Deep Learning Insights into Lanthanides Complexation Chemistry

Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. We have a series of chemically similar lanthanide ions, and we have collected data on complexes’ stability, built models, predicting stability constants and decoded the models to obtain key fragments responsible for complexation efficiency. The results are in good correlation with the experimental ones, as well as modern theories of complexation. It was shown that the main influence on the constants had a mutual location of the binding centers.

Advancing understanding of actinide(iii) (Ac, Am, Cm) aqueous complexation chemistry

Actinide complexation from aqueous acetic acid/acetate buffered solutions is described. The number of water ligands was directly correlated with the acetate concentration and characterized by X-ray absorption and optical spectroscopy.

Rapidly responsive smart adhesive-coated micropillars utilizing catechol–boronate complexation chemistry

Mussel-inspired smart adhesives were combined with an array of micropillars to create a pH-responsive adhesive with the ability to transform rapidly between its adhesive and non-adhesive states.