Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells-Reference-Cited by-同舟云学术

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Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

Published:2019-12-15 Issue:7 Volume:30 Page:1907259
ISSN:1616-301X
Container-title:Advanced Functional Materials
language:en
Short-container-title:Adv. Funct. Mater.

Author:

Majeed Nahdia¹,Saladina Maria²,Krompiec Michal³⁴,Greedy Steve¹,Deibel Carsten²,MacKenzie Roderick C. I.¹^ORCID

Affiliation:

1. Faculty of EngineeringUniversity of Nottingham Nottingham NG7 2RD UK

2. Institut für PhysikTechnische Universität Chemnitz 09107 Chemnitz Germany

3. Merck Chemicals Ltd. Chilworth Technical CentreUniversity Parkway Southampton SO16 7QD UK

4. School of ChemistryUniversity of Southampton Highfield Southampton SO17 1BJ UK

Publisher

Wiley

Subject

Electrochemistry,Condensed Matter Physics,Biomaterials,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.201907259

Reference58 articles.

1. Solar cell efficiency tables (version 50)

2. Silicon solar cell production

3. Re-assessment of net energy production and greenhouse gas emissions avoidance after 40 years of photovoltaics development

4. Environmental payback time analysis of a roof-mounted building-integrated photovoltaic (BIPV) system in Hong Kong

5. Enhancing silicon solar cells with singlet fission: the case for Förster resonant energy transfer using a quantum dot intermediate

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