Liquid-Phase Modeling in Heterogeneous 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.

Rational design of water splitting electrocatalysts through computational insights

Electrocatalytic water splitting is vital for the sustainable production of green hydrogen. Electrocatalysts, including those for the hydrogen evolution reaction at the cathode and the oxygen evolution reaction at the anode, are crucial in determining the overall performance of water splitting. Traditional methods for electrocatalyst development often rely on trial-and-error, which can be time-consuming and inefficient. Recent advancements in computational techniques provide more systematic and predictive strategies for catalyst design. This review article explores the role of computational insights in the development of water-splitting electrocatalysts. We start by giving an introduction of electrocatalytic water splitting mechanisms. Then, fundamental theories such as the Sabatier principle and scaling relationships are reviewed, which provide a theoretical basis for catalytic activity. We also discuss thermodynamic, electronic, and geometric descriptors used to guide catalyst design and provide an in-depth discussion of their applications and limitations. Advanced computational approaches, including high-throughput screening, machine learning, solvation models and Ab initio molecular dynamics, are also highlighted for their ability to accelerate catalyst discovery and simulate realistic reaction conditions. Finally, we propose future research directions aimed at searching universal descriptors, expanding data sets, and integrating developing interpretable models with catalyst design.

Modeling interfacial electric fields and the ethanol oxidation reaction at electrode surfaces

The electrochemical environment present at surfaces can have a large effect on intended applications. Such environments may occur, for instance, at battery or electrocatalyst surfaces. Solvent, co-adsorbates, and electrical field effects may strongly influence surface chemistry. Understanding these phenomena is an on-going area of research, especially in the realm of electrocatalysis. Herein, we modeled key steps in the ethanol oxidation reaction (EOR) over a common EOR catalyst, Rh(111), using density functional theory. We assessed how the presence of electrical fields may influence important C-C and C-H bond scission and C-O bond formation reactions with and without co-adsorbed water. We found that electric fields combined with the presence of water can significantly affect surface chemistry, including adsorption and reaction energies. Our results show that C-C scission (necessary for the complete oxidation of ethanol) is most likely through CHxCO adsorbates. With no electric field or solvent present C-C scission of CHCO has the lowest reaction energy and dominates the oxidation of ethanol. But when applying strong negative fields (with or without solvent), the C-C scission of CH2CO and CHCO becomes competitive. The current work provides insights into how electric fields and water solvent affect EOR, especially when simulated using density functional theory.

Rationally designed Ru catalysts supported on TiN for highly efficient and stable hydrogen evolution in alkaline conditions

Electrocatalysis holds the key to enhancing the efficiency and cost-effectiveness of water splitting devices, thereby contributing to the advancement of hydrogen as a clean, sustainable energy carrier. This study focuses on the rational design of Ru nanoparticle catalysts supported on TiN (Ru NPs/TiN) for the hydrogen evolution reaction in alkaline conditions. The as designed catalysts exhibit a high mass activity of 20 A mg-1Ru at an overpotential of 63 mV and long-term stability, surpassing the present benchmarks for commercial electrolyzers. Structural analysis highlights the effective modification of the Ru nanoparticle properties by the TiN substrate, while density functional theory calculations indicate strong adhesion of Ru particles to TiN substrates and advantageous modulation of hydrogen adsorption energies via particle-support interactions. Finally, we assemble an anion exchange membrane electrolyzer using the Ru NPs/TiN as the hydrogen evolution reaction catalyst, which operates at 5 A cm-2 for more than 1000 h with negligible degradation, exceeding the performance requirements for commercial electrolyzers. Our findings contribute to the design of efficient catalysts for water splitting by exploiting particle-support interactions.

PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics

PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.

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.

