ORNL Staff Associates
(Energy & Transportation Science Division )
Samuel A. Lewis,
Materials and Techniques for Forensics
The ORNL forensics research team is working with numerous collaborators (Naval Research Laboratory, University of Tennessee, and other research groups) to identify robust and fieldable methods (including chemical development, SERS imaging, and reflective scattering techniques) for the detection of latent fingerprints that would otherwise remain undetected using traditional techniques. ORNL is teaming with Local and Federal agencies (Knoxville Police Department, ATF, NRL) to enhance one of the most widely used forensic techniques used to identify an individual associated with a crime or national-security incident. Under both normal aging and extreme-event conditions, ORNL is studying the changes in latent fingerprint compositions and identifying associated in-growth products. As information is gained from these experiments, new instrumental and chemical methods of detecting latent fingerprints that key off of surviving fingerprint constituents are being identified and assessed. One new instrumental method being developed with a manufacturing partner (ChemImage, Inc.) is surface enhanced macro-Raman spectral (SERS) imaging of latent fingerprints. Sponsors of the ORNL forensics research team have included the National Institute of Justice (NIJ), Federal Bureau of Investigations (FBI), OSD Counter Narcoterrorism Technology Program Office (CNTPO), and the Technical Support Working Group (TSWG).
Develop field portable methods of locating and imaging latent fingerprints that would otherwise remain undetected. As illustrated below, a new visualization technique utilizing differential light scatter resulting from the presence of untreated and invisible fingerprint residues has evolved from the team’s research. ORNL is working to detail the scatter mechanism in order to further develop and exploit this methodology for the detection of fingerprints that would otherwise remain undetected.
The ultimate program goal is to develop robust, efficient fingerprint visualization techniques supporting rapid assessment field and laboratory needs.