Effect of Mixed-Solvent Environments on the Selectivity of Acid-Catalyzed Dehydration Reactions

Solvation effect enabled visualized discrimination of multiple metal ions

Highly efficient detection of environmental residual potentially toxic species is of concern worldwide as their presence in an excessive amount would greatly endanger the health of human beings as well as environmental sustainability. The solvation effect is a critical factor to be considered for understanding chemical reaction progress as well as the photophysical behaviors of substances and thus is promising for visualized detection of metal ions. Herein, by applying 5-amino-1,10-phenanthroline (APT) as the optical probe, a sensing strategy was proposed based on the solvation effect modulated complexation of APT towards different metal ions to achieve the visualized discrimination of four critical ions (Cu(II), Zn(II), Cd(II), and Al(III)). How the crucial intrinsic properties of the solvent (e.g., polarity, solvent free energy, and electrostatic potential) influenced the complexation and the product emission was clarified, and the detection performances were systematically evaluated with detection limits as low as the nM level and good recognition selectivity. Furthermore, a portable sensing chip was developed with potential for highly efficient analysis in complicated scenes; thus, this strategy offers a new insight into determining multiple metal ions or other critical substances upon solvation manipulation.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

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.

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.

Influence of solvent structure and hydrogen bonding on catalysis at solid-liquid interfaces

Solvent molecules interact with reactive species and alter the rates and selectivities of catalytic reactions by orders of magnitude. Specifically, solvent molecules can modify the free energies of liquid phase and surface species via solvation, participating directly as a reactant or co-catalyst, or competitively binding to active sites. These effects carry consequences for reactions relevant for the conversion of renewable or recyclable feedstocks, the development of distributed chemical manufacturing, and the utilization of renewable energy to drive chemical reactions. First, we describe the quantitative impact of these effects on steady-state catalytic turnover rates through a rate expression derived for a generic catalytic reaction (A → B), which illustrates the functional dependence of rates on each category of solvent interaction. Second, we connect these concepts to recent investigations of the effects of solvents on catalysis to show how interactions between solvent and reactant molecules at solid-liquid interfaces influence catalytic reactions. This discussion demonstrates that the design of effective liquid phase catalytic processes benefits from a clear understanding of these intermolecular interactions and their implications for rates and selectivities.

Trace mild acid-catalysed Z → E isomerization of norbornene-fused stilbene derivatives: intelligent chiral molecular photoswitches with controllable self-recovery

Stilbene derivatives have long been known to undergo “acid-catalyzed” Z → E isomerization, where a strong mineral acid at high concentration is practically necessary. Such severe reaction conditions often cause undesired by-reactions and limit their potential application. Herein, we present a trace mild acid-catalyzed Z → E isomerization found with stilbene derivatives fused with a norbornene moiety. By-reactions, such as the migration of the C Created by potrace 1.16, written by Peter Selinger 2001-2019 ]]> C double bond and electrophilic addition reactions, were completely inhibited because of the ring strain caused by the fused norbornene component. Direct photolysis of the E isomers at selected wavelengths led to the E → Z photoisomerization of these stilbene derivatives and thus constituted a unique class of molecular switches orthogonally controllable by light and acid. The catalytic amount of acid could be readily removed, and the Z → E isomerization could be controlled by turning on/off the irradiation of a photoacid, which allowed repeated isomerization in a non-invasive manner. Moreover, the Z isomer produced by photoisomerization could spontaneously self-recover to the E isomer in the presence of a catalytic amount of acid. The kinetics of Z → E isomerization were adjustable by manipulating catalytic factors and, therefore, unprecedented molecular photoswitches with adjustable self-recovery were realized.

Quantitative Z → E isomerization was catalyzed by trace mild acids to offer molecular switches orthogonally controllable by acid and light.

