The accurate prediction of standard vaporization enthalpy (ΔvapHm°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔvapHm° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications. In this work, we present a data driven, explainable supervised ML model to predict ΔvapHm° of VOCs. The model was built on an established experimental database of 2410 unique molecules and 223 VOCs categorized by chemical groups. Using supervised ML regression algorithms, the Random Forest successfully predicted VOCs' ΔvapHm° with a mean absolute error of 3.02 kJ mol−1 and a 95% test score. The model was successfully validated through the prediction of ΔvapHm° for a known database of VOCs and through molecular group hold-out tests. Through chemical feature importance analysis, this explainable model revealed that VOC polarizability, connectivity indexes and electrotopological state are key for the model's prediction accuracy. We thus present a replicable and explainable model, which can be further expanded towards the prediction of other thermodynamic properties of VOCs. © 2024 The Authors

Graphene-based materials modified with transition metals, and their potential utilization as hydrogen storage devices, are extensively studied in the last decades. Despite this widespread interest, a comprehensive understanding of the intricate interplay between graphene-based transition metal systems and H2 molecules remains incomplete. Beyond fundamental H2 adsorption, the activation of H2 molecule, crucial for catalytic reactions and hydrogenation processes, may occur on the transition metal center. In this study, binding modes of H2 molecules on the circumcoronene (CC) decorated with Cr or Fe atoms are investigated using the DFT methods. Side-on (eta 2-dihydrogen bond), end-on and dissociation modes of H2 binding are explored for high (HS) and low (LS) spin states. Spin state energetics, reaction energies, QTAIM and DOS analysis are considered. Our findings revealed that CC decorated with Cr (CC-Cr) emerges as a promising material for H2 storage, with the capacity to store up to three H2 molecules on a single Cr atom. End-on interaction in HS is preferred for the first two H2 molecules bound to CC-Cr, while the side-on LS is favored for three H2 molecules. In contrast, CC decorated with Fe (CC-Fe) demonstrates the capability to activate H2 through H-H bond cleavage, a process unaffected by the presence of other H2 molecules in the vicinity of the Fe atom, exclusively favoring the HS state. In summary, our study sheds light on the intriguing binding and activation properties of H2 molecules on graphene-based transition metal systems, offering valuable insights into their potential applications in hydrogen storage and catalysis.

Glycerol (propane-1,2,3-triol), a byproduct of biodiesel production, serves as a valuable precursor for numerous products including hydrogen, propylene glycol (propane-1,2-diol), propane-1,3-diol, lactide, acrolein, hydroxyacetone, pyruvaldehyde, acetaldehyde, ethylene glycol, glyceraldehyde, lactic acid, acetic acid, formic acid, glyceric acid, tartronic acid, oxalic acid, glycolic acid, glyoxylic acid, and pyruvic acid, among others. Utilizing glycerol for the production of these diverse compounds not only enhances the sustainability of biodiesel production but also contributes to the economic viability of the entire process. The primary challenge in realizing the conversion of glycerol into the aforementioned chemicals lies in the need for catalysts with adequate activity, selectivity, and stability for the various processes involved. To address this, researchers have frequently employed cost-effective Ni-Cu-based catalysts in studies focused on glycerol conversion. These catalysts can be effectively modified to adjust their activity, selectivity, and stability, thereby enabling the conversion of glycerol into valuable products. This review provides a comprehensive overview of recent achievements related to Ni-Cu catalysts utilized in glycerol conversion to valuable products. It explores and discusses general principles governing the catalytic properties of these catalysts. Special attention is paid to the modification of the reaction mechanisms by varying catalyst morphology and composition or adjusting reaction conditions. These modifications play a crucial role in achieving the desired products effectively. The knowledge gained on modifying the reaction mechanism by modulating Ni-Cu catalysts can be further utilized in the design of catalysts with improved characteristics for glycerol conversion.

<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>

This communication presents a novel approach to set up a machine learning-ready database for epoxidation reactions, focusing on vanadium catalysts. Utilising data driven analysis, we identified key reaction yield trends through chemical descriptors, providing insights for catalyst design and reaction optimisation. This communication presents a novel approach to set up a machine learning-ready database for epoxidation reactions, focusing on vanadium catalysts.