Evidence map›Paper›PMID 34683289›Full record

ArticleMicromachines2021

A Sensitivity-Enhanced Electrolyte-Gated Graphene Field-Effect Transistor Biosensor by Acoustic Tweezers.

Yan Chen, Wenpeng Liu, Hao Zhang, Daihua Zhang, Xiaoliang Guo

Abstract read
In one paragraph

Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Yan ChenBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0001-5519-9764
Wenpeng LiuCollege of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.ORCID 0000-0002-2932-1321
Hao ZhangCollege of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Daihua ZhangCollege of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Xiaoliang GuoCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Funding

National Natural Science Foundation of China 61904010Natural Science Foundation of Beijing Municipality 4204109the Fundamental Research Funds for the Central Universities FRF-TP-19-032A1
6 · The paper itself

Abstract

Low-abundance biomolecule detection is very crucial in many biological and medical applications. In this paper, we present a novel electrolyte-gated graphene field-effect transistor (EGFET) biosensor consisting of acoustic tweezers to increase the sensitivity. The acoustic tweezers are based on a high-frequency bulk acoustic resonator with thousands of MHz, which has excellent ability to concentrate nanoparticles. The operating principle of the acoustic tweezers to concentrate biomolecules is analyzed and verified by experiments. After the actuation of acoustic tweezers for 10 min, the IgG molecules are accumulated onto the graphene. The sensitivities of the EGFET biosensor with accumulation and without accumulation are compared. As a result, the sensitivity of the graphene-based biosensor is remarkably increased using SMR as the biomolecule concentrator. Since the device has advantages such as miniaturized size, low reagent consumption, high sensitivity, and rapid detection, we expect it to be readily applied to many biological and medical applications.

Indexed as

acoustic tweezerselectrolyte-gated graphene field-effect transistorssolid mounted resonator (SMR)

Identifiers

PMID34683289
PMCPMC8541680

What OpenQuestion holds

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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.