DOE OSTI · 3382425
Scalable multiplexed machine learning gas sensor chips for food classification
Abstract
Multiplexed gas sensor arrays combined with machine learning have unlocked previously inaccessible applications for scent-based sensing. Current platforms are limited by overlapping sensing materials with similar compositions, leading to highly correlated responses, or multistep deposition processes that hinder scalability. In this work, we developed a 16-element monolithic chip with fully distinct sensing layers, enabling a truly heterogeneous array. The system consists of highly sensitive carbon nanotube field effect transistors that are functionalized through a single-step microdispensing method compatible with automated pipetting systems. The resulting chip produces characteristic signal patterns in response to object-specific scent profiles and, when combined with machine learning algorithms, can perform automated object identification. We demonstrate the classification of 16 different objects, including food spoilage and nut allergens, with a 92.6% overall prediction accuracy.
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Bassil, Carla [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0009000087688976), Lee, Kichul [University of California, Berkeley, CA (United States); Korea Advanced Institute Science and Technology (KAIST), Daejeon (Korea, Republic of)] (ORCID:0000000278386447), Liao, Xun [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0009000185381599), Krishnan, Divya [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Zhan, Yifei [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0009000989744738), Wijaya, Theodorus Jonathan [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)] (ORCID:0000000339470029), Hester, Edward [University of California, Berkeley, CA (United States)], Kim, Minhyun [Korea Advanced Institute Science and Technology (KAIST), Daejeon (Korea, Republic of)] (ORCID:0009000988246606), Kim, Il-Doo [Korea Advanced Institute Science and Technology (KAIST), Daejeon (Korea, Republic of)] (ORCID:0000000299702218), Park, Inkyu [Korea Advanced Institute Science and Technology (KAIST), Daejeon (Korea, Republic of)] (ORCID:0000000157617739), Javey, Ali [University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Kavli Energy NanoScience Institute, Berkeley, CA (United States)] (ORCID:0000000172147931). 2026-06-19. Scalable multiplexed machine learning gas sensor chips for food classification. https://doi.org/10.1126/sciadv.aec7965
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