A methodology comprising careful consideration of sample preparation, reactor design, experimental procedures and data evaluation routines for precise analysis of the kinetics of gas-solid reactions, specifically the oxidation of carbonaceous materials, has been developed and validated. The well-controlled solvent-free deposition of the carbonaceous material onto cordierite monolith substrates ensures experimental studies in the absence of diffusion limitations, temperature gradients and hot zones. These critical aspects are supported also by theoretical considerations. Temperature-programmed oxidation and isothermal oxygen stepresponse experiments in continuous gas-flow reactor using a homogeneous synthetic carbonblack material demonstrate excellent reproducibility and the conversion profiles agree well with previously reported data. An independent set of global kinetic parameters was estimated ⇤ To whom correspondence should be addressed 1 for each 5% sub-conversion interval using linear regression such that the conversion dependence of each parameter could be analyzed separately and compared to previously published data. The results show that the evolution of reactive carbons cannot be described with a single global reaction order. This is supported by intermittent ex situ measurements of the specific surface area of the carbon-black material during the course of isothermal oxidation, which reveals a developing microporous structure at high conversions. Physically the changes in carbon reaction order are interpreted as changes in fraction of accessible reactive carbon atoms during progressing oxidation. Moreover, at high conversions, the carbon reaction order approaches 0.7 implying that the evolution of the concentration of reactive carbon atoms is not only proportional to the external surface area of shrinking spheres but also that these spheres have approximately the same size.

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The framework of a multi‐scale model that couples a deep neural network, a widely used machine learning approach, with a partial differential equation solver and provides understanding of the relationship between the pore‐scale electrode structure reaction and device‐scale electrochemical reaction uniformity within a redox flow battery is introduced. A deep neural network is trained and validated using 128 pore‐scale simulations that provide a quantitative relationship between battery operating conditions and uniformity of the surface reaction for the pore‐scale sample. Using the framework, information about surface reaction uniformity at the pore level to combined uniformity at the device level is upscaled. The information obtained using the framework and deep neural network against the experimental measurements is also validated. Based on the multi‐scale model results, a time‐varying optimization of electrolyte inlet velocity is established, which leads to a significant reduction in pump power consumption for targeted surface reaction uniformity but little reduction in electric power output for discharging. The multi‐scale model coupled with the deep neural network approach establishes the critical link between the micro‐structure of a flow‐battery component and its performance at the device scale, thereby providing rationale for further operational or material optimization.

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