Evidence map›Paper›PMID 35891677›Full record

ArticleDiamond and related materials2022

Design and finite element modeling of two-dimensional nanomechanical biosensors for SARS-CoV-2 detection.

J Payandehpeyman, N Parvini, K Moradi, N Hashemian

Abstract read
In one paragraph

Article in Diamond and related materials, 2022. 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

4 authors.

J PayandehpeymanDepartment of Mechanical Engineering, Hamedan University of Technology, Hamedan, Iran.
N ParviniCellular and Molecular Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran.
K MoradiDepartment of Mechanical Engineering, Hamedan University of Technology, Hamedan, Iran.
N HashemianFaculty of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SARS-CoV-2 is the causative agent of COVID-19 disease. The development of different variants has increased the prevalence, pathogenicity, and mortality of the SARS-CoV-2. Prompt diagnosis and timely initiation of therapy can undoubtedly minimize the damage caused by this virus. In this study, a wide range of emerging single layer two-dimensional materials (SL2DMs), including graphene, grapheme oxide (GO), reduced graphene oxide (rGO), hexagonal boron nitride (h-BN), Ti3C2Tx MXene, and MoS2that can be used to fabricate highly sensitive biosensors, are analyzed using the finite element method based on antigen-antibody interaction. Important design parameters including sensor size, sensor aspect ratio, number of viruses, and applying in-plane strain on sensor performance are analyzed using frequency shift technique. In the following, an analytical relationship that can predict the limit of detection (LOD) according to the above parameters is proposed. The results show that all the above materials have a good performance in detecting viruses in the sample range of 10-100 viruses. This range can be reduced significantly by applying strains of less than 0.1. Also, applying strain increases shift frequency index by 2 to 3 times, which is a significant result. The maximum and minimum sensor performance are obtained for GO and Ti3C2Tx, respectively. The results of this paper can be used to build a new generation of two-dimensional biosensors for rapid detection of COVID-19 and other viruses.

Indexed as

COVID–19Finite element methodNanobiosensorTwo–dimensional materials

Identifiers

PMID35891677
PMCPMC9303063

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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.