Light-harvesting properties of photocatalyst supports—no photon left behind

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-10-08 DOI:10.1038/s41524-024-01409-0
Athanasios A. Tountas, Anselm Dreher, Wenjie Zhou, Abhinav Mohan, Nazir P. Kherani, Geoffrey A. Ozin, Mohini M. Sain
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Abstract

In this work, we set out to elucidate the light-harvesting properties of various random and ordered photocatalyst supports (PSs) with different macropore sizes. To accomplish this, we propose two studies of increasing relevance, enabled by computed tomography (CT) reconstructions and ray-tracing COMSOL Multiphysics simulations: (a) a 360-degree light release study approximating a PS situated within a compound parabolic concentrator (CPC) or cylindrical LED reactor with open ends; and (b) the same system as before but with closed ends. The ordered geometry is of interest, as it can be 3D printed at scale with a tailored morphology and porosity, and it can potentially be refined using machine learning models to optimize its light-harvesting properties. As will be shown, the local volumetric light absorption (LVLA) data suggests that an ordered PS with a more open pore interior and a smaller pore exterior would begin to approach the more isophotonic light-harvesting properties of random PSs.

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光催化剂支架的光收集特性--不留下任何光子
在这项工作中,我们致力于阐明具有不同大孔尺寸的各种无序和有序光催化剂载体 (PS) 的光收集特性。为此,我们通过计算机断层扫描 (CT) 重建和射线追踪 COMSOL 多物理场仿真,提出了两项相关性越来越高的研究:(a) 360 度光释放研究,近似于位于复合抛物面聚光器 (CPC) 或圆柱形 LED 反应器内、两端开放的 PS;(b) 与之前相同的系统,但两端封闭。有序的几何形状令人感兴趣,因为它可以按比例三维打印,具有量身定制的形态和孔隙率,而且有可能利用机器学习模型对其进行改进,以优化其光收集特性。如图所示,局部体积光吸收(LVLA)数据表明,内部孔隙更开放、外部孔隙更小的有序聚苯乙烯将开始接近随机聚苯乙烯更等光子的光收集特性。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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