Utilizing Data Mining for the Synthesis of Functionalized Tungsten Oxide with Enhanced Oxygen Vacancies for Highly Sensitive Detection of Triethylamine.

The optimal combination of metal ions and ligands for sensing materials was estimated by using a data-driven model developed in this research. This model utilized advanced computational algorithms and a data set of 100,000 literature pieces. The semiconductor metal oxide (SMO) that is most suitable for detecting triethylamine (TEA) with the highest probability was identified by using the Word2vec model, which employed the maximum likelihood method.