Daniel W. Armstrong
R.A. Welch Distinguished Professor, Department of Chemistry and Biochemistry, University of Texas at Arlington, USA
Main research aims? New approaches in identifying chiral disease biomarkers, peptide epimers and isotopic compounds. Enhanced analysis via both faster, more effective techniques and peak processing. Ionic liquid development for separations and spectrometry.
A problem that could be tackled interdisciplinarily… Much, perhaps most, analytical research is interdisciplinary in nature. Depending on the task, it can involve materials science, pharmacology, biology, computer science, etc. Currently, we are working with mathematicians, biologists, spectroscopists, organic chemists, inorganic chemists and engineers on various different projects. One interesting “bio-analytical” research project involves the means by which insects selectively acquire resistance to pesticides by developing symbiotic relationships with gut bacteria that can detoxify certain compounds. Such research involves having the wherewithal to grow hundreds of different bacteria, being able to analyze their degradation of dozens of pesticides and finally to mine genomic databases for possible relationships. Obviously, the biological aspects of such a project are huge and essential.
Most exciting trend today? Clearly artificial intelligence (AI) is attracting a lot of attention, although its actual contributions to chemistry research have been limited thus far. Many if not most articles in this area are “smoke and mirrors” or hyperbole based on what is likely to happen. Simple, labor intensive imputing of large amounts of published and/or unpublished data and concluding that computers can evaluate such databases faster than humans is neither new nor surprising. The fact that AI can “write” general introductions to a variety of topics is newer but not surprising – and it often contains errors or miss-statements, not to mention ethical problems. Of course, I’ve seen a number of reviews and introductions to papers over the last several decades that also contain errors, miss-statements and apparently bias. However, the new tendency may be to place the blame on AI rather than the author – hopefully we can negate the worst of this. One of the exciting aspects of AI in measurement science is using chatbots to implement ideas and solve task-specific problems by posing targeted questions. Can the chatbot complete the assignment with minimal human direction or data input (which we refer to as unguided) or can it complete complex tasks with a bit more human direction (which we refer to as guided)? In other words, can AI complete some scientific tasks that take researchers months to years to complete, in a matter of minutes to perhaps days? This is coming; however, we have shown that such results must be continually tested and verified for correctness. This must be an essential part of any/all such processes.
