Surface modeling of photocatalytic materials for water splitting

Best practices of modeling complex materials in electrocatalysis, exemplified by oxygen evolution reaction on pentlandites

Pentlandites are natural ores with structural properties comparable to that of [FeNi] hydrogenases. While this class of transition-metal sulfide materials - (Fe,Ni)9S8 - with a variable Fe : Ni ratio has been proven to be an active electrode material for the hydrogen evolution reaction, it is also discussed as electrocatalyst for the alkaline oxygen evolution reaction (OER), corresponding to the bottleneck of anion exchange membrane electrolyzers for green hydrogen production. Despite the experimental evidence for the use of (Fe,Ni)9S8 as an OER catalyst, a detailed investigation of the elementary reaction steps, including consideration of adsorbate coverages and limiting steps under anodic polarizing conditions, is still missing. We address this gap in the present manuscript by gaining atomistic insights into the OER on an Fe4.5Ni4.5S8(111) surface through density functional theory calculations combined with a descriptor-based analysis. We use this system to introduce best practices for modeling this rather complex material by pointing out hidden pitfalls that can arise when using the popular computational hydrogen electrode approach to describe electrocatalytic processes at the electrified solid/liquid interface for energy conversion and storage.

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

Two-Dimensional Layered Heterojunctions for Photoelectrocatalysis

Two-dimensional (2D) layered nanomaterial heterostructures, arising from the combination of 2D materials with other low-dimensional species, feature a large surface area to volume ratio, which provides a high density of active sites for catalytic applications and for (photo)electrocatalysis (PEC). Meanwhile, their electronic band structure and high electrical conductivity enable efficient charge transfer (CT) between the active material and the substrate, which is essential for catalytic activity. In recent years, researchers have demonstrated the potential of a range of 2D material interfaces, such as graphene, graphitic carbon nitride (g-C3N4), metal chalcogenides (MCs), and MXenes, for (photo)electrocatalytic applications. For instance, MCs such as MoS2 and WS2 have shown excellent catalytic activity for hydrogen evolution, while graphene and MXenes have been used for the reduction of carbon dioxide to higher value chemicals. However, despite their great potential, there are still major challenges that need to be addressed to fully realize the potential of 2D materials for PEC. For example, their stability under harsh reaction conditions, as well as their scalability for large-scale production are important factors to be considered. Generating heterojunctions (HJs) by combining 2D layered structures with other nanomaterials is a promising method to improve the photoelectrocatalytic properties of the former. In this review, we inspect thoroughly the recent literature, to demonstrate the significant potential that arises from utilizing 2D layered heterostructures in PEC processes across a broad spectrum of applications, from energy conversion and storage to environmental remediation. With the ongoing research and development, it is likely that the potential of these materials will be fully expressed in the near future.

Periodic DFTB for Supported Clusters: Implementation and Application on Benzene Dimers Deposited on Graphene

The interest for properties of clusters deposited on surfaces has grown in recent years. In this framework, the Density Functional based Tight Binding (DFTB) method appears as a promising tool due to its ability to treat extended systems at the quantum level with a low computational cost. We report the implementation of periodic boundary conditions for DFTB within the deMonNano code with k-points formalism and corrections for intermolecular interactions. The quality of DFTB results is evaluated by comparison with dispersion-corrected DFT calculations. Optimized lattice properties for a graphene sheet and graphite bulk are in agreement with reference data. The deposition of both benzene monomer and dimers on graphene are investigated and the observed trends are similar at the DFT and DFTB levels. Moreover, interaction energies are of similar orders of magnitude for these two levels of calculation. This study has evidenced the high stability of a structure made of two benzene molecules deposited close to each other on the graphene sheet. This work demonstrates the ability of the new implementation to investigate surface-deposited molecular clusters properties.