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Schletz, Daniel, Breidung, Morten, Fery, Andreas (2023) Validating and Utilizing Machine Learning Methods to Investigate the Impacts of Synthesis Parameters in Gold Nanoparticle Synthesis. The Journal of Physical Chemistry C, 127 (2) 1117-1125 doi:10.1021/acs.jpcc.2c07578

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Reference TypeJournal (article/letter/editorial)
TitleValidating and Utilizing Machine Learning Methods to Investigate the Impacts of Synthesis Parameters in Gold Nanoparticle Synthesis
JournalThe Journal of Physical Chemistry C
AuthorsSchletz, DanielAuthor
Breidung, MortenAuthor
Fery, AndreasAuthor
Year2023 (January 19)Volume127
Issue2
PublisherAmerican Chemical Society (ACS)
DOIdoi:10.1021/acs.jpcc.2c07578Search in ResearchGate
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Mindat Ref. ID15678973Long-form Identifiermindat:1:5:15678973:0
GUID0
Full ReferenceSchletz, Daniel, Breidung, Morten, Fery, Andreas (2023) Validating and Utilizing Machine Learning Methods to Investigate the Impacts of Synthesis Parameters in Gold Nanoparticle Synthesis. The Journal of Physical Chemistry C, 127 (2) 1117-1125 doi:10.1021/acs.jpcc.2c07578
Plain TextSchletz, Daniel, Breidung, Morten, Fery, Andreas (2023) Validating and Utilizing Machine Learning Methods to Investigate the Impacts of Synthesis Parameters in Gold Nanoparticle Synthesis. The Journal of Physical Chemistry C, 127 (2) 1117-1125 doi:10.1021/acs.jpcc.2c07578
In(2023, January) The Journal of Physical Chemistry C Vol. 127 (2) American Chemical Society (ACS)

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