Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning.

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative.

Comparison of portable and benchtop electrochemical instruments for detection of inorganic and organic gunshot residues in authentic shooter samples.

Analysis of gunshot residue currently lacks effective screening methods that can be implemented in real time at the crime scene. Historically, SEM-EDS has been the standard for analysis; however, advances in technology have brought portable instrumentation to the forefront of forensic science disciplines, including the screening of GSR. This study proposes electrochemical methods with disposable screen-printed carbon electrodes for GSR screening at the laboratory and points of care due to their rapid, cost-efficient, and compact platform.

The metabolism of valerylfentanyl using human liver microsomes and zebrafish larvae.

Valerylfentanyl, a novel synthetic opioid less potent than fentanyl, has been reported in biological samples, but there are limited studies on its pharmacokinetic properties. The goal of this study was to elucidate the metabolism of valerylfentanyl using an in vitro human liver microsome (HLM) model compared with an in vivo zebrafish model. Nineteen metabolites were detected with N-dealkylation-valeryl norfentanyl and hydroxylation as the major metabolic pathways.

Evaluation of the Simultaneous Analysis of Organic and Inorganic Gunshot Residues Within a Large Population Data Set Using Electrochemical Sensors* .

The increasing demand for rapid methods to identify both inorganic and organic gunshot residues (IGSR and OGSR) makes electrochemical methods, an attractive screening tool to modernize current practice. Our research group has previously demonstrated that electrochemical screening of GSR samples delivers a simple, inexpensive, and sensitive analytical solution that is capable of detecting IGSR and OGSR in less than 10 min per sample. In this study, we expand our previous work by increasing the number of GSR markers and applying machine learning classifiers to the interpretation of a larger population data set.