从光扩散到光催化速率：强散射多孔板的紧凑标度律
From Light Diffusion to Photocatalytic Rates: Compact Scaling Laws for Strongly Scattering Porous Slabs
AI总结：
本文提出了一种紧凑的分析框架，统一了纳米粒子负载光催化板中的多重散射、边界损失、光化学效率和表面动力学过程，通过有限板扩散模型推导出辐照场的闭式表达式，并结合光化学量子效率和一级表面动力学，得到体积和面积速率常数的依赖关系。
AI中文摘要：
强散射多孔光催化材料中的光传输决定了吸收光子的空间分布，从而驱动光催化反应的载流子生成。然而，将此类介质的测量光学特性转化为内在反应速率常数仍然具有挑战性，因为这需要同时考虑多重散射、边界损失、光化学效率和表面动力学。本文开发了一种紧凑的分析框架，用于纳米粒子负载光催化板中的这些过程。使用有限板扩散模型并扩展边界，推导出辐照场的闭式表达式，并将其与光化学量子效率和一级表面动力学耦合。所得预测器产生内在体积和面积速率常数，其对传输均自由程、光学厚度和表面积/体积比的依赖关系清晰可见。蒙特卡罗光子迁移模拟验证显示，扩散近似在辐照度和生成速率剖面的再现上具有适度的乘法偏差，通常在1.20-1.39倍范围内，取决于各向异性和散射相函数。这种一致性与已知的扩散近似限制一致，并足以实现可靠的、面向设计的预测。本文引入的分析描述符，如l*、S/V和扩展长度zb是通用的、物理可解释的，并可直接整合到数据驱动优化和几何工程反应器设计中。因此，该框架为光催化系统提供了多功能且物理基础的工具，适用于多种应用，包括VOC光降解、室内空气净化和太阳能燃料生产。
英文摘要：
Light transport in strongly scattering porous photocatalytic materials governs the spatial distribution of absorbed photons and therefore the generation of charge carriers driving photocatalytic reactions. Yet translating measured optical properties of such media into intrinsic reaction rate constants remains challenging, as it requires simultaneously accounting for multiple scattering, boundary losses, photochemical efficiency, and surface kinetics. Here we develop a compact analytical framework that unifies these processes for nanoparticle-loaded photocatalytic slabs. Using a finite-slab diffusion model with extrapolated boundaries, we derive closed-form expressions for the fluence field and couple them to a photochemical quantum efficiency and first-order surface kinetics. The resulting predictors yield intrinsic volumetric and areal rate constants whose dependence on the transport mean free path, optical thickness, and surface-to-volume ratio emerges transparently. Validation against Monte-Carlo photon-migration simulations shows that the diffusion approximation reproduces the fluence and generation-rate profiles with a modest multiplicative mismatch, typically within a factor of about 1.20-1.39, depending on the anisotropy and scattering phase function. This level of agreement is consistent with the known limits of the diffusion approximation and is sufficient to enable reliable, design-oriented predictions. The analytical descriptors introduced here, such as l*, S/V and the extrapolation length zb are general, physically interpretable, and directly integrable into data-driven optimisation and geometry-engineered reactor design. The framework thus provides a versatile and physically grounded tool for photocatalytic systems across diverse applications, including VOC photo-degradation, indoor air purification, and solar-fuel production.
