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The switch from a carbon-based to a hydrogen-based economy requires environmentally friendly methods for hydrogen production. CO2-reforming of methane promises to be a greener alternative to steam-methane reforming, which accounts for the majority of hydrogen production today. For this dry process to become industrially competitive, challenges such as catalyst deactivation and degradation through coke formation must be better understood and ultimately overcome. While bulk characterisation methods provide a wealth of useful information about the carbon formed during coking, spatially resolved techniques are required to understand the type and extent of degradation of supported catalyst particles themselves under coking conditions. Here, lab-based X-ray nano-computed tomography, in conjunction with a range of complementary techniques, is utilised to understand the effects of the nickel-to-cobalt ratio on the degradation of individual supported catalyst particles. Findings suggest that a bimetallic system greatly outperforms monometallic catalysts, with the ratio between nickel and cobalt having a significant impact on the type and quantity of the carbon formed and on the extent of supported catalyst breakdown.

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%.