Exploring the biointerfaces: ab initio investigation of nano-montmorillonite clay, and its interaction with unnatural amino acids

We investigated the interaction between biomimetic Fe and Mg co-doped montmorillonite nanoclay and eleven unnatural amino acids. Employing three different functionals (PBE-GGA, PBE-GGA + U, and HSE06), we examined the clay's structural, electronic, and magnetic properties. Our results revealed the necessity of using PBE-GGA + U with U ≥ 4 eV to accurately describe key clay properties. We identified amino acids that strongly interacted with the clay surface, with steric orientation playing a crucial role in facilitating binding. Our DFT calculations highlighted significant electrostatic interactions between the amino acids and the clay slab, with the amino group's predominant role in this interaction. These findings hold promise for designing amino acids for clay-amino acid systems, leading to innovative bio-material composites for various applications. Additionally, our ab-initio molecular dynamics simulations confirmed the stability of clay-amino acid systems under ambient conditions, and the introduction of an implicit water solvent enhanced the binding energy of amino acids on the clay surface.

A Combined Reaction Path Search and Hybrid Solvation Method for the Systematic Exploration of Elementary Reactions at the Solid-Liquid Interface

We present a combined simulation method of single-component artificial force induced reaction (SC-AFIR) and effective screening medium combined with the reference interaction site model (ESM-RISM), termed SC-AFIR+ESM-RISM. SC-AFIR automatically and systematically explores the chemical reaction pathway, and ESM-RISM directly simulates the precise electronic structure at the solid-liquid interface. Hence, SC-AFIR+ESM-RISM enables us to explore reliable reaction pathways at the solid-liquid interface. We applied it to explore the dissociation pathway of an H2O molecule at the Cu(111)/water interface. The reaction path networks of the whole reaction and the minimum energy paths from H2O to H2 + O depend on the interfacial environment. The qualitative difference in the energy diagrams and the resulting change in the kinematically favored dissociation pathway upon changing the solvation environments are discussed. We believe that SC-AFIR+ESM-RISM will be a powerful tool to reveal the details of chemical reactions in surface catalysis and electrochemistry.

Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials

Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate ab initio MD simulations by up to 4 orders of magnitude while reproducing with high fidelity the geometrical features of water in the bulk and at metal-water interfaces. Using these ML-accelerated simulations, we accurately predicted key catalytic quantities such as the adsorption energies of CO*, OH*, COH*, HCO*, and OCCHO* on Cu surfaces and the free energy barriers of C-H scission of ethylene glycol over Cu and Pd surfaces, as validated with ab initio calculations. We envision that such simulations will pave the way towards detailed and realistic studies of solvated catalysts at large time- and length-scales.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Evaluating Adsorbate-Solvent Interactions: Are Dispersion Corrections Necessary?

Incorporating solvent-adsorbate interactions is paramount in models of aqueous (electro)catalytic reactions. Although a number of techniques exist, they are either highly demanding in computational terms or inaccurate. Microsolvation offers a trade-off between accuracy and computational expenses. Here, we dissect a method to swiftly outline the first solvation shell of species adsorbed on transition-metal surfaces and assess their corresponding solvation energy. Interestingly, dispersion corrections are generally not needed in the model, but caution is to be exercised when water-water and water-adsorbate interactions are of similar magnitude.

Nanoporous Gold: From Structure Evolution to Functional Properties in Catalysis and Electrochemistry

Nanoporous gold (NPG) is characterized by a bicontinuous network of nanometer-sized metallic struts and interconnected pores formed spontaneously by oxidative dissolution of the less noble element from gold alloys. The resulting material exhibits decent catalytic activity for low-temperature, aerobic total as well as partial oxidation reactions, the oxidative coupling of methanol to methyl formate being the prototypical example. This review not only provides a critical discussion of ways to tune the morphology and composition of this material and its implication for catalysis and electrocatalysis, but will also exemplarily review the current mechanistic understanding of the partial oxidation of methanol using information from quantum chemical studies, model studies on single-crystal surfaces, gas phase catalysis, aerobic liquid phase oxidation, and electrocatalysis. In this respect, a particular focus will be on mechanistic aspects not well understood, yet. Apart from the mechanistic aspects of catalysis, best practice examples with respect to material preparation and characterization will be discussed. These can improve the reproducibility of the materials property such as the catalytic activity and selectivity as well as the scope of reactions being identified as the main challenges for a broader application of NPG in target-oriented organic synthesis.

An Electrostatically Embedded QM/MM Scheme for Electrified Interfaces

Atomistic modeling of electrified interfaces remains a major issue for detailed insights in electrocatalysis, corrosion, electrodeposition, batteries, and related devices such as pseudocapacitors. In these domains, the use of grand-canonical density functional theory (GC-DFT) in combination with implicit solvation models has become popular. GC-DFT can be conveniently applied not only to metallic surfaces but also to semiconducting oxides and sulfides and is, furthermore, sufficiently robust to achieve a consistent description of reaction pathways. However, the accuracy of implicit solvation models for solvation effects at interfaces is in general unknown. One promising way to overcome the limitations of implicit solvents is going toward hybrid quantum mechanical (QM)/molecular mechanics (MM) models. For capturing the electrochemical potential dependence, the key quantity is the capacitance, i.e., the relation between the surface charge and the electrochemical potential. In order to retrieve the electrochemical potential from a QM/MM hybrid scheme, an electrostatic embedding is required. Furthermore, the charge of the surface and of the solvent regions has to be strictly opposite in order to consistently simulate charge-neutral unit cells in MM and in QM. To achieve such a QM/MM scheme, we present the implementation of electrostatic embedding in the VASP code. This scheme is broadly applicable to any neutral or charged solid/liquid interface. Here, we demonstrate its use in the context of GC-DFT for the hydrogen evolution reaction (HER) over a noble-metal-free electrocatalyst, MoS₂. We investigate the effect of electrostatic embedding compared to the implicit solvent model for three contrasting active sites on MoS₂: (i) the sulfur vacancy defect, which is rather apolar; (ii) a Mo antisite defect, where the active site is a surface bound highly polar OH group; and (iii) a reconstructed edge site, which is generally believed to be responsible for most of the catalytic activity. According to our results, the electrostatic embedding leads to almost indistinguishable results compared to the implicit solvent for the apolar system but has a significant effect on polar sites. This demonstrates the reliability of the hybrid QM/MM, electrostatically embedded solvation model for electrified interfaces.

Differences in solvation thermodynamics of oxygenates at Pt/Al2O3 perimeter versus Pt(111) terrace sites

A prominent role of water in aqueous-phase heterogeneous catalysis is to modify free energies; however, intuition about how is based largely on pure metal surfaces or even homogeneous solutions. Using multiscale modeling with explicit liquid water molecules, we show that the influence of water on the free energies of adsorbates at metal/support interfaces is different than that on pure metal surfaces. We specifically compute free energies of solvation for methanol and its constituents on a Pt/Al2O3 catalyst and compare the results to analogous values calculated on a pure Pt catalyst. We find that the more hydrophilic Pt/Al2O3 interface leads to smaller (more positive) free energies of solvation due to an increased entropy penalty resulting from the additional work necessary to disrupt the interfacial water structure and accommodate the interfacial species. The results will be of interest in other fields, including adsorption and proteins.

Hydrothermal Synthesis and Properties of Nanostructured Silica Containing Lanthanide Type Ln-SiO2 (Ln = La, Ce, Pr, Nd, Eu, Gd, Dy, Yb, Lu)

