On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Aqueous phase heterogeneous catalysis is important to various industrial processes, including biomass conversion, Fischer-Tropsch synthesis, and electrocatalysis. Accurate calculation of solvation thermodynamic properties is essential for modeling the performance of catalysts for these processes. Explicit solvation methods employing multiscale modeling, e.g., involving density functional theory and molecular dynamics have emerged for this purpose. Although accurate, these methods are computationally intensive. This study introduces machine learning (ML) models to predict solvation thermodynamics for adsorbates on a Pt(111) surface, aiming to enhance computational efficiency without compromising accuracy. In particular, ML models are developed using a combination of molecular descriptors and fingerprints and trained on previously published water-adsorbate interaction energies, energies of solvation, and free energies of solvation of adsorbates bound to Pt(111). These models achieve root mean square error values of 0.09 eV for interaction energies, 0.04 eV for energies of solvation, and 0.06 eV for free energies of solvation, demonstrating accuracy within the standard error of multiscale modeling. Feature importance analysis reveals that hydrogen bonding, van der Waals interactions, and solvent density, together with the properties of the adsorbate, are critical factors influencing solvation thermodynamics. These findings suggest that ML models can provide rapid and reliable predictions of solvation properties. This approach not only reduces computational costs but also offers insights into the solvation characteristics of adsorbates at Pt(111)-water interfaces.

Spatial Scale Matters: Hydrolysis of Aryl Methyl Ethers over Zeolites

The local environment of the active site, such as the confinement of hydronium ions within zeolite pores, significantly influences catalytic turnover, similar to enzyme functionality. This study explores these effects in the hydrolysis of guaiacols─lignin-derived compounds─over zeolites in water. In addition to the interesting catechol products, this reaction is advantageous for study due to its bimolecular hydrolysis pathway, which involves a single energy barrier and no intermediates, simplifying kinetic studies and result interpretation. As in alcohol dehydration, hydronium ions show enhanced activity in ether hydrolysis due to undercoordination and increased electrophilicity when confined within zeolite pores, compared to bulk water. In addition, a volcano-shaped relationship between hydronium ion activity and Brønsted acid density was observed. However, unlike alcohol dehydration, this activity distribution cannot be attributed to variations in ionic strength within the pores, as the rate-determining step in the hydrolysis of guaiacols involves the attack of a neutral water molecule, unaffected by ionic strength. Instead, a detailed transition state analysis revealed a significant thermodynamic energy compensation effect, driven by the spatial organization of the transition state. This organization is influenced by the available reaction space, the interaction between the reacting species and the zeolite environment, leading to the volcano-shaped dependence. This phenomenon also explains the unusual reactivity order of the 4-R-guaiacol derivatives (R = H, Me, Et, Pr) with zeolite catalysis, extending beyond the traditional steric and electronic effects to provide a deeper understanding of reactant reactivity. The work concludes that the critical spatial parameters for fast ether hydrolysis─resulting in the highest hydronium activity─are determined by a combination of zeolite properties (topology and acid density) and reactant size.

Recent Advances in Fluorescent Based Chemical Probes for the Detection of Perchlorate Ions

This review highlights recent advancements and challenges in fluorescence-based chemical sensors for selective and sensitive detection of perchlorate, a persistent environmental pollutant and global concern due to its health and safety implications. Perchlorate is a highly persistent inorganic pollutant found in drinking water, soil, and air, with known endocrine-disruptive properties due to its interference with iodide uptake by the thyroid gland. Human exposure mainly occurs through contaminated water and food. Additionally, perchlorates are prevalent in improvised explosives, causing numerous civilian casualties, making their detection important in a worldwide aspect. Fluorescence-based chemical sensors provide a valuable tool for the selective detection of perchlorate ions due to their simplicity and applicability across various fields, including biology, pharmacology, military, and environmental science. This review article overviews perchlorate chemistry, occurrence, and remediation strategies, compares regulatory limits, and examines fluorescence-based detection mechanisms. It systematically summarizes recent advancements in designing at least a dozen fluorescence-based chemical materials for detecting perchlorate in the environment over the past decade. Key focus areas include the design and molecular architecture of synthetic chemical chromophores for perchlorate sensing and the photochemistry mechanisms driving their effectiveness. The main findings indicate that there has been significant progress in the development of reliable and robust fluorescence-based sensors with higher selectivity and sensitivity for perchlorate detection. However, several challenges remain, such as improving detection limits and sensor stability. The review outlines potential future research directions, emphasizing the need for further innovation in sensor design and development. It aims to enhance understanding and spur advances that could create more efficient and robust chemical scaffolds for perchlorate sensing. By addressing current limitations and identifying opportunities for improvement, the review provides a comprehensive resource for researchers working to develop better detection methods for this significant environmental pollutant.

Bimetallic Single-Atom Catalysts for Water Splitting

Green hydrogen from water splitting has emerged as a critical energy vector with the potential to spearhead the global transition to a fossil fuel-independent society. The field of catalysis has been revolutionized by single-atom catalysts (SACs), which exhibit unique and intricate interactions between atomically dispersed metal atoms and their supports. Recently, bimetallic SACs (bimSACs) have garnered significant attention for leveraging the synergistic functions of two metal ions coordinated on appropriately designed supports. BimSACs offer an avenue for rich metal-metal and metal-support cooperativity, potentially addressing current limitations of SACs in effectively furnishing transformations which involve synchronous proton-electron exchanges, substrate activation with reversible redox cycles, simultaneous multi-electron transfer, regulation of spin states, tuning of electronic properties, and cyclic transition states with low activation energies. This review aims to encapsulate the growing advancements in bimSACs, with an emphasis on their pivotal role in hydrogen generation via water splitting. We subsequently delve into advanced experimental methodologies for the elaborate characterization of SACs, elucidate their electronic properties, and discuss their local coordination environment. Overall, we present comprehensive discussion on the deployment of bimSACs in both hydrogen evolution reaction and oxygen evolution reaction, the two half-reactions of the water electrolysis process.

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.