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Keeping Up with the Power List: Part 1

What are the most exciting developments and emerging trends in analytical science today? We asked the 2024 Power List

12/19/2024 | 4 min read | Discussion

Apart from the exciting technical advances in analytical science which continue unabated, to me the most exciting development is the convergence of analytical and computer science. The work our group has substantially focused on is the analysis of proteins as molecular entities consisting of a string of amino acids, at times further modified or processed post synthesis. The convergence of analytical and computational methods now make it possible to computationally extrapolate from the results generated by analytical techniques focused on the proteome to extrapolate to the structure, function and cellular context of the detected molecules, thus moving the analytical techniques closer to understanding biological processes and phenotypes.

In my area, there is a lot of exciting work trying to uncover more of the exposome, i.e., the total human exposure and associated diseases in a broader One Health concept. This perfectly aligns with strengthening the links between environmental analysis, hygiene and epidemiology in using wastewater as a source of health-related information in the population. While wastewater-based surveillance that focuses on the use of illicit drugs has already been well established on a European and international level, we are only now beginning to realise its huge potential. That area, of course, received a boost during the pandemic for SARS-CoV-2 analysis using quantitative polymerase chain reaction assays. We need to maintain that momentum to revolutionise future public health surveillance by pathogen and biomarker analysis in wastewater.

The exciting fact is that analytical science is developing in two completely different directions. One is the application of sophisticated equipment to get lower detection limits. The second is the development of simple procedures that are accessible to everyday users.

I think that such trends are miniaturization and, just to avoid “AI-washing”, I would say “creative re-use of previously acquired treasure troves of data.”

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.

Here I would like to mention the fantastic possibilities offered by artificial intelligence (AI) for molecular (micro)spectroscopy to extract more and deeper information out of the spectroscopic data. The fruitful interplay between artificial intelligence (AI) and molecular spectroscopy has only just begun. There are great synergies between optics and AI, which are just being tapped in the context of molecular (micro)spectroscopy. Molecular (micro)spectroscopy forms an ideal platform for the application of AI. First and foremost, is the automated interpretation of large data sets. Analyzing spectroscopic or (micro)spectroscopic data sets with powerful AI methods instead of the naked eye opens entirely new possibilities in the derivation of secondary data and conclusions from the primary information. In particular, the field of "Explainable AI" should be mentioned here, i.e., the exploration of visualization concepts that will change the black-box character of nonlinear AI methods to interpretable models.

The advancement in mass spectrometry to become even more sensitive, to increase dramatically resolution as well as new ways to separate compounds (e.g., ion mobility)

Recently developed mass spectrometry technologies have transcended traditional roles of studies of gas phase ions to studies of large biomolecules in their native solution environment.

I'm impressed with the continued improvements in mass spectrometry in terms of speed, resolution, and sensitivity. Allowing the merging of this with microscale methods (microfluidics, nano LC etc.) is creating lots of opportunities in analysis including high-throughput, ultrasmall samples (single cells).

For me, it is the development of low-cost sustainable analytical devices.

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