Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity.

Wang, Fengrui; Qin, Shiyi; Acevedo-Vélez, Claribel; Van Lehn, Reid C; Zavala, Victor M; Lynn, David M

Wang, Fengrui; Qin, Shiyi; Acevedo-Vélez, Claribel; Van Lehn, Reid C; Zavala, Victor M; Lynn, David M.

Affiliation

Wang F; Department of Chemistry, University of Wisconsin-Madison, 1101 University Ave., Madison, Wisconsin 53706, United States.
Qin S; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, Wisconsin 53706, United States.
Acevedo-Vélez C; Department of Chemical Engineering, University of Puerto Rico-Mayagüez, Call Box 9000, Mayagüez, PR 00681-9000, United States.
Van Lehn RC; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, Wisconsin 53706, United States.
Zavala VM; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, Wisconsin 53706, United States.
Lynn DM; Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Ave, Lemont, Illinois 60439, United States.

ACS Appl Mater Interfaces ; 15(43): 50532-50545, 2023 Nov 01.

Article in En | MEDLINE | ID: mdl-37856671

Key words

amphiphiles; liquid crystals; machine learning; neural networks; surfactants

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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: ACS Appl Mater Interfaces Journal subject: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Year: 2023 Document type: Article Affiliation country: United States Country of publication: United States

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