The nanostructured lanthanide-silica materials of the Ln-SiO₂ type (Ln = La, Ce, Pr, Nd, Eu, Gd, Dy, Yb, Lu) were synthesized by the hydrothermal method at 100 °C, using cetyltrimethylammonium as a structural template, silica gel and sodium silicate as a source of silicon, and lanthanide oxides, with Si/Ln molar ratio = 50. The resulting materials were calcined at 500 °C using nitrogen and air, and characterized by X-ray diffraction (XRD), Fourier-Transform infrared absorption spectroscopy, scanning electron microscopy, thermogravimetry (TG), surface area by the BET method and acidity measurements by n-butylamine adsorption. The XRD and chemical analysis indicated that the SiO₂ presented a hexagonal structure and the incorporation of lanthanides in the structure changes the properties of the Ln-SiO₂ materials. The heavier the lanthanide element, the higher the Si/Ln ratio. The TG curves showed that the decomposition of the structural template occurs in the materials at temperatures below 500 °C. The samples showed variations in specific surface area, mean pore diameter and silica wall thickness, depending on the nature of the lanthanide. The incorporation of different lanthanides in the silica generated acid sites of varied strength. The hydrothermal stability of the Ln-SiO₂ materials evaluated at high temperatures, evidenced that the properties can be controlled for application in adsorption and catalysis processes.

Insight into the solvent effects on ethanol oxidation on Ir(100)

Solvent effects have always been a non-negligible factor for aqueous catalytic reactions, though few studies have been devoted towards the molecular understanding and impact of solvent effects on catalysis. In this work, we investigated ethanol dehydrogenation and C-C bond cleavage over Ir(100) in an aqueous solution using density functional theory calculations with both the implicit and explicit solvent models and transition state theory-based kinetics simulations. The results show that solvent polarization assists the α- and β-dehydrogenation of ethanol on Ir(100) in the aqueous solution and hydrogen bonding also assists the ethanol β-dehydrogenation and C-C bond cleavage in CH₂CO. The hydrogen bond between the ethanol and water molecule hinders ethanol hydroxyl dehydrogenation while the CHCO⋯H2O hydrogen bond radically alters the adsorption configuration of CHCO, which leads to an increase in the C-C cleavage barrier by 2.5 fold. Furthermore, the solvent changes the reaction pathways significantly. In an aqueous solution, ethanol β-dehydrogenation on Ir(100) is the dominant ethanol dehydrogenation pathway and C-C bond cleavage occurs predominantly via CH₂CO species.

Liquid-Phase Effects on Adsorption Processes in Heterogeneous Catalysis

Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values. Computational, implicit solvation models predict exergonic solvation effects for phenol adsorption, failing to agree with measurements even qualitatively. In this study, an explicit, hybrid quantum mechanical/molecular mechanical approach for computing solvation free energies of adsorption is developed, solvation free energies of phenol adsorption are computed, and experimental data for solvation free energies of phenol adsorption are reanalyzed using multiple adsorption isotherm models. Explicit solvation calculations predict an endergonic solvation free energy for phenol adsorption that agrees well with measurements to within the experimental and force field uncertainties. Computed adsorption free energies of solvation of carbon monoxide, ethylene glycol, benzene, and phenol over the (111) facet of Pt and Cu suggest that liquid water destabilizes all adsorbed species, with the largest impact on the largest adsorbates.

Multilevel Computational Studies Reveal the Importance of Axial Ligand for Oxygen Reduction Reaction on Fe-N-C Materials

The systematic improvement of Fe-N-C materials for fuel cell applications has proven challenging, due in part to an incomplete atomistic understanding of the oxygen reduction reaction (ORR) under electrochemical conditions. Herein, a multilevel computational approach, which combines ab initio molecular dynamics simulations and constant potential density functional theory calculations, is used to assess proton-coupled electron transfer (PCET) processes and adsorption thermodynamics of key ORR intermediates. These calculations indicate that the potential-limiting step for ORR on Fe-N-C materials is the formation of the Fe^III-OOH intermediate. They also show that an active site model with a water molecule axially ligated to the iron center throughout the catalytic cycle produces results that are consistent with the experimental measurements. In particular, reliable prediction of the ORR onset potential and the Fe(III/II) redox potential associated with the conversion of Fe^III-OH to Fe^II and desorbed H₂O requires an axial H₂O co-adsorbed to the iron center. The observation of a five-coordinate rather than four-coordinate active site has significant implications for the thermodynamics and mechanism of ORR. These findings highlight the importance of solvent-substrate interactions and surface charge effects for understanding the PCET reaction mechanisms and transition-metal redox couples under realistic electrochemical conditions.

