Data mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells
Data mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells
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DOI：
10.1039/c4cp02645d
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发表时间：
2014-01-01
影响因子：
3.3
通讯作者：
Kawase, Takeshi
中科院分区：
文献类型：
--
作者：
Cole, Jacqueline M.;Low, Kian Sing;Kawase, Takeshi
合适染料的一个主要缺陷是抑制了染料敏化太阳能电池（DSC）工业的发展。材料发现策略已经提供了许多新的染料;然而，相应的基于溶液的DSC器件性能在二十多年前使用N719染料实现的11%效率上几乎没有改善。然而，对这些染料的研究揭示了染料的分子结构与其相关DSC效率之间的关系。在这里，这样的结构-性质关系已被编入分子染料设计规则的形式，这已经明智地在算法中排序，以使大规模的数据挖掘的染料结构与最佳的DSC性能。这首次提供了一种DSC特定的染料发现策略，该策略通过测量一组代表性的化学空间来预测新的染料类别。实验验证了这些预测中的一种铅材料，其DSC效率可与许多知名的有机染料相媲美。这证明了这种方法的力量。
A major deficit in suitable dyes is stifling progress in the dye-sensitised solar cell (DSC) industry. Materials discovery strategies have afforded numerous new dyes; yet, corresponding solution-based DSC device performance has little improved upon 11% efficiency, achieved using the N719 dye over two decades ago. Research on these dyes has nevertheless revealed relationships between the molecular structure of dyes and their associated DSC efficiency. Here, such structure-property relationships have been codified in the form of molecular dye design rules, which have been judiciously sequenced in an algorithm to enable large-scale data mining of dye structures with optimal DSC performance. This affords, for the first time, a DSC-specific dye-discovery strategy that predicts new classes of dyes from surveying a representative set of chemical space. A lead material from these predictions is experimentally validated, showing DSC efficiency that is comparable to many well-known organic dyes. This demonstrates the power of this approach.