Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909101180583936 |
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| author | Chen, Zhuo Zhou, Binghan Xiao, Mingfei Bhowmick, Tynee Kannan, Padmanathan Karthick Occhipinti, Luigi G. Gardner, Julian William Hasan, Tawfique |
| author_facet | Chen, Zhuo Zhou, Binghan Xiao, Mingfei Bhowmick, Tynee Kannan, Padmanathan Karthick Occhipinti, Luigi G. Gardner, Julian William Hasan, Tawfique |
| contents | Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimising the morphology and doping of the printed structures, we achieve a record-high response of 15.23 percent for 1 parts-per-million formaldehyde and an ultralow detection limit of 8.02 parts-per-billion consuming only 130 uW power. Based on measured dynamic response snapshots, we also develop an intelligent computational algorithm for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room-temperature sensors. Our framework in combining materials engineering, structural design and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_13156 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments Chen, Zhuo Zhou, Binghan Xiao, Mingfei Bhowmick, Tynee Kannan, Padmanathan Karthick Occhipinti, Luigi G. Gardner, Julian William Hasan, Tawfique Applied Physics Materials Science Instrumentation and Detectors Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimising the morphology and doping of the printed structures, we achieve a record-high response of 15.23 percent for 1 parts-per-million formaldehyde and an ultralow detection limit of 8.02 parts-per-billion consuming only 130 uW power. Based on measured dynamic response snapshots, we also develop an intelligent computational algorithm for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room-temperature sensors. Our framework in combining materials engineering, structural design and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature. |
| title | Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments |
| topic | Applied Physics Materials Science Instrumentation and Detectors |
| url | https://arxiv.org/abs/2309.13156 |