Evidence map›Paper›PMID 39697598›Full record

ArticleData in brief2024

Molecular structure data and modelling roadmap for optimized oxidized graphene quantum dot and epoxy interface and mechanical properties.

Prathamesh Deshpande, Robert Chan-Jobe, Ozgur Keles

Abstract read
In one paragraph

Article in Data in brief, 2024. 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

3 authors.

Prathamesh DeshpandeSan Jose State University, 1 Washington Sq., San Jose, CA 95192, United States.
Robert Chan-JobeSan Jose State University, 1 Washington Sq., San Jose, CA 95192, United States.
Ozgur KelesSan Jose State University, 1 Washington Sq., San Jose, CA 95192, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hybrid epoxy composites are highly considered for low-density applications due to the excellent specific strength and specific stiffness. Enhancements made to the epoxy matrix by addition of nanofillers like carbon nanotubes (CNTs) and graphene (GNPs) have been studied in detail over the course of few decades. These enhancements not only help elevate the material properties of the matrix but also activate different failure mitigating mechanisms in the composite. Although highly beneficial, there are few shortcomings due to the challenging fabrication process of integrating such. Common problems like filler agglomeration, formation of voids, wrinkling and more can result in poor load transfer within the composite. Graphene quantum dots (GQDs), on the other hand are the smallest carbon-based filler which are known to promote more intimate contact with the matrix. Their small size enables simultaneous enhancement of stiffness, strength and toughness. In addition, functionalization of these materials enables other supramolecular interactions like hydrogen bonding which improve the interfacial interaction with the epoxy. This study provides a molecular dynamics (MD) workflow to model a single functionalized GQD embedded in an epoxy matrix and the effective mechanical response of the nanocomposite. Ten chemistries were developed with different oxygen-based functional groups which capture the effect of GQD on the mechanical properties of the nanocomposite. Uniaxial strain simulations revealed that a maximum strength gain of 56 % and stiffness gain of 18 % was computed by the oxidized GQD-epoxy nanocomposite.

Indexed as

Epoxy nanocompositeGraphene oxide quantum dotsMechanical responseMolecular dynamicsMolecular interactions

Identifiers

PMID39697598
PMCPMC11652893

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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.