Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
396 Sam Houston State University (142284)
Principal Investigator: Yu, Chi C.
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 5,796
Exceeds $250,000 (Is it flagged?): No
Start and End Dates: 10/1/22 - 9/30/23
Restricted Research: YES
Academic Discipline: Criminal Justice
Department, Center, School, or Institute: Department of Criminal Justice
Title of Contract, Award, or Gift: Development of Carbon Nanotubes Assisted Headspace Chemical Analysis and Artificial Intelligence for Fire Debris Analysis
Name of Granting or Contracting Agency/Entity:
Forensic Sciences Foundation
CFDA: n/a
Program Title: n/a
Note:
SAMs 1.1.1: Among all the items used to ignite fires, flammable or combustible liquids accounted for 59% and 19% of the annual averages of deaths/injuries and property losses, respectively. The ignitable liquids (ILs) are used as an accelerant to speed up the escalation of the fire and provide enough energy necessary for fire development. Because gasoline is readily available on the market and easily transported, it has become the most widely used accelerant by arsonists. The current gold standards for forensic gasoline identification employ liquid extraction and gas chromatography/mass spectrometry (GC/MS) analysis based on ASTM E1412-19. As for fire debris analysis, ASTM E1618-19 is the go-to standard. These standard testing procedures usually involve long thermal extraction time for ILs (2 to 24 hours), an elution procedure by an organic solvent, and an essential chromatographic separation process. The time-consuming sample preparation not only increases the turnaround time of evidence analysis in forensic laboratories but also impacts the preservation of evidence on the fire scene when multiple sample collections are needed. In addition, using an organic solvent for eluting analytes, such as carbon disulfide (CS2), harms analysts’ health and the environment. More importantly, challenges in chromatogram interpretation are commonly encountered during data analysis due to weathering, microbial activities, and matrix effects resulting from the pyrolysis products in fire debris. The substantial contributions from those pyrolysis products in the chromatograms can often complicate the interpretation of chromatographic data for gasoline identification. In this work, an innovative workflow that incorporates the emerging nanotechnology and deep learning technique is proposed to solve the potential challenges in modern fire debris analysis as addressed above. This work will investigate a nanomaterial, carbon nanotubes (CNTs), as a new adsorbent for fire debris analysis because of CNTs’ excellent non-covalent interactions with hydrophobic substances. This study will also explore the potential and benefits of artificial intelligence (AI) to assist the analyst in eliminating human bias and errors in data interpretation. The desirable merging of the two techniques offers new opportunities to produce a more sustainable and reliable analytical workflow for fire debris analysis.
Discussion: No discussion notes