Machine-Learning-Accelerated Development of Efficient Mixed Protonic-Electronic Conducting Oxides as the Air Electrodes for Protonic Ceramic Cells

Harnessing High-Throughput Computational Methods to Accelerate the Discovery of Optimal Proton Conductors for High-Performance and Durable Protonic Ceramic Electrochemical Cells

The pursuit of high-performance and long-lasting protonic ceramic electrochemical cells (PCECs) is impeded by the lack of efficient and enduring proton conductors. Conventional research approaches, predominantly based on a trial-and-error methodology, have proven to be demanding of resources and time-consuming. Here, this work reports the findings in harnessing high-throughput computational methods to expedite the discovery of optimal electrolytes for PCECs. This work methodically computes the oxygen vacancy formation energy (EV), hydration energy (EH), and the adsorption energies of H2O and CO2 for a set of 932 oxide candidates. Notably, these findings highlight BaSnxCe0.8-xYb0.2O3-δ (BSCYb) as a prospective game-changing contender, displaying superior proton conductivity and chemical resilience when compared to the well-regarded BaZrxCe0.8-xY0.1Yb0.1O3-δ (BZCYYb) series. Experimental validations substantiate the computational predictions; PCECs incorporating BSCYb as the electrolyte achieved extraordinary peak power densities in the fuel cell mode (0.52 and 1.57 W cm-2 at 450 and 600 °C, respectively), a current density of 2.62 A cm-2 at 1.3 V and 600 °C in the electrolysis mode while demonstrating exceptional durability for over 1000-h when exposed to 50% H2O. This research underscores the transformative potential of high-throughput computational techniques in advancing the field of proton-conducting oxides for sustainable power generation and hydrogen production.

Closed-Loop Error-Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials

The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways. Here, historical data is incorporated, and is updated using experimental feedback by employing error-correction learning (ECL). This is achieved by learning from prior datasets and then adapting the model to differences in synthesis and characterization that are otherwise difficult to parameterize. This strategy is thus applied to discovering thermoelectric materials, where synthesis is prioritized at temperatures <300 °C. A previously unexplored chemical family of thermoelectric materials, PbSe:SnSb, is documented, finding that the best candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power factor more than 2× that of PbSe. The investigations herein reveal that a closed-loop experimentation strategy reduces the required number of experiments to find an optimized material by a factor as high as 3× compared to high-throughput searches powered by state-of-the-art machine-learning (ML) models. It is also observed that this improvement is dependent on the accuracy of the ML model in a manner that exhibits diminishing returns: once a certain accuracy is reached, factors that are instead associated with experimental pathways begin to dominate trends.

Three-dimensional multiphysics coupling numerical simulation of a proton conductor solid oxide fuel cell based on multi-defect transport

The conductivity of the electrolyte of a proton conductor solid oxide fuel cell is not only related to temperature, but also related to the humidity and oxygen partial pressure of the cathode and anode. The gas partial pressure and temperature of the cell have significant inhomogeneity in three-dimensional space, so it is extremely important to develop a multi-field coupled three-dimensional model to explore the electrochemical performance of the cell. In this study, a model is constructed that takes into account macroscopic heat and mass transfer, microscopic defect transport, and the reaction kinetics of defects. The results show that for thin cathodes, the ribs significantly affect the oxygen partial pressure and the concentration of defects on the cathode side. On both sides of the electrolyte membrane, the concentration of hydroxide ions increases with increasing gas humidity. The hydroxide ion concentration increases along the flow direction, but the concentration of O-site small polarons increases on the anode side and decreases on the cathode side. The conductivity of hydroxide ions is more sensitive to the humidity of the anode side, while the conductivity of O-site small polarons is more sensitive to the humidity of the cathode side. Increasing the humidity of the cathode side results in a significant decrease in the conductivity of the O-site small polarons. The contribution of the conductivity of oxygen vacancies to the total conductivity is negligible. The total conductivity on the cathode side is greater than that on the anode side; it is dominated by hydroxide ions on the anode side, and co-dominated by hydroxide ions and O-site small polarons on the cathode side. Increasing temperature significantly increases both partial and total conductivity. When hydrogen depletion occurs, the partial conductivities and the total conductivity exhibit a sharp increase downstream of the cell.

Water Electrolysis toward Elevated Temperature: Advances, Challenges and Frontiers

Since severe global warming and related climate issues have been caused by the extensive utilization of fossil fuels, the vigorous development of renewable resources is needed, and transformation into stable chemical energy is required to overcome the detriment of their fluctuations as energy sources. As an environmentally friendly and efficient energy carrier, hydrogen can be employed in various industries and produced directly by renewable energy (called green hydrogen). Nevertheless, large-scale green hydrogen production by water electrolysis is prohibited by its uncompetitive cost caused by a high specific energy demand and electricity expenses, which can be overcome by enhancing the corresponding thermodynamics and kinetics at elevated working temperatures. In the present review, the effects of temperature variation are primarily introduced from the perspective of electrolysis cells. Following an increasing order of working temperature, multidimensional evaluations considering materials and structures, performance, degradation mechanisms and mitigation strategies as well as electrolysis in stacks and systems are presented based on elevated temperature alkaline electrolysis cells and polymer electrolyte membrane electrolysis cells (ET-AECs and ET-PEMECs), elevated temperature ionic conductors (ET-ICs), protonic ceramic electrolysis cells (PCECs) and solid oxide electrolysis cells (SOECs).