Operando X-ray imaging of NMC811 electrode revealing a non-linear cracking profile as the particles delithiate.

Anion exchange membrane water electrolysers are held back by the low durability of the ionomer in the membrane and catalyst layers. Studying ionomer degradation in these systems is challenging because the main mechanisms ‐ which result in catalyst detachment, membrane thinning, and loss of cationic functionality ‐ have opposing effects on the cell potential. Electrochemical measurements alone are therefore insufficient for elucidating the underlying causes of degradation. To address this, a bespoke miniature‐electrolyser‐cell is developed for X‐ray microtomography imaging of membrane electrode assemblies at 1.6 µm resolution. This setup enables the study of the entire active volume of the electrolyser under static and operando conditions and is validated against standard 5 cm ² laboratory cells. An operando investigation of degradation in Fumasep‐based catalyst‐coated membranes reveals both significant membrane thinning and loss of membrane ionic conductivity during stability testing, leading to increased ohmic resistance and cell potential. In contrast, a Selemion membrane shows minimal changes in thickness and conductivity and is significantly more stable compared to Fumasep when exposed to synchrotron radiation. This platform has relevance for operando studies of electrochemical materials and devices generally, including proton exchange membrane electrolysers, fuel cells, and CO ₂ electrolysers using both lab‐based and synchrotron X‐ray sources.

Crack formation during electrode drying is a potential hindrance to the manufacture of high energy density electrodes. In this study, highly sensitive digital volume correlation is used to identify the precise moment of crack initiation.

The transient nature of the internal pore structure of particulate wall flow filters, caused by the continuous deposition of particulate matter, makes studying their flow and filtration characteristics challenging. In this article we present a new methodology and first experimental demonstration of time resolved in-situ synchrotron micro X-ray computed tomography (micro-CT) to study aerosol filtration. We directly imaged in 4D (3D plus time) pore scale deposits of TiO${}_2$ nanoparticles (nominal mean primary diameter of 25 nm) with a pixel resolution of 1.6 $\mathsf{\mu }$m. We obtained 3D tomograms at a rate of ∼1 per minute. The combined spatial and temporal resolution allows us to observe pore blocking and filling phenomena as they occur in the filter’s pore space. We quantified the reduction in filter porosity over time, from an initial porosity of 0.60 to a final porosity of 0.56 after 20 min. Furthermore, the penetration depth of particulate deposits and filtration rate was quantified. This novel image-based method offers valuable and statistically relevant insights into how the pore structure and function evolves during particulate filtration. Our data set will allow validation of simulations of automotive wall flow filters. Evolutions of this experimental design have potential for the study of a wide range of dry aerosol filters and could be directly applied to catalysed automotive wall flow filters.

Battery_xct_workflows is an open, Python notebook-based method for deriving quantitative quality assurance (QA) metrics from X-ray computed tomography (XCT) of cylindrical Li-ion cells. The workflows target three recurring industrial QA questions: (i) are electrode overhang regions within design tolerance, (ii) is the canister geometry and alignment acceptable, and (iii) is the internal winding uniform and free from gross defects. The method combines preprocessing, segmentation (including optional pre-trained U-Net models), and metric calculation into a series of executable notebooks that can be run locally or via Binder using public example datasets.

During battery operation, cracking occurs in Nickel Manganese Cobalt (NMC) oxide secondary particles. Cracked particles appear darker in micro‐computed tomography (micro‐CT) images due to the partial volume effect, where voxels containing both void and solid yield intermediate grey‐levels. This work presents an automated method for tracking grey‐level changes caused by this effect in large, statistically meaningful micro‐CT datasets containing over 10 000 individual particles. It extends earlier work using the GREAT algorithm to analyze NMC particles in tomography images. The new GREAT2 algorithm increases processing speed, from around 1,400 particles per day with GREAT to over 10 000 particles in under a minute. Furthermore, this work introduces methods for automated tracking of grey‐level intensity changes in individual particles through different states of charge in an operando experiment. This capability enables temporal analysis of particle degradation mechanisms. Additional data processing methods are presented that extract useful insights. Through this work we show that the large sample sizes, enabled by this method and GREAT2, allow for statistically robust analysis of particle populations. These advances significantly accelerate the tomographic study of cracking in battery electrodes. The GREAT2 algorithm and associated workflows have been made available as the GRAPES Python toolkit.