Evidence map›Paper›PMID 37485314›Full record

ReviewFrontiers in bioengineering and biotechnology2023

Advances in flexible graphene field-effect transistors for biomolecule sensing.

Bo Hu, Hao Sun, Jinpeng Tian, Jin Mo, Wantao Xie, Qiu Ming Song, Wenwei Zhang, Hui Dong

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2023. 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. 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

8 authors.

Bo HuSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China.
Hao SunSchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
Jinpeng TianSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China.
Jin MoSchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
Wantao XieSchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.
Qiu Ming SongSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China.
Wenwei ZhangSino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen, China.
Hui DongSchool of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increasing demand for biomarker detection in wearable electronic devices, flexible biosensors have garnered significant attention. Additionally, graphene field-effect transistors (GFETs) have emerged as key components for constructing biosensors, owing to their high sensitivity, multifunctionality, rapid response, and low cost. Leveraging the advantages of flexible substrates, such as biocompatibility, adaptability to complex environments, and fabrication flexibility, flexible GFET sensors exhibit promising prospects in detecting various biomarkers. This review provides a concise summary of design strategies for flexible GFET biosensors, including non-encapsulated gate without dielectric layer coverage and external gate designs. Furthermore, notable advancements in sensing applications of biomolecules, such as proteins, glucose, and ions, are highlighted. Finally, we discuss the future challenges and prospects in this field, aiming to inspire researchers to address these issues in their further investigations.

Indexed as

biomarkerbiomoleculebiosensorflexiblegraphene field-effect transistor

Identifiers

PMID37485314
PMCPMC10361656

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.