Author ORCID Identifier

https://orcid.org/0000-0002-9969-887X

Semester

Spring

Date of Graduation

2025

Document Type

Dissertation

Degree Type

PhD

College

Eberly College of Arts and Sciences

Department

Chemistry

Committee Chair

Tatiana Trejos

Committee Co-Chair

Glen Jackson

Committee Member

Luis Arroyo

Committee Member

Stephen Valentine

Committee Member

Peng Li

Abstract

The information derived from the forensic examination of gunshot residue (GSR) can be critical during the investigative and trial stages in the criminal justice system. However, the complexity of the GSR deposition, transfer, and persistence mechanisms calls for meticulous handling and identification processes. Novel advances in evidence interpretation and analysis via instrumental methods are current areas of growing interest that must be pushed along the chain from research to practice in a timely manner. Therefore, this research focuses on developing both rapid and comprehensive methods for detecting gunshot residue evidence and effectively communicating its significance to the investigators or trier of fact. This dissertation accomplishes this goal through three primary objectives: the investigation of 1) a rapid screening technique for organic gunshot residues by direct analysis in real-time mass spectrometry (DART-MS), 2) an in-depth examination of the behavior of gunshot residues before, during, and after the deposition process, as well as risks associated with potential involvement in firearm-related scenarios and 3) the classification of manufacturer-grade ammunitions using statistical analysis and machine learning techniques.

First, this research investigates the feasibility and capabilities of DART-MS over 330 authentic OGSR samples as a rapid, straightforward technique for analyzing OGSR specimens, ranging from low complexity items (smokeless powder) to the highest complexity (residues from a shooter’s hands). Other sample types of immense evidentiary value, such as residues recovered from fired clothing and spent cartridge cases demonstrate promising performance rates (73% to 100% true positive rate, depending on sample type and classification criteria). Detection limits range from 0.075 to 12 ng depending on analyte and analysis mode, with inter-day reproducibility of 0.0012% CV. The method allows for isomeric differentiation due to collection of mass spectra at multiple in-source collision induced dissociation levels. These capabilities provide the groundwork for rapid adoption of DART-MS for OGSR screening into forensic laboratories, custom borders, or areas of interest for homeland security surveillance.

Second, this work uses a combined multi-sensor and visualization approach to perform a novel analysis of airborne GSR in real-time, immediately following firing and up to three hours after the firing event. Atmospheric particulate monitoring methods including custom-made particle counters, aerodynamic particle sizing, and laser sheet scattering are used to provide foundational knowledge of GSR flow, while spectrometry techniques including SEM-EDS and LC-MS/MS are used for complementary analysis of resultant GSR deposition. Among the factors of interest are the flow dynamics and duration of airborne particulates in various environmental conditions and the assessment of direct versus indirect deposition risks on a person of interest. This study brings to light the dichotomy of GSR deposition depending on chemical characteristics, finding that individuals are more likely exposed to indirect IGSR deposition than OGSR.

Finally, this research performs comprehensive characterization and classification of organic gunshot residues by LC-MS/MS and chemometric techniques including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (KNN) and support vector machines (SVM) to classify and correlate smokeless powders, cartridge cases, and shooter’s hand samples based on their manufacturer. Classification by discriminant analysis methods is powerful for smokeless powders, reaching correct classification rates as high as 83.7%. More challenging residues, such as those recovered from a shooter’s hands, show lower correct classification rates up to 62.7%. Results from this study demonstrate that the incorporation of OGSR analysis can provide key insight into the classification of multiple types of samples, being particularly applicable in situations where evidence is limited.

The range of knowledge uncovered throughout this research is expected to increase confidence and effectiveness in the use of OGSR evidence, provide rapid analysis solutions, and aid investigators in determining important characteristics of evidence when traditional methods may fall short.

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