<jats:p>This communication presents a novel approach to set up a machine learning-ready database for epoxidation reactions, focusing on vanadium catalysts.</jats:p>

Hydrogen has emerged as a promising new energy source that can be produced in renewable mode, for example, from biomass derivatives reforming or water splitting. However, the conventional catalysts used for hydrogen production in renewable mode suffer from limitations in activity, selectivity, and/or stability. To overcome these limitations, nanostructured catalysts with multicomponent active phases, particularly trimetallic catalysts, are being explored. This catalyst formulation significantly enhances catalyst activity and effectively suppresses the undesired production of CO, CH4, or coke during the reforming of biomass derivatives for hydrogen formation. Moreover, the success of this approach extends to water splitting catalysis, where trimetallic based catalysts have demonstrated good performance in hydrogen production. Notably, trimetallic catalysts, composed of Ni, Fe, and a third metal, prove to be highly efficient in water splitting, bypassing the problems associated with traditional catalysts. That is, the high material costs of state-of-the-art catalysts as well as the limited activity and stability of alternative ones. Furthermore, theoretical methods play a vital role in understanding catalyst activity and/or selectivity, as well as in the design of catalysts with improved characteristics. These enable a comprehensive study of the complete reaction mechanism on a target catalyst and help in identifying potential reaction descriptors, allowing for efficient screening and selection of catalysts for enhanced hydrogen production.Overall, this critical review shows how the exploration of trimetallic catalysts, combined with the insights from theoretical methods, holds great promise in advancing hydrogen production through renewable means, paving the way for sustainable and efficient energy solutions.

BackgroundThe widespread use of new engineered nanomaterials (ENMs) in industries such as cosmetics, electronics, and diagnostic nanodevices, has been revolutionizing our society. However, emerging studies suggest that ENMs present potentially toxic effects on the human lung. In this regard, we developed a machine learning (ML) nano-quantitative-structure-toxicity relationship (QSTR) model to predict the potential human lung nano-cytotoxicity induced by exposure to ENMs based on metal oxide nanoparticles.ResultsTree-based learning algorithms (e.g., decision tree (DT), random forest (RF), and extra-trees (ET)) were able to predict ENMs' cytotoxic risk in an efficient, robust, and interpretable way. The best-ranked ET nano-QSTR model showed excellent statistical performance with R-2 and Q(2)-based metrics of 0.95, 0.80, and 0.79 for training, internal validation, and external validation subsets, respectively. Several nano-descriptors linked to the core-type and surface coating reactivity properties were identified as the most relevant characteristics to predict human lung nano-cytotoxicity.ConclusionsThe proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks.

Biopurification is a challenging and growing market. Despite great efforts in the past years, current purification strategies still lack specificity, efficiency, and cost-effectiveness. The development of more sustainable functional materials and processes needs to address pressing environmental goals, efficiency, scale-up, and cost. Herein, L-leucine (LEU)-molecularly imprinted polymers (MIPs), LEU-MIPs, are presented as novel biomolecular fishing polymers for affinity sustainable biopurification. Rational design was performed using quantum mechanics calculations and molecular modeling for selecting the most appropriate monomers. LEU-MIPs were synthesized for the first time by two different green approaches, supercritical carbon dioxide (scCO(2)) technology and mechanochemistry. A significant imprinting factor of 12 and a binding capacity of 27 mg LEU/g polymer were obtained for the LEU-MIP synthesized in scCO(2) using 2-vinylpyridine as a functional monomer, while the LEU-MIP using acrylamide as a functional monomer synthesized by mechanochemistry showed an imprinting factor of 1.4 and a binding capacity of 18 mg LEU/g polymer, both systems operating at a low binding concentration (0.5 mg LEU/mL) under physiological conditions. As expected, at a higher concentration (1.5 mg LEU/mL), the binding capacity was considerably increased. Both green technologies show high potential in obtaining ready-to-use, stable, and low-cost polymers with a molecular recognition ability for target biomolecules, being promising materials for biopurification processes.