Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules
Open accessAbstract
Insights into the fascinating molecular world of biological processes are crucial for understanding diseases, developing diagnostics, and effective therapeutics. These processes are complex as they involve interactions between four major classes of biomolecules, i.e., proteins, nucleic acids, carbohydrates, and lipids, which makes it important to be able to discriminate between all these different biomolecular species. In this work, a deep learning‐augmented, chemically‐specific nanoplasmonic technique that enables such a feat in a label‐free manner to not disrupt native processes is presented. The method uses a highly sensitive multiresonant plasmonic metasurface in a microfluidic device, which enhances infrared absorption across a broadband mid‐IR spectrum and in water, despite its strongly overlapping absorption bands. The real‐time format of the optofluidic method enables the collection of a vast amount of spectrotemporal data, which allows the construction of a deep neural network to discriminate accurately between all major classes of biomolecules. The capabilities of the new method are demonstrated by monitoring of a multistep bioassay containing sucrose‐ and nucleotides‐loaded liposomes interacting with a small, lipid membrane‐perforating peptide. It is envisioned that the presented technology will impact the fields of biology, bioanalytics, and pharmacology from fundamental research and disease diagnostics to drug development.
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Dimensionality reduction of spectral data using machine learning algorithms is an invaluable aid to VOC sensors.
“Therefore, dimensionality reduction of spectral data using ML algorithms is an invaluable aid to VOC sensors.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as John-Herpin et al. 2021
AI algorithms can facilitate the analysis and processing of data by VOC sensors in a prompt, direct, and automated manner.
“AI algorithms can facilitate the analysis and processing of data by VOC sensors in a prompt, direct, and automated manner (John-Herpin et al. 2021; Kuhner et al. 2019).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as John-Herpin et al. 2021
A deep learning method using multi-resonant plasmonic nanoantennas augments dynamics monitoring between four major classes of biomolecule from absorption spectra at three working wavelengths of 3.4 μm, 6.5 μm, and 9 μm.
“Leveraging the multi-resonant PNAs, the deep learning method is proposed to augment the dynamics monitoring between four major classes of biomolecule (lipids, proteins, nucleic acids, and carbohydrates) from the absorption spectra in three working wavelengths of 3.4 μm, 6.5 μm, and 9 μm50.”
From Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy (2022) · cites this paper as [50]
In PNA-based SEIRAS, the great challenge is the limited bandwidth of nanoantenna resonances compared with the ultra-broadband vibrational absorption fingerprints.
“In PNA-based surface-enhanced IR absorption spectroscopy (SEIRAS), the great challenge is the limited bandwidth of nanoantenna resonances compared with the ultra-broadband vibrational absorption fingerprints.”
From Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy (2022) · cites this paper as [50]
Previous works show many solutions using multiple resonances of nanoantenna structures to capture plasmonic enhanced molecular fingerprints in separate wavelength ranges for sensing different molecule species.
“Previous works show many solutions using multiple resonances of nanoantenna structures to capture the plasmonic enhanced molecular fingerprints in separate wavelength ranges for sensing different molecule species22,39,41,50,54–56.”
From Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy (2022) · cites this paper as [50]
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- Authors
- John‐Herpin, A., Kavungal, D., von Mücke, L., & Altug, H.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Institute of Bioengineering École Polytechnique Fédérale de Lausanne (EPFL) Lausanne 1015 Switzerland
- Corresponding author
- Altug, Hatice
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2021-02-22
- Volume
- 33
- Issue
- 14
- Pages
- e2006054-e2006054
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- PMC
- PMC11469153
- OpenAlex
- W3132124173
- Cited by
- 124
- References
- 60
- Citations by year
- 2026: 12 · 2025: 26 · 2024: 26 · 2023: 20 · 2022: 25 · 2021: 15
- FWCI
- 6.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced Materials (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- European Research Council (682167)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Plasmonic and Surface Plasmon Research
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Gold and Silver Nanoparticles Synthesis and Applications
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Metamaterials and Metasurfaces Applications
- Keywords
- infrared metasurfaces, nanoplasmonics, deep learning, label-free biosensing, optofluidics, microfluidics, mid-infrared spectroscopy, liposomes, nucleotides, sucrose, drug development
- MeSH terms
- Deep Learning, Biological Assay, Infrared Rays, Liposomes, Nucleic Acids, Nucleotides, Proteins, Spectrophotometry, Infrared, Sucrose, Lab-On-A-Chip Devices
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