Electrocatalysis: From Planar Surfaces to Nanostructured Interfaces

The reactions critical for the energy transition center on the chemistry of hydrogen, oxygen, carbon, and the heterogeneous catalyst surfaces that make up electrochemical energy conversion systems. Together, the surface-adsorbate interactions constitute the electrochemical interphase and define reaction kinetics of many clean energy technologies. Practical devices introduce high levels of complexity where surface roughness, structure, composition, and morphology combine with electrolyte, pH, diffusion, and system level limitations to challenge our ability to deconvolute underlying phenomena. To make significant strides in materials design, a structured approach based on well-defined surfaces is necessary to selectively control distinct parameters, while complexity is added sequentially through careful application of nanostructured surfaces. In this review, we cover advances made through this approach for key elements in the field, beginning with the simplest hydrogen oxidation and evolution reactions and concluding with more complex organic molecules. In each case, we offer a unique perspective on the contribution of well-defined systems to our understanding of electrochemical energy conversion technologies and how wider deployment can aid intelligent materials design.

Unifying the ORR and OER with surface oxygen and extracting their intrinsic activities on platinum

Common half-cell measurements deliver oxygen reduction activities linked to the chosen scan rate, limiting their value for fundamental and comparative studies on platinum. Here we show a deconvolution of the intrinsic kinetics from the effect of surface oxygen on platinum. We find an electronic effect of the surface oxygen, substantiate a Tafel slope of ~120 mV/decade, obtain an exchange current density of 13 ± 4 µA/cm2, and an activity of 7 mA/cm2 at 900 mV. Eventually, we broaden the scope of this analysis to the effects of surface rearrangement, alloying, and supported Pt nanoparticles, the latter providing insight into discrepancies between half-cell and fuel cell measurements. We find through computational methods that binding energies of intermediates would be weakened by the presence of highly coordinated oxygen atoms. Finally, we obtain a phenomenological rate equation for the oxygen reduction and evolution reaction, suggesting that both reactions follow a shared mechanism.

High power density output and durability of microbial fuel cells enabled by dispersed cobalt nanoparticles on nitrogen-doped carbon as the cathode electrocatalyst

To endow microbial fuel cells (MFCs) with low cost, long-term stability and high-power output, a novel cobalt-based cathode electrocatalyst (Nano-Co@NC) is synthesized from a polygonal metal-organic framework ZIF-67. After calcining the resultant ZIF-67, the as-synthesized Nano-Co@NC is characteristic of cobalt nanoparticles (Nano-Co) embedded in nitrogen-doped carbon (NC) that inherits the morphology of ZIF-67 with a large surface area. The Nano-Co particles that are highly dispersed and firmly fixed on NC not only ensure electrocatalytic activity of Nano-Co@NC toward the oxygen reduction reaction on the cathode, but also inhibit the growth of non-electrogenic bacteria on the anode. Consequently, the MFC using Nano-Co@NC as the cathode electrocatalyst demonstrates excellent performance, delivering a comparable initial power density and exhibiting far better durability than that using Pt/C (20 wt%) as the cathode electrocatalyst. The low cost and the excellent performance of Nano-Co@NC make it promising for MFCs to be used in practice.

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO₂ activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO₂ adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (ε_d), surface metal electronegativity (χ_M), and valence electron number of metal atoms (M_V) as key descriptors for CO₂ activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO₂ activation and their posterior usage.

Low-Loading Sub-3 nm PtCo Nanoparticles Supported on Co-N-C with Dual Effect for Oxygen Reduction Reaction in Proton Exchange Membrane Fuel Cells

Developing low-loading Pt-based catalysts possessing glorious catalytic performance can accelerate oxygen reduction reaction (ORR) and hence significantly advance the commercialization of proton exchange membrane fuel cells. In this report, we propose a hybrid catalyst that consists of low-loading sub-3 nm PtCo intermetallic nanoparticles carried on Co-N-C (PtCo/Co-N-C) via the microwave-assisted polyol procedure and subsequent heat treatment. Atomically dispersed Co atoms embedded in the Co-N-C carriers diffuse into the lattice of Pt, thus forming ultrasmall PtCo intermetallic nanoparticles. Owing to the dual effect of the enhanced metal-support interaction and alloy effect, as-fabricated PtCo/Co-N-C catalysts deliver an extraordinary performance, achieving a half-wave potential of 0.921 V, a mass activity of 0.700 A mg_Pt^-1@0.9 V, and brilliant durability in the acidic medium. The fuel cell employing PtCo/Co-N-C as the cathode catalyst with an ultralow Pt loading of 0.05 mg cm^-2 exhibits an impressive peak power density of 0.700 W cm^-2, higher than that of commercial Pt/C under the same condition. Furthermore, the enhanced intrinsic ORR activity and stability are imputed to the downshifted d-band center and the strengthened metal-support interaction, as revealed by density functional theory calculations. This report affords a facile tactic to fabricate Pt-based alloy composite catalysts, which is also applicable to other alloy catalysts.

Molecular dynamics simulations of the impurity effect on packing structures, local stresses, and thermodynamics of Ih silver clusters

Molecular dynamics simulations are performed to investigate impurity effects on the changes of packing structures, atomic level pressure, and thermodynamics for icosahedral Ag clusters having 55 and 147 atoms. The simulation results indicate that, doping Cu atoms in different cluster shells can significantly affect their thermal stability and thermodynamic values. For the small size clusters, the doping in the interior apparently releases the stress, and improves the stability. The thermodynamic values of entropy and free energy can be used to indicate the order degree of the packing structures and structural transition. The shape changes are related to the local stresses at elevated temperatures.

Synergistic engineering of shell thickness and core ordering to boost the oxygen reduction performance

When benchmarked against the extended Pt(111), slightly weaker adsorption and stronger cohesion properties of surface Pt are required to improve activity and durability for the oxygen reduction reaction, respectively, making it challenging to meet both requirements on one surface. Here, using Pt(111) over-layers stressed and modified by Pt-TM (TM = Fe, Co, Ni, V, Cu, Ag, and Pd) intermetallics as examples, we theoretically identified ten promising catalysts by synergistically tailoring the skin thickness and substrate chemical ordering to simultaneously achieve weak adsorption and strong cohesion. More specifically, compared with Pt(111), all candidates exhibit 10-fold enhanced activity, half of which show improved durability, such as mono-layer skin on L1₂-Pt₃Co or Pt₃Fe, double-layer Pt on L1₃-Pt₃Ni or Pt₃Cu, and triple-layer skin on L1₁-PtCu, while double- or triple-layer skin on L1₀-PtCo or PtNi and double-layer skin on L1₂-PtFe₃ show slightly poor durability. Although L1₀ and L1₂ based nanocrystals have been demonstrated extensively as outstanding catalysts, L1₁ and L1₃ ones hold great application potential. The coexistence of high activity and durability on the same surface is because of the different responses of surface adsorption and cohesion properties to the strain effects and ligand effects. When intermetallic-core@Pt-shell nanocrystals are constructed using this slab model, the necessity of protecting or eliminating low-coordinated Pt and the possibility of maximizing Pt(111) facets and core ordering by morphology engineering were highlighted. The current discovery provides a new paradigm toward the rational design of promising cathodic catalysts.

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.

Heterogeneous Trimetallic Nanoparticles as Catalysts

The development and application of trimetallic nanoparticles continues to accelerate rapidly as a result of advances in materials design, synthetic control, and reaction characterization. Following the technological successes of multicomponent materials in automotive exhausts and photovoltaics, synergistic effects are now accessible through the careful preparation of multielement particles, presenting exciting opportunities in the field of catalysis. In this review, we explore the methods currently used in the design, synthesis, analysis, and application of trimetallic nanoparticles across both the experimental and computational realms and provide a critical perspective on the emergent field of trimetallic nanocatalysts. Trimetallic nanoparticles are typically supported on high-surface-area metal oxides for catalytic applications, synthesized via preparative conditions that are comparable to those applied for mono- and bimetallic nanoparticles. However, controlled elemental segregation and subsequent characterization remain challenging because of the heterogeneous nature of the systems. The multielement composition exhibits beneficial synergy for important oxidation, dehydrogenation, and hydrogenation reactions; in some cases, this is realized through higher selectivity, while activity improvements are also observed. However, challenges related to identifying and harnessing influential characteristics for maximum productivity remain. Computation provides support for the experimental endeavors, for example in electrocatalysis, and a clear need is identified for the marriage of simulation, with respect to both combinatorial element screening and optimal reaction design, to experiment in order to maximize productivity from this nascent field. Clear challenges remain with respect to identifying, making, and applying trimetallic catalysts efficiently, but the foundations are now visible, and the outlook is strong for this exciting chemical field.

A Comparative Study of Oxygen and Hydrogen Adsorption on Strained and Alloy-Supported Pt(111) Monolayers

A comparative study of the unreacted and reacted uniaxially strained Pt(111) and the layered (111)-Pt/Ni/Pt3Ni and (111)-Pt/Ni/PtNi3 surfaces has been performed using density functional theory (DFT). An in-depth study of the unreacted surfaces has been performed to evaluate the importance of geometric, magnetic and ligand effects in determining the reactivity of these different Pt surfaces. An analysis of the binding energies of oxygen and hydrogen over the high-symmetry binding positions of all surfaces has been performed. The study has shown that O and H tend to bind more strongly to the (111)-Pt/Ni/Pt3Ni surface and less strongly to the (111)-Pt/Ni/PtNi3 surface compared to binding on the equivalently strained Pt(111) surfaces. Changes in the surface magnetisation of the surfaces overlaying the ferromagnetic alloys during adsorption are discussed, as well as the behaviour of the d-band centre across all surfaces, to evaluate the potential mechanisms for these differences in binding. An accompanying comparison of the accessible density functionals has been included to estimate the error in the computational binding energies.

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

In this work, we propose the use of the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening.

In a conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of the d-band for improved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of the d-band center of solid surfaces, but it requires density functional calculations that can be quite costly for the purposes of large scale screening of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening. This pair of descriptors is then combined with machine learning methods, namely, neural network (NN) and kernel ridge regression (KRR). We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a mean absolute deviation error of 0.05 eV. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV. In addition, the results of testing the method with other facets such as (111) terrace and (211) step sites suggest that the present model is also capable of handling different coordination environments effectively. As an example of the practical application of this machine, we identified Cu₃Y@Cu* as an active and cost-effective electrochemical CO₂ reduction catalyst to produce CO with an overpotential ∼1 V lower than a Au catalyst.