Record link: 
http://hdl.handle.net/20.500.12708/232339
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Title: 
Towards machine learning approaches in plasmonic solid–molecule systems for sensing and catalysis
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Citation: 
Guselnikova, O. (2026). Towards machine learning approaches in plasmonic solid–molecule systems for sensing and catalysis. In OePG-CMD-JOINTMEETING 2026 75th Annual Meeting of the Austrian Physical Society and 32nd Meeting of the Condensed Matter Division of the European Physical Society Program & Abstracts (pp. 143–143).
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Publication Type: 
Inproceedings - Abstract Book Contribution
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Language: 
English
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Authors: 
Guselnikova, Olga 
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Organisational Unit: 
E134-02 - Forschungsbereich Applied Interface Physics 
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Published in: 
OePG-CMD-JOINTMEETING 2026 75th Annual Meeting of the Austrian Physical Society and 32nd Meeting of the Condensed Matter Division of the European Physical Society Program & Abstracts 
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Date (published): 
2026
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Event name: 
75. Jahrestagung der Österreichischen Physikalischen Gesellschaft 32. Treffen der Sektion Kondensierte Materie der Europäischen Physikalischen Gesellschaft (ÖPG-CMD JOINT MEETING 2026) 
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Event date: 
20-Sep-2026 - 25-Sep-2026
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Event place: 
Graz, Austria
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Number of Pages: 
1
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Keywords: 
Plasmonics; machine learning; SERS; plasmon catalysis; multiplexed sensing; high-throughput screening
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Research Areas: 
Special and Engineering Materials: 50%
Structure-Property Relationsship: 50%
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Science Branch: 
1040 - Chemie: 50%
1030 - Physik, Astronomie: 50%
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Appears in Collections:
Conference Paper

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