Conventional GC-MS methods for the analysis of ignitable liquids (IL) are usually time-consuming and the data produced are difficult to interpret. A fast IL screening method using DART-MS is conducted in this study. In addition, gasoline is one of the most common accelerants found at arson crime scenes, but GC-MS method is incapable of distinguishing the gasoline with different brands. Identifying the brand of gasoline used as an accelerant has great forensic significance as it potentially allows traceback to the fuel service station the gasoline was purchased from, and subsequently identification of suspects through the surveillance camera or payment card transactions at the fuel service stations. Our preliminary study indicates that DART-MS is a promising technique for the analysis of characteristic compounds in gasoline, the differentiation of brands of gasoline, and the classification of different IL.(1,2) The goal of the project is to exploit the unique capabilities of DART-MS in the detection of IL residues and discrimination between different brands of gasoline from various substrates and fire debris samples. 

In 2023, Moungi G. Bawendi, Louis E. Brus, and Alexei I. Ekimov were awarded the Nobel Prize in Chemistry for their discovery and synthesis of Quantum dots (QDs). QDs are semiconductor particles a few nanometres in size with unique optical and electronic properties. Understanding the details of QD surface chemistry is vital to understanding and controlling their behavior. Improper surface coordination can diminish or destroy otherwise excellent catalytic activity, colloidal stability, chemical stability, electronic and optical properties, programmed self-assembly, and biocompatibility. It has long been established that NC properties can be modified through postsynthetic ligand exchange processes, but in many cases, the details of these ligand exchange processes are not fully understood. Our group is the first to develop the DART-MS method to characterize colloidal QD surface ligands. The method allows the identification of ligand species with various functional groups, even in complex, mixed-ligand samples. Bound and unbound molecules can be distinguished based on the desorption temperature. In ideal cases, the desorption profile for a given molecule can be analyzed according to methods adapted from thermal desorption spectroscopy (TDS) to estimate desorption activation energy for QD-bound ligands. Results are presented and discussed for different nanocrystal and ligand types. The method is a promising complement to the range of existing tools for QD ligand analysis.

The thermal desorption/pyrolysis-DART-MS (TD/Py-DART-MS) method was developed for the analysis of fibers in this study. The fiber samples were pyrolyzed with a temperature gradient and the pyrolysis products were determined by DART-MS. The pyrogram from the TD/Py-DART-MS fiber analysis was found to be associated with the physical properties such as the melting points. At the same time, the TD/Py-DART-MS allows the analyst to obtain the chemical information such as polymeric backbone structures and dyes on the fiber. The pyrolysis profiles of common polymeric fibers in textile materials such as cotton, cellulose triacetate (CT), poly(caprolactam) (nylon 6), poly(hexamethylene adipamide) (nylon 6/6), poly(acrylonitrile) (PAN), poly(ethylene terephthalate) (PET), poly(butylene terephthalate) (PBT), poly(propylene) (PP), and polytrimethylene terephthalate (PTT) and their respective characteristic mass spectra were reported in this study. The statistical methods including principal component analysis (PCA) and Pearson product moment correlation (PPMC) were applied to classify and associate the fibers based on their mass spectral data. The strong correlation between the reference fiber mass spectral profiles and tested fiber mass spectral profiles was observed by using the PPMC method. When combined with the mass spectral and pyrogram data, the types of fibers, including the blended fibers, were identified effectively. The TD/Py-DART-MS method also demonstrated the promising capability for identifying dyes on fibers. 

Ginsenosides are the major constituent responsible for the health effects of American ginseng. The ginsenoside profile of wild American ginseng is ultimately the result of germplasm, climate, geography, vegetation species, water, and soil conditions. We are analyzing the ginsenoside profile of wild American ginseng grown in Tennessee (TN), the third leading state for the production of wild American ginseng. In the present study, ten major ginsenosides in wild American ginseng roots grown in TN, including Rb1, Rb2, Rb3, Rc, Rd, Re, Rf, Rg1, Rg2, and Rg3, were determined simultaneously. The chemotypic differences among TN wild ginseng, cultivated American ginseng, and Asian ginseng were assessed based on the widely used markers of ginsenoside profiling, including the top three ginsenosides, ratios of PPD/PPT, Rg1/Rb1, Rg1/Re, and Rb2/Rc. Our findings showed marked variation in the ginsenoside profile for TN wild ginseng populations. This work is critical for the ecological and biological assessments of wild American ginseng so as to facilitate the long-term sustainability of the wild population. (1) We also study the polysaccharides in the ginseng extracts. 

This study characterizes the constituents in particulate matter (PM) from wildland or forest fire smoke from different locations in order to understand their pollution-related risks. To understand air pollution-associated health issues for firefighters exposed to wildfire smoke, it is important to characterize and analyze the composition of the ambient air of the Wildland-Urban Interface (WUI) in fire-impacted areas with analytical methods for ultra-trace levels of volatile organic compounds (VOCs) as well as polar organics, polycyclic aromatic hydrocarbons (PAHs), and metals bound in particulate matters. We investigate the correlation between smoke constituents and toxicants known to impact human health with immediate exposure and exposure after containment of the fire. This includes reviewing the air quality monitoring data collected at the site of wildfires in order to understand the temporal trend for the dispersion of air pollutants and the deposition pathways of the pollutants resulting from the fires so that sound recommendations can be provided to firefighters to limit exposure during evacuation and resettlement activities.

This project also develops novel and efficient analytical methods for the determination of both polar and non-polar compounds simultaneously. Combined with traditional techniques such as gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), Fourier Transform infrared spectrometry (FTIR), and inductively coupled plasma mass spectrometry (ICP-MS), we evaluate health-related chemical components in wildfire PM samples extensively.

Chemometrics is a critical component of our research. It helps us improve the understanding of chemical information and to correlate quality parameters or physical properties to analytical instrument data. Almost all of our publications have a component of chemometrics. We have commercial chemometric software to process data and also MATLAB for developing and applying new algorithms. We also collaborate with USDA Lab to develop data processing programs for the chemical data from agricultural research.