Evidence map›Paper›PMID 42230545›Full record

ArticleMicrosystems & nanoengineering2026

Sensitive and wafer-scale olfactory sensory neurons.

Wenjian Zhang, Hegeng Li, Zhixiang Hu, Lanpeng Guo, Jingqiu Chen, Huanhuan Zhang, Rongyu Mao, Hua-Yao Li, Yang Zhang, Huan Liu

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 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

10 authors.

Wenjian Zhang *School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Hegeng Li *School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.ORCID http://orcid.org/0000-0002-0522-747X
Zhixiang Hu *School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Lanpeng GuoSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Jingqiu ChenSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Huanhuan ZhangSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Rongyu MaoSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Hua-Yao LiSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China.
Yang ZhangState Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China. zhang_yang@semi.ac.cn.
Huan LiuSchool of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, Hubei, PR China. huan@hust.edu.cn.ORCID http://orcid.org/0000-0002-4222-2299

Funding

Natural Science Foundation of Hubei Province (Hubei Provincial Natural Science Foundation) 2022CFA035
6 · The paper itself

Abstract

Accurate and energy-efficient gas sensing remains a key challenge for next-generation artificial olfactory systems, particularly in applications such as unmanned aerial vehicles, wearable electronics, and humanoid robotic actuators. Inspired by the inherent selectivity and low-power information-processing mechanisms of biological olfactory neurons, we report an integrated olfactory sensory neuron (OSN) that simultaneously achieves high sensitivity and selectivity, signal stability, and neuromorphic spiking output. The device combines iodine-passivated colloidal quantum dots (CQDs) as molecular receptor layers with a wafer-level high-electron-mobility transistor (HEMT) transduction platform. The intrinsically low subthreshold swing and high on/off ratio of the HEMT enable efficient amplification of gas-induced charge modulation and allow the direct generation of spike-like signals. The artificial OSN achieves an ultralow detection limit of 0.5 ppb for NO₂ and successfully differentiates NO₂ from NO via principal component analysis (PCA). These results establish a scalable approach for neuromorphic olfactory modules, providing greater degrees of freedom for AI-driven olfaction and enabling compact, high-performance sensing in robotics, environmental monitoring, and healthcare applications.

Identifiers

PMID42230545
PMCPMC13230934

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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.