AI-enabled materials discovery for advanced ceramic electrochemical cells

Ceramic electrochemical cells(CECs)are promising devices for clean and efficient energy conversion and storage due to their high energy efficiency,more extended system durability,and less expensive materials.However,the search for suitable materials with desired properties,including high ionic and electronic conductivity,thermal stability,and chemical compatibility,presents ongoing challenges that impede widespread adoption and further advancement in the field.Artificial intelligence(AI)has emerged as a versatile tool capable of enhancing and expediting the materials discovery cycle in CECs through data-driven modeling,simulation,and optimization techniques.Herein,we comprehensively review the state-of-the-art AI applications for materials design and optimization for CECs,covering various material aspects,database construction,data pre-processing,and AI methods.We also present some representative case studies of AI-predicted and synthesized materials for CECs and provide insightful highlights about their approaches.We emphasize the main implications and contributions of the AI approach for advancing the CEC technology,such as reducing the trial-and-error experiments,exploring the vast materials space,discovering novel and optimal materials,and enhancing the understanding of the materials-performance relationships.We also discuss the AI approach’s main limitations and future directions for CECs,such as addressing the data and model challenges,improving and extending the AI models and methods,and integrating with other computational and experimental techniques.We conclude by suggesting some potential applications and collaborations for AI in materials design for CECs.

Engineered wettability-gradient porous structure enabling efficient water manipulation in regenerative fuel cells

Regenerative fuel cells can operate alternately as an electrolyzer and as a fuel cell,frequently involving water as a reactant or product.Modifying the electrode surface to manipulate water can prevent electrode flooding and enhance the electrode's mass transfer efficiency by facilitating better contact with gaseous reactants.However,conventional electrodes face difficulties in allowing water droplets to penetrate in a single direction leaving electrodes.In this work to address this issue,a wettability gradient electrode is designed and fabricated for efficient water manipulation in regenerative fuel cells.The findings demonstrate that the water removal strategy in the electrolyzer mode yields the highest ammonia yield and Faradaic efficiency of 3.39×10-10 mol s-1 cm-2 and 0.49%,respectively.Furthermore,in the fuel cell mode,the discharging process sustains for approximately 20.5 h,which is six times longer than the conventional strategy.The ability to sustain the discharging process for extended periods is particularly advantageous in regenerative fuel cells,as it enables the cells to operate for longer periods without the need for regeneration.

Mathematical modeling of fuel cells fed with an electrically rechargeable liquid fuel

Lately,utilizing a novel electrically rechargeable liquid fuel(e-fuel),a fuel cell has been designed and fabricated,which is demonstrated to achieve a much better performance than alcoholic liquid fuel cells do.However,its current performance,which thus hampers its wide application,demands further improvement to meet up with industrial requirement.Therefore,to attain a better performance for this system,an in-depth understanding of the complex physical and chemical processes within this fuel cell is essential.To this end,in this work,a two-dimensional transient model has been developed to gain an extensive knowledge of a passive e-fuel cell and analyze the major factors limiting its performance.The effects of various structural parameters and operating conditions are studied to identify the underlying performance-limiting factors,where deficient mass transport is found to be one of the major causes.The increment of anode porosity and thickness are found to be effective methods of improving the cell performance.This study therefore provides insights on achieving further per-formance advancement of the fuel cell in the future.

Machine learning for advanced energy materials

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relation-ships has recently become one of the hot and trending topics in energy materials due to the diverse challenges,including low success probabilities,high time consumption,and high computational cost associated with the traditional methods of developing energy materials.Following this,new research concepts and technologies to promote the research and development of energy materials become necessary.The latest advancements in ar-tificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of en-ergy materials.Furthermore,the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment.In this article,the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented.A comprehensive introduction to the fundamentals of machine learning is also provided,including open-source databases,feature engineering,machine learning algorithms,and analysis of machine learning model.Afterwards,the latest progress in data-driven materials science and engineering,including alkaline ion battery materials,pho-tovoltaic materials,catalytic materials,and carbon dioxide capture materials,is discussed.Finally,relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.

Water flooding behavior in flow cells for ammonia production via electrocatalytic nitrogen reduction

The green production of ammonia,in an electrochemical flow cell under ambient conditions,is a promising way to replace the energy-intensive Haber-Bosch process.In the operation of this flow cell with an alkaline electrolyte,water is produced at the anode but also required as an essential reactant at the cathode for nitrogen reduction.Hence,water from the anode is expected to diffuse through the membrane to the cathode to compensate for the water needed for nitrogen reduction.Excessive water permeation,however,tends to increase the possibility of water flooding,which would not only create a large barrier for nitrogen delivery and availability,but also lead to severe hydrogen evolution as side reaction,and thus significantly lower the ammonia production rate and Faradaic efficiency.In this work,the water flooding phenomenon in flow cells for ammonia production via electrocatalytic nitrogen reduction is verified via the visualization approach and the electrochemical cell performance.In addition,the effects of the nitrogen flow rate,applied current density,and membrane thickness on the water crossover flux and ammonia production rate are comprehensively studied.The underlying mechanism of water transport through the membrane,including diffusion and electro-osmotic drag,is precisely examined and specified to provide more insight on water flooding behavior in the flow cell.