Evidence map›Paper›PMID 31952151›Full record

ArticleMicromachines2020

Low-Concentration Ammonia Gas Sensors Manufactured Using the CMOS-MEMS Technique.

Wei-Chun Shen, Po-Jen Shih, Yao-Chuan Tsai, Cheng-Chih Hsu, Ching-Liang Dai

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
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

12 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
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  8. A Review of Gas Measurement Set-Ups.Sensors (Basel, Switzerland) · 2022
    Review
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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

5 authors at 3 institutions in 1 country.

Wei-Chun ShenDepartment of Mechanical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
Po-Jen ShihDepartment of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.
Yao-Chuan TsaiDepartment of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung 402, Taiwan.
Cheng-Chih HsuDepartment of Electro-Optical Engineering, National United University, Miaoli 360, Taiwan.
Ching-Liang DaiDepartment of Mechanical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
National Chung Hsing University · TWNational Taiwan University · TWNational United University · TW

Funding

Ministry of Science and Technology, Taiwan MOST 108-2221-E-005 -065 -MY2
6 · The paper itself

Abstract

This study describes the fabrication of an ammonia gas sensor (AGS) using a complementary metal oxide semiconductor (CMOS)-microelectromechanical system (MEMS) technique. The structure of the AGS features interdigitated electrodes (IDEs) and a sensing material on a silicon substrate. The IDEs are the stacked aluminum layers that are made using the CMOS process. The sensing material; polypyrrole/reduced graphene oxide (PPy/RGO), is synthesized using the oxidation-reduction method; and the material is characterized using an electron spectroscope for chemical analysis (ESCA), a scanning electron microscope (SEM), and high-resolution X-ray diffraction (XRD). After the CMOS process; the AGS needs post-processing to etch an oxide layer and to deposit the sensing material. The resistance of the AGS changes when it is exposed to ammonia. A non-inverting amplifier circuit converts the resistance of the AGS into a voltage signal. The AGS operates at room temperature. Experiments show that the AGS response is 4.5% at a concentration of 1 ppm NH

Indexed as

ammoniaCMOS processgas sensorlow concentrationMEMS

Identifiers

PMID31952151
PMCPMC7019987
OpenAlexW2999356885

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.