Optimizing the architecture of cathodic catalyst layers is significant in improving the catalyst utilization and enhancing the performance and durability of proton exchange membrane fuel cells (PEMFCs). Nanofibre electrodes possess continuous proton and electron transport channels, and are advantageous over conventional electrodes for mass transport. However, the structural stability of the nanofibre electrodes has been rarely reported in the literature. Herein, a highly stable esterified nanofibre electrode was prepared by esterifying poly (acrylic acid) (PAA) with poly (vinyl alcohol) (PVA) to address the structural stability of the nanofibre electrodes during the operation of PEMFCs. Both the ex situ and in situ tests confirm that the esterified nanofibre electrode has enhanced structural stability compared with conventional nanofibre electrodes. The concept of an esterified nanofibre electrode inspires a facile approach for stabilizing polymer binders in fuel cell catalyst layers, especially for nanofibre electrodes.

The capacity of Lithium-ion batteries degrades over the time, making accurate prediction of their Remaining Useful Life (RUL) crucial for maintenance and product lifespan design. However, diverse aging mechanisms, changing working conditions and cell-to-cell variation lead to the inhomogeneous cell lifespan and complicated life prediction. In this work, a data-driven algorithm based on stacked Long Short Term Memory (LSTM) encoder–decoders is proposed for RUL prediction. The encoder and upstream decoder form an autoencoder framework for feature extraction. The encoder and the downstream decoder form the encoder–decoder framework for RUL prediction. To enhance generalization during training, the Maximum Mean Discrepancy (MMD) loss is included in the autoencoder framework. The similarity of aging patterns is analyzed during splitting source and target datasets through k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The Euclidean metric with accumulated Equivalent Cycle Number (ECN) sequence during aging shows better performance for similarity-based data splitting than the Dynamic Time Wrapping (DTW) distance metric based on capacity fading trajectory. The experimental results indicate that the proposed algorithm can provide accurate RUL prediction using 5% fading data and shows good generalization with Coefficient of Determination (R²) score of 0.98.