Inverse design of chemoenzymatic epoxidation of soyabean oil through artificial intelligence-driven experimental approach.

This paper presents an inverse design methodology that utilizes artificial intelligence (AI)-driven experiments to optimize the chemoenzymatic epoxidation of soyabean oil using hydrogen peroxide and lipase (Novozym 435). First, experiments are conducted using a systematic 3-level, 5-factor Box-Behnken design to explore the effect of input parameters on oxirane oxygen content (OOC (%)). Based on these experiments, various AI models are trained, with the support vector regression (SVR) model being found to be the most accurate.

Ferrocene-derived Fe-metalated porous organic polymer for the core planarity-triggered detoxification of chemical warfare agents.

Herein, we demonstrate the successful construction of two Fe-metalated porous organic polymers having planar (Fe-Tt-POP) and non-planar (Fe-Rb-POP) geometry the ternary copolymerization strategy for the catalytic oxidative decontamination of different sulfur-based mustard gas simulants (HD). Fe-Tt-POP exhibits superior catalytic performance for the oxidation of thioanisole (TA) in comparison with Fe-Rb-POP. Interestingly, this activity difference can be further explored by operando DRIFTS and DFT computational studies.