Individual-particle analysis is well established as an alternative to bulk analysis of airborne particulates. It yields size and chemical data on a particle-by-particle basis, information that is critical in predicting the behavior of air pollutants. Individual-particle analysis is especially important for particles with diameter < 1 μm, because particles in this size range have a disproportionately large effect on atmospheric visibility and health.
The main advantage of modern natural language processing methods is a possibility to turn an amorphous
human-readable task into a strict mathematic form. That allows to extract chemical data and insights from
articles and to find new semantic relations. We propose a universal engine for processing chemical and
biological texts. We successfully tested it on various use-cases and applied to a case of searching a
therapeutic agent for a COVID-19 disease by analyzing PubMed archive.