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

Direct Transformation of Glycerol to Propanal using Zirconium Phosphate-Supported Bimetallic Catalysts

Selective transformation of glycerol to propanal (PA) provides a feasible route towards the sustainable synthesis of high value-added chemicals. In this work, zirconium phosphate (ZrP) was studied as support and Ru and Co as metal sites for glycerol hydrogenolysis in a continuous-flow reactor. It was found that ZrP-supported Co-O species had a moderate selectivity to PA (49.5 %) in glycerol hydrogenolysis. Notably, once Ru species were doped into CoO/ZrP, the resulting catalyst exhibited not only an outstanding catalytic performance for glycerol hydrogenolysis to PA (a selectivity of 80.2 % at full conversion), but also a high stability at least a 50 h long-term performance. The spent catalyst could be regenerated by calcining in air to remove carbonaceous deposits. Characterization indicated that the acid sites on ZrP played a very critical role in the dehydration of glycerol into acrolein (AE), that the distribution of Co was uniform, basically consistent with that of Zr, P and Ru, and that an especially close contact between Co-O and Ru species was formed on Ru/CoO/ZrP catalyst. The further activity tests and characterizations confirmed that there was a strong interaction between the dispersed Co-O species and Ru⁰ nanoparticles, which endowed Ru sites with high electronic density. This effect could play a role in facilitating the dissociation of H₂ , and thus in promoting the hydrogenation reaction. Besides, DFT calculations suggested that the Co-O species can adsorb more strongly the C=C bond of the intermediate AE on a highly coordinatively unsaturated Co (Co_cus ) site and thus lead to preferential hydrogenation at the C=C bond of AE to PA.

Structure and solvation of confined water and water-ethanol clusters within microporous Brønsted acids and their effects on ethanol dehydration catalysis

Water networks confined within zeolites solvate clustered reactive intermediates and must rearrange to accommodate transition states that differ in size and polarity, with thermodynamic penalties that depend on the shape of the confining environment.

Aqueous-phase reactions within microporous Brønsted acids occur at active centers comprised of water-reactant-clustered hydronium ions, solvated within extended hydrogen-bonded water networks that tend to stabilize reactive intermediates and transition states differently. The effects of these diverse clustered and networked structures were disentangled here by measuring turnover rates of gas-phase ethanol dehydration to diethyl ether (DEE) on H-form zeolites as water pressure was increased to the point of intrapore condensation, causing protons to become solvated in larger clusters that subsequently become solvated by extended hydrogen-bonded water networks, according to in situ IR spectra. Measured first-order rate constants in ethanol quantify the stability of S_N2 transition states that eliminate DEE relative to (C₂H₅OH)(H⁺)(H₂O)_n clusters of increasing molecularity, whose structures were respectively determined using metadynamics and ab initio molecular dynamics simulations. At low water pressures (2–10 kPa H₂O), rate inhibition by water (–1 reaction order) reflects the need to displace one water by ethanol in the cluster en route to the DEE-formation transition state, which resides at the periphery of water–ethanol clusters. At higher water pressures (10–75 kPa H₂O), water–ethanol clusters reach their maximum stable size ((C₂H₅OH)(H⁺)(H₂O)_4–5), and water begins to form extended hydrogen-bonded networks; concomitantly, rate inhibition by water (up to –3 reaction order) becomes stronger than expected from the molecularity of the reaction, reflecting the more extensive disruption of hydrogen bonds at DEE-formation transition states that contain an additional solvated non-polar ethyl group compared to the relevant reactant cluster, as described by non-ideal thermodynamic formalisms of reaction rates. Microporous voids of different hydrophilic binding site density (Beta; varying H⁺ and Si–OH density) and different size and shape (Beta, MFI, TON, CHA, AEI, FAU), influence the relative extents to which intermediates and transition states disrupt their confined water networks, which manifest as different kinetic orders of inhibition at high water pressures. The confinement of water within sub-nanometer spaces influences the structures and dynamics of the complexes and extended networks formed, and in turn their ability to accommodate the evolution in polarity and hydrogen-bonding capacity as reactive intermediates become transition states in Brønsted acid-catalyzed reactions.