Computational screening for selective catalysts: Cleaving the C C bond during ethanol electro-oxidation reaction

Balancing C- and O-species activation for enhanced synergistic electrooxidation of alcohols and formic acid on grain boundary-engineered PtPdNi alloy aerogels

Direct liquid fuel cell offer high theoretical energy density, portability, and low environmental impact; however, commercialization remains hindered by challenges such as intermediate-induced catalyst poisoning, the high cost and scarcity of platinum, and inefficient CC bond cleavage during ethanol oxidation. To overcome these limitations, this study develops a grain boundary-rich PtPdNi ternary aerogel alloy as a high-performance catalyst for direct liquid fuel cell anodic reactions. Theoretical calculations indicate the engineered grain boundaries enhance active site density and modulate the catalyst's electronic structure, greatly expanded the adsorption energy range of CO* and OH*. Electrochemical diagnostics validate the catalyst's exceptional structural stability and durability attributed to the grain boundary architecture. Compared to PtPdNi single-crystalline counterpart without grain boundary, PtPdNi grain boundary-rich catalyst achieves specific activities 1.4-, 1.7-, and 2.5-fold higher for methanol, ethanol, and formic acid oxidation, respectively. Advanced in situ infrared data provide the analyses of the mechanism of small molecule electro-oxidation by grain boundaries in catalysts. This work introduces a grain boundary engineering strategy to elevate catalytic performance, providing critical insights into the design of robust direct liquid fuel cell catalysts.

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.

How machine learning can accelerate electrocatalysis discovery and optimization

Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.

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.

Highly Porous Pt2Ir Alloy Nanocrystals as a Superior Catalyst with High-Efficiency C-C Bond Cleavage for Ethanol Electrooxidation

Achieving high catalytic performance with high CO₂ selectivity is critical for commercialization of direct ethanol fuel cells. Here, we report carbon-supported highly porous Pt₂Ir alloy nanocrystals (p-Pt₂Ir/C) for an ethanol oxidation reaction (EOR) that displays nearly 7.2-fold enhancement in mass activity and promotes antipoisoning ability and durability for the EOR as compared with the commercial Pt/C-JM. Moreover, the catalyst exhibits high CO₂ selectivity, 3.4-fold at 0.65 V (vs. SCE) and 4.1-fold at 0.75 V (vs. SCE) higher as compared with the carbon-supported porous Pt nanocrystals (p-Pt/C). The highly porous structure is composed of interconnected one-dimensional (1D) rough branches with an average diameter of only 1.9 nm, largely promoting Pt utilization efficiency and accelerating mass transfer. The 1D rough branch surface exposed many atomic steps/corners endowed with abundant high activity sites. Alloying with Ir can significantly improve the antipoisoning ability, durability, and C-C bond cleavage ability, thereby evidently enhancing its EOR performance.

Ethanol Electrooxidation on Phase- and Morphology-Controlled Ni(OH)2 Microspheres

The electrooxidation kinetics of ethanol is key to making direct ethanol fuel cells and electrocatalytically reforming ethanol viable technologies for a more sustainable energy conversion. In this study, the electrooxidation of ethanol was investigated on nickel hydroxide (Ni(OH)2) catalysts synthesized using a facile solvothermal method. Variations in the temperature, heating time, and the addition of oleylamine in the precursor enabled the phase and morphology control of the catalysts. X-ray diffraction and scanning electron microscopy show that the addition of oleylamine in the precursor resulted in microspheres with a high surface area, but favored the formation of β-phase Ni(OH)2. Elevated temperatures or prolonged periods of heating in a controlled environment, on the other hand, can lead to the formation of the ethanol oxidation reaction-active α-phase. Among the synthesized catalysts, the α-Ni(OH)2 microspheres with nanoflakes achieved the highest activity for ethanol oxidation with a current density of 24.4 mA cm−2 at 1.55 V (vs. RHE, reversible hydrogen electrode) in cyclic voltammetry tests and stable at 40 mA cm−2 in chronoamperometric tests at the same potential, comparatively higher than other Ni-based catalysts found in the literature. While the overpotential is beyond the useful range for direct ethanol fuel cells, it may be useful for understanding the mechanism of ethanol oxidation reactions on transition metal hydroxides at their oxidizing potential for ethanol electroreforming.

Chemical Design of Palladium-Based Nanoarchitectures for Catalytic Applications

Palladium (Pd) plays an important role in numerous catalytic reactions, such as methanol and ethanol oxidation, oxygen reduction, hydrogenation, coupling reactions, and carbon monoxide oxidation. Creating Pd-based nanoarchitectures with increased active surface sites, higher density of low-coordinated atoms, and maximized surface coverage for the reactants is important. To address the limitations of pure Pd, various Pd-based nanoarchitectures, including alloys, intermetallics, and supported Pd nanomaterials, have been fabricated by combining Pd with other elements with similar or higher catalytic activity for many catalytic reactions. Herein, recent advances in the preparation of Pd-based nanoarchitectures through solution-phase chemical reduction and electrochemical deposition methods are summarized. Finally, the trend and future outlook in the development of Pd nanocatalysts toward practical catalytic applications are discussed.