Evidence map›Paper›PMID 36133524›Full record

ArticleNanoscale advances2022

Graphene BioFET sensors for SARS-CoV-2 detection: a multiscale simulation approach.

A Toral-Lopez, D B Kokh, E G Marin, R C Wade, A Godoy

Open access · goldAbstract read
In one paragraph

Article in Nanoscale advances, 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
1.0field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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 2 countries.

A Toral-LopezDepartamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada Spain atoral@ugr.es agodoy@ugr.es.ORCID https://orcid.org/0000-0001-5612-0536
D B KokhMolecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies Schloss-Wolfsbrunnenweg 35 69118 Heidelberg Germany.ORCID https://orcid.org/0000-0002-4687-6572
E G MarinDepartamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada Spain atoral@ugr.es agodoy@ugr.es.ORCID https://orcid.org/0000-0002-0302-3764
R C WadeMolecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies Schloss-Wolfsbrunnenweg 35 69118 Heidelberg Germany.ORCID https://orcid.org/0000-0001-5951-8670
A GodoyDepartamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada Spain atoral@ugr.es agodoy@ugr.es.ORCID https://orcid.org/0000-0002-3014-8765
Universidad de Granada · ESHeidelberg Institute for Theoretical Studies · DEHeidelberg University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological Field-Effect Transistors (BioFETs) have already demonstrated enormous potential for detecting minute amounts of ions and molecules. The use of two-dimensional (2D) materials has been shown to boost their performance and to enable the design of new applications. This combination deserves special interest in the current pandemic caused by the SARS-CoV-2 virus which demands fast, reliable and cheap detection methods. However, in spite of the experimental advances, there is a lack of a comprehensive and in-depth computational approach to capture the mechanisms underlying the sensor behaviour. Here, we present a multiscale platform that combines detailed atomic models of the molecules with mesoscopic device-level simulations. The fine-level description exploited in this approach accounts for the charge distribution of the receptor, its reconfiguration when the target binds to it, and the consequences in terms of sensitivity on the transduction mechanism. The results encourage the further exploration of improved sensor designs and 2D materials combined with diverse receptors selected to achieve the desired specificity.

Identifiers

PMID36133524
PMCPMC9418999
OpenAlexW4284883712

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.