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Recent advances in forensic science research

For immediate release, acs news service weekly presspac: april 20, 2022.

Forensic scientists collect and analyze evidence during a criminal investigation to identify victims, determine the cause of death and figure out “who done it.” Below are some recent papers published in ACS journals reporting on new advances that could help forensic scientists solve crimes. Reporters can request free access to these papers by emailing  newsroom@acs.org .

“Insights into the Differential Preservation of Bone Proteomes in Inhumed and Entombed Cadavers from Italian Forensic Caseworks” Journal of Proteome Research March 22, 2022 Bone proteins can help determine how long ago a person died (post-mortem interval, PMI) and how old they were at the time of their death (age at death, AAD), but the levels of these proteins could vary with burial conditions. By comparing bone proteomes of exhumed individuals who had been entombed in mausoleums or buried in the ground, the researchers found several proteins whose levels were not affected by the burial environment, which they say could help with AAD or PMI estimation.

“Carbon Dot Powders with Cross-Linking-Based Long-Wavelength Emission for Multicolor Imaging of Latent Fingerprints” ACS Applied Nanomaterials Jan. 21, 2022 For decades, criminal investigators have recognized the importance of analyzing latent fingerprints left at crime scenes to help identify a perpetrator, but current methods to make these prints visible have limitations, including low contrast, low sensitivity and high toxicity. These researchers devised a simple way to make fluorescent carbon dot powders that can be applied to latent fingerprints, making them fluoresce under UV light with red, orange and yellow colors.

“Proteomics Offers New Clues for Forensic Investigations” ACS Central Science Oct. 18, 2021 This review article describes how forensic scientists are now turning their attention to proteins in bone, blood or other biological samples, which can sometimes answer questions that DNA can’t. For example, unlike DNA, a person’s complement of proteins (or proteome) changes over time, providing important clues about when a person died and their age at death.

“Integrating the MasSpec Pen with Sub-Atmospheric Pressure Chemical Ionization for Rapid Chemical Analysis and Forensic Applications” Analytical Chemistry May 19, 2021 These researchers previously developed a “MasSpec Pen,” a handheld device integrated with a mass spectrometer for direct analysis and molecular profiling of biological samples. In this article, they develop a new version that can quickly and easily detect and measure compounds, including cocaine, oxycodone and explosives, which can be important in forensics investigations.

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Forensic anthropology

Criminalistics, forensic engineering.

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forensic science , the application of the methods of the natural and physical sciences to matters of criminal and civil law. Forensic science can be involved not only in investigation and prosecution of crimes such as rape , murder , and drug trafficking but also in matters in which a crime has not been committed but in which someone is charged with a civil wrong ( see tort ), such as willful pollution of air or water or causing industrial injuries.

Almost any science can be a forensic science because almost any science can contribute to solving a crime or evaluating a civil harm. In fact, with few exceptions, forensic sciences are no different in what they study than traditional sciences. The only difference is that forensic scientists apply the methods and techniques of established sciences to legal matters.

Short descriptions of each of the main areas of forensic science follow.

There are a number of applications of anthropology to the forensic sciences. A large part of physical anthropology deals with skeletal biology, which includes bone and bone system structures and their relationships to characteristics such as gender, age , race , socioeconomic status, and so forth. That knowledge can be applied to the examination of characteristics of skeletal remains that are part of a crime scene. In such cases, the goal of the analysis may be to determine the identity of the deceased person and, perhaps, the cause of death . To those ends, forensic anthropologists make use of a number of unique techniques.

Two major types of human-remains evidence confront the forensic anthropologist. First is the single bone or bone fragment or small group of bones. When that is the only type of evidence present, the forensic anthropologist seeks to determine if the bone is human and, if not, what type of animal the bone belongs to. If the sample is human bone, then the anthropologist will determine the part of the body from which it came. For example, if a single human arm bone is recovered from a field, there will most likely be other human bones belonging to the same individual around also.

The second major type of forensic anthropological evidence is the complete (or nearly complete) skeleton . From that evidence, the accomplished forensic anthropologist may be able to determine gender, race , approximate age , stature, and approximate socioeconomic status . If there is damage to some of the bones, the anthropologist may be able to determine what type of trauma caused it. If the skull is present, it may be possible to prepare an approximate face on the skull using skull superimposition—building a face out of clay using average thickness measurements developed by anatomists, pathologists, and anthropologists. Investigators may then publish a picture of the face to see if it evokes a response from a relative of a missing person. If a possible match to the skeleton is found and there are antemortem pictures available, then a new video superimposition technique may be used. That technique utilizes two cameras to superimpose the skull over the picture of the actual face to determine if the skull could be the right one.

Criminalistics can be defined as the application of scientific methods to the recognition, collection, identification, and comparison of physical evidence generated by criminal or illegal civil activity. It also involves the reconstruction of such events by evaluation of the physical evidence and the crime scene.

Criminalists, usually called “forensic scientists,” analyze evidence such as body fluids in order to determine if DNA in those fluids matches blood found at a crime scene ( see DNA fingerprinting ). Other forensic scientists may help identify, collect, and evaluate physical evidence at a crime scene.

Forensic engineering uses the concepts of mechanical , chemical , civil , and electrical engineering as tools in the reconstruction of crimes and accidents and the determination of their cause. A major component of that work involves traffic accident reconstruction. To determine what may have caused the accident, forensic engineers use evidence such as skid marks; damage to cars and their positions after the accident; road and environmental conditions; injuries to drivers, passengers, and pedestrians; and witness accounts. In developing their explanations, engineers may work in concert with forensic pathologists, toxicologists, criminalists, and other engineers. Some forensic engineers specialize in marine incidents or aircraft crashes.

Another major area of forensic engineering is failure analysis. Mechanical, chemical, civil, and structural engineers all bring their skills to bear on problems involving how and why buildings or other structures deteriorate or fail prematurely. An example of such work was the collapse of a walkway high above the lobby of the Kansas City Hyatt Regency Hotel in 1981, which killed and injured many people. Forensic engineers were called in to determine why the balcony collapsed.

A somewhat unusual application of forensic engineering involves animals on farms where high-voltage power lines or communication transmission lines pass overhead. For many years, there have been suggestions by farmers that transient currents from these power lines affect the health of their animals, including cows ’ ability to give milk . Many electrical engineers have studied this problem and cases have ended up in court.

Forensic engineers are usually educated engineers who have earned a doctorate and who develop expertise in one or more of the forensically important disciplines . There are no university graduate programs in forensic engineering; most of the expertise is developed on the job, perhaps working with more-experienced practitioners.

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• Monographs In collaboration with toxicology labs, medical examiner and coroner offices, crime laboratories, clinical partners, and the National Institute of Justice (NIJ), the CFSRE is documenting the first reports of NPS in the United States through analysis of drug materials and/or biological samples. These reports are generated using comprehensive analytical techniques (e.g., GC-MS, LC-QTOF-MS, NMR) and include available information about the new substances identified at the time of reporting, as well as the analytical data generated during testing. Learn More >
• Trend Reports The CFSRE is developing quarterly trend reports associated with NPS occurrence in the United States. These trend reports are intended to provide near real-time information regarding NPS prevalence, positivity, and turnover. Testing was performed using biological samples, sample extracts, and/or datafiles. The CFSRE received funding from the National Institute of Justice (NIJ) of the Department of Justice (DOJ) to develop this initiative. Learn More >
• Public Alerts The CFSRE is developing Public Alerts and related reports to increase public awareness regarding NPS involvement in adverse intoxications, mass overdoses, and fatalities. These reports are generated based on subsets of data collected at or near the time of first report or incidence and may not necessarily reflect all results for a specific emerging NPS. Learn More >
• Drug Checking The CFSRE is collaborating with public health agencies to collect up-to-date information regarding the drug supply in various cities and communities across the United States. Drug checking allows individuals to draw scientifically backed opinions and understand complex drug data based on accurate and reliable testing protocols. Our leading team of toxicologists and chemists help acquire data and interpret results based on years of knowledge and experience. Learn More >
• Clinical Reports Drug use can lead to adverse events and overdose scenarios where individuals present to emergency departments for clinical evaluation and/or treatment. The culprit can be traditional drugs (e.g., heroin, fentanyl, cocaine, methamphetamine) or novel psychoactive substances (NPS); however, proper drug testing methodologies must be employed for accurate identification and characterization. The CFSRE is collaborating with clinicians and emergency department physicians to employ comprehensive drug testing of clinical biological specimens collected after suspected NPS-related overdoses in various cities across the United States. Learn More >
• Scope Recommendations The NPS landscape is changing rapidly, requiring laboratories to constantly remain abreast of new and emerging drugs locally, nationally, and internationally. To meet individualized needs, laboratories amend existing methods or develop new ones for detection and confirmation. This can be challenging for scientists as information about NPS detections can be regionalized and/or out-of-date, making it difficult to determine which drugs should be prioritized at a given time. The CFSRE and the SOFT NPS Committee have established recommendations for NPS scope based on information from extensive collaborations, partnerships, and initiatives which yield national perspectives. Learn More >
• Analytical Toolkits The CFSRE is developing NPS Discovery Toolkits as a consolidation of our program outcomes into a comprehensive new document detailing relevant information about the detection and characterization of a specified NPS. This “toolkit” includes basic drug information, date of first appearance, prevalence, temporal trends, geographical trends, demographics, poly-drug combinations (including with other NPS), metabolism, methods for identification and confirmation, reference concentration ranges, and much more. Learn More >

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Center for Advanced Research in Forensic Science (CARFS)

Center overview.

The Center for Advanced Research in Forensic Science (CARFS) serves as a platform to conduct interdisciplinary and innovative research to address the needs of the forensic science laboratory community. CARFS brings together forensic science leaders in state-of-the-art research laboratories, specialized facilities, and institutes across the nation to solve the most complex problems facing forensic science today.

The combined knowledge of the forensic faculty allows CARFS to address a wide range of forensic science problems, whether in the form of long-term, high-risk discovery or shorter term, deliverable-driven research. CARFS produces novel findings and tools for a range of stakeholders, including forensic science practitioners, research laboratories, industrial partners, government, and private forensic science end users.

CARFS' goal is to develop, implement, and commercialize tools that benefit the national forensic science research enterprise by uniting experts in multidisciplinary backgrounds to address the direct needs of industrial members, ultimately providing products, techniques, strategies, and methodologies for the end-user community in the forensic science enterprise.

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• Legal psychology.

CARFS plans to expand to include other forensic disciplines.

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Forensic science: defending justice

Shanghai Key Laboratory of Forensic Medicine,Shanghai Forensic Service Platform, Institute of Forensic Science, Ministry of Justice, PRC, Shanghai, China, nc.djfss@mnehs

Duarte Nuno Vieira

Faculty of Medicine, University of Coimbra, Coimbra, Portugal

Forensic science is the application of scientific knowledge and methodology for the resolution of legal questions and problems for individuals and society. It involves the observation, documentation, collection, analysis, assessment and scientific interpretation of evidence during the course of an investigation required for the different fields of law, including criminal, civil, work, family and administrative. Forensic scientists also testify as expert witnesses and can work for either the prosecution or the defence.

Over the past few years, an abundance of new insights and technologies has caused the forensic science field to grow rapidly. Moreover, advances in technology mean that forensic scientists are able to do much more with the same resources than they were before, so the value of the forensic laboratory has increased significantly.

The role of a scientific journal is to disseminate science, to be a tool for international research communication. Today there are only 15 Science Citation Index (SCI) forensic science journals in the world, and their impact factors are at a much lower level than journals in other disciplines. All 15 of these SCI journals are almost monopolized by European and North American publications, with few Asian journals indexed by the SCI. More and more scientific achievements in forensic science are emerging, and forensic scientists face the challenge of increasing the quantity and quality of their research. These journals cannot meet all the needs of these developments. The range of journals available is narrower for forensic science than for many other fields. To meet this demand and further serve the needs of academia, industry and policy analysis, we are pleased to announce the launch of a new global and multidisciplinary journal, called Forensic Sciences Research ( FSR ).

