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Title: Design-to-Device Approach Affords Panchromatic Co-sensitized Solar Cells

Journal Article · · Advanced Energy Materials
ORCiD logo [1];  [2];  [3];  [3];  [4];  [4];  [2];  [5];  [6];  [7];  [7];  [8];  [8];  [9];  [10];  [6];  [7];  [11]
  1. Univ. of Cambridge, Cambridge (United Kingdom); Rutherford Appleton Lab, Oxon (United Kingdom)
  2. Univ. of Cambridge, Cambridge (United Kingdom)
  3. Argonne National Lab. (ANL), Argonne, IL (United States)
  4. STFC Rutherford Appleton Lab, Didcot (United Kingdom)
  5. Indian Inst. of Technology Roorkee, Roorkee (India)
  6. Tianjin Univ. of Technology, Tianjin (People's Republic of China)
  7. Hong Kong Baptist Univ., Hong Kong (People's Republic of China)
  8. Univ. of Zaragoza-CSIC, Zaragoza (Spain)
  9. Univ. of Naples Federico II, Naples (Italy)
  10. Indian Inst. of Technology, Roorkee, Roorkee (India)
  11. Univ. of Cambridge, Cambridge (United Kingdom); Rutherford Appleton Lab, Oxon (United Kingdom); Argonne National Lab. (ANL), Argonne, IL (United States); STFC Rutherford Appleton Lab, Didcot (United Kingdom)

Abstract Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
Spanish Ministerio de Economia y Competitividad (MINECO); Research Grants Council (RGC) of Hong Kong; USDOE
Grant/Contract Number:
AC02-06CH11357; DE‐AC02‐06CH11357
OSTI ID:
1494584
Alternate ID(s):
OSTI ID: 1484818
Journal Information:
Advanced Energy Materials, Vol. 9, Issue 5; ISSN 1614-6832
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 41 works
Citation information provided by
Web of Science

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Cited By (4)

Growing field of materials informatics: databases and artificial intelligence journal January 2020
A Photoelectrochemical Study of Bioinspired 2-Styryl-1-Benzopyrylium Cations on TiO2 Nanoparticle Layer for Application in Dye-Sensitized Solar Cells journal December 2019
Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges journal January 2020
Comparative dataset of experimental and computational attributes of UV/vis absorption spectra journal December 2019

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