Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase
Open accessAbstract
The use of molecularly modified Si nanowire field effect transistors (SiNW FETs) for selective detection in the liquid phase has been successfully demonstrated. In contrast, selective detection of chemical species in the gas phase has been rather limited. In this paper, we show that the application of artificial intelligence on deliberately controlled SiNW FET device parameters can provide high selectivity toward specific volatile organic compounds (VOCs). The obtained selectivity allows identifying VOCs in both single-component and multicomponent environments as well as estimating the constituent VOC concentrations. The effect of the structural properties (functional group and/or chain length) of the molecular modifications on the accuracy of VOC detection is presented and discussed. The reported results have the potential to serve as a launching pad for the use of SiNW FET sensors in real-world counteracting conditions and/or applications.
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Using ANN improves the selectivity and sensitivity of sensors in mixtures containing counteracting compounds with similar chemical structures and physical properties.
“Here, using ANN would improve the selectivity and sensitivity of sensors in mixtures containing counteracting compounds with similar chemical structures and physical properties [216].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [216]
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- Authors
- Wang, B., Cancilla, J. C., Torrecilla, J. S., & Haick, H.
- Affiliations
- Technion – Israel Institute of Technology (IL)
- Universidad Complutense de Madrid (ES)
- Department of Chemical Engineering, Complutense University of Madrid, Madrid 28040, Spain
- Corresponding author
- Haick, Hossam
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-01-21
- Volume
- 14
- Issue
- 2
- Pages
- 933-938
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2101896997
- Cited by
- 236
- References
- 37
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 13 · 2023: 11 · 2022: 15 · 2021: 22 · 2020: 18 · 2019: 17 · 2018: 32 · 2017: 26 · 2016: 28 · 2015: 26 · 2014: 9
- FWCI
- 15.39
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- Nano Letters (publishedVersion)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- Council for Higher Education
- Yükseköğretim Kurulu
- FP7 Health
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Nanowire Synthesis and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Gas Sensing Nanomaterials and Sensors
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Keywords
- silicon nanowire transistors, gas sensing, volatile organic compounds, artificial intelligence, sensor selectivity, molecular functionalization, concentration estimation, functional group effects, chain length effects
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