An Optoelectrically Switched, Dual‐Mode Neuromorphic Sensor for Transient and Accumulative Gas Detection
Abstract Conventional gas sensors typically focus on detecting transient gases with critical gas concentrations but lack the ability to detect hazards resulting from cumulative gas exposure. Here, the study demonstrates a dual‐mode nitrogen dioxide (NO 2 ) gas sensor utilizing carbon nanotube thin‐film transistors, which features a transient detection mode for sensitive detection of transient gas inflow and accumulation detection mode for monitoring cumulative gas exposure, offering efficient and compact analysis of both immediate and prolonged NO 2 exposure. The proposed sensor is capable of detecting NO 2 gas through the charge trapping and detrapping mechanisms of gas molecules. The unique capability to switch between the transient detection and accumulation recognition modes is achieved via the controlled modulation of electrical bias and ultraviolet light. More importantly, the gate‐bias adjustment facilitates precise sensitivity control by regulating the device's electrical properties, while the UV exposure promotes efficient desorption of attached gas molecules. These features may pave the way for the development of multifunctional gas sensors that can perform both real‐time detection and long‐term exposure monitoring of toxic gases in compact device architectures.
Authors
- Jong Wook Shin (ORCID: https://orcid.org/0000-0002-3988-1680)
- Sung Kyu Park (ORCID: https://orcid.org/0000-0001-9617-2541)
- Youngwoo Jang
- Sung Woon Cho (ORCID: https://orcid.org/0000-0001-8392-3668)
- Jae‐Won Shin (ORCID: https://orcid.org/0000-0003-4823-6373)
- Yong‐Hoon Kim (ORCID: https://orcid.org/0000-0001-5209-4908)
- Seung‐Han Kang
- Jeong‐Wan Jo (ORCID: https://orcid.org/0000-0001-6168-5013)
Institutions
- Sunchon National University (KR)
- University of Cambridge (GB)
- Chung-Ang University (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1002/adfm.202504636
- Citations
- 4
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.92
Funders
- National Research Foundation
- Ministry of Trade, Industry and Energy
- Ministry of Science, ICT and Future Planning
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea