Local tetragonal distortion of Pt alloy catalysts for enhanced oxygen reduction reaction efficiency

Platinum-based alloy nanoparticles are the most attractive catalysts for the oxygen reduction reaction at present,but an in-depth understanding of the relationship between their short-range structural information and catalytic performance is still lacking.Herein,we present a synthetic strategy that uses transition-metal oxide-assisted thermal diffusion.PtCo/C catalysts with localized tetragonal distortion were obtained by controlling the thermal diffusion process of transition-metal elements.This localized structural distortion induced a significant strain effect on the nanoparticle surface,which further shortened the length of the Pt-Pt bond,improved the electronic state of the Pt surface,and enhanced the performance of the catalyst.PtCo/C catalysts with special short-range structures achieved excellent mass activity(2.27 Amg_(Pt)^(-1))and specific activity(3.34 A cm^(-2)).In addition,the localized tetragonal distortion-induced surface compression of the Pt skin improved the stability of the catalyst.The mass activity decreased by only 13% after 30,000 cycles.Enhanced catalyst activity and excellent durability have also been demonstrated in the proton exchange membrane fuel cell configuration.This study provides valuable insights into the development of advanced Pt-based nanocatalysts and paves the way for reducing noble-metal loading and increasing the catalytic activity and catalyst stability.

Facile grafting strategy synthesis of single-atom electrocatalyst with enhanced ORR performance

Single-atom catalysts(SACs)have become one of the most considered research directions today,owing to their maximum atom utilization and simple structures,to investigate structure-activity relationships.In the field of non-precious-metal electrocatalysts,atomically dispersed Fe-N4 active sites have been proven to possess the best oxygen reduction activity.Yet the majority of preparation methods remains complex and costly with unsatisfying controllability.Herein,we have designed a surface-grafting strategy to directly synthesize an atomically dispersed Fe-NVC electrocatalyst applied to the oxygen reduction reaction(ORR).Through an esterification process in organic solution,metal-containing precursors were anchored on the surface of carbon substrates.The covalent bonding effect could suppress the formation of aggregated particles during heat treatment.Melamine was further introduced as both a cost-effective nitrogen resource and blocking agent retarding the migration of metal atoms.The optimized catalyst has proven to have abundant atomically dispersed Fe-N4 active sites with enhanced ORR catalytic performance in acid condition.This method has provided new feasible ideas for the synthesis of SACs.

Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review

Proton exchange membrane fuel cells (PEMFCs) as energy conversion devices for hydrogen energy are crucial for achieving an eco-friendly society, but their cost and performance are still not satisfactory for large-scale commercialization. Multiple physical and chemical coupling processes occur simultaneously at different scales in PEMFCs. Hence, previous studies only focused on the optimization of different components in such a complex system separately. In addition, the traditional trial-and-error method is very inefficient for achieving the performance breakthrough goal. Machine learning (ML) is a tool from the data science field. Trained based on datasets built from experimental records or theoretical simulation models, ML models can mine patterns that are difficult to draw intuitively. ML models can greatly reduce the cost of experimental attempts by predicting the target output. Serving as surrogate models, the ML approach could also greatly reduce the computational cost of numerical simulations such as first-principle or multiphysics simulations. Related reports are currently trending, and ML has been proven able to speed up tasks in this field, such as predicting active electrocatalysts, optimizing membrane electrode assembly (MEA), designing efficient flow channels, and providing stack operation strategies. Therefore, this paper reviews the applications and contributions of ML aiming at optimizing PEMFC performance regarding its potential to bring a research paradigm revolution. In addition to introducing and summarizing information for newcomers who are interested in this emerging cross-cutting field, we also look forward to and propose several directions for future development.

Boosting the optimization of membrane electrode assembly in proton exchange membrane fuel cells guided by explainable artificial intelligence

The utilization of environmentally friendly hydrogen energy requires proton exchange membrane fuel cell de-vices that offer high power output while remaining affordable.However,the current optimization of their key component,i.e.,the membrane electrode assembly,is still based on intuition-guided,inefficient trial-and-error cycles due to its complexity.Hence,we introduce an innovative,explainable artificial intelligence(AI)tool trained as a reliable assistant for a variable analysis and optimum-value prediction.Among the 8 algorithms considered,the surrogate model built with an artificial neural network achieves high replaceability in the experimentally validated multiphysics simulation(R^(2)=0.99845)and a much lower computational cost.For interpretation,partial dependence plots and the Shapley value method are applied to black-box models to intelligently simulate the impact of each parameter on performance.These methods show that a tradeoff existed in the catalyst layer thickness.The AI-guided optimization suggestions regarding catalyst loading and the ion-omer content are fully supported by the experimental results,and the final product achieves 3.2 times the Pt utilization of commercial products with a time cost orders of magnitude smaller.