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.

This project involves developing a comprehensive database, programs, and analytical methods to identify secondary metabolites in foods and botanicals. This research primarily focuses on complex plant metabolites such as flavonoids, phenolic acids, ginsenosides, and glucosinolates. The database consists of chemical fingerprints for plant materials and dietary supplements, along with UV and mass spectral data for major secondary metabolites. Using this system, data processing tools can extract secondary metabolite information from ultra-high-performance liquid chromatography (UHPLC) and high-resolution accurate-mass mass spectrometry (HRAM-MS) data matrices by leveraging their characteristic chemical properties (e.g., UV absorbance from a diode array detector and MS patterns from a mass spectrometer) to identify and quantify secondary metabolites. Over the past several years, USDA funding has supported the development of tools such as FlavonQ, GLS Finder, and ANOVA-PCA programs, which have been applied to various studies, including the analysis of secondary metabolites in daylily, strawberry, and lettuce. Additionally, an online platform is under construction to host the database of reference standards, secondary metabolite composition in foods and plants, and associated data processing programs. This work aims to facilitate natural product analysis, support flavonoid research (e.g., intake evaluations and clinical investigations), and enhance dietary recommendations. 

Metabolomics has become increasingly important for understanding biological systems, as metabolic pathways play central roles in regulating cellular function. In this study, we collaborated with Dr. Shouan Zhu (OHF Ralph S. Licklider, D.O. Professor in Orthopedic Research and Associate Director for OMNI) to perform both untargeted and targeted metabolomics analyses. The objective was to evaluate the impact of Acetyl-CoA Carboxylase 1 (ACC1) knockout on the regulation of tricarboxylic acid (TCA) cycle metabolites, given ACC1’s involvement in osteoarthritis development. Tissue extracts from wild-type and ACC1 knockout mice maintained on high-fat diet (HFD) and low-fat diet (LFD) regimens were analyzed. Key TCA cycle–related metabolites in mammalian tissues—including amino acids (e.g., arginine, alanine, asparagine, isoleucine, leucine, glutamine, glycine, phenylalanine, proline, valine, and serine) as well as central intermediates (e.g., acetyl-CoA, pyruvate, oxaloacetate, aconitate, malate, fumarate, succinate, citrate, isocitrate, and α-ketoglutarate)—were quantified. Complementary lipidomics analyses are currently underway. 

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. 

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.