Evidence map›Paper›PMID 29352756›Full record

ArticleJournal of molecular modeling2018

Influence of defect locations and nitrogen doping configurations on the mechanical properties of armchair graphene nanoribbons.

Ahmet Emin Senturk, Ahmet Sinan Oktem, Alp Er S Konukman

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of molecular modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Ahmet Emin SenturkDepartment of Mechanical Engineering, Gebze Technical University, Gebze, 41400, Kocaeli, Turkey.
Ahmet Sinan OktemDepartment of Mechanical Engineering, Gebze Technical University, Gebze, 41400, Kocaeli, Turkey.
Alp Er S KonukmanDepartment of Mechanical Engineering, Gebze Technical University, Gebze, 41400, Kocaeli, Turkey. konukman@gtu.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effect of defect locations on the mechanical properties of armchair graphene nanoribbons (AGNRs) and the various configurations of nitrogen (N) doping on the mechanical properties of AGNRs were examined using molecular dynamics (MD) simulations. The variation of the Young's modulus (YM) and the ultimate tensile strength (UTS) of pyridinic-N, graphitic-N, and pyrrolic-N by increasing the concentration of N doping was investigated. The results of MD simulations show that the defect location has a significant effect on the UTS and failure strain (FS) of AGNRs in both vertical and horizontal directions. In the horizontal direction, variations of the UTS and FS are lower than in the vertical direction. On the other hand, the variations of the YM is almost similar in vertical and horizontal directions. The results of this work indicate that the UTS and FS of AGNRs are more sensitive than the YM of AGNRs for different defect directions. Pyridinic-N improves the mechanical properties of the defective AGNR and performs better YM and UTS values than the graphitic-N. Substitution N atoms, which are located at the defective sites and/or at the edges of AGNRs, are mechanically more favorable. Pyrrolic-N configuration has the lowest mechanical properties among the other configurations. Furthermore, pyrrolic-N with Stone-Wales-1 (SW-1) type of defect has higher mechanical properties than pyrrolic-N with Stone-Wales-2 (SW-2) type of defect.

Indexed as

Armchair graphene nanoribbonDefect locationMechanical propertiesMolecular dynamicsNitrogen doping configurations

Identifiers

What OpenQuestion holds

Textmetadata
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.