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Informatics Driven Materials Innovation for a Regenerative Economy: Harnessing NLP for Safer Chemistry in Manufacturing of Solar Cells

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The objective of this paper is to demonstrate the use of natural language processing tools to map the propensity of usage of specific solvents used for the fabrication of different perovskite photovoltaic (PV) systems. This article focuses on implementing contextual natural language processing (NLP) tool to identify different chemicals that appear in the synthesis of perovskite solar cells. This information is linked to the known level of hazard of those solvents. This work serves to demonstrate how by harnessing the tools of informatics, in this case NLP, offers a powerful framework to guide environmentally conscious selection of chemicals used in the manufacturing of perovskite solar cells.

Original languageEnglish
Title of host publicationREWAS 2022
Subtitle of host publicationDeveloping Tomorrow’s Technical Cycles
EditorsAdamantia Lazou, Katrin Daehn, Camille Fleuriault, Mertol Gökelma, Elsa Olivetti, Christina Meskers
PublisherSpringer Science and Business Media Deutschland GmbH
Pages11-19
Number of pages9
ISBN (Print)9783030925628
DOIs
StatePublished - 2022
Event7th Installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on developing tomorrow’s technical cycles, 2022 - Anaheim, United States
Duration: Feb 27 2022Mar 3 2022

Publication series

NameMinerals, Metals and Materials Series
ISSN (Print)2367-1181
ISSN (Electronic)2367-1696

Conference

Conference7th Installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on developing tomorrow’s technical cycles, 2022
Country/TerritoryUnited States
CityAnaheim
Period02/27/2203/3/22

Keywords

  • NLP
  • Perovskite
  • Solvent

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