Evidence map›Paper›PMID 40645924›Full record

ArticleMicrosystems & nanoengineering2025

An enhanced-performance multisensing progressive cellular μGC: design advances and blind test results.

Declan Winship, Weilin Liao, Hsueh-Tsung Lu, Irene Lara-Ibeas, Xiangyu Zhao, Qu Xu, Tao Qian, Robert Gordenker, Yutao Qin, Yogesh B Gianchandani

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. 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.

Declan Winship *Department of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.ORCID http://orcid.org/0000-0002-3994-1153
Weilin Liao *Department of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.
Hsueh-Tsung LuDepartment of Mechanical Engineering, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.
Irene Lara-IbeasDepartment of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.
Xiangyu ZhaoDepartment of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.ORCID http://orcid.org/0000-0002-3525-9747
Qu XuDepartment of Integrative Systems + Design, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.
Tao QianDepartment of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.
Robert GordenkerDepartment of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA.
Yutao QinDepartment of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA. yutaoqin@umich.edu.ORCID http://orcid.org/0000-0003-4589-4485
Yogesh B GianchandaniDepartment of Electrical Engineering and Computer Science, and Center for Wireless Integrated MicroSensing and Systems (WIMS²), University of Michigan, Ann Arbor, MI, 48109, USA. yogesh@umich.edu.

Funding

ODNI | Intelligence Advanced Research Projects Activity (IARPA) FA8650-19-C-9105
6 · The paper itself

Abstract

Many environmental, industrial, and security applications demand in-field analysis of chemical vapors. Whereas microscale gas chromatographs (µGCs) are promising candidates, reliable in-field chemical analysis particularly demands repeatability, humidity tolerance, and in-field reference. Using a µGC with substantial monolithic integration (of preconcentrators, separation columns, and capacitive and photoionization detectors), this paper reports chip-level and system-level advancements towards reliable chemical analysis. Thermal management is advanced using tailored heater designs to compensate for boundary conditions and cooling. Fence electrodes are incorporated into on-chip photoionization detectors, reducing responses due to humidity by >98%. The repeatability of retention time is advanced by introducing closed-loop flow control, reducing the relative standard deviation of retention time to only 0.29-0.43%, which represents a 4-5× improvement over open-loop flow control. A miniature reservoir for a chemical reference standard is also incorporated on board, providing the ability to correct for drifts in retention time and the ability to directly measure retention times relative to the reference chemical. A set of blind false alarm tests was performed for fixed target analytes in the presence of partially coeluting interferent species. A separate set of blind chemical recognition tests was also performed for various analytes of concealed identities. Overall, the results were largely successful and showed the promise of the reported µGC instrument and modules for broad chemical screening and long-term in-field deployment.

Identifiers

PMID40645924
PMCPMC12254352

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Registered trials

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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.