Evidence map›Paper›PMID 36005043›Full record

ReviewBiosensors2022

Advancement and Challenges of Biosensing Using Field Effect Transistors.

Gokuraju Thriveni, Kaustab Ghosh

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  13. Surface-modified CMOS biosensors.Frontiers in bioengineering and biotechnology · 2024
    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

2 authors.

Gokuraju ThriveniDepartment of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bengaluru 560074, India.
Kaustab GhoshCentre for Nanoelectronics and VLSI Design, Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai 600127, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Field-effect transistors (FETs) have become eminent electronic devices for biosensing applications owing to their high sensitivity, faster response and availability of advanced fabrication techniques for their production. The device physics of this sensor is now well understood due to the emergence of several numerical modelling and simulation papers over the years. The pace of advancement along with the knowhow of theoretical concepts proved to be highly effective in detecting deadly pathogens, especially the SARS-CoV-2 spike protein of the coronavirus with the onset of the (coronavirus disease of 2019) COVID-19 pandemic. However, the advancement in the sensing system is also accompanied by various hurdles that degrade the performance. In this review, we have explored all these challenges and how these are tackled with innovative approaches, techniques and device modifications that have also raised the detection sensitivity and specificity. The functional materials of the device are also structurally modified towards improving the surface area and minimizing power dissipation for developing miniaturized microarrays applicable in ultra large scale integration (ULSI) technology. Several theoretical models and simulations have also been carried out in this domain which have given a deeper insight on the electron transport mechanism in these devices and provided the direction for optimizing performance.

Indexed as

Biosensing TechniquesCOVID-19HumansPandemicsSARS-CoV-2Spike Glycoprotein, CoronavirusTransistors, ElectronicSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2biosensorsDebye lengthfield effect transistorsfunctionalizationscreeningsensitivityspecificity

Identifiers

PMID36005043
PMCPMC9405812

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.