Solvent-mediated outer-sphere CO2 electro-reduction mechanism over the Ag111 surface

The electrocatalytic CO2 reduction reaction (CO2RR) is one of the key technologies of the clean energy economy. Molecular-level understanding of the CO2RR process is instrumental for the better design of electrodes operable at low overpotentials with high current density. The catalytic mechanism underlying the turnover and selectivity of the CO2RR is modulated by the nature of the electrocatalyst, as well as the electrolyte liquid, and its ionic components that form the electrical double layer (EDL). Herein we demonstrate the critical non-innocent role of the EDL for the activation and conversion of CO2 at a high cathodic bias for electrocatalytic conversion over a silver surface as a representative low-cost model cathode. By using a multiscale modeling approach we demonstrate that under such conditions a dense EDL is formed, which hinders the diffusion of CO2 towards the Ag111 electrocatalyst surface. By combining DFT calculations and ab initio molecular dynamics simulations we identify favorable pathways for CO2 reduction directly over the EDL without the need for adsorption to the catalyst surface. The dense EDL promotes homogeneous phase reduction of CO2 via electron transfer from the surface to the electrolyte. Such an outer-sphere mechanism favors the formation of formate as the CO2RR product. The formate can undergo dehydration to CO via a transition state stabilized by solvated alkali cations in the EDL.

Mechanism of Oxygen Reduction Reaction in Alkaline Medium on Nitrogen-Doped Graphyne and Graphdiyne Families: A First Principles Study

Using extensive first principles protocols, a systematic investigation is performed to probe the oxygen reduction reaction (ORR) mechanism on nitrogen (N) doped graphynes (Gys, e. g. αGy, βGy, γGy and 6,6,12Gy) and graphdiyne (Gdy) in alkaline medium. We considered both associative and dissociative pathways, as well as two distinct intermediate forks for each of them depending on the first protonation site(s). Following the dissociative approach, the activation energy to form an O₂ dissociated configuration is found as a function of the distances migrated by the O atoms over the catalyst surface and the amount of charge transferred from the C atoms linked to N. N doped αGy and 6,6,12Gy emerged as the best electrocatalyst comparing both pathways having lowest overpotentials of 0.88 and 0.82 V, respectively. The rate-limiting steps for the two different intermediate routes are observed to be dependent on the first protonation site(s) and related to the desorption of the OH radical from the sp hybridized C atom site(s) linked to N. Hence, the OH adsorption energy is identified as a descriptor for the efficiency of the ORR for the considered systems. The stabilities of the ORR intermediates are further elaborated in terms of pH and electrode potential.

Influence of an electrified interface on the entropy and energy of solvation of methanol oxidation intermediates on platinum(111) under explicit solvation

Liquid water and electric fields play significant roles in phenomena occurring at catalytic and electrocatalytic interfaces; however, how their interplay influences interfacial energetics remains uncertain. Electric fields control the orientations of water molecules, which we hypothesized would influence the solvation thermodynamics of surface species. To explore this hypothesis, we used multiscale simulations involving density functional theory and classical molecular dynamics. We computed the energies and entropies of solvation of surface species on Pt(111), specifically, adsorbed CH₃OH, COH, and CO, which are intermediates in the pathway of methanol oxidation, in the presence of electric fields spanning -0.5 to +0.5 V Å^-1. We found that both the energy and entropy of solvation depend on the strength and direction of the field, with the entropy of solvation being significantly impacted. Both the energy and entropy dependence on the field can be ascribed to water molecule orientations. Specifically, more positive fields orient water molecules so that they can more effectively hydrogen bond with surface species, which strengthens the energies of solvation. However, at more negative fields, competition with the surface species causes interfacial water molecules to reorient, which leads to disorder in the water structure and hence increased entropy.

