Britni Skillman
- Assistant Professor
- (936) 294-4319
- bns017@shsu.edu
- CFS 221D
- Curriculum Vitae (PDF)
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Dr. Britni Skillman is an Assistant Professor of Forensic Science. She obtained a PhD in Forensic Science with a research focus in forensic toxicology. Dr. Skillman is a member of the Society of Forensic Toxicologists (SOFT), the American Academy of Forensic Sciences (AAFS), and the International Association of Forensic Toxicologists (TIAFT) where she presents at meetings and publishes in associated peer-reviewed journals. She is also board certified as a fellow of the American Board of Forensic Toxicology (ABFT). Dr. Skillman has previous experience as a toxicologist in an accredited laboratory performing development and validation activities, as well as a bench-level casework activities, where she gained experience working with opioids, opiates, over-the-counter medications, antipsychotics, stimulants, benzodiazepines, sedative hypnotics, fentanyl analogs, ethanol and other volatiles, and many other compounds in a wide variety of biological specimens. Her research interests include sedative hypnotics, novel psychoactive substances, liquid chromatography and high-resolution mass spectrometry methods, and the improvement of efficiency in the crime laboratory through the development of novel analytical techniques.
Dr. Skillman’s current research group endeavors:
Dr. Skillman’s research interests are primarily in forensic toxicology with some interest in seized drug analysis. Her experience in toxicology involves the detection of a wide variety of substances in various biological specimens using advanced instrumentation techniques such as liquid chromatography tandem mass spectrometry (LC/MS/MS) and liquid chromatography high resolution mass spectrometry (HRMS). Dr. Skillman’s long-term research goals are to improve efficiency of laboratory processes by incorporating novel psychoactive substances in existing scopes of traditionally tested compounds which would allow laboratories to update existing methodologies, thereby decreasing analysis times and backlogs.
Major areas of interest include: