Sulfide corrosion of Cu is rapid, and hydrogen atoms produced by its cathodic half-reaction could adsorb on the Cu surface and diffuse into the Cu, potentially leading to hydrogen embrittlement. However, in solutions with low concentrations of SH⁻, absorption of hydrogen into Cu is not observed by ex situ hydrogen analysis, although it is unclear whether this is due to the lack of absorption, the outgassing of hydrogen from the Cu before it can be measured, or another mitigation mechanism. Herein, hydrogen uptake into Cu and the development of Cu2S layers during corrosion by SH⁻ were studied by in situ neutron reflectometry and electrochemical impedance spectroscopy. The method relies on a 4-nm Ti layer beneath 50 nm of Cu to trap hydrogen that may penetrate the Cu. Additionally, elastic recoil detection analysis and Rutherford backscattering spectrometry were used to measure hydrogen. While no increase in hydrogen was detected in either the Ti or Cu layers, a higher concentration of hydrogen was observed in the outer Cu2S layer (2560 ppm) than in the underlying Cu (244 ppm), demonstrating that bisulfide-driven corrosion does not lead to hydrogen absorption into the Cu. These results have implications for deep geological repositories utilizing Cu corrosion barriers.

Fatigue damage prognosis always requires a degradation model describing the damage evolution with time; thus, the prognostic performance highly depends on the selection of such a model. The best model should probably be case specific, calling for the fusion of multiple degradation models for a robust prognosis. In this context, this paper proposes a scheme of online fusing multiple models in a particle filter (PF)-based damage prognosis framework. First, each prognostic model has its process equation built through a physics-based or data-driven degradation model and has its measurement equation linking the damage state and the measurement. Second, each model is independently processed through one PF to provide one group of particles. Then, the particles from all models are adopted for remaining useful life prediction. Finally, the particles from each PF are fused with those from all the other PFs to improve their particle diversity, and consequently, to provide better estimation and prognostic performance. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a guided wave measurement system.

Understanding the relationship between gas diffusion electrode (GDE) structures and the performance of electrochemical CO₂ reduction reaction (CO₂RR) is crucial to developing industrial-scale technologies to convert CO₂ to valuable products. We studied how the microporous layer (MPL) on GDE's coated with silver nanoparticle catalysts affects the electrochemical CO₂ conversion to CO in a flow cell electrolyser. We demonstrate a convenient method to measure the rate of catholyte seepage through a GDE during CO₂RR experiments and used this method to show how the MPL thickness affects flooding of the GDE. We found the GDE with the thickest MPL (39BB) had the best selectivity for CO and stability at current densities above 100 mA cm⁻² as the thick MPL minimized flooding. However, at low current densities the 39BB electrode achieved a lower CO selectivity than the GDE with thinner MPL. These results suggest opportunities to improve CO₂ electrolyser performances at high current by optimisation of the MPL structure and wettability.