Machine Learning-Assisted Carbon Dot Synthesis: Prediction of Emission Color and Wavelength.

Senanayake, Ravithree D; Yao, Xiaoxiao; Froehlich, Clarice E; Cahill, Meghan S; Sheldon, Trever R; McIntire, Mary; Haynes, Christy L; Hernandez, Rigoberto

Senanayake, Ravithree D; Yao, Xiaoxiao; Froehlich, Clarice E; Cahill, Meghan S; Sheldon, Trever R; McIntire, Mary; Haynes, Christy L; Hernandez, Rigoberto.

Afiliação

Senanayake RD; Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.
Yao X; Department of Chemistry, University of Minnesota-Twin Cities, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.
Froehlich CE; Department of Chemistry, University of Minnesota-Twin Cities, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.
Cahill MS; Department of Chemistry, University of Minnesota-Twin Cities, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.
Sheldon TR; Department of Chemistry, University of Minnesota-Twin Cities, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.
McIntire M; Department of Chemistry, University of Minnesota-Twin Cities, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.
Haynes CL; Department of Chemistry, University of Minnesota-Twin Cities, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States.
Hernandez R; Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States.

J Chem Inf Model ; 62(23): 5918-5928, 2022 Dec 12.

Article em En | MEDLINE | ID: mdl-36394850

Assuntos

Carbono; Pontos Quânticos; Solventes; Temperatura; Aprendizado de Máquina

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carbono / Pontos Quânticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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