Evidence map›Paper›PMID 35957069›Full record

ArticleNanomaterials (Basel, Switzerland)2022

COVID-19 Detection via Silicon Nanowire Field-Effect Transistor: Setup and Modeling of Its Function.

Asma Wasfi, Falah Awwad, Juri George Gelovani, Naser Qamhieh, Ahmad I Ayesh

Open access · goldAbstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

9 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
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  8. Sugar Molecules Detection via CNanomaterials (Basel, Switzerland) · 2023
    Article
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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 2 institutions in 2 countries.

Asma WasfiDepartment of Electrical Engineering, College of Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates.ORCID 0000-0003-3182-6684
Falah AwwadDepartment of Electrical Engineering, College of Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates.
Juri George GelovaniCollege of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates.ORCID 0000-0002-8413-6161
Naser QamhiehDepartment of Physics, College of Science, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates.ORCID 0000-0002-0292-0367
Ahmad I AyeshPhysics Program, Department of Mathematics, Statistics and Physics, College of Arts and Science, Qatar University, Doha P.O. Box 2713, Qatar.ORCID 0000-0002-0442-5941
United Arab Emirates University · AEQatar University · QA

Funding

United Arab Emirates University 31N443United Arab Emirates University 31R128
6 · The paper itself

Abstract

Biomolecular detection methods have evolved from simple chemical processes to laboratory sensors capable of acquiring accurate measurements of various biological components. Recently, silicon nanowire field-effect transistors (SiNW-FETs) have been drawing enormous interest due to their potential in the biomolecular sensing field. SiNW-FETs exhibit capabilities such as providing real-time, label-free, highly selective, and sensitive detection. It is highly critical to diagnose infectious diseases accurately to reduce the illness and death spread rate. In this work, a novel SiNW-FET sensor is designed using a semiempirical approach, and the electronic transport properties are studied to detect the COVID-19 spike protein. Various electronic transport properties such as transmission spectrum, conductance, and electronic current are investigated by a semiempirical modeling that is combined with a nonequilibrium Green's function. Moreover, the developed sensor selectivity is tested by studying the electronic transport properties for other viruses including influenza, rotavirus, and HIV. The results indicate that SiNW-FET can be utilized for accurate COVID-19 identification with high sensitivity and selectivity.

Indexed as

COVID-19FET biosensorsemiempirical modeling

Identifiers

PMID35957069
PMCPMC9370568
OpenAlexW4289203916

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.