Evidence map›Paper›PMID 42308309›Full record

ArticleScience advances2026

Scalable multiplexed machine learning gas sensor chips for food classification.

Carla Bassil, Kichul Lee, Xun Liao, Divya Krishnan, Yifei Zhan, Theodorus Jonathan Wijaya, Edward Hester, Minhyun Kim, Il-Doo Kim, Inkyu Park and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Carla BassilDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.ORCID 0009-0000-8768-8976
Kichul LeeDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.ORCID 0000-0002-7838-6447
Xun LiaoDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.ORCID 0009-0001-8538-1599
Divya KrishnanDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
Yifei ZhanDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.ORCID 0009-0009-8974-4738
Theodorus Jonathan WijayaDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.ORCID 0000-0003-3947-0029
Edward HesterDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
Minhyun KimDepartment of Materials Science and Engineering, KAIST, Daejeon 34141, Republic of Korea.ORCID 0009-0009-8824-6606
Il-Doo KimDepartment of Materials Science and Engineering, KAIST, Daejeon 34141, Republic of Korea.ORCID 0000-0002-9970-2218
Inkyu ParkDepartment of Mechanical Engineering, KAIST, Daejeon 34141, Republic of Korea.ORCID 0000-0001-5761-7739
Ali JaveyDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.ORCID 0000-0001-7214-7931

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biosensing TechniquesFoodFood AnalysisGasesMachine LearningClassification AlgorithmsNanotubes, CarbonOdorantsGasesNanotubes, Carbon

Identifiers

PMID42308309
PMCPMC13274616

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.