Understanding Adsorption of Organics on Pt(111) in the Aqueous Phase: Insights from DFT Based Implicit Solvent and Statistical Thermodynamics Models

Adsorption of organics in the aqueous phase is an area which is experimentally difficult to measure, while computational techniques require extensive configurational sampling of the solvent and adsorbate. This is exceedingly computationally demanding, which excludes its routine use. If implicit solvent could be applied instead, this would dramatically reduce the computational cost as configurational sampling of solvent is not needed. Here, using statistical thermodynamic arguments and DFT calculations with implicit solvent models, we show that semiquantitative values for the free energy and entropy change of adsorption in the aqueous phase (ΔG_ads^solv and ΔS_ads^solv) for small organics can be calculated, for a range of coverages. We parametrize the soft sphere based solute dielectric cavity to an approximated free energy of solvation for a single Pt atom at the (111) facet, forming upper and lower bounds based on the entropy of water at the aqueous metal interface (ΔG_solv(Pt) = -4.35 to -7.18 kJ mol^-1). This captures the decrease in ΔG_ads^solv compared to the free energy of adsorption in the vacuum phase (ΔG_ads^vac), while solvent models with electron density based cavities fail to do so. For a range of oxygenated aromatics, the adsorption energetics using horizontal gas phase geometries significantly overestimate ΔG_ads^solv compared to experiment by ∼100 kJ mol^-1, but they agree with ab initio MD simulations using similar geometries. This suggests oxygenated aromatic compounds adsorb perpendicular to the metallic surface, while the ΔG_ads^solv for vertical geometries of furfural and cyclohexanol agree to within 20 kJ mol^-1 of experimental studies. The proposed techniques provide an inexpensive toolset for validation and prediction of adsorption energetics on solvated metallic surfaces, which could be further validated by the future availability of more experimental measurements for the aqueous entropy/free energy of adsorption.

Solvent effects on the kinetics of 4-nitrophenol reduction by NaBH4 in the presence of Ag and Au nanoparticles

The reduction of 4-nitrophenol (4-NiP) to 4-aminophenol (4-AP) with an excess of sodium borohydride is commonly used as a model reaction to assess the catalytic activity of metallic nanoparticles. This reaction is considered both a potentially important step in industrial water treatment and an attractive, commercially relevant synthetic pathway. Surprisingly, an important factor, the role of the reaction medium on the reduction performance, has so far been overlooked. Here, we report a pronounced effect of the solvent on the reaction kinetics in the presence of silver and gold nanoparticles. We demonstrate that the addition of methanol, ethanol, or isopropanol to the reaction mixture leads to a dramatic decrease in the reaction rate. For typical concentrations of reactants, the reduction is completely suppressed in the presence of 50 vol% alcohols. 4-NiP reduction rate in aqueous alcohol mixtures can, however, be improved noticeably by increasing the borohydride concentration or the reaction temperature. The analysis of various factors responsible for solvent effects reveals that the decrease in the reduction rate in the presence of alcohols is related, amongst others, to a substantially higher oxygen solubility in alcohols compared to water. The results of this work show that the effects of solvent properties on reaction kinetics must be considered for unambiguous comparison and optimization of the catalytic performance of metallic nanoparticles in the liquid phase 4-NiP reduction.

The presence of methanol, ethanol, or isopropanol in the reaction mixture substantially affects the kinetics of 4-nitrophenol reduction in aqueous medium.

Recent Progress and Prospects in Catalytic Water Treatment

Presently, conventional technologies in water treatment are not efficient enough to completely mineralize refractory water contaminants. In this context, the implementation of catalytic processes could be an alternative. Despite the advantages provided in terms of kinetics of transformation, selectivity, and energy saving, numerous attempts have not yet led to implementation at an industrial scale. This review examines investigations at different scales for which controversies and limitations must be solved to bridge the gap between fundamentals and practical developments. Particular attention has been paid to the development of solar-driven catalytic technologies and some other emerging processes, such as microwave assisted catalysis, plasma-catalytic processes, or biocatalytic remediation, taking into account their specific advantages and the drawbacks. Challenges for which a better understanding related to the complexity of the systems and the coexistence of various solid-liquid-gas interfaces have been identified.

