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.

Technologies involving a solvent|surface interface, such as nanotechnology, electrochemistry, and energy storage applications, are actively pursuing ecologically responsible and sustainable development practices. In response to this pressing need, deep eutectic solvents have emerged as a promising solution to bridge the gap between technological requirements and environmental concerns. In this work, we present the results of a molecular dynamics simulation study of the interface between a monocrystalline gold surface and the deep eutectic solvent ethaline, where a molar ratio of 1:2 choline chloride:ethylene glycol was used for ethaline. The simulations covered a range of temperatures from 313 K to 343 K and applied charge values ranging from 0 to +/- 24 mu C cm-2. Several key interfacial properties were thoroughly analyzed, including among others, charge density profiles, radial distribution functions, hydrogen bond close contacts, and molecular orientation. Additionally, we examined how the differential capacitance varied upon the applied potential. Our findings reveal that, at neutral surfaces, all components of the solvent are present in the innermost layer, with ethylene glycol molecules being the most prevalent, followed by choline cations and a residual amount of chloride anions. For lower applied charges, this mixed composition at the boundary layer persists, despite the growing accumulation of ionic species with charges opposite to that of the electrode. As surface polarization increases, unique innermost boundary layers composed exclusively of one of the ionic species and the hydrogen bond donor molecules are observed, forming a multilayer structure, with subsequent layers enriched of paired counterions. Interestingly, even at higher applied charges, choline cations and ethylene glycol molecules tended to orient themselves in a parallel fashion toward the electrodes. Differential capacitance curves exhibited a camel-shaped behavior, suggesting a complex interplay of electrochemical processes at the DES|Au(100) interface. In summary, our study provides valuable insights into the interfacial properties of deep eutectic solvents on gold surfaces and their response to changes in temperature and potential, which are crucial for understanding and optimizing deep eutectic solventbased electrochemical systems.

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.

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.