Progress in electrochemical applications of ionic liquids builds on an understanding of electrical double layer. This computational study focuses on structure-determined quantities - maximum packing density, potentials, and capacitances - evaluated using a one-electrode electrical double layer model. Interfaces of the 40 studied ions are grouped into four distinct classes according to their characteristic packing at the model surface. The simulations suggest that the exact screening by a monolayer of counter-ions (preceding the crowding of ions) is unlikely for ions in known air- and water-stable ionic liquids within their electrochemical stability window. This work discusses how the assessed structure-determined quantities can guide the experimental tuning of (electro/mechano)chemical properties and characterize the structure of ionic liquid-electrode interfaces.

Magnetic ionic liquids (MILs) stand out as a remarkable subclass of ionic liquids (ILs), combining the desirable features of traditional ILs with the unique ability to respond to external magnetic fields. The incorporation of paramagnetic species into their structures endows them with additional attractive features, including thermochromic behavior and luminescence. These exceptional properties position MILs as highly promising materials for diverse applications, such as gas capture, DNA extractions, and sensing technologies. The present Review synthesizes key experimental findings, offering insights into the structural, thermal, magnetic, and optical properties across various MIL families. Special emphasis is placed on unraveling the influence of different paramagnetic species on MILs' behavior and functionality. Additionally, the Review highlights recent advancements in computational approaches applied to MIL research. By leveraging molecular dynamics (MD) simulations and density functional theory (DFT) calculations, these computational techniques have provided invaluable insights into the underlying mechanisms governing MILs' behavior, facilitating accurate property predictions. In conclusion, this Review provides a comprehensive overview of the current state of research on MILs, showcasing their special properties and potential applications while highlighting the indispensable role of computational methods in unraveling the complexities of these intriguing materials. The Review concludes with a forward-looking perspective on the future directions of research in the field of magnetic ionic liquids.

<jats:p>Scientists often disagree on the best theory to describe a scientific event. While such debates are a natural part of healthy scientific discourse, the timeframe for scientists to converge on an ideal method may not always align with real-life knowledge dynamics. In this article, I use an event from the history of chemistry as inspiration to develop Agent-Based Models of epistemic networks, exploring method selection within a scientific community. These models reveal several situations where incorrect, simpler methods can persist, even when substantial evidence supports a more complex method. This becomes particularly evident when different evidence-sharing timeframes are analyzed. The network structure connecting the scientists plays a crucial role in determining how and when convergence on the correct method is achieved, guided by real-world evidence. This framework provides a foundation for further exploration of scientists’ behavior in past and future discoveries, as well as how agents internalize scientific information.</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.

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.