双连续催化剂载体结构中的产气反应流
Gas-generating reactive flows in bicontinuous catalyst support structures
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中文总结 AI 辅助
本研究针对产气反应中催化剂载体结构的气泡滞留问题，采用新型中心矩碰撞算子的反应流颜色梯度格子玻尔兹曼模拟，发现双连续界面受阻乳液凝胶（bijels）可实现高转化效率，且可通过超亲水表面涂层优化其性能。
中文摘要 AI 辅助
多相催化领域的一大挑战是选择最优的催化剂载体结构。市售结构可大规模制造，但其随机特性导致化学和传输性能欠佳，这对产气反应尤为相关——多孔结构的不均匀性会引发气泡滞留，阻碍反应物流向催化剂位点，导致转化效率低下。此前实验研究表明，旋节线衍生的结构，尤其是双连续界面受阻乳液凝胶（bijels），可缓解这些问题并提供优异性能，但据我们所知，尚未开展针对该形态优化操作条件的数值研究。本研究旨在通过采用新型中心矩碰撞算子的反应流颜色梯度格子玻尔兹曼模拟填补这一空白，基于模拟数据开发分析模型预测催化剂性能。研究结果表明，该类形态可实现极高的转化效率，此外，我们证明可通过超亲水表面涂层优化其催化剂性能。
英文摘要
A major challenge in the field of heterogeneous catalysis is selecting an optimal catalyst support structure. Commercially available structures can be easily manufactured at scale, but their stochastic nature makes their chemical and transport properties suboptimal. This is particularly relevant for gas-generation reactions, where non-uniformity of a porous structure leads to bubble trapping. Such trapping impedes the flow of reactants to catalyst sites, leading to conversion inefficiencies. Previous experimental work demonstrated that spinodally-derived architectures, in particular bicontinuous interfacially jammed emulsion gels (bijels), can alleviate these issues and deliver superior performance. However, to the best of our knowledge, numerical studies to optimize the operating conditions for such a morphology have not been performed yet. In this work, we aim to close this gap using color-gradient lattice Boltzmann simulations of reactive flows with a novel central moments collision operator. We develop an analytical model to predict catalyst performance based on our simulation data. Our findings show that this type of morphology can achieve very high conversion efficiencies. Moreover, we demonstrate that its catalyst performance can be optimized using superhydrophilic surface coatings.
