Evidence map›Paper›PMID 34690390›Full record

ArticleJournal of molecular liquids2022

A molecular dynamic study on the ability of phosphorene for designing new sensor for SARS-CoV-2 detection.

Aliyeh Mehranfar, Mohammad Khavani, Mohammad Izadyar

Open access · greenAbstract read
In one paragraph

Article in Journal of molecular liquids, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 20 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

3 authors at 2 institutions in 2 countries.

Aliyeh MehranfarResearch Center for Modeling and Computational Sciences, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran.
Mohammad KhavaniDepartment of Chemistry and Materials Science, School of Chemical Engineering, Aalto University, P.O. Box 16100, FI-00076 Aalto, Finland.
Mohammad IzadyarResearch Center for Modeling and Computational Sciences, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran.
Ferdowsi University of Mashhad · IRAalto University · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the dramatic increase in the number of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), designing new selective and sensitive sensors for the detection of this virus is of importance. In this research, by employing full atomistic molecular dynamics (MD) simulations, the interactions of the receptor-binding domain (RBD) of the SARS-CoV-2 with phosphorene and graphene nanosheets were analyzed to investigate their sensing ability against this protein. Based on the obtained results, the RBD interactions with the surface of graphene and phosphorene nanosheets do not have important effects on the folding properties of the RBD but this protein has unique dynamical behavior against each nanostructure. In the presence of graphene and phosphorene, the RBD has lower stability because due to the strong interactions between RBD and these nanostructures. This protein spreads on the surface and has lower structural compaction, but in comparison with graphene, RBD shows greater stability on the surface of the phosphorene nanosheet. Moreover, RBD forms a more stable complex with phosphorene nanosheet in comparison with graphene due to greater electrostatic and van der Waals interactions. The calculated Gibbs binding energy for the RBD complexation process with phosphorene and graphene are -200.37 and -83.65 kcal mol

Indexed as

CoronavirusElectrostatic interactionGrapheneMD simulationNanostructuresPhosphoreneSensor

Identifiers

PMID34690390
PMCPMC8520178
OpenAlexW3207800840

What OpenQuestion holds

Textmetadata
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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.