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Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy

Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic res...

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Autores principales: Ren, Zhihao, Zhang, Zixuan, Wei, Jingxuan, Dong, Bowei, Lee, Chengkuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256719/
https://www.ncbi.nlm.nih.gov/pubmed/35790752
http://dx.doi.org/10.1038/s41467-022-31520-z
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author Ren, Zhihao
Zhang, Zixuan
Wei, Jingxuan
Dong, Bowei
Lee, Chengkuo
author_facet Ren, Zhihao
Zhang, Zixuan
Wei, Jingxuan
Dong, Bowei
Lee, Chengkuo
author_sort Ren, Zhihao
collection PubMed
description Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.
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spelling pubmed-92567192022-07-07 Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy Ren, Zhihao Zhang, Zixuan Wei, Jingxuan Dong, Bowei Lee, Chengkuo Nat Commun Article Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics. Nature Publishing Group UK 2022-07-05 /pmc/articles/PMC9256719/ /pubmed/35790752 http://dx.doi.org/10.1038/s41467-022-31520-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ren, Zhihao
Zhang, Zixuan
Wei, Jingxuan
Dong, Bowei
Lee, Chengkuo
Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
title Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
title_full Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
title_fullStr Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
title_full_unstemmed Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
title_short Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
title_sort wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256719/
https://www.ncbi.nlm.nih.gov/pubmed/35790752
http://dx.doi.org/10.1038/s41467-022-31520-z
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