Effects of surface wettability on (001)-WO and (100)-WSe: A spin-polarized DFT-MD study

An extensive understanding of WO and WSe bulk crystalline structures and explicit solvent effects on (001)-WO and (100)-WSe facets are essential for design of efficient (photo) electrocatalysts. The atomistic level understanding of both WO and WSe bulk solids and how water solvation processes occur on WO and WSe facets are nowadays characterized by a noticeable lack of knowledge. Herein, forefront Density Functional Theory-based molecular dynamics have been conducted for assessing the role of an explicit water environment in the characterization of solid surfaces. Water at the interface and H-bonds environment, as well as WO and WSe surface activity, will be described in terms of surface wettability and interfacial water dynamics, revealing the relevance of treating explicitly liquid water and its dynamics in assessing catalytic features. We provide pieces of evidence of the hydrophobic character shown by (001)-WO and (100)-WSe facets. A preferential in-plane hydration structure of the first water layer has been detected at both (001)-WO and (100)-WSe water interface, in which the electric dipole moment of water molecules is re-oriented in a sort of 2-dimensional H-bond network. Bulk property calculations of WO and WSe are also provided

Enabling Direct Photoelectrochemical H₂ Production using Alternative Oxidation Reactions on WO₃

The efficient and inexpensive conversion of solar energy into chemical bonds, such as in H2 via the photoelectrochemical splitting of H2O, is a promising route to produce green industrial feedstocks and renewable fuels, which is a key goal of the NCCR Catalysis. However, the oxidation product of the water splitting reaction, O2, has little economic or industrial value. Thus, upgrading key chemical species using alternative oxidation reactions is an emerging trend. WO3 has been identified as a unique photoanode material for this purpose since it performs poorly in the oxygen evolution reaction in H2O. Herein we highlight a collaboration in the NCCR Catalysis that has gained insights at the atomic level of the WO3 surface with ab initio computational methods that help to explain its unique catalytic activity. These computational efforts give new context to experimental results employing WO3 photoanodes for the direct photoelectrochemical oxidation of biomass-derived 5-(hydroxymethyl) furfural. While yield for the desired product, 2,5-furandicarboxylic acid is low, insights into the reaction rate constants using kinetic modelling and an electrochemical technique called derivative voltammetry, give indications on how to improve the system

Enabling Direct Photoelectrochemical H₂ Production using Alternative Oxidation Reactions on WO₃

The efficient and inexpensive conversion of solar energy into chemical bonds, such as in H2 via the photoelectrochemical splitting of H2O, is a promising route to produce green industrial feedstocks and renewable fuels, which is a key goal of the NCCR Catalysis. However, the oxidation product of the water splitting reaction, O2, has little economic or industrial value. Thus, upgrading key chemical species using alternative oxidation reactions is an emerging trend. WO3 has been identified as a unique photoanode material for this purpose since it performs poorly in the oxygen evolution reaction in H2O. Herein we highlight a collaboration in the NCCR Catalysis that has gained insights at the atomic level of the WO3 surface with ab initio computational methods that help to explain its unique catalytic activity. These computational efforts give new context to experimental results employing WO3 photoanodes for the direct photoelectrochemical oxidation of biomass-derived 5-(hydroxymethyl) furfural. While yield for the desired product, 2,5-furandicarboxylic acid is low, insights into the reaction rate constants using kinetic modelling and an electrochemical technique called derivative voltammetry, give indications on how to improve the system

Two Novel Dinuclear Cobalt Polypyridyl Complexes in Electro- and Photocatalysis for Hydrogen Production: Cooperativity Increases Performance

Syntheses and mechanisms of two dinuclear Co-polypyridyl catalysts for the H2 evolution reaction (HER) were reported and compared to their mononuclear analogue (R1). In both catalysts, two di-(2,2’-bipyridin-6-yl)-methanone units were linked by either 2,2’-bipyridin-6,6’-yl or pyrazin-2,5-yl. Complexation with CoII gave dinuclear compounds bridged by pyrazine (C2) or bipyridine (C1). Photocatalytic HER gave turnover numbers (TONs) of up to 20000 (C2) and 7000 (C1) in water. Electrochemically, C1 was similar to the R1, whereas C2 showed electronic coupling between the two Co centers. The E(CoII/I) split by 360 mV into two separate waves. Proton reduction in DMF was investigated for R1 with [HNEt3](BF4) by simulation, foot of the wave analysis, and linear sweep voltammetry (LSV) with in-line detection of H2. All methods agreed well with an (E)ECEC mechanism and the first protonation being rate limiting (≈104 m−1 s−1). The second reduction was more anodic than the first one. pKa values of around 10 and 7.5 were found for the two protonations. LSV analysis with H2 detection for all catalysts and acids with different pKa values [HBF4, pKa(DMF)≈3.4], intermediate {[HNEt3](BF4), pKa(DMF)≈9.2} to weak [AcOH, pKa(DMF)≈13.5] confirmed electrochemical H2 production, distinctly dependent on the pKa values. Only HBF4 protonated CoI intermediates. The two metals in the dualcore C2 cooperated with an increase in rate to a competitive 105 m−1 s−1 with [HNEt3](BF4). The overpotential decreased compared to R1 by 100 mV. Chronoamperometry established high stabilities for all catalysts with TONlim of 100 for R1 and 320 for C1 and C2

Electronic Communication and Photoinduced Intramolecular Electron Transfer in Hybrid Ru(II)-Re(I) Complexes Using Eigenstate-Based and Diabatic-State-Based Models

Photoinduced intramolecular electron transfer (PIET) plays a vital role in the efficiency of electronics communication in transition metal complexes catalysing oxidation-reduction reaction. In this work, we theoretically calculate the rate of electron transfer(ET) in Ru(II)-BL-Ru(I) hybrid complexes; where BL is bridging ligand. A brief concept of ET in the basis of Marcus theory, which is extended to address a variety of different type of ET, is provided. We show that, in the case of Ru(II)-BL-Ru(I) complex, ET involves a non-adiabatic state which thanks to a fast electronics communication between donor and acceptor connected by BL and becomes rigid complex. Single electron transferring in Ru(II)-BL-Ru(I) complex governed by PIET constructed by potential energy curve as change of structural transformation over time-evolution. We also investigate the mechanism of PIET involving a redox reaction in excited state, wherein the oxidation state of Ru(II) (donor) and Ru(I) (acceptor) changes. To access non-adiabatic state of Ru(II)-BL-Ru(I), we use constrained density functional theory to allow ground state calculation to be performed along with geometry constraints. We also systematically study the role of distance of donor-acceptor separation on kinetics of PIE

How ab initio Molecular Dynamics Can Change the Understanding on Transition Metal Catalysed Water Oxidation

Artificial water splitting is a promising technology that allows the storage of renewable energy in the form of energy-rich compounds. This mini-review showcases how theoretical studies contribute to the under-standing of existing water oxidation catalysts (WOCs) as well as inspiring the development of novel WOCs. In order to understand the chemical complexity of transition metal complexes and their interaction with the solvent environment, the use of sophisticated simulation protocols is necessary. As an illustration, a family of ruthe- nium-based WOCs is presented which were investigated employing a wide range of forefront computational methods with emphasis on ab initiomolecular dynamic based approaches. In those studies a base assisted oxygen–oxygen bond formation was identified as the energetically most favourable reaction mechanism. By examining the role of local environmental effects at ambient temperature and the effect of modifications in the ligand framework, a comprehensible picture of the WOCs can be given, where the latter can serve as a guideline for further experimental and computational studies. In this mini-review, we provide a description of the methods, and the findings of our previous computational studies in compacted form, aimed at scientists with a theoretical as well as experimental background

How ab initio Molecular Dynamics Can Change the Understanding on Transition Metal Catalysed Water Oxidation

Artificial water splitting is a promising technology that allows the storage of renewable energy in the form of energy-rich compounds. This mini-review showcases how theoretical studies contribute to the under-standing of existing water oxidation catalysts (WOCs) as well as inspiring the development of novel WOCs. In order to understand the chemical complexity of transition metal complexes and their interaction with the solvent environment, the use of sophisticated simulation protocols is necessary. As an illustration, a family of ruthe- nium-based WOCs is presented which were investigated employing a wide range of forefront computational methods with emphasis on ab initiomolecular dynamic based approaches. In those studies a base assisted oxygen–oxygen bond formation was identified as the energetically most favourable reaction mechanism. By examining the role of local environmental effects at ambient temperature and the effect of modifications in the ligand framework, a comprehensible picture of the WOCs can be given, where the latter can serve as a guideline for further experimental and computational studies. In this mini-review, we provide a description of the methods, and the findings of our previous computational studies in compacted form, aimed at scientists with a theoretical as well as experimental background

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

We present Deep learning for Collective Variables (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials

Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space

Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search for hidden CVs of the reaction of interest

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

We present Deep learning for Collective Variables (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials