Evidence map›Paper›PMID 36616952›Full record

ArticleSensors (Basel, Switzerland)2022

Electrolyte-Gated Graphene Field Effect Transistor-Based Ca

Rong Zhang, Tiantian Hao, Shihui Hu, Kaiyang Wang, Shuhui Ren, Ziwei Tian, Yunfang Jia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. From Sensing Mechanisms to System Co-Design: Graphene-Based Sensors and Readout Circuits.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. Review
  3. Recent Advances in Xenes Based FET for Biosensing Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  4. Review
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

7 authors.

Rong ZhangCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Tiantian HaoCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Shihui HuCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Kaiyang WangCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Shuhui RenCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Ziwei TianCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Yunfang JiaCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.ORCID 0000-0002-2093-2118

Funding

National Natural Science Foundation of China 61771260National Natural Science Foundation of China 62271269
6 · The paper itself

Abstract

Flexible electrolyte-gated graphene field effect transistors (Eg-GFETs) are widely developed as sensors because of fast response, versatility and low-cost. However, their sensitivities and responding ranges are often altered by different gate voltages. These bias-voltage-induced uncertainties are an obstacle in the development of Eg-GFETs. To shield from this risk, a machine-learning-algorithm-based LgGFETs' data analyzing method is studied in this work by using Ca

Indexed as

GraphiteElectrolytesElectrolytesGraphiteCa2+ detectioncalibration-freeelectrolyte-gated graphene field effect transistorflexiblemachine learningregression model

Identifiers

PMID36616952
PMCPMC9824237

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.