Evidence map›Paper›PMID 37445019›Full record

ArticleMaterials (Basel, Switzerland)2023

Size- and Chirality-Dependent Structural and Mechanical Properties of Single-Walled Phenine Nanotubes.

Yanjun Liu, Ruijie Wang, Liya Wang, Jun Xia, Chengyuan Wang, Chun Tang

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2023. 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

6 authors.

Yanjun LiuFaculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.
Ruijie WangFaculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.
Liya WangFaculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.
Jun XiaFaculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.
Chengyuan WangZienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Bay Campus, Swansea University, Swansea SA1 8EN, Wales, UK.ORCID 0000-0002-1001-2537
Chun TangFaculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China.ORCID 0000-0002-7767-2126

Funding

National Natural Science Foundation of China 12072134
6 · The paper itself

Abstract

Phenine nanotubes (PNTs) have recently been synthesized as a promising new one-dimensional