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Iron-associated reductants play a crucial role in providing electrons for various reductive transformations. However, developing reliable predictive tools for estimating abiotic reduction rate constants (logk) in such systems has been impeded by the intricate nature of these systems. Our recent study developed a machine learning (ML) model based on 60 organic compounds toward one soluble Fe(II)-reductant. In this study, we built a comprehensive kinetic dataset covering the reactivity of 117 organic and 10 inorganic compounds towards four major types of Fe(II)-associated reductants. Separate ML models were developed for organic and inorganic compounds, and the feature importance analysis demonstrated the significance of resonance structures, reducible functional groups, reductant descriptors, and pH in logk prediction. Mechanistic interpretation validated that the models accurately learned the impact of various factors such as aromatic substituents, complexation, bond dissociation energy, reduction potential, LUMO energy, and dominant reductant species. Finally, we found that 38% of the 850,000 compounds in the Distributed Structure-Searchable Toxicity (DSSTox) database contain at least one reducible functional group, and the logk of 285,184 compounds could be reasonably predicted using our model. Overall, the study is a significant step towards reliable predictive tools for anticipating abiotic reduction rate constants in iron-associated reductant systems. 

2D particle surfactants are attractive for the formation of highly stable emulsions and use as templates to prepare composite structures with performance properties dependent on the composition. Cobalt oxide nanosheets (CONs) are a relatively understudied class of 2D particle surfactants that can be produced by the chemical exfoliation of lithium cobalt oxide, a transition metal oxide known for excellent gas-sensing, catalytic, and electrochemical properties. Here, we report a simple method to access CONs stabilized oil-in-water Pickering emulsions and use these as templates to prepare particles with a core of polymer and shell of CONs. Salt-flocculated CONs produce emulsions with droplets of hydrophobic monomer ( e.g. , styrene) in water that are stable for at least 24 hours, and suspension free radical polymerization is used to produce CON-armored particles. Characterization by X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA) confirmed the presence of CONs on the surface of the polymer core. We then demonstrated the CON-armored polymer particles can activate the oxidant peroxymonosulfate (PMS) for the degradation of bisphenol A (BPA). Freshly prepared and artificially aged CON-armored particles showed full degradation of BPA in less than an hour and no decrease in activity was observed after two uses. CON-armored particles combine high surface area of the nanosheets with the ease of recoverability of the particles.