Catalyst Degradation in Fuel Cell Electrodes: Accelerated Stress Tests and Model-based Analysis

Towards a realistic prediction of catalyst durability from liquid half-cell tests

Liquid half-cell measurements provide a convenient laboratory method for determining relevant parameters of electro-catalysts applied in e.g. polymer electrolyte membrane fuel cells. While these measurements may be effective in certain contexts, their applicability to real-world systems, such as single-cells in a membrane electrode assembly (MEA) configuration, is not always clear. This is particularly true when assessing the stability of these systems through accelerated stress tests (ASTs). Due to different electrode compositions and operating conditions, nanoscale degradation proceeds differently. Nevertheless, given the high demands of MEA measurements in terms of time, testing equipment complexity, and amount of catalyst material, application-relevant predictions of catalyst durability from liquid half-cell tests are highly desirable. This study combines electrochemical and nanoparticle analysis based on transmission electron microscopy to conduct a typical voltage cycling AST for rotating disc electrode (RDE) measurements, showing that the loss of the electrochemically active surface area (ECSA) of the used Pt/Vulcan catalyst is strongly enhanced at 80 °C compared to room temperature, which goes along with increased nanoparticle coarsening. Additionally, a high ionomer/carbon mass ratio (I/C = 0.7) accelerates the ECSA loss, and further investigations of its influence suggest a combination of several factors, including the high local proton concentration and the presence of adsorbing anions. At the same temperature (80 °C) and I/C ratio (0.7), the ECSA loss vs. AST cycle number of the Pt/Vulcan catalyst is essentially identical for a voltage cycling AST conducted in either an RDE half-cell or an MEA configuration, suggesting that liquid electrolyte half-cell based ASTs can provide application-relevant results. Thus, our study points out a way for predicting the stability of electro-catalysts in MEAs based on RDE experiments that require less specialized equipment and only μg-quantities of catalysts.

Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

Identifiability Analysis of Degradation Model Parameters from Transient CO2 Release in Low-Temperature PEM Fuel Cell under Various AST Protocols

The detrimental effects of the catalyst degradation on the overall envisaged lifetime of low-temperature proton-exchange membrane fuel cells (LT-PEMFCs) represent a significant challenge towards further lowering platinum loadings and simultaneously achieving a long cycle life. The elaborated physically based modeling of the degradation processes is thus an invaluable step in elucidating causal interaction between fuel cell design, its operating conditions, and degradation phenomena. However, many parameters need to be determined based on experimental data to ensure plausible simulation results of the catalyst degradation models, which proves to be challenging with the in situ measurements. To fill this knowledge gap, this paper demonstrates the application of a mechanistically based PEMFC modeling framework, comprising real-time capable fuel cell performance, and platinum and carbon support degradation models, to model transient CO2 release rates in the LT-PEMFCs with the consistent calibration of reaction rate parameters under multiple different accelerated stress tests at once. The results confirm the credibility of the physical and chemical modeling basis of the proposed modeling framework, as well as its prediction and extrapolation capabilities. This is confirmed by an increase of only 29% of root mean square deviations values when using a model calibrated on all three data sets at once in comparison to a model calibrated on only one data set. Furthermore, the unique identifiability and interconnection of individual model calibration parameters are determined via Fisher information matrix analysis. This analysis enables optimal reduction of the set of calibration parameters, which results in the speed up of both the calibration process and the general simulation time while retaining the full extrapolation capabilities of the framework.

First-principles investigation of electrochemical dissolution of Pt nanoparticles and kinetic simulation

Dissolution is the primary route of Pt nanoparticle degradation in electrochemical devices, e.g., fuel cells. Investigation of potential-dependent dissolution kinetics of Pt nanoparticles is crucial to optimize the nanoparticle size and operating conditions for better performance. A mean-field kinetic theory under the steady-state approximation, combined with atomistic thermodynamics and Wulff construction, was developed to study the interplay between oxygen chemisorption, electrode potential, and particle size on the dissolution of Pt nanoparticles. We found that although oxygen chemisorption from electrode potential-induced water splitting can stabilize Pt nanoparticles through decreasing the surface energy and increasing the redox potential, the electrode potential plays a more decisive role in facilitating the dissolution of Pt nanoparticles. In comparison with the minor effect of oxygen chemisorption, an increase in the particle size, though reducing the dispersion, has a more significant effect on the suppression of the dissolution. These theoretical understandings on the effects of electrode potential and particle size on the dissolution are crucial for optimizing the nanoparticle size under oxidative operating conditions.