An evaluation of solvent effects and ethanol oxidation

Understanding liquid-metal interfaces in catalysis is important, as the liquid can speed up surface reactions, increase the selectivity of products, and open up new favorable reaction pathways. In this work we modeled using density functional theory various steps in ethanol oxidation/decomposition over Rh(111). We considered implicit (continuum), explicit, and hybrid (implicit combined with explicit) solvation approaches, as well as two solvents, water and ethanol. We focused on modeling adsorption steps, as well as C-C/C-H bond scission and C-O bond formation reactions. Implicit solvation had very little effect on adsorption and reaction free energies. However, using the explicit and hybrid models, some free energies changed significantly. Furthermore, ethanol solvent had a more considerable impact than water solvent. We observed that preferred reaction pathways for C-C scission changed depending on the solvation model and solvent choice (ethanol or water). We also applied the bond-additivity solvation method to calculate heats of adsorption. Heats of adsorption and reaction using the bond-additivity model followed the same trends as the other solvation models, but were ∼1.1 eV more endothermic. Our work highlights how different solvation approaches can influence analysis of the oxidation/decomposition of organic surface species.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

Insights into protonation for cyclohexanol/water mixtures at the zeolitic Brønsted acid site

Proton transfer from Brønsted acid sites (BASs) to alcohol molecules ignites the acid-catalyzed alcohol dehydration reactions. For aqueous phase dehydration reactions in zeolites, the coexisting water molecules around BASs in the zeolite pores significantly affect the alcohol dehydration activity. In the present work, proton transfer processes among the BASs of H-BEA zeolites, the adsorbed cyclohexanol and surrounding water clusters with different sizes up to 8 water molecules were investigated using ab initio molecular dynamics (AIMD) simulations combined with the multiple-walker well-tempered metadynamics algorithm. The plausible proton locations and proton transfer processes were characterized using two/three-dimensional free energy landscapes. The strong proton affinity makes the protonated cyclohexanol stable species until a water trimer is formed. The proton either is shared between protonated cyclohexanol and the water trimer or remains with the water trimer (H7O3+). With a further increase in water concentrations, the proton prefers to remain with the water clusters.

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

H2 -Free Re-Based Catalytic Dehydroxylation of Aldaric Acid to Muconic and Adipic Acid Esters

As one of the most demanded dicarboxylic acids, adipic acid can be directly produced from renewable sources. Hexoses from (hemi)cellulose are oxidized to aldaric acids and subsequently catalytically dehydroxylated. Hitherto performed homogeneously, we present the first heterogeneous catalytic process for converting an aldaric acid into muconic and adipic acid. The contribution of leached Re from the solid pre-reduced catalyst was also investigated with hot-filtration test and found to be inactive for dehydroxylation. Corrosive or hazardous (HBr/H2 ) reagents are avoided and simple alcohols and solid Re/C catalysts in an inert atmosphere are used. At 120 °C, the carboxylic groups are protected by esterification, which prevents lactonization in the absence of water or acidic sites. Dehydroxylation and partial hydrogenation yield monohexenoates (93 %). For complete hydrogenation to adipate, a 16 % higher activation barrier necessitates higher temperatures.

Aqueous-phase effects on ethanol decomposition over Ru-based catalysts

Liquid water decelerates ethanol reforming over Ru(0001) but increases the H2 selectivity due to accelerated WGS and suppressed methanation.

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.

Dependency of solvation effects on metal identity in surface reactions

Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O-H and C-H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface. A significant metal dependence on aqueous solvation effects was observed that suggests solvation effects must be studied in detail for every reaction system. The main reason for this dependence could be traced back to a different amount of charge-transfer between the adsorbed moieties and metals in the reactant and transition states for the different metal surfaces.