High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning.

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Bibliographic Details
Title: High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning.
Authors: Zhang J; Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland., Perrin ML; Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland., Barba L; Machine Learning and Optimization Laboratory, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland., Overbeck J; Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland.; Department of Physics and Swiss Nanoscience Institute, University of Basel, CH-4056 Basel, Switzerland., Jung S; Micro- and Nanosystems, Department of Mechanical and Process Engineering, ETH Zurich, CH-8092 Zurich, Switzerland., Grassy B; Machine Learning and Optimization Laboratory, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland., Agal A; Machine Learning and Optimization Laboratory, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland., Muff R; Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland., Brönnimann R; Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland., Haluska M; Micro- and Nanosystems, Department of Mechanical and Process Engineering, ETH Zurich, CH-8092 Zurich, Switzerland., Roman C; Micro- and Nanosystems, Department of Mechanical and Process Engineering, ETH Zurich, CH-8092 Zurich, Switzerland., Hierold C; Micro- and Nanosystems, Department of Mechanical and Process Engineering, ETH Zurich, CH-8092 Zurich, Switzerland., Jaggi M; Machine Learning and Optimization Laboratory, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland., Calame M; Laboratory for Transport at Nanoscale Interfaces, Empa, Swiss Federal Laboratories for Materials Science and Technology, CH-8600 Dübendorf, Switzerland.; Department of Physics and Swiss Nanoscience Institute, University of Basel, CH-4056 Basel, Switzerland.
Source: Microsystems & nanoengineering [Microsyst Nanoeng] 2022 Feb 10; Vol. 8, pp. 19. Date of Electronic Publication: 2022 Feb 10 (Print Publication: 2022).
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101695458 Publication Model: eCollection Cited Medium: Internet ISSN: 2055-7434 (Electronic) Linking ISSN: 20557434 NLM ISO Abbreviation: Microsyst Nanoeng Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2055-7434
DOI:10.1038/s41378-022-00350-w