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                            Funding & Awards
 Identification of Minor Dye Components of Fibers via Integrating-Cavity-Enhanced Raman Spectroscopy
Award Information
Awardee
              WASHINGTON STATE UNIVERSITY
Award #
              2018-DU-BX-0178
Location
                    PULLMAN, WA
Congressional District
              5
Status
              Closed
Funding First Awarded
              2018
Total funding (to date)
              $580,622
Original Solicitation
              NIJ FY18 Research and Development in Forensic Science for Criminal Justice Purposes
Description of original award (Fiscal Year 2018, $580,622)
The characterization and analysis of trace evidence is part of the standard protocol during forensic investigations. Trace evidence can potentially be used to link a suspect with a victim, or a suspect/victim with a location. Textile fibers are one type of trace evidence, and the color of the fiber is one of its most important properties.

Fibers are typically analyzed using a variety of techniques and compared with a reference. First, nondestructive techniques such as light microscopy and UV/Vis microspectrophotometry are used. If these dont yield conclusive results, destructive techniques such as thin-layer chromatography and gas-chromatography mass spectrometry are used.

Recently, Raman spectroscopy has been evaluated as an analytical tool for the characterization of fibers. Besides its non-destructive nature, Raman spectroscopy offers other advantages such as: requiring almost no sample preparation, yielding more distinctive spectra than UV/VIS/NIR spectra, and providing unique sample information. While Raman spectroscopy is a promising tool, it has its own set of limitations, the main one being sample fluorescence which is typically orders of magnitude stronger than the actual Raman signal. Fluorescence can, in principle, be avoided by measuring anti-Stokes Raman spectra instead of Stokes Raman spectra. However, the intensity of anti-Stokes Raman spectra is typically too low.

Textile fibers are often colored using a mixture of dyes. Due to the limitations described above, however, Raman spectroscopy can often only identify the dye with the highest concentration or the one resulting in the strongest Raman scattering peaks. Changing the wavelength of the Raman laser can sometimes help in identifying a secondary dye. Given that there exist thousands of dyes, however, the ability to identify only one or two dyes within a dye mixture can severely limit the usefulness of Raman spectroscopy for forensic purposes.

The goal of this project is to evaluate integrating-cavity-enhanced Raman spectroscopy (ICERS) to measure anti-Stokes Raman spectra for the characterization of dyed fibers. ICERS has been developed for the ultrasensitive identification and characterization of materials, and enhancements of five orders of magnitude have been demonstrated. Using ICERS to measure anti-Stokes Raman spectra eliminates the fluorescence background, while the cavity design amplifies the anti-Stokes Raman signal. The combination of the two makes it possible to detect, identify, and characterize minor dye components without interference from fluorescence. Such an approach is expected to reveal more minor dye components which could help narrow down the source of the fiber in question.

This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR  200.210(a)(14).

CA/NCF



  
Grant-Funded Publications
        
                      Comparison of Novel Raman Techniques for Fiber Forensics
                  Comparison of Novel Raman Techniques for Fiber Forensics
                  Synthetic dyes: A mass spectrometry approach and applications
                  The characterization of disperse dyes in polyester fibers using DART mass spectrometry
                  Identification of Minor Dye Components of Fibers via Integrating-Cavity-Enhanced Raman Spectroscopy
                  Synthetic dyes: A mass spectrometry approach and applications




  
Grant-Funded Datasets
        
                      https://doi.org/10.7273/000003928

  
Date Created: September 27, 2018
    
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