Electro-, photo-, and photoelectrocatalysis play a critical role toward the realization of a sustainable energy economy. They facilitate numerous redox reactions in energy storage and conversion systems, enabling the production of chemical feedstock and clean fuels from abundant resources like water, carbon dioxide, and nitrogen. One major obstacle for their large-scale implementation is the scarcity of cost-effective, durable, and efficient catalysts. A family of two-dimensional transition metal carbides, nitrides, and carbonitrides (MXenes) has recently emerged as promising earth-abundant candidates for large-area catalytic energy storage and conversion due to their unique properties of hydrophilicity, high metallic conductivity, and ease of production by solution processing. To take full advantage of these desirable properties, MXenes have been combined with other materials to form MXene hybrids with significantly enhanced catalytic performances beyond the sum of their individual components. MXene hybridization tunes the electronic structure toward optimal binding of redox active species to improve intrinsic activity while increasing the density and accessibility of active sites. This review outlines recent strategies in the design of MXene hybrids for industrially relevant electrocatalytic, photocatalytic, and photoelectrocatalytic applications such as water splitting, metal-air/sulfur batteries, carbon dioxide reduction, and nitrogen reduction. By clarifying the roles of individual material components in the MXene hybrids, we provide design strategies to synergistically couple MXenes with associated materials for highly efficient and durable catalytic applications. We conclude by highlighting key gaps in the current understanding of MXene hybrids to guide future MXene hybrid designs in catalytic energy storage and conversion applications.

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Rational Design of Two-Dimensional Transition Metal Carbide/Nitride (MXene) Hybrids and Nanocomposites for Catalytic Energy Storage and Conversion

Conversion of naturally abundant dinitrogen (N2) to ammonia (NH3) is one of the most attractive and challenging topics in chemistry. Current studies mainly focus on electrocatalytic nitrogen reduction reaction (NRR) using metal-based electrocatalysts, while metal-free and solar-driven photocatalysts have been rarely explored. Here, on the basis of the "σ donation-π* back-donation" concept, single B atom supported on holey g-CN (B@g-CN) can serve as metal-free photocatalyst for highly efficient N2 fixation and reduction under visible and even infrared spectra. Our results reveal that N2 can be efficiently activated and reduced to NH3 with extremely low overpotential of 0.15 V and activation barrier of 0.61 eV, lower than most of metal-based NRR catalysts, thereby guaranteeing low energy cost and fast kinetics of NRR. The inherent properties of B@g-CN, such as centralized spin-polarization on the B atom, efficient prohibition of competitive hydrogen evolution reaction (HER), and reduced exciton binding energy, are responsible for the high selectivity and Faradaic efficiency for NRR under ambient conditions. Moreover, for the first time, we theoretically disclose that the external potential provided by photogenerated electrons for NRR/HER endowing B@g-CN spontaneous NRR and inaccessible HER. This work may provide a promising lead for designing efficient and robust metal-free single atom catalysts toward photocatalytic NRR under visible/infrared spectrum.

Metal-Free B@ g -CN: Visible/Infrared Light-Driven Single Atom Photocatalyst Enables Spontaneous Dinitrogen Reduction to Ammonia.

Hydrogen production from water splitting using renewable electric energy is an interesting topic towards the carbon neutral future. Single atom catalysts (SACs) have emerged as a new frontier in the field of catalysis such as hydrogen evolution reaction (HER), owing to their intriguing properties like high activity and excellent chemical selectivity. The catalytic active moiety is often comprised of a single metal atom and its neighboring environment from the supports. Recent published reviews about electricdriven HER tend to classify these SACs by the species of active center atom, nevertheless the influence of their neighboring coordinated atoms from the supports is somehow neglected. Thus we classify the SACs for HER through the type of supports, highlighting the electronic metal-support interaction and their coordination environment from support. Then, we put forward some structural designing strategies including regulating of the central atoms, coordination environments, and metal-support interactions. Finally, the current challenges and future research perspectives of SACs for HER are briefly proposed. 

https://link.springer.com/content/pdf/10.1007/s12274-022-4265-y.pdf

Regulations of active moiety in single atom catalysts for electrochemical hydrogen evolution reaction

“More is Different:” Synergistic Effect and Structural Engineering in Double‐Atom Catalysts

MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machin...

Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene

The electrocatalytical process is the most efficient way to produce ammonia (NH3) under ambient conditions, but developing a highly efficient and low-cost metal-free electrocatalysts remains a major scientific challenge. Hence, single atom and double boron (B) atoms doped 2D graphene-like carbon nitride (C2N-h2D) electrocatalysts have been designed (B@C2N and B2@C2N), and the efficiency of N2 reduction reaction (NRR) is examined by density functional theory calculation. The results show that the single and double B atoms can both be strongly embedded in natural nanoporous C2N with superior catalytic activity for N2 activation. The reaction mechanisms of NRR on the B@C2N and B2@C2N are both following an enzymatic pathway, and B2@C2N is a more efficient electrocatalyst with extremely low overpotential of 0.19 eV comparing to B@C2N (0.29 eV). In the low energy region, the hydrogenation of N2 is thermodynamically more favorable than the hydrogen production, thereby improving the selectivity for NRR. Based on these results, a new double-atom strategy may help guiding the experimental synthesis of highly efficient NRR electrocatalysts.

https://iopscience.iop.org/article/10.1088/1361-6528/ab1d01/pdf

Single and double boron atoms doped nanoporous C2N-h2D electrocatalysts for highly efficient N2 reduction reaction: a density functional theory study.

Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials

In this study, machine learning (ML) models combined with density functional theory (DFT) calculations and Gibbs free energy of hydrogen adsorption (ΔGH*) were employed to facilitate the high-throu...

High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials

Two-dimensional (2D) materials have been developed into various catalysts with high performance, but employing them for developing highly stable and active nonprecious hydrogen evolution reaction (HER) catalysts still encounters many challenges. To this end, the machine learning (ML) screening of HER catalysts is accelerated by using genetic programming (GP) of symbolic transformers for various typical 2D MA2Z4 materials. The values of the Gibbs free energy of hydrogen adsorption (ΔGH*) are accurately and rapidly predicted via extreme gradient boosting regression by using only simple GP-processed elemental features, with a low predictive root-mean-square error of 0.14 eV. With the analysis of ML and density functional theory (DFT) methods, it is found that various electronic structural properties of metal atoms and the p-band center of surface atoms play a crucial role in regulating the HER performance. Based on these findings, NbSi2N4 and VSi2N4 are discovered to be active catalysts with thermodynamical and dynamical stability as ΔGH* approaches to zero (-0.041 and 0.024 eV). In addition, DFT calculations reveal that these catalysts also exhibit good deuterium evolution reaction (DER) performance. Overall, a multistep workflow is developed through ML models combined with DFT calculations for efficiently screening the potential HER and DER catalysts from 2D materials with the same crystal prototype, which is believed to have significant contribution to catalyst design and fabrication.

Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials.

Rational design and constructing earth-abundant electrocatalysts for efficient electrocatalytic water splitting is a crucial challenge. Herein, we report a simple and efficient one-step electrochemical synthetic route of the NiFe2O4@FeOOH composite electrocatalyst for the oxygen evolution reaction. The unique morphology of the NiFe2O4 nanoflowers loaded on FeOOH nanosheets allows more active sites to be exposed and promote charge transfer as well as gas release, and the resulting electrode enables a current density of 10 mA cm−2 at a low overpotential of 255 mV with outstanding stability at a current density of 100 mA cm−2 for 300 h.

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Jingnan Zheng

Papers

Single and double boron atoms doped nanoporous C2N-h2D electrocatalysts for highly efficient N2 reduction reaction: a density functional theory study.

Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials

High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials

Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials.

Design and Preparation of NiFe2O4@FeOOH Composite Electrocatalyst for Highly Efficient and Stable Oxygen Evolution Reaction