Optimizing the Combination of Natural Pigments for Co-Sensitization of Panchromatic TiO2 Dye Sensitized Solar Cells

Note: The Proquest link to the M.S. Thesis with a more complete description of this work is provided under the "Related Links" header at the bottom of this page. This repository contains UV/VIS and JV measurement .csv files for DSSC’s produced with a variety of natural dye combinations of Anthocyanins (A), Betalins (B), Chlorophyll (C), Xanthonoids (M), Curcuminoids (K) and Phycobilins (P). Note: A 1:1 combination of A & B is denoted as “AB”. Data is available for 42 unique combinations, plus 6 so-called “absorbance optimized combinations” that exhibit light harvesting efficiency (LHE) profiles which are highly commensurable with the AM1.5G solar irradiance spectrum (data courtesy of NREL). This work hypothesizes that dye combinations exhibiting these highly commensurable LHE spectra will also exhibit characteristically high DSSC performance. For convenience, both of these .csv files have been plotted to make the data available in a graphical format. The repository also contains the original .py file used to perform the Radial Basis Function (RBF) interpolation for analysis of a further 2,568 dye combinations. For example, given a set of 6 constituent dyes, this script yields a surface in R7 which interpolates all measured absorbance data at a given wavelength. Assuming that absorbance data is available for n combinations of these 6 constituent dyes, this would be a function interpolating n points in R7. This process is repeated for all wavelengths for which data is available allowing each interpolation function to be sampled and concatenated to yield a spectrum for an arbitrary dye combination. The script then samples many arbitrary combinations using this method and prints to the terminal the combinations which maximize each of the 3 objective functions uses to assess AM1.5G commensurability. The input data to this script can be modified allowing for similar analysis of proprietary data. Specifically, this can be achieved by the appropriate modification of the “Empirical_Dye_Solutions_Volume_Fractions.csv” and either of the “UV/VIS_Absorbance_Anode_Adsorbed.csv” or “UV/VIS_Absorbance_Bulk_Solution.csv” files. The following videos (uploaded to YouTube) have been produced to assist readers in understanding the purpose of both the RBF interpolation and the differences of the 3 objective functions: https://www.youtube.com/watch?v=KSHNrELYn9g https://www.youtube.com/watch?v=D9Z7w32d_Ts The results obtained in this work indicate that panchromaticity alone is not sufficient to predict DSSC performance. However, a 1:1 combination of a-Mangostin and Curcuminoids was found to exhibit much higher DSSC efficiency (TiO2 anode, Pt-counter electrode, I-/I3- electrolyte) compared to any of the other analyzed combinations. This combination was successfully co-adsorbed with CDCA and exhibited strong UV stability over time.注：本研究完整描述的硕士学位论文的ProQuest链接已置于本页面底部的"相关链接"板块下。 本仓库包含采用多种天然染料组合制备的染料敏化太阳能电池（DSSC）的紫外-可见（UV/VIS）与电流-电压（JV）测量CSV文件，所用天然染料包括花青素（A）、甜菜素类（B）、叶绿素（C）、氧杂蒽类化合物（M）、姜黄素类（K）与藻胆蛋白类（P）。注：A与B的1:1组合记为"AB"。本数据集包含42种独特染料组合的测量数据，另有6种所谓的"吸光度优化组合"，其光收集效率（LHE）曲线与AM1.5G标准太阳辐照光谱高度匹配（数据由美国国家可再生能源实验室（NREL）提供）。本研究提出假设：具备此类高度匹配的LHE光谱的染料组合，其染料敏化太阳能电池性能也将表现出典型的高水平特性。为便于使用，本仓库已将两份CSV文件的数据绘制成可视化图表格式。 本仓库同时包含用于对额外2568种染料组合开展分析的径向基函数（RBF）插值原始Python脚本。例如，当给定6种组分染料时，该脚本将生成R⁷空间中的曲面，以插值特定波长下的所有实测吸光度数据。若这6种组分染料共有n种组合具备吸光度数据，则该函数将实现R⁷空间中n个点的插值。针对所有具备实测数据的波长重复该流程，即可对每个插值函数进行采样并拼接，从而得到任意染料组合的光谱曲线。随后该脚本将基于此方法采样大量任意染料组合，并在终端输出可最大化3项用于评估AM1.5G匹配度的目标函数的组合。该脚本的输入数据可修改，以支持对专有数据集开展类似分析，具体可通过适当修改"Empirical_Dye_Solutions_Volume_Fractions.csv"文件，以及"UV/VIS_Absorbance_Anode_Adsorbed.csv"或"UV/VIS_Absorbance_Bulk_Solution.csv"其中任一文件实现。 以下为上传至YouTube的辅助视频，旨在帮助读者理解径向基函数插值的原理，以及3项目标函数之间的差异： https://www.youtube.com/watch?v=KSHNrELYn9g https://www.youtube.com/watch?v=D9Z7w32d_Ts 本研究结果表明，仅具备全光谱吸收特性不足以预测染料敏化太阳能电池的性能。然而，研究发现α-倒捻子素与姜黄素类的1:1组合，其染料敏化太阳能电池效率（采用二氧化钛阳极、铂对电极与碘/三碘电解质体系）远高于其余所有分析过的组合。该组合可与鹅脱氧胆酸（CDCA）成功共吸附，并展现出优异的长期紫外稳定性。