The Institute of Forensic Science (IFS) attached to Ministry of Justice, P.R. China, will be the sponsor of FSR . It is the oldest full-scale institute specializing in multi-disciplinary forensic scholarship, with nearly 100 years of history in China. The IFS is very active in international scholarship exchanges and has developed international training programmes with many forensic institutes across the world. The IFS will provide a valuable foundation for the successful development of FSR . China has a long history of forensic science. The first written account of using medicine and entomology to solve criminal cases is attributed to the book of Xi Yuan Ji Lu (translated as The Washing Away of Wrongs ), which was written in Chinese by Song Ci (1186–1249) in 1248, during the Song Dynasty. The book offered advice on how to determine a drowning case (water in the lungs) or strangulation (broken neck cartilage), along with using other evidence from corpses to distinguish deaths caused by murder, suicide or accident. Over the past 30 years, there has been an extremely rapid development of the Chinese economy, and the Chinese government now invests a large amount of funding to scientific research, technology and education every year. The number of research papers published in China has significantly increased to be second only to United States in the past few years. Many of these research papers have a high citation rate by well-regarded academic journals. Additionally, China has a strong ethos of promoting academic communication through scholarly publication. As early as the 1930s, Professor Lin Ji, the founder of modern Chinese forensic science, founded the Journal of DIE GERICHTLICH-MEDIZINISCHE MONATSSCHRIFT in Shanghai.

In the subsequent decades, Chinese forensic scientists have shared and communicated a multitude of scholarly works with researchers and scholars worldwide, through major academic journals such as Forensic Science International , Journal of Forensic Sciences , International Journal of Legal Medicine , Science & Justice and Forensic Science, Medicine, and Pathology . Many other Chinese papers of high quality have also been published in various fields of forensic medicine and science including forensic pathology, clinical forensic medicine, forensic psychiatry, forensic toxicology, forensic biology, forensic genetics, anthropology, document examination, forensic entomology and forensic odontology, to effectively promote the application of their research outcomes. Ultimately, Chinese scholars make strong contributions to forensic science internationally. According to Web of Science, Chinese papers published in these 15 SCI journals increase every year, reaching 297 in 2014 and 2015.

FSR will be published as a peer-reviewed journal, sponsored by the IFS with the cooperation of the Taylor & Francis Group in England. Initially it will be a quarterly periodical publication in English. The mission of this journal is to offer an academic platform for forensic scientists and researchers to publish and exchange interesting, challenging and innovative research findings across various disciplines related to collecting, preserving and analysing scientific evidence during the course of a forensic investigation. We hope that FSR will provide an effective communication platform for all forensic scholars in this era of globalization, and serve as a role model for other Chinese forensic science journals to follow in the future.

FSR welcomes forensic science and technology manuscripts in all forensic sciences areas, namely in forensic pathology, clinical forensic medicine, forensic psychiatry, forensic toxicology and chemistry, forensic biology, forensic genetics, anthropology, criminalistics, document examination, accident investigation, crime scene investigation, explosives, quality assurance, forensic entomology, forensic odontology, digital & media sciences, gunshot injury and engineering sciences, etc., as well as investigations of value to public health in its broadest sense, and the important cross-disciplinary areas where science and medicine interact with the law. FSR is intended for publishing original research, scholarly reviews, opinion papers and research highlights/commentaries in forensic science. Through these features, FSR aims to build a communication platform for international researchers to share scholarly achievements effectively. In addition to full scientific research or technological and application papers, shorter papers of academic interest, reports on conferences and reviews of books in relevant fields are also welcome.

FSR has adopted internationally acknowledged standards for and approaches to its operation. The journal is governed by a highly esteemed academic editorial board consisting of internationally recognized world-class scholars from different countries, including the United States, the United Kingdom, Germany, France, Australia and China. The Board membership has met the high academic standards required for an international scholarly journal for submission, peer-review ( FSR has established an editorial manager) and publication (production and hosting by the Taylor & Francis Group).

After a year of preparatory work, FSR is now ready to be launched. On this special occasion, we would like to express our sincere thanks to all those authors that gave their contribution for this first issue. Thanks also go to all members of the Editorial Board of FSR , our publisher Taylor & Francis Group and expert reviewers who have made significant contributions to the publication of this journal. It is only with their support that the editorial team has been able to put together this inaugural issue.

We hope that FSR is successful in serving as an international academic forum for scholars to communicate and exchange their ideas, research approaches, results and experiences. FSR should become the journal of choice for international forensic practitioners and scholars to share and advance knowledge and to learn from each other across the forensic sciences. We welcome your comments on this first issue and suggestions for future ones, as well as on any other aspect of the FSR . It is our goal to work together for FSR to uphold high standards, integrity and excellence in its publication. Your support will be a strong driving force for us in our efforts to continually improve the quality and influence of this journal.

Center for Advanced Research in Forensic Science

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The Center for Advanced Research in Forensic Science (CARFS)  is a partnership-based collaboration between academic researchers, government laboratories, industry leaders, and the end-user community with an aim to develop, implement and commercialize tools which benefit the national forensic science research enterprise. CARFS' structure is based on the National Science Foundation’s Industry-University Cooperative Research Center (IUCRC) model to promote innovation in product development, techniques, strategies, and methodologies for application by stakeholders.

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Researcher Spotlight

Britni Skillman, Ph.D., F-ABFT; Assistant Professor, Department of Forensic Science, College of Criminal Justice, Sam Houston State University

The Science of Success

Established 2017 and funded though 2028.

• ~$2M In Phase I Research Funds
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CARFS Poster Reception at AAFS , February 21, 2024

CARFS Announces Poster Presentations During the American Academy of Forensic Sciences 2024, in Denver/CO - Download the list of posters

CARFS hosts a Poster Session during the American Academy of Forensic Sciences 2023, in Orlando/FL - Download the list of posters

FIU CARFS Hosts Miami-Dade College and Braddock Senior High School Faculty and Students - Read the article

By having the problem delivered to them, researchers can focus on finding a solution instead of trying to solve a problem that might not be a priority for the industry. — Kenneth G. Furton, Director, CARFS; Executive Director, Global Forensic and Justice Center, Florida International University

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https://www.nist.gov/news-events/news/2024/09/nist-report-outlines-strategic-opportunities-advance-forensic-science-us

NIST Report Outlines Strategic Opportunities to Advance Forensic Science in the U.S.

Report identifies four challenges facing the forensic science community and provides strategies for addressing them through r&d and use of standards..

The National Institute of Standards and Technology (NIST) has published Strategic Opportunities to Advance Forensic Science in the United States: A Path Forward Through Research and Standards , which identifies four “grand challenges” facing the forensic science community in the United States.

The report provides a strategic roadmap for addressing these challenges through scientific research and standards. It also assesses the state of the field 15 years after a landmark report from the National Academy of Sciences identified critical gaps in the scientific foundations of several forensic disciplines.

“NIST looks forward to continuing to strengthen the science that supports forensic science by working together with stakeholders in the forensic science and criminal justice communities to drive significant advancements in the practice of forensic science in the years ahead,” said NIST Director Laurie Locascio in a foreword to the report.

The forensic science community has a significant opportunity to strengthen the validity, reliability, and consistency of existing methods and techniques for the analysis of forensic evidence; develop new methods to analyze complex forensic evidence; advance the development of science-based standards and guidelines; and promote the adoption and use of these advances by forensic service providers and officers of the court. By addressing these shared challenges, the forensic science community will strengthen the foundations of our criminal justice system, ensuring fairness and impartiality while also preserving public safety and public trust.

To complete this report, NIST researchers conducted an extensive literature review and collected input from academic and government researchers, forensic science practitioners, legal experts, statistics experts and feedback from NIST subject matter experts. The report outlines the following four “grand challenges” facing the forensic science community and provides strategies for addressing them through research and development efforts and adoption of standards:

• Accuracy and reliability of complex methods and techniques for analysis of forensic evidence. Quantify and establish statistically rigorous measures of accuracy and reliability of complex methods and techniques for forensic evidence analysis that clearly demonstrate their validity when applied to evidence of varying quality.
• New methods and techniques for analysis of forensic evidence. Develop new methods and techniques for forensic evidence analysis, including those that leverage the benefits of algorithms and next-generation technologies, such as AI, to provide rapid analyses of forensic evidence and produce new analytical insights from complex forensic evidence.
• Science-based standards and guidelines for forensic science practices. Develop rigorous science-based standards, conformity assessment schemes, and guidelines across forensic science disciplines to support consistent and comparable results from forensic analyses among laboratories and jurisdictions.
• Adoption and use of advanced forensic analysis methods, techniques, standards and guidelines. Promote the adoption and use of advances in forensic science standards, guidelines, methods, and techniques aimed at improving the validity, reliability, and consistency of forensic science practices.

This report builds upon earlier efforts NIST conducted to assess the forensic science environment to inform strategic planning. The resulting Forensic Science Environmental Scan 2023 report captured salient issues and trends across five different landscapes: governance, economic, societal, scientific and technological, and legal and regulatory. In addition, NIST held a roundtable workshop with forensic science thought leaders from across forensic disciplines to discuss the long-term vision and strategic priorities for forensic science in the United States.

NIST’s Forensic Science Program works to strengthen the scientific basis of forensic methods and practices so that evidence is appropriately collected, accurately analyzed, and effectively communicated. These NIST efforts are intended to bring the best possible forensic science methods and practices to the criminal justice system, thereby eliminating potential bias in measurements, analysis, and interpretation of evidence.    

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Exploring Forensic Science Careers: Pathways, Challenges, and Emerging Trends

Explore the diverse careers in forensic science, the pathway to becoming a forensic scientist, and the future of this dynamic field. Uncover the rewards and challenges of working in forensic science.

Introduction

Forensic science is a fascinating and ever-evolving field that serves as the bridge between scientific inquiry and the legal system. At its core, forensic science involves the meticulous application of various scientific principles to uncover the mysteries surrounding criminal investigations. Forensic scientists play a crucial role in examining evidence, reconstructing crime scenes, and revealing the intricate details that can shed light on the circumstances surrounding a crime.

This captivating field offers a diverse range of career paths, each with its unique set of responsibilities and specializations. From ballistics and fingerprint analysis to DNA profiling and bloodstain pattern interpretation, forensic scientists are at the forefront of uncovering the truth and ensuring that justice is served.

The Path to Becoming a Forensic Scientist

The diverse career paths in forensic science, the rewards and challenges of a forensic science career, the importance of ethics and integrity in forensic science, rapid dna analysis, artificial intelligence and machine learning, advanced imaging techniques, forensic genetics and phenotyping, forensic cybersecurity, interdisciplinary collaboration, the role of professional organizations and continuing education, forensic science education and training programs, forensic science careers: opportunities and outlook, forensic science in popular culture and media, ethical considerations and challenges in forensic science, the intersection of forensic science and emerging technologies, the diverse realms of forensic science.

Forensic science encompasses a vast spectrum of disciplines, each contributing a unique perspective to the investigative process. Here are some of the most prominent areas within this multifaceted field:

Experts in this domain meticulously examine firearms, ammunition, and the intricate trajectories of projectiles. Their analyses can shed light on crucial details, such as the weapon used in a crime, the distance from which a shot was fired, and the sequence of events that unfolded. Read More…

Arson and explosives investigators possess an in-depth understanding of accelerants, explosives, and the telltale signs of intentional fires or detonations. Their expertise is invaluable in determining the cause and origin of fires, as well as identifying potential suspects.

Fingerprints are a powerful tool in forensic investigations, as they can link individuals to crime scenes or establish their presence at specific locations. Fingerprint analysts employ cutting-edge techniques to identify, enhance, and compare these unique impressions, often playing a crucial role in criminal prosecutions. Read More…

Even the most meticulous criminals can inadvertently leave behind microscopic traces of evidence, such as fibers, hair, or skin cells. Forensic scientists specializing in trace evidence possess the skills and technology to detect, analyze, and interpret these minute clues, potentially unlocking vital information about a crime. Read More…

In the aftermath of accidents, forensic scientists specializing in reconstruction meticulously examine physical evidence, such as skid marks, vehicle positioning, and debris patterns, to piece together the sequence of events and determine the underlying causes.

The intricate patterns formed by bloodstains can reveal valuable insights into the dynamics of a crime scene. Bloodstain pattern analysts use their expertise to interpret these patterns, shedding light on factors such as the positioning of individuals, the type of weapon used, and the sequence of events that transpired. Read More

Forensic pathologists play a crucial role in medicolegal investigations, conducting autopsies and examining medical records to determine the cause and manner of death. Their findings can be instrumental in criminal trials, providing critical evidence and expert testimony. R ead More

Forensic scientists specializing in serology and toxicology analyze bodily fluids and substances to identify the presence of drugs, toxins, or other substances that may have played a role in a crime or accident. Their expertise is invaluable in cases involving impaired driving, poisoning, or overdoses. Read More

DNA analysis has revolutionized the field of forensic science, enabling investigators to identify individuals with unprecedented accuracy. Forensic DNA analysts employ cutting-edge techniques to extract, analyze, and compare DNA samples, potentially linking suspects to crime scenes or exonerating the innocent.

In cases where traditional identification methods are insufficient, forensic odontologists utilize their expertise in dental records and bite mark analysis to identify victims and establish critical connections between individuals and crime scenes.

When faced with skeletal remains or severely decomposed bodies, forensic anthropologists employ their knowledge of human anatomy and osteology to determine crucial details such as age, sex, ancestry, and the circumstances surrounding the individual’s death. Read More..

The study of insects and their interactions with decomposing remains can provide invaluable insights into the timeline of a crime. Forensic entomologists analyze the insect species present, their life cycles, and their feeding patterns to estimate the time of death and other critical details. Read More

The journey to becoming a forensic scientist is both challenging and rewarding. While the specific requirements may vary depending on the area of specialization, several common steps are typically involved:

Most entry-level positions in forensic science require a minimum of a bachelor’s degree in a relevant field, such as chemistry, biology, forensic science, or criminal justice. These programs provide a solid foundation in scientific principles, laboratory techniques, and investigative methodologies.

Many forensic scientists pursue advanced degrees, such as a master’s or doctoral degree, to deepen their knowledge and specialize in a particular area of forensic science. Graduate programs offer opportunities for intensive research, hands-on training, and the development of advanced analytical skills.

Hands-on experience is invaluable in the field of forensic science. Many aspiring forensic scientists seek internships or entry-level positions in crime laboratories, law enforcement agencies, or private forensic companies to gain practical experience and develop their skills under the guidance of experienced professionals.

The field of forensic science is constantly evolving, with new technologies, techniques, and best practices emerging regularly. Forensic scientists must remain committed to lifelong learning and stay current with the latest developments in their field through seminars, workshops, and professional conferences.

The field of forensic science offers a wide range of career opportunities, each with its unique set of responsibilities and specializations. Here are some of the most common roles within this dynamic field:

Forensic science technicians play a crucial role in crime scene investigations, collecting and analyzing physical evidence. They may specialize in areas such as fingerprint analysis, ballistics, or trace evidence examination. Their work often involves meticulous documentation, adherence to chain-of-custody protocols, and the preparation of detailed reports. Read More

Forensic science technicians play a crucial role in crime scene investigations, collecting and analyzing physical evidence. They may specialize in areas such as fingerprint analysis, ballistics, or trace evidence examination. Their work often involves meticulous documentation, adherence to chain-of-custody protocols, and the preparation of detailed reports. Read More…

Forensic psychologists apply their knowledge of human behavior and mental processes to the legal system. They may be involved in evaluating criminal suspects, assessing the risk of reoffending, assisting victims of crime, or providing expert testimony in court proceedings. Read Mor e

Fingerprint examiners are highly specialized forensic scientists who focus on the identification, analysis, and comparison of fingerprints. Their work is crucial in linking suspects to crime scenes or exonerating individuals wrongfully accused of crimes. Read More…

Criminal investigators play a pivotal role in the investigative process, conducting interviews, gathering evidence, obtaining search warrants, and collaborating with forensic scientists to build comprehensive cases. Their expertise in investigative techniques and legal procedures is essential in ensuring the integrity of criminal proceedings.

Forensic accountants combine their knowledge of accounting principles with investigative skills to uncover financial crimes, such as fraud, embezzlement, and money laundering. They analyze financial records, trace transactions, and provide expert testimony in legal proceedings related to financial matters. Read More..

Forensic nurses bridge the gap between healthcare and the legal system. They are trained to recognize and document evidence of physical or sexual abuse, collect and preserve forensic evidence, and provide expert testimony in court proceedings related to medical or nursing practices. Read More

In the digital age, forensic scientists specializing in digital forensics play a crucial role in recovering and analyzing electronic evidence from various devices and storage media. Their expertise is invaluable in cases involving cybercrime, data breaches, and other technology-related offenses. Read More

Forensic artists utilize their artistic skills to create composite sketches, facial reconstructions, and age progressions based on witness descriptions or skeletal remains. Their work can be instrumental in identifying suspects or victims and generating leads in criminal investigations. Read More..

Pursuing a career in forensic science can be both rewarding and challenging. On one hand, the opportunity to contribute to the pursuit of justice and the resolution of complex cases can be deeply fulfilling. Forensic scientists play a vital role in uncovering the truth, providing closure to victims and their families, and ensuring that the guilty are held accountable.

However, the nature of the work can also be emotionally and mentally demanding. Forensic scientists often encounter disturbing crime scenes, gruesome evidence, and traumatic situations. They must maintain a high level of professionalism and objectivity, even in the face of difficult circumstances.

Additionally, the field of forensic science is highly competitive, with a limited number of job openings and a growing number of qualified applicants. Forensic scientists must be prepared to continually update their skills, adapt to new technologies, and remain vigilant in their pursuit of professional development opportunities.

Despite these challenges, those who possess a strong commitment to justice, a passion for scientific inquiry, and the ability to handle the demands of the profession can find immense satisfaction in a career in forensic science.

Forensic science plays a crucial role in the administration of justice, and as such, it is imperative that forensic scientists maintain the highest standards of ethics and integrity. The credibility of their work and the weight of their testimony in legal proceedings rely heavily on their adherence to strict ethical principles.

Forensic scientists must be objective and impartial, ensuring that their analyses and conclusions are based solely on scientific evidence and are not influenced by personal biases or external pressures. They must also maintain meticulous documentation and chain-of-custody protocols to ensure the integrity of the evidence they handle.

Additionally, forensic scientists must remain committed to ongoing education and training, staying up-to-date with the latest advancements in their field and adhering to best practices and established protocols. This not only ensures the accuracy and reliability of their work but also contributes to the overall credibility and trustworthiness of the forensic science profession.

By upholding the highest ethical standards and maintaining a steadfast commitment to integrity, forensic scientists can ensure that their work contributes to the fair and impartial administration of justice, protecting the rights of both victims and the accused.

The Future of Forensic Science: Emerging Trends and Technologies

The field of forensic science is constantly evolving, driven by advancements in technology and scientific research. As we look to the future, several exciting trends and emerging technologies are poised to shape the landscape of forensic investigations:

Rapid DNA analysis techniques are revolutionizing the speed and efficiency of DNA profiling. These cutting-edge methods allow for the rapid extraction, amplification, and analysis of DNA samples, potentially providing crucial information within hours rather than days or weeks.

The integration of artificial intelligence (AI) and machine learning into forensic science holds immense potential. AI algorithms can assist in tasks such as pattern recognition, image analysis, and data processing, enhancing the accuracy and efficiency of forensic investigations.

Cutting-edge imaging technologies, such as 3D scanning, virtual reality, and advanced microscopy, are providing forensic scientists with unprecedented levels of detail and insight. These techniques can aid in crime scene reconstruction, evidence analysis, and the visualization of complex data.

Advancements in forensic genetics and phenotyping are enabling forensic scientists to derive increasingly detailed information from DNA samples. These techniques can potentially reveal physical characteristics, such as eye color, hair color, and facial features, providing valuable leads in investigations.

As cybercrime continues to evolve, the field of forensic cybersecurity is gaining increasing importance. Forensic cybersecurity experts are tasked with investigating and analyzing digital evidence, tracking down cybercriminals, and developing strategies to combat emerging cyber threats.

The future of forensic science lies in interdisciplinary collaboration, where experts from various fields, such as chemistry, biology, computer science, and engineering, work together to tackle complex forensic challenges. This collaborative approach fosters innovation and enhances the effectiveness of forensic investigations.

As these trends and technologies continue to emerge, forensic scientists must remain adaptable and committed to lifelong learning, embracing new developments and integrating them into their practices to stay at the forefront of this dynamic field.

Professional organizations play a vital role in the forensic science community, fostering collaboration, promoting best practices, and providing ongoing education and training opportunities. These organizations serve as valuable resources for forensic scientists, offering a platform for networking, knowledge-sharing, and professional development.

Some of the most prominent professional organizations in the field of forensic science include:

• American Academy of Forensic Sciences (AAFS)
• International Association for Identification (IAI)
• American Society of Crime Laboratory Directors (ASCLD)
• Association of Forensic Science Providers (AFSP)
• Forensic Science Society (FSSoc)

These organizations offer a range of services and resources, including:

• Certification programs and accreditation standards
• Continuing education courses and workshops
• Annual conferences and seminars
• Peer-reviewed journals and publications
• Networking opportunities and mentorship programs
• Advocacy and lobbying efforts for the advancement of forensic science

Actively participating in professional organizations and pursuing ongoing education and training opportunities are essential for forensic scientists to stay current with the latest developments, techniques, and best practices in their field. These organizations also provide a platform for sharing research, collaborating on complex cases, and addressing challenges and ethical considerations within the forensic science community.

By embracing the resources and opportunities offered by professional organizations, forensic scientists can enhance their skills, expand their knowledge, and contribute to the advancement of their profession, ultimately strengthening the integrity and effectiveness of the forensic science field.

To embark on a career in forensic science, individuals must pursue specialized education and training programs. These programs are designed to equip students with the necessary knowledge, skills, and practical experience to excel in this dynamic field.

Many universities and colleges offer bachelor’s degree programs in forensic science or related disciplines such as chemistry, biology, or criminal justice. These programs typically cover a broad range of topics, including:

• Criminalistics
• Crime scene investigation
• Evidence collection and preservation
• Forensic biology and DNA analysis
• Forensic chemistry and toxicology
• Forensic anthropology
• Forensic psychology
• Legal and ethical considerations

In addition to coursework, many undergraduate programs incorporate hands-on laboratory work, internships, and research opportunities, providing students with valuable practical experience.

For those seeking advanced expertise or specialized knowledge, graduate-level programs in forensic science are available. These programs may lead to a master’s degree (M.S.) or a doctoral degree (Ph.D.) and often allow students to concentrate in a specific area of forensic science, such as:

• Forensic chemistry
• Forensic toxicology
• Digital forensics

Graduate programs typically involve rigorous coursework, research projects, and the completion of a thesis or dissertation, preparing students for careers in academia, research, or highly specialized forensic science roles.

In addition to formal education, many forensic science professionals pursue certifications and accreditations to demonstrate their expertise and adherence to industry standards. These credentials are often offered by professional organizations or accrediting bodies and may require a combination of education, experience, and successful completion of examinations.

Some examples of certifications and accreditations in the field of forensic science include:

• Certified Crime Scene Investigator (CCSI)
• Certified Forensic Photographer (CFPH)
• Forensic Anthropology Certification
• Forensic Toxicology Certification
• Digital Forensics Certification

Obtaining these certifications and accreditations can enhance a forensic scientist’s credibility, career opportunities, and professional standing within the field.

By pursuing high-quality education and training programs, as well as relevant certifications and accreditations, aspiring forensic scientists can gain the knowledge, skills, and credentials necessary to excel in this challenging and rewarding profession.

The field of forensic science offers a diverse range of career opportunities, with roles spanning various sectors, including law enforcement, government agencies, private laboratories, and academic institutions. As technology continues to advance and the demand for forensic expertise grows, the job outlook for Forensic science remains promising. According to the U.S. Bureau of Labor Statistics, employment of forensic science technicians is projected to grow 11% from 2021 to 2031, faster than the average for all occupations.

This growth can be attributed to several factors, including the increasing use of scientific evidence in criminal investigations and the need for highly trained professionals to analyze and interpret this evidence. Additionally, the emergence of new technologies and techniques, such as rapid DNA analysis and advanced imaging techniques, is driving demand for forensic scientists with specialized skills.

While the job market for forensic scientists can be competitive, individuals with strong academic credentials, relevant certifications, and practical experience may find numerous opportunities in various sectors. Some potential career paths include:

Law Enforcement Agencies: Forensic scientists can work for local, state, or federal law enforcement agencies, such as police departments, crime laboratories, or specialized units like the Federal Bureau of Investigation (FBI) or the Drug Enforcement Administration (DEA). In these roles, they may be involved in crime scene investigation, evidence analysis, and providing expert testimony in court.

Government Agencies: Various government agencies, such as the Department of Justice, the Department of Homeland Security, and the Environmental Protection Agency, employ forensic scientists to investigate crimes related to their respective jurisdictions. These roles may involve analyzing evidence related to terrorism, cybercrimes, environmental crimes, or other specialized areas.

Private Forensic Laboratories: Many private forensic laboratories offer services to law enforcement agencies, attorneys, and other clients. Forensic scientists in these laboratories may work on a wide range of cases, from criminal investigations to civil litigation matters.

Academic and Research Institutions: Universities, research centers, and other academic institutions employ forensic scientists to conduct research, teach courses, and contribute to the advancement of the field. These roles often involve developing new techniques, publishing research papers, and training future forensic scientists.

Consulting and Expert Witness Services: Experienced forensic scientists may choose to work as independent consultants or expert witnesses, providing their expertise to attorneys, law enforcement agencies, or other clients on a case-by-case basis.

Regardless of the specific career path, forensic scientists must possess a strong foundation in scientific principles, critical thinking skills, attention to detail, and the ability to communicate complex findings effectively. Additionally, staying up-to-date with the latest technologies, techniques, and best practices through ongoing professional development is essential for career advancement and success in this dynamic field.

The fascinating world of forensic science has captured the public’s imagination, leading to its widespread representation in popular culture and media. From television shows and movies to books and video games, forensic science has become a source of intrigue and entertainment for audiences worldwide.

One of the most notable examples of forensic science’s popularity is the long-running television series “CSI: Crime Scene Investigation” and its various spinoffs. These shows, while dramatized for entertainment purposes, have introduced millions of viewers to the world of forensic science, showcasing the various techniques and methodologies used by forensic scientists to solve complex cases.

However, it is important to note that these fictional representations often take creative liberties and may not accurately depict the realities of forensic science work. In reality, forensic investigations can be time-consuming, meticulous, and heavily reliant on teamwork and collaboration among various experts.

Despite the potential for sensationalism, the popularity of forensic science in popular culture has had some positive impacts. It has sparked public interest in the field, encouraging more individuals to pursue careers in forensic science and related disciplines. Additionally, it has raised awareness about the importance of scientific evidence in criminal investigations and the role forensic scientists play in the pursuit of justice.

Nonetheless, it is crucial for the public to understand the limitations and complexities of forensic science, as well as the ethical and legal considerations that govern its application. Forensic scientists must adhere to strict protocols, maintain objectivity, and ensure that their findings are based on sound scientific principles and rigorous methodology.

As the field of forensic science continues to evolve and new technologies emerge, it is likely that its representation in popular culture will also adapt and evolve. However, it is essential for both the media and the forensic science community to strive for accurate and responsible portrayals that educate and inform the public while maintaining the integrity and credibility of the profession.

While forensic science plays a vital role in the pursuit of justice, it is not without its ethical challenges and considerations. As forensic scientists wield significant influence in legal proceedings, it is imperative that they maintain the highest standards of integrity, objectivity, and professionalism.

One of the primary ethical challenges in forensic science is the potential for bias or error. Forensic scientists must be vigilant in recognizing and mitigating any sources of bias, whether conscious or unconscious, that could potentially influence their analyses and interpretations. This includes personal biases, cognitive biases, and external pressures from stakeholders or agencies involved in the investigation.

Another ethical consideration is the handling and preservation of evidence. Forensic scientists are responsible for maintaining the chain of custody and ensuring that evidence is properly collected, stored, and analyzed without compromising its integrity. Any lapses in these protocols can undermine the credibility of the evidence and potentially lead to miscarriages of justice.

The use of emerging technologies and techniques in forensic science also raises ethical questions. While advancements such as rapid DNA analysis and advanced imaging techniques offer promising opportunities, they also introduce new challenges related to privacy, data protection, and the potential for misuse or overreliance on these technologies.

Furthermore, forensic scientists may encounter ethical dilemmas when faced with conflicting interests or competing priorities. For example, they may be pressured to expedite their analyses or reach specific conclusions, which could compromise the scientific integrity of their work. In such situations, forensic scientists must remain steadfast in their commitment to ethical conduct and prioritize the pursuit of truth over external influences.

To address these ethical challenges, the forensic science community has established professional codes of ethics and best practices. These guidelines emphasize the importance of objectivity, transparency, continuous education, and adherence to scientific principles. Additionally, ongoing training and education in ethics and professional responsibility are essential for fostering a culture of integrity within the field.

Ultimately, the credibility and effectiveness of forensic science rely heavily on the ethical conduct of the professionals involved. By upholding the highest ethical standards and addressing ethical challenges proactively, forensic scientists can ensure that their work contributes to the fair and impartial administration of justice, while maintaining public trust in the scientific processes that underpin the legal system.

The field of forensic science is constantly evolving, driven by advancements in technology and scientific research. As new technologies emerge, they present both opportunities and challenges for forensic scientists, reshaping the way investigations are conducted and evidence is analyzed.

One of the most promising areas of technological integration in forensic science is the use of artificial intelligence (AI) and machine learning. These technologies have the potential to revolutionize various aspects of forensic investigations, from crime scene reconstruction to pattern recognition and data analysis.

AI algorithms can be trained to analyze vast amounts of data, such as surveillance footage, digital evidence, or biological samples, identifying patterns and anomalies that may be difficult for human analysts to detect. Additionally, machine learning techniques can be applied to automate and streamline processes like fingerprint analysis, ballistics comparisons, and DNA profiling, increasing efficiency and reducing the potential for human error.

Another emerging technology with significant implications for forensic science is the use of advanced imaging techniques. Three-dimensional (3D) scanning and modeling, virtual reality (VR), and advanced microscopy are providing forensic scientists with unprecedented levels of detail and insight into crime scenes and evidence.

For example, 3D scanning and modeling can be used to create highly accurate digital representations of crime scenes, allowing investigators to virtually revisit and analyze the scene from multiple angles and perspectives. VR technology can be used to immerse investigators in these virtual environments, providing a more comprehensive understanding of the spatial relationships and dynamics involved.

Advanced microscopy techniques, such as scanning electron microscopy (SEM) and atomic force microscopy (AFM), are enabling forensic scientists to examine evidence at the nanoscale level, revealing details that were previously impossible to detect with traditional microscopes.

Furthermore, the integration of blockchain technology into forensic science practices is being explored as a means to enhance the integrity and transparency of evidence handling and chain of custody procedures. By creating an immutable and decentralized record of evidence movements and analyses, blockchain technology could help to mitigate the risk of tampering or mishandling, further strengthening the credibility of forensic evidence in legal proceedings.

However, the adoption of these emerging technologies in forensic science is not without its challenges. Issues related to data privacy, cybersecurity, and the potential for misuse or overreliance on these technologies must be carefully considered and addressed. Additionally, forensic scientists will need to undergo specialized training and education to effectively leverage these new tools and techniques.

As the field of forensic science continues to evolve, it is essential for practitioners and researchers to stay abreast of emerging technologies and their potential applications. By embracing innovation while maintaining a commitment to scientific rigor and ethical principles, forensic scientists can harness the power of these technologies to enhance the accuracy, efficiency, and credibility of their work, ultimately strengthening the pursuit of justice and public safety.

Forensic Analyst by Profession. With Simplyforensic.com striving to provide a one-stop-all-in-one platform with accessible, reliable, and media-rich content related to forensic science. Education background in B.Sc.Biotechnology and Master of Science in forensic science.

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Forensic science is a critical element of the criminal justice system.  Forensic scientists examine and analyze evidence from crime scenes and elsewhere to develop objective findings that can assist in the investigation and prosecution of perpetrators of crime or absolve an innocent person from suspicion. 

Common forensic science laboratory disciplines include forensic molecular biology (DNA), forensic chemistry, trace evidence examination (hairs and fibers, paints and polymers, glass, soil, etc.), latent fingerprint examination, firearms and toolmarks examination, handwriting analysis, fire and explosives examinations, forensic toxicology, and digital evidence.  Some forensic disciplines practiced outside forensic laboratories include forensic pathology, forensic nursing, forensic psychiatry, forensic entomology, and forensic engineering.  Practitioners of these disciplines are most often found in medical examiner or coroner offices, in universities, or in private practices. 

The Department of Justice maintains forensic laboratories at the Bureau of Alcohol, Tobacco, Firearms, and Explosives, the Drug Enforcement Administration, and the Federal Bureau of Investigation.  The Department, through the National Institute of Justice, is a sponsor of cutting-edge research.  Its labs serve as a model for government forensic agencies at the federal, state and local levels.  The Department strives to set the global standard for excellence in forensic science and to advance the practice and use of forensic science by the broader community.

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In January 2017, the Department issued Supplemental Guidance for Prosecutors Regarding Criminal Discovery Involving Forensic Evidence.  The Supplemental Guidance provided new Department-wide guidance on criminal discovery in cases with forensic evidence.   The guidance has been incorporated into the U.S. Attorneys Manual (USAM) at section 9-5.003 and assists prosecutors in meeting their discovery obligations regarding forensic evidence and experts so that defendants have a fair opportunity to understand the evidence that could be used against them.

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• Published: 08 September 2024

Quantitative matching of forensic evidence fragments using fracture surface topography and statistical learning

• Geoffrey Z. Thompson 1 ,
• Bishoy Dawood 2 ,
• Tianyu Yu 2 ,
• Barbara K. Lograsso 3 ,
• John D. Vanderkolk 4 ,
• Ranjan Maitra   ORCID: orcid.org/0000-0002-3515-8532 1 ,
• William Q. Meeker   ORCID: orcid.org/0000-0002-5366-0294 1 &
• Ashraf F. Bastawros   ORCID: orcid.org/0000-0003-4547-8588 2  

Nature Communications volume  15 , Article number:  7852 ( 2024 ) Cite this article

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• Mechanical engineering
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The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a “match” by using comparative microscopy and tactile pattern analysis. The material intrinsic properties and microstructures, as well as the exposure history of external forces on a fragment of forensic evidence have the premise of uniqueness at a relevant microscopic length scale (about 2–3 grains for cleavage fracture), wherein the statistics of the fracture surface become non-self-affine. We utilize these unique features to quantitatively describe the microscopic aspects of fracture surfaces for forensic comparisons, employing spectral analysis of the topography mapped by three-dimensional microscopy. Multivariate statistical learning tools are used to classify articles and result in near-perfect identification of a “match” and “non-match” among candidate forensic specimens. The framework has the potential for forensic application across a broad range of fractured materials and toolmarks, of diverse texture and mechanical properties.

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Introduction.

Consider the example of a crime scene where investigators have found the tip of a knife or other tool that appears to have broken off from the rest of the object. Later, investigators recover a base that appears to topographically match, as indicated in Fig.  1 a, b and they wish to show that the two pieces are from the same knife in order to use that evidence later at trial. To this extent, the analyst comparison relies on subjective pattern recognition methodologies. Scientific testimony used in a criminal or civil trial must be “not only relevant but reliable”, according to the Supreme Court decision Daubert v. Merrell Dow Pharmaceuticals, Inc (1993) . The application of this ruling forced a reconsideration of some previously acceptable forensic evidence and a re-evaluation of the scientific validation of its premises and techniques 1 . In 2009, The National Academy of Sciences issued a report 2 that evaluated the state of forensic science and concluded that,

a Visual jigsaw match of the macroscopic crack trajectory at the typical examination scale. b Physical pattern match with comparative microscopy, with analyst focusing on macroscopic topological features. c 3D representation of a pair of fracture surfaces, showing detailed topographic features at the relevant comparison scale ( ∽ 20 grains), utilized in the current work. The fracture surface shows a biased orientation of the low-frequency texture in the direction of crack propagation, along the x -axis. d Height-height correlation variation with the size of the correlation window, showing the domain of the self-affine deformation and the deviation of the fracture surface characteristics at higher length scales ( >50–70 μm), which could be used for matching purposes. e For quantitative analysis of the fracture surface pairs, a series of aligned topographical images were taken, relative to a reference coordinate w.r.t. the right edge of the fractured article. A series of k  = 9 topographical images with 75% overlap between successive images, rendering three fully independent sequel images on the fracture surface.

…much forensic evidence—including, for example, bite marks and firearm and toolmark identification—is introduced in criminal trials without any meaningful scientific validation, determination of error rates, or reliability testing to explain the limits of the discipline 2 .

However, it should be noted that a considerable amount of prior work has been done to provide a quantitative and scientific basis for firearm and tool mark identification, for example, with the consecutive matching striae (CMS) method 3 , 4 , 5 . The report highlighted the need to develop new methods that have meaningful scientific validation and are accompanied by statistical tools to determine error rates and the reliability of the methods. To that end, the American Association for the Advancement of Science has published reports on the state of fire investigation 6 and latent fingerprint examination 7 .

The proposed framework focuses on fracture matching, the forensic discipline of determining whether two pieces came from the same fractured object. The fracture mechanisms leave surface marks on both surfaces that could be utilized for matching fragments. The basis for physical matching is the assumption that there is an indefinite number of matches all along the fracture surface. The irregularities of the fracture surfaces are considered to be distinctive and may be exploited to individualize or distinguish correlated pairs of fracture surfaces 8 , 9 . Current forensic practice for fracture matching involves visually inspecting the complex jagged trajectory of fracture surfaces to recognize a match, either by an examiner or even by a layperson on a jury. The process uses comparative microscopy and tactile pattern analysis 8 , 10 , where macro-features on a pair of fracture fragments are correlated as demonstrated in Fig.  1 a, b. Previous research has supported that the observed fracture patterns in metals are unique 11 , 12 and that inspection via a microscope of the fracture surfaces by examiners can reliably validate matches 13 . However, experience, understanding, and judgment are needed by a forensic expert, to make reliable examination decisions using comparative microscopy and physical pattern match as indicated in Fig.  1 b to identify correlated macroscopic topological features. The comparative process relies on subjective comparison without a statistical foundation, which may be flawed, as the 2009 NAS report argues:

But even with more training and experience using newer techniques, the decision of the toolmark examiner remains a subjective decision based on unarticulated standards and no statistical foundation for estimation of error rates 2 .

Indeed, the microscopic details of the non-contiguous crack edges on the observation surface of Fig.  1 a, b cannot always be directly linked to a pair of fracture surfaces, except possibly by a highly experienced examiner. There are many published studies and case reports concerning fracture or pattern matching of different materials such as rubber shoe soles, wood, glass, tape, paper, skin, fishing line, cable, and, most commonly, metal 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 . However, at about one-tenth the scale of Fig.  1 b, the 3D microscopic details imprinted on the topographical fracture surface of Fig.  1 c carry considerable information that could provide a quantitative forensic comparison with higher evidentiary value. Forensically, glass and metal fracture surfaces have been shown to have highly stochastic fracture-branches due to the randomness of the microstructure and grain sizes 11 , 30 , with limited prior attempts to quantitatively match two measured fracture surface topographies 13 , 16 . It is therefore desirable to develop more objective methods using quantitative measures that can be validated with less human input for use in a criminal or civil trial.

In this work, we propose using the fractal nature of fracture surface topography and their transition to non-self-affine properties 31 (where self-affinity means the roughness scales with the observation window) to define a suitable comparison scale. We also aim to develop supporting statistical methods for forensic fracture matching using three-dimensional (3D) topological imaging of fracture surface details. Fracture surface topography exhibits unique characteristics across various length scales, offering significant insights into damage initiation and propagation. The material microstructure controls the micro-mechanisms of fracture and the microscopic crack growth path, while the loading direction determines the macroscopic crack trajectory 32 . Mandelbrot et al. 31 first demonstrated the self-affine nature of fractured surfaces, relating their roughness to the material’s resistance to fracture through the fractal dimension. This self-affine roughness has been experimentally verified for various materials (metals, ceramics, and glasses) and under static and dynamic loading conditions 33 , 34 , 35 , 36 , 37 . A key finding is the variation of the surface descriptors when measured parallel to the crack front and along the direction of propagation 38 . The cut-off length scale of the self-affine behavior has been suggested as a unique scale to characterize the microscale fracture process in ductile 34 , 39 , 40 and brittle/semi-brittle materials 34 , 41 , 42 . Motivated by observations about the self-affine nature of fracture surfaces, we hypothesize that a randomly propagating crack will exhibit distinctive topographical details when observed from a global coordinate that does not recognize the direction of crack propagation. This work explores the existence of such distinctions at relevant length scales, which implies they can be used to individualize and distinguish paires of fracture surfaces. Our approach leverages the distinctive attributes of microscopic fracture surface features at relevant length scales, arising from the interaction of the propagating crack-tip process-zone and microstructure details, as shown in Fig.  1 c. The corresponding surface roughness analysis is shown in Fig.  1 d using a height-height correlation function, \(\delta h(\delta {{{\bf{x}}}})=\sqrt{{\langle {[h({{{\bf{x}}}}+\delta {{{\bf{x}}}})-h({{{\bf{x}}}})]}^{2}\rangle }_{{{{\bf{x}}}}}}\) , where the 〈 ⋯  〉 operator denotes averaging over the x -direction. At the small length scale of less than 10–20 μm, the roughness characteristic is self-affine (i.e. proportional to the analysis window scale). However, at larger length scales (>50–70 μm), the roughness characteristic deviates and reaches a saturation level, highlighting the individuality of the surface topography at such scale. The height-height correlation function at this transition scale, as shown in Fig.  1 d, captures the uniqueness of the fracture surfaces. We use this transition scale to set the observation scales (i.e., field of view (FOV) and imaging resolution) for comparing matching and non-matching surfaces, and creating a statistical model for classification. This imaging scale should be greater than about 10-times the self-affine transition scale to avert signal aliasing. Multiple observations at different spectral topographical frequency bands (around the transition scale of fracture surface topography) can be combined into one model to improve discrimination between surfaces of the same class or from similar manufacturing processes. This statistical model can produce a likelihood ratio or log-odds ratio for classifying new surface sets, similar to methods used in fingerprint identification and bullet matching 43 , 44 , 45 , 46 , 47 , 48 , 49 . This model can estimate misclassification probabilities and compare them to actual rates in test data. For example, in fingerprint identification, features (minutiae) on reference and latent prints are marked and scored based on their match, forming part of a probabilistic model that reports a likelihood ratio 50 . Similarly, the Congruent Matching Cells approach in ballistics divides scanned cartridge breech face surfaces into cells, searches for matches, and uses this input for a statistical model to output a likelihood ratio 51 , 52 .

After presenting an overview of the method and the study objectives, we provide an evaluation of the method and several experiments to guide choices in imaging and in the parameters for the statistical model. We also examine the general application of the framework to different modes of failure under generalized loading, mimicking mixed mode-I and mode-III loading in fracture mechanics. Finally, we discuss our results and illustrate how it may be applied in a forensic context. In the method section, we describe the sample generation and the imaging process used to create training and forensically relevant data sets. We then provide a description of the statistical model which discriminates the matching fracture surfaces from the non-matching surfaces.  Supplementary materials provide additional information about the methods and materials. An R 53 software package to perform the model fitting and analysis, MixMatrix , and code to reproduce the analysis and figures is available 54 .

In this section, we demonstrate the developed framework for matching fragments and discuss some of its attributes, generalities, and limitations.

Imaging scale for comparison

When comparing characteristic features on a fractured surface, identifying the proper magnification and FOV are critical. An optical image obtained by high magnification and a small field of view will possess a visually indistinguishable characteristic. This is the range where surface roughness shows a self-affine or fractal nature as noted in Fig.  1 d. In this range, the material intrinsic local fracture mechanism shows similar topographical surface features over the fractured surface (e.g. local cleavage steps and river patterns, and/or dimples and voids). On the contrary, employing lower magnifications will result in a lower power of identifying the class characteristics of the surface. However, we showed that the transition scale of the height-height correlation function captures the uniqueness of the fracture surfaces. We found that this transition scale is about 2–3 times the average grain size for the class of materials examined here and undergoes cleavage fracture. Interestingly, this scale is consistent with the average cleavage critical distance for the local stresses to reach the critical fracture stress 34 , 55 required for cleavage fracture initiation and typically extends to 2–3 times the grain size, or around 50–75 μm for the tested material system. This critical microstructural size scale for cleavage crack initiation is stochastic in nature as it statistically encompasses the location of the critical fracture-triggering microscopic inclusion or particle 34 , 56 , 57 .

Accordingly, the surface characteristic becomes statistically unique and non-self-affine at a larger scale. This scale sets; (i) the observation FOV to be around 10-periods of such scale. And (ii) the range of wavelengths or frequencies to perform correlations on pairs of fragments. When correlating the frequency bands in the range of 5–20 mm −1 (i.e. 50–200 μm wavelength) full separation and clustering can be clearly observed in Fig.  2 a for matched and non-match fracture surface pairs. Furthermore, beyond this frequency range, the match and non-match correlations overlap, as noted on Fig.  2 b. The identified imaging scale (which should be established for each class of materials) coupled with the statistical analysis framework provides a promising quantitative forensic comparison for a wide class of materials. However, it is crucial to acquire precise 3D topographical representations of the fracture surface without imaging artifacts for the comparison process. Two main issues may pose additional problems for the technique 49 . (1) The comparison technique is well-suited for materials that exhibit cleavage fracture, which typically possesses a relatively planar fracture surface within several hundred microns. The planarity of the cleavage fracture surface ensures that the imaging depth resolution remains in the sub-micron range across the entire surface topography. However, if the fracture surface exhibits ductile tearing with large, tortuous fracture path and millimeter-scale morphological variations 58 , additional mathematical treatments will be needed, similar to the comparison of cylindrical surfaces like cartridge cases 59 , 60 . (2) Surface anomalies, such as grain fall-out and fracture surface corrosion, will reduce fracture-pair correlations among matches, making the matching and non-matching classes less distinct. In such cases, a larger image set will be necessary to maintain the same power of separation.

a Scatter plot of correlations for 81 matched pairs and 648 non-matched pairs from training set K-1-1 for the 5–10 and 10–20 mm −1 frequency ranges on a Fisher-z (nonlinear) axis. We see that true matches and true non-matches are distinguished in this example by features in the 5–10 and 10–20 mm −1 frequency ranges. The connected points show the values of nine overlapping images from the same surface, indicating that while some individual images may not distinguish matches from non-matches, taking an ensemble of images from the surface importantly improves the ability to discriminate between the two classes in this data set. b Histograms of correlations of true matches and true non-matches for the same data set split by frequency band. Lower frequencies are well-separated, but higher frequencies begin to have more substantial overlap.

Classification performance

There are two datasets from the knives and two from the steel bars: “K-1-1” is the first set of images from the first set of knives (Supplementary Fig.  1 ), and the imaging is independently repeated generating additional sets of images “K-1-2”and “K-1-3” for repeat analysis. “K-2” indicates the other set of knives, whereas “S-1” and “S-2” indicate the two steel bar samples (Supplementary Fig.  2 ). Figure  3 a shows the classifications obtained by training on each of the four datasets, represented by one of the color boxes, with all 9 images per sample and classifying on all the other sets of surfaces using the matrix-variate t distribution and a common degrees of freedom parameter, ν  = 3, 5, 10, 15, 20, and 30, and prior probability of being a match of 0.5 (for example, training on the first set and testing on sets 2, 3, and 4, and continuing the same process with the other sets as the training set). The output (Supplementary Table  1 ) given in terms of the log-odds of being a match—log-odds larger than zero ( p  = 0.5) indicate classification as a match. While initially there are no false positives or false negatives, as the degrees of freedom parameter (DF or ν ) increases, there is one false positive, though this probability is very close to 0.5 and all of the true positives have a probability close to 1, which suggests using a classification threshold other than 0.5 would yield perfect classification in this set of data. A different threshold can be chosen by selecting a low probability (such as 10 −4 ) as a probability of a false alarm and using the distribution of log-odds of the true non-matches to fix that threshold conservatively by selecting an upper confidence bound of that quantile 61 . Using the upper 95% confidence bound for the threshold at which the false alarm probability based on the distribution of true negatives is 10 −4 sets the threshold at a probability of 0.8814 for the most conservative training set at the setting of ν  = 10, for example, which still results in perfect classification. Additionally, we may consider the probability in the range of 0.5 >  P  > 0.88 to bound the range of inconclusive decisions.

a Log-odds of being a match split by training set and true class membership for matrix −  t distributions with 3, 5, 10, 15, 20, and 30 degrees of freedom. A log-odds ratio greater than 0 indicates greater odds of being a match than a non-match. The predictions for each training set are performed on all four sets of fracture surfaces. b Individual true match correlations for three repetitions of topographical imaging of the K-1 set of 9 knives and with 9 images per knife. The similarity among the three distributions demonstrates that similar results will be obtained upon re-imaging the same surface, which is important in forensic applications. The large dots indicate the means of the sets, and we display the covariance matrices through the 99% ellipses of concentration of their distributions.

Reproducibility of results

In order to determine the reproducibility of results for a given sample, we re-imaged one of the knife samples three times and examined the distributions of the true match image correlations in Fig.  3 b. The different re-imaged sets are labeled “K-1-1”, “K-1-2”, and “K-1-3”. The means of the distributions (indicated by the large shapes) are similar and the covariance matrices, visualized using 99% confidence ellipses, are also similar. Using the two-sample Peacock test, a two-dimensional extension of the Kolmogorov-Smirnov test 62 , 63 , there is no evidence these distributions differ significantly ( H 0 : distributions are the same for 1 and 2, p  = 0.21; H 0 for 1 and 3, p  = 0.32; H 0 for 2 and 3, p  = 0.25). We conclude that the imaging and analysis processes are reproducible for the analyzed samples.

Selecting DF ( ν )

The training sets do not have a sufficient number of observations in both classes to estimate ν in the MxV t model. However, the analysis in the previous section indicates it has some influence on the results. We performed a leave-one-out cross-validation (LOOCV) procedure to provide guidance about the effects of changing the parameter. For each surface in a training set, a model was trained on the set of observations excluding that surface and tested on the observations using the excluded surface. This was done for k  = 9 images on training sets S-1 and S-2 and using k  = 5 images (restricting to the images with only 50% overlap) and k  = 3 images (restricting to the non-overlapping images) on all four training sets. The procedure was performed only on sets S-1 and S-2 for k  = 9 because nine surfaces are needed to fit the model and K-1-1 and K-2 have only nine fracture pairs, while S-1 and S-2 have ten fracture pairs. Figure  4 a shows the results for k  = 3, 5, and 9 respectively. The parameter ν varied from 3 to 30. In all cases, the true matches and true non-matches were perfectly classified using a threshold probability of 0.5 (log-odds of 0). Higher values of ν had more separation between the classes. Using 9 images with 75% overlap had greater separation than 5 images with 50% overlap and greater separation between the identification of true matches. However, given that there is perfect classification in all cases, this finding does not provide much guidance on the selection of ν .

a Cross-validation results for models fit using k  = 3, 5, and 9 images of each surface. The cross-validation was done to provide guidance about the number of images and the choice of DF ( ν ). There were no false positives or false negatives in this analysis, so it did not provide any conclusive results. b Rates of false positive and false negative classifications (in %) using models trained on the four different sets of surfaces and tested on consecutive subsets of those images for k  = 2, 3, …, 9. A full summary of the results is provided in Supplementary Table  1 . c Distributions of the log-odds of a match using models trained on the four different sets of surfaces and tested on subsets of k consecutive images for k  = 2, 3, …, 9, for a model with ν  = 10. d Rates of false positive classifications (in %) using models trained on the four different sets of surfaces using only the images with at most 50% overlap and tested on subsets of k consecutive images for k  = 2, 3, 4, 5 and using only the 3 non-overlapping images and tested on subsets of k consecutive images for k  = 2, 3. A full summary of the results is in Supplementary Tables  2 and 3 .

Required number of images for discrimination and model selection

Due to the existence of morphological disturbances in some images (e.g., grains fall out from the fracture surface or substantially large out-of-plane curvature within the range of comparisons), there is no perfect separation between all image pairs for the matches and non-matches. This can be seen in Fig.  2 a where some image pairs have a correlation coefficient of less than 0.50 for the two bands of frequency analysis. To mitigate the influence of local topographical disturbances when deciding whether a pair of fragments represents a match or not, multiple observations are needed. To determine how many images are needed to optimize classification performance, we started by training models using all nine images from each base-tip pair in each training set as before. We again used the MxV t model with ν  = 3, 5, 10, 15, 20, and 30, and then tested them on subsets of consecutive overlapping images of size k , for k  = 2, 3, …9 with the model reduced to considering only the selected images and the training set for each model excluded from testing. A summary of the complete results is given in the Supplementary Section  S.4 .

In Fig.  4 b, models with higher ν have higher false negative rates for all values of k . For values of k over 4, only 20 and 30 DF have false negatives (specifically, they each have one false negative result, Supplementary Table  1 ). Low values of the degrees of freedom parameter have false positives. All of this suggests that choosing a value near ν  = 10 and k  ≥ 5 images is sufficient for error-free classification in the examined sample sets. Figure  4 c displays complete results for a model with ν  = 10. As k increases, the typical classification results become more separated. However, even with only two images considered in the test cases for the ν  = 10 model, the accuracy is very high. The worst case of a false positive is classified with only a probability of 0.8314. The worst case of a false negative is classified with a probability of 0.504. Again, this is the range of match probabilities where an inconclusive match result could be assimilated for 0.5 >  P  > 0.88 as noted in the classification performance section.

Percentage of imaging overlap

Guided by the results of Fig.  4 c, it is apparent that we need at least 5 to 6 images for error-free discrimination in this particular example, and that performance improves with additional images. We reassessed the imaging procedure to gauge the role of the image-overlap ratio. The initial experiment involved imaging surfaces using nine images with 75% overlap between images, which provides three observations for each point on the surface, apart from the edges. However, a similar area can be imaged using 5 images with 50% overlap, which produces two observations of each point on the surface apart from the edges, or using 3 non-overlapping images, which raises the question of whether anything is gained by having an additional third image of the same area and, if so, what level of overlap is best.

We can evaluate this by providing an analysis similar to that done previously: looking at the classification results when restricted to cases with the specified overlap. We train classifiers on the same sets as before, except using 5 images with 50% overlap instead of 9 images with 75% overlap and then test the models on the other sets excluding the set used to train the model by classifying pairs of surfaces using all possible subsets of those images on the surface of sizes 2, 3, 4, and 5. When restricted to the case of 50% overlap, Fig.  4 d only shows perfect classification when all four or five images are included and ν  < 20. In all cases, there are no false negatives.

We perform a similar exercise in the case of the non-overlapping images. There are three non-overlapping images per surface which can be used to train the classifiers and the models can then be tested on subsets of those images on each surface of sizes 2 and 3. In the case of non-overlapping images, no model results in perfect classification. The false positives for each model are also shown in Fig.  4 d. There are no false negatives in the classification decisions.

This suggests that, while having more images is generally better, using 5 images with 50% overlap appears to be sufficient if all the images are used. Imaging the entire surface with 50% overlap outperforms imaging the entire surface with 75% overlap in the sense that it works for all of the classes of model. However, if training with 9 images with 75% overlap is possible, testing on new surfaces is feasible with as few as 5 test images with an appropriate choice of the degrees of freedom parameter in the model.

Calibration of output probabilities

The models present the outputs as probabilities, therefore we need to assess how well the probabilities in the models reflect the underlying probabilities in the matching and non-matching populations. Figure  5 displays a calibration plot comparing the output probabilities for all predictions to the empirical proportions in each class with a line drawn by a local regression smoother (LOESS) for each model 64 , 65 . These predictions can be compared to the reference line on the plot, y  =  x , to judge the calibration. The true matches correspond with y  = 1 and the true non-matches with y  = 0. The vast majority of the model classifications are correct with probabilities of being a match of either  <0.001 for non-matches or  >0.999 for matches. The relative lack of samples in the middle range makes it hard to judge the calibration. The lowest probability of a match among the true matches was 0.3709. Among the various models, the 99th percentile of the predictions for non-matches was, in the worst case, 0.1437. Only outliers overlapped in middle range. We note that our evaluation of the calibration is limited by the sample size in the experiment—with more samples and more observations with match probabilities between 0.1 and 0.9, a better evaluation of the calibration could be made.

The lines should be compared to the diagonal reference line. Matches are indicated at y  = 1 and non-matches are indicated at y  = 0.

Examining the framework capabilities on a twisted-fracture knife set

All examined sets of fractured articles were tested in tension or bending. This is mode-I cleavage fracture where the crack propagation direction is normal to the loading axis. The fracture surface showed topographical features normal to the fracture surface, similar to those shown in the scanning electron microscope (SEM) image of Fig.  1 b. However for a general forensic article such as a knife or a pry tool, an edge could be broken due to bending and twisting of the article. This would impose a mixed mode of loading including mode-I opening and mode-III twisting of a crack. To understand the effect of external loading mode on the generality of the proposed analysis framework, a set of nine knives from the same manufacturer similar to the previously used sets were fractured at random using the same fixture (Supplementary Fig.  1 b) and forming set of twisted knives shown in Supplementary Fig.  1 e. A typical twisted knife fracture topography is very different at both the macro and micro scales. At the macro-scale, the crack trajectory is no longer planer with curvilinear or twisted trajectory (Supplementary Fig.  1 e). At the micro-scale, the SEM image of Fig.  6 b shows twisted fracture morphology in the plane of the crack that is very different than those under mode-I loading of Fig.  6 a. This unique texture would probably further enhance the individuality of the fracture surface. We will attempt to examine the validity of the analysis protocol on such general case of fractured articles. The twisted knife set was imaged using the same procedure discussed in “Sample Generation and Imaging” section and the same magnification of 20X. However, due to the excessive tortuosity of the crack path, five images ( k  = 5) with 75% overlap between adjacent images were employed. Using the models previously trained on the four training sets loaded in tension or bending, and restricted to 5 images and setting the degrees of freedom ν  = 10. The results for this set, shown in Fig.  6 c, are similar to those obtained in Fig.  3 a despite the use of a different external loading of mode-I tensile cleavage fracture. The true match cases were identified with a probability exceeding 99.999% and the true non-match was identified with a probability not exceeding 0.05% for all different training sets. This suggests the scale of comparison, derived from the self-affine saturation scale of the fracture surface topography is more general and tied to the microstructure scale (grain size) for the class of materials failing by cleavage fracture (similar to hardened tool materials). This result is far more reaching with practical implications. As long as the cleavage fracture is the dominant mode of failure, a single robust training data set under simplified loading conditions for the same material class would be sufficient to help in discriminating; (i) articles that were exposed to complex external loading (i.e. mixed mode of fracture). (ii) articles from different classes of materials, but share the same grain size distributions, and (iii) articles with different grain sizes, which would only require changes of the FOV to cover 20-grains while changing the comparison frequency bands to cover the corresponding 2–4 and 4–8 grain size ranges. It is conceivable to extend these results to glassy metals, polymers and ceramics, that undergo cleavage and/or brittle or semi-brittle fracture. In such cases, the limits of the fractal scale should be examined and compared to the critical microstructure scale of the fracture surface topography, such as river and herringbone patterns. Though, additional experimental verification is needed for these classes of non-crystalline materials.

a SEM image of a typical fracture surface bent of a knife broken in bending, showing topological details normal to the imaging plane. b SEM image of a typical fracture surface of a broken knife in torsion, showing in-plane swirl textures. c Classification performance on a set of nine knives broken by twisting. The models were trained on different training bending and tensile fracture sets using five images and ν  = 10.

This paper provides a formal quantitative basis for matching metal fragments found at crime scenes. Our proposed approach combines fracture mechanics with statistics and machine learning to quantify, given a prior probability, the posterior probability that two candidate specimens are a match. Our methodology utilizes 3D spectral analysis of the fracture surface topography, mapped by white light non-contact surface profilometers. Specifically, our framework realizes the distinctive attributes for a pair of fragment surfaces when viewed at a length scale defined by the transition of fracture surface topography to become non-self-affine, and uses them to do a quantitative physical match analysis of metal fragments. Fracture surface morphology has been analyzed for many classes of materials and external loading conditions including tensile, bending and twisting of articles, and shown to be self-affine within a microscopic scale relevant to the fracture surface topography.

The transition scale of the height-height correlation function, shown in Fig.  1 d is used to set the FOV and imaging resolution. For the examined class of materials, the saturation level is observed at a length scale ( >50–70 μm). Moreover, the examined class of materials has an average grain size of approximately d g  = 25–35 μm. This will determine the transition scale, where the individuality and uniqueness of the fracture surfaces become apparent, to be approximately two-grain diameters. This relationship, characterized by a constant fractal dimension and related to the material average grain size, has been observed in some cleavage fracture mechanics studies. Dauskardt et al. 34 have examined the topography of a wide range of brittle failure of well-characterized mild steel at extremely low temperature and observed two ranges over which the fractal dimension is constant. The first range is 1–10 μm corresponding to the cleavage step. This range of cleavage steps will be non-unique as it will be found in all surfaces of the same alloy that exhibit cleavage failure. The second range is of the order of twice to three times the grain size. It is shown that the fractal dimension is constant over a range of the order of twice to three times the grain size range for transgranular cleavage fracture, about twice the grain size range for intergranular fracture, and of the order of the grain size for the quasicleavage fracture 34 . Some fracture mechanics studies have demonstrated that cleavage failure occurs when the local stress ahead of the crack tip exceeds the fracture strength of the material over a characteristic distance, equal to about two grain diameter 34 , 55 . This critical scale is required for cleavage crack initiation. However, it is apparent that such critical scale is also embedded in the topography of the fracture surface. When a microcrack is initiated at a hard-particle, it may be arrested if there is insufficient global driving forces to continue crack propagation 57 . Accordingly, the requirement of reaching critical stress over a microstructure critical distance will be maintained for continued crack propagation until the macroscopic crack reach an unstable propagation domain, and thereby set-forth the critical fractal scale on the topography of the fracture surface. It is also important to note that the reported fractographic details are reported for mild steel, examined at extremely low temperature, below the ductile to brittle transition temperature (DTBTT) of (−95  o C ), where fracture occurs before general yielding due to slip-induced cleavage. For the current examined alloy of AISI 440C stainless steel, a common alloy for cutlery and knives, the alloy has up to 1.2% carbon content in order to make the alloy hard and remain sharp. Such carbon content also shifts the DBTT to be above the room temperature 66 . Accordingly, it is no surprise that the examined alloy in the form of rods or knives at room temperature shows similar fractal character to mild steel alloys tested below their DTBTT 34 , 55 . The requirement of local stress ahead of a crack to exceed the fracture stress over a microstructurally significant distance 55 should be viewed in statistical terms. The characteristic dimension represents the location of the weakest link for the fracture process to occur 57 . The cleavage fracture process zone is statistical in nature 56 , as a finite volume of the material ahead of the crack tip should include a local defect to nucleate the cleavage crack. Such statistical argument is used to explain the large scatter in the cleavage fracture toughness data, wherein two nominally identical articles from the same material lot might show very different toughness (resistance to fracture) and failure strength values. By extension, we speculate also that such statistical differences will result in different local fracture surface topography because of the statistical randomness of the microscopic spatial location of the critical fracture-triggering particle. Susceptibility of cleavage fracture is sensitive to microstructure (grain size and carbide population), yield strength, stress state (triaxiality), and environment (temperature and radiation). Similarly, the fracture topography will exhibit unique microscopic feature signatures that exist on the entire fracture surface. We extend these parameters to generally include the material microstructure, the intrinsic material resistance to fracture, the direction of the applied load, and the statistical distribution of imperfections within the microstructure. The proposed framework’s ability to classify large sets of fracture surface pairs under various macroscopic loading conditions (both controlled and random) reinforces the foundational aspects of fracture mechanics in forensic comparisons. By leveraging the fractal nature of fracture surface topography and the statistical nature of cleavage fracture initiation, this approach establishes the critical length scale required for imaging comparisons and identifies the unique attributes of the fracture surface for forensic applications.

We exploit these distinctive features to quantitatively distinguish the microscopic features on fracture surfaces. Statistical learning tools are used to classify specimens. Using at least 5–6 images in the case of 75% image overlap or five images with 50% image overlap, we found that the matrix-variate t-distribution with 10–15 degrees of freedom, and a first-order autoregressive correlation structure to describe between-image correlation provides highly effective discrimination between matching and non-matching surface pairs. Our results show the distinctive individuality and the lack of identified discrepancies for a pair of fractured surfaces at wavelengths in the range of 2–8 grain diameters (50–200 μm, or the frequency range of 5–20 mm −1 for the examined tool-steel). Near-perfect discrimination was achieved in the four training sets totaling 38 samples along with a set of 9 twisted samples, even in cases where some images on a surface had correlations that were not distinguishable from non-matching images. Challenges to this technique arise from high topographical details with a large aspect ratio that might shadow the surrounding details and might disturb one of the frequency bands. Statistical methods using two frequency bands and an extended number of base-tip image pairs yielded highly accurate match decisions. Among the range of training sample sets, this domain of distinctive individuality was found to be persistent and easily identified.

Our results suggest that for the class of materials that undergoes cleavage fracture, a single robust training data set would be needed for the identification of different classes of materials that share the same grain size distribution, but exposed to different and complex loading conditions. Furthermore, a framework is provided for performing matching of fragments with recommendations for model parameters, procedures for training models on a similar class of materials and setting the imaging scale and comparison bands as a function of the grain sizes, and procedures for testing new samples. Repeated imaging on the same surfaces consistently provided similar results. Our framework provided near-perfect matching with high confidence and so has the potential to be of significant impact, providing the ability to introduce more formality into how forensic match comparisons are conducted, through a rigorous mathematical framework. Our framework is also general enough to be applied, after suitable modifications and identification of the proper imaging scale, to a broad range of fractured materials and/or toolmarks, with diverse textures and mechanical properties. In doing so, we expect our proposed methodology and findings to help forensic scientists and practitioners place forensic decision-making on a firmer scientific footing. This can help formalize the scientific basis for conclusive matching of fragments leading to quantitative and more objective forensic decisions.

Sample generation and imaging

To mimic forensic articles found in a crime scene that might undergo comparative analysis, we consider two main material classes 67 : sets of rectangular rods of a common tool steel material (SS-440C) fractured under control tension and bending configurations (Supplementary Fig.  2 b, d), and sets of knives (Supplementary Fig.  1 c, d) from the same manufacturer, fractured at random employing the fixture shown in Supplementary Fig.  1 b. Figure  1 a shows a typical pair of fragments, generated for this study. The average grain size for both groups was approximately d g  = 25–35 μm. Four different sets of samples were established with nine specimens in the two sets of knives and ten specimens in the two sets of steel rods. To show the generalization of the approach for modes of loading, an additional set of 9-knives (Supplementary Fig.  1 e) was tested by random twisting utilizing the same fixture (Supplementary Fig.  1 b). The fracture surface topography would be influenced by a combination of fracture loading modes; that is mixed mode of the tensile mode-I and tearing mode-III loading as shown in the SEM images of Fig.  6 b. The SEM images show subtle differences between the Modes of loading. Figure  6 a shows cleaved grains in a direction normal to the imaging plane due to the pulling action (mode-I) under bending. Figure  6 (b) shows swirl texture due to the combined out of plane tensile (mode-I) and in plane tearing (mode-III) loading. These topographical textures are very different and clearly show the critical role of external loading direction. Further details about sample preparations are given in Supplementary Section  1 .

For clarity, we refer to the surface attached to the knife handle as the base and the surface from the tip portion of the knife as the tip and apply the same terminology to samples from the rectangular steel rods. The microscopic features of pairs of fracture surfaces were analyzed by a standard non-contact 3D optical interferometer (Zygo-NewView 6300), which provides a height resolution of 20 nm. Utilizing the results of the height-height correlations of Fig.  1 d, the transition scale commences at around 50–70 μm to become non-self-affine and saturate, rendering a required imaging FOV of about 500 μm. For the examined material systems, this scale amounts to 2–3 times the grain size (consistent with the fracture process zone for cleavage fracture 55 ), and the FOV should cover 20–30 grain diameters. Accordingly, an optical magnification of 20X is employed, providing a 550 μm FOV and 0.55 μm/pixel resolution (Fig.  1 c). Two fragments were aligned for imaging relative to their rectangular edges and their lower right corner. Image mis-registration can greatly affect the correlation estimations between a pair of images. However, the implemented procedure in this work to utilize the spectral (frequency) space is very tolerant to linear mis-registration of up to 20% of the FOV and several degrees of angular miss-registration, further elaborated in Supplementary Section  2 .

A series of k -overlapping surface height 3D topographic maps were acquired from the pairs of fracture surfaces (Fig.  1 e; k  = 9), and quantified using Fourier transform based power spectral analysis as summarized in Fig.  7 a in the image analysis step. The choice of overlap means there are three full independent sequential images on a surface. Multiple overlapping images were needed to overcome problems arising from missing grains between pairs of the fracture surface and/or the special circumstances of complex tortuous path of fracture. The effect of the number of images and overlapping ratio are further discussed in the result section. Additionally, having a super-image of stitched FOVs results in misregistration at the overlapping boundary of the stitched images, leading to an additional interfering frequency within the frequency bands of comparison 49 .

Steps include ( a ) image spectral analysis, ( b ) model training, and ( c ) classification of new objects to provide classification probabilities. For a new field-find object, an examiner would use ( a ) to image the object and perform ( c ) for object classification, using a model trained in ( b ) on samples of the same class to guide forensic conclusions.

Image spectral analysis and frequency correlations

From the 3D imaging of the fracture surface, the measured height distribution function h ( x ) is acquired to define the topography of the fracture surface at every spatial point, x on the fracture surface of a pair of fragments, shown in Fig.  7 a. Each wavelength on the fracture surface has a distribution, in the frequency domain H ( f ), which is acquired using a Fast Fourier Transform (FFT) operator. For example, grain size has a distribution of frequencies across the spectrum rather than one specific frequency. Similarly, other microscopic fracture features have a range of spectral distributions 67 , 68 , 69 . For a pair of fractured surfaces, the population of these features contains relevant information about the physical fracture processes present at each length scale (e.g. cleavage steps, dimples and voids at the sub-microscale, and river marks at scales of tens of microns). The spectral space analysis provides a straightforward segmentation of the surface topographical frequency ranges for comparison. After calculating the spectra of each pair of images, each spectrum was divided into multiple radial sectors. The segmented angular sectors for the frequency range (0 ∘ , 180 ∘ ) represent the entire data set because the amplitude of H ( f ) exhibits inversion symmetry. The spectral array size is proportional to 2 n , as this is a mathematical feature of the FFT. For the image size employed in this work, a spectral array of 1024 by 1024 is acquired, although only the upper half is utilized because of symmetry. The radial segments for comparison in the frequency domain (marked on the FFT spectral representation in Fig.  7 a) are chosen to reflect the physical process scales and the corresponding wavelength, identified from the height-height correlation of Fig.  1 d.

For comparison, we use the frequency amplitude, \(\bar{H}({{{\boldsymbol{f}}}})\) for each surface spectral frequency. To compare the surfaces of two fragments, two-dimensional statistical correlations between their spectra are computed in banded radial frequencies, producing a similarity measure for each frequency band across the corresponding k pairs of images. As noted earlier, the increments for the bands’ frequency are determined by the scale of the image and the material microstructure, covering the transition scale of the height-height correlation of Fig.  1 d. A training data set with N fracture surfaces is utilized to estimate the correlation distribution among the two selected frequency bands on all k image pairs for both the population of true matches and true non-matches fracture surfaces. For establishing a statistical match, these distributions, shown in Fig.  2 (a), form the basis for our classification and matching process strategy, following the two modules summarized in Fig.  4 , (b) Model training on an initial data set, and (c) performing classification of new sample(s).

Model training/fitting

A statistical model will be developed to distinguish matching from non-matching fracture surfaces 70 . Employing a training data set, the behavior of the frequency band correlations in the population of matches and non-matches has to be estimated and modeled. The proposed framework provides a separate model for each class (i.e., match and non-match). The model training process, highlighted in Fig.  4 b, entails:

Choice of controlled and robustly characterized data set of fractured pairs to train the model.

Computation of the correlations for the frequency bands for the sets of k images for all N matching and N ( N  − 1)/2 non-matching surface pairs.

Employing the Fisher’s z transformation on the correlation data to stabilize variance 71 .

Fitting the models using a matrix-variate distribution (as detailed later in this section) to describe the distribution of true matches and true non-matches. The matrix-variate models account for the difference in the location of the correlations and account for the covariance of the repeated observations across the surface.

The frequency band correlations for one of the examined data sets (K-1-1) are shown in Fig.  2 . The proposed method’s discrimination ability can be judged from the clear separation of matching and non-matching surfaces within two separate clusters. The data in this illustration were derived from N  = 9 base-tip pairs from fractured knives. A series of k  = 9 overlapping images were taken from each base and tip fracture surface, resulting in N  × 2 k  = 162 total images (81 from the tips and 81 from the bases). Additional details are given in the Supplementary Section  2 for different data sets. In this example, image pairs for when the tip and base surfaces were from the same knife are true matches ( N  ×  k  = 81 matched-pairs), while those pairs for which the tip and base surfaces were from different knives are true non-matches ( N ( N  − 1) ×  k  = 648 unmatched-pairs). Furthermore, there is one image-pair among the true matches in Fig.  2 a which cannot be distinguished from the true non-matches and three other pairs that are ambiguous. To further improve the discrimination, considering multiple k  − observations from the same surface would distinguish it from the non-matches, since the other observations on that surface are well-separated from the non-matches. In the current framework, we take the information from every pair of images and collectively based on the model, a decision is driven accounting for the fact that the images are not independent, i.e. overlapping and coming from the same fracture surface. The role of imaging repetition or overlap, may improve the signal to noise ratio. Figure  2 b summarizes the correlation analysis over several ranges of frequency bands. A clear separation (lower values for the true non-matches and higher values for the true matches) can be observed for the 5–10 and 10–20 mm −1 frequency-band ranges. Beyond these frequency ranges, there is some overlap, where the true match and the true non-match correlation distributions become less distinct and overlap more.

For the presented data set of k  = 9 overlapping images for each fracture surface and two (or more) comparison frequencies, each comparison between a pair of fracture surfaces based on the ensemble of nine images provides a 2 × 9 matrix of correlations. Our model needs to account for the lack of independence in the images from the same specimens. Accordingly, we propose using a matrix-variate distribution 72 , 73 to model the densities of the matching and non-matching populations, and, specifically, a matrix-variate t distribution (MxV t ) because the data for the individual comparisons are approximately elliptically distributed but have heavier tails than a normal distribution. A definition of the distribution is in the Supplementary Section  3 and the density is defined in Supplementary Equation  1 .

We use matrix-variate distributions to model the relationship between the two frequency bands in each image comparison and across all the images being compared for each of the base and tip pairs (e.g. Fig.  2 a). Because of the overlapping-image structure of the data source, our model allows between-image correlations in the matrix-variate model to be related according to an autoregressive model of order 1 (or AR(1)) model (implying that immediately adjacent images can be correlated). The AR(1) model implies that the mean correlations in the two frequency bands remain the same across the images on a surface in the model. The parameters of the model are estimated using an expectation-maximization (EM) algorithm developed for the matrix-variate t distribution 74 .

Classification of a new object

Figure  7 c sumarizes the classification procedure. Suppose the fitted model has been trained on a set of k -images per fracture surface, yielding probability density functions f 1 corresponding to the population of true matches and f 2 corresponding to the population of true non-matches. Suppose also that there is a new pair of fracture surfaces that may or may not match. First, the correlations for the k -aligned image pairs in the chosen frequency bands are computed and transformed, yielding a new observation X , which is a matrix of observations of correlations with one row for each frequency band and one column for each pair of images—here, a 2 ×  k matrix. Then, presuming prior probability p of being a true match and prior probability 1 −  p of being a true non-match, we can find, by combining prior probabilities and the match and non-match densities from the model, the posterior probability that the two surfaces match as follows:

Alternatively, a likelihood ratio (LR) can be calculated as f 1 ( X )/ f 2 ( X ), a common method in forensic applications 43 , 44 , 45 , 46 , 47 , 48 , 49 . These LR results can then be used to express the uncertainty about the strength of evidence under different sets of assumptions 75 . The likelihood ratio can be combined with prior odds ( p  / (1 −  p )) to produce posterior odds:

with the conversion of odds O to probability P performed by the formula P  =  O  / (1 +  O ). In this paper, odds and likelihood ratios are employed and reported on the logarithmic scale. Once the posterior odds are obtained, classification decisions can be made according to the rules of evidence in setting prior probability relevant to each forensic case. For the purposes of illustrating the method, we are using an equal prior probability of being a match or non-match (i.e., p  = 0.5 or a log prior ratio of 0). In an actual criminal or civil case, choosing a prior match probability would require carefully considering any other evidence or relevant information previously presented, but such considerations are beyond the scope of this paper.

Data availability

The experimental data generated in this study for fracture match samples have been deposited in a public access database 54 at https://github.com/gzt/fracturematching . The processed data set 54 will help to reproduce the figures and analysis in the paper.

Code availability

An R 53 software package to perform the model fitting and analysis MixMatrix , is available 54 . A GitHub repository containing the code required to reproduce the figures and analysis in the paper is available at https://github.com/gzt/fracturematching .

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Acknowledgements

This research is supported by U.S. Department of Justice under contracts No. 2015-DN-BX-K056, 2018-R2-CX-0034 and 15PNIJ-21-GG-04141-RESS. The content of this paper however is solely the responsibility of the authors and does not represent the official views of the NIJ.

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Bishoy Dawood, Tianyu Yu & Ashraf F. Bastawros

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G.Z. Thompson carried out the statistical analysis. B. Dawood, T. Yu, and B.K. Lograsso performed the experimental studies. J.D. Vanderkolk is retired and provided the forensic perspective and design of forensic data collection protocols. R. Maitra, W.Q. Meeker, and A.F. Bastawros supervised the work.

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Thompson, G.Z., Dawood, B., Yu, T. et al. Quantitative matching of forensic evidence fragments using fracture surface topography and statistical learning. Nat Commun 15 , 7852 (2024). https://doi.org/10.1038/s41467-024-51594-1

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New method for fingerprint analysis holds great promise

A groundbreaking study has made it possible to extract much more information from fingerprints as evidence than what is currently achievable.

A new study from the Department of Forensic Medicine at Aarhus University is the first in the world to analyze fingerprints on gelatin lifters using chemical imaging. This could be crucial in criminal cases where current methods fall short.

Danish police frequently collect fingerprints at crime scenes using so-called gelatin lifters. Unlike tape, these lifters are easy to use and are suitable for lifting fingerprints from delicate surfaces, such as peeling wall paint, and irregular objects like door handles.

Once collected, the fingerprints are photographed digitally so they can be processed through fingerprint databases. However, traditional photography cannot separate overlapping fingerprints, which are often found at crime scenes. Very faint prints are also problematic. As a result, many fingerprints that could otherwise contribute to investigations unfortunately have to be discarded.

A fine spray of solvent

A solution is presented in the new study from the Department of Forensic Medicine at Aarhus University, recently published in the scientific journal Analytical Chemistry .

"We are presenting a method that has the potential to be integrated into the police's traditional workflow. If this happens, more fingerprints from crime scenes could be used and evaluated both visually and chemically," says postdoc Kim Frisch, who is behind the study.

The method is based on a technique called Desorption Electrospray Ionization Mass Spectrometry (DESI-MS), which works by measuring the chemical compounds in fingerprints based on their mass.

"We send a very fine spray of solvent, consisting of electrically charged droplets of methanol. This releases and ionizes substances on the surface of the fingerprint on the gelatin lifter. The substances are then drawn into the instrument, where their masses are measured individually," explains Kim Frisch.

DESI-MS was invented about 20 years ago and was developed for general surface analysis. In 2008, it was shown that the technique could be used for chemical imaging of fingerprints on glass surfaces and tape.

"But now we show that the technique can also be used to analyze fingerprints collected on gelatin lifter, which are used by police in many countries, including Denmark. This is analytical chemistry used in a forensic context, and it has great potential," says the researcher.

Revealing fingerprints where traditional optical imaging fails

Overlapping fingerprints pose a significant challenge for investigators because they are difficult to separate. The study shows that the new method can be used to separate overlapping fingerprints (Figure 2) and to enhance faint fingerprints in situations where optical imaging fails.

So far, the method has been tested on fingerprints lifted in the laboratory, but the researchers are now testing the method on fingerprints from crime scenes. For this purpose, they have received fingerprints collected by the National Special Crime Unit of the Danish Police , and there are high hopes for the results at the Department of Forensic Medicine.

Can we analyze gender, age, and dietary habits?

The method is still under development, and the researchers are now focusing more on analyzing the chemical composition of fingerprints.

A fingerprint is much more than a unique pattern -- it also contains a variety of chemical compounds from the person who left the print. These compounds include natural lipids, amino acids, and peptides secreted from the skin. However, the fingerprint can also contain nicotine, caffeine, drugs, cosmetic ingredients, and potentially incriminating substances such as lubricant from condoms and explosives that have been secreted through the skin or contaminated the skin upon contact.

Chemical imaging could potentially be used for profiling the person who left the fingerprint.

Many researchers around the world are working to develop methods for this purpose -- not only using the technique employed at the Department of Forensic Medicine in Aarhus. There are examples in the literature that fingerprints can reveal whether people have ingested or touched substances of abuse such as cocaine, cannabis, and ayahuasca.

Studies have also been conducted with the aim of determining individuals' gender, age, and lifestyle factors such as diet, medication, and smoking from their fingerprints. The Department of Forensic Medicine continues to work on the study, supported by the Danish Victims Fundand ongoing for two and a half years, in an effort to maximize the information that can be obtained from fingerprints.

Research focused on practical application

The research is conducted in close collaboration with the National Special Crime Unit of the Danish Police because it is important that the work is aimed at practical application.

So far, the results suggest that the method could be used in practice.

"When the police collect fingerprints at a crime scene, the gelatin lifters can, in principle, be sent to the Department of Forensic Medicine, where we scan the samples. However, the scanning process is time consuming, which means that we would not be able to analyze samples in the hundreds, as we do with, for example, blood samples. We expect that the method will be used in the future as a special analysis in more serious cases such as murder and rape," says Kim Frisch.

• Medical Topics
• Pain Control
• Forensic Research
• Legal Issues
• Surveillance
• Protein structure
• Chemical bond
• Biochemistry
• Vitreous humour
• Erikson's stages of psychosocial development
• Hydrochloric acid

Story Source:

Materials provided by Aarhus University . Original written by Line Rønn. Note: Content may be edited for style and length.

Journal Reference :

• Kim Frisch, Kirstine L. Nielsen, Jo̷rgen B. Hasselstro̷m, Rikke Fink, Stine V. Rasmussen, Mogens Johannsen. Desorption Electrospray Ionization Mass Spectrometry Imaging of Powder-Treated Fingermarks on Forensic Gelatin Lifters and its Application for Separating Overlapping Fingermarks . Analytical Chemistry , 2024; DOI: 10.1021/acs.analchem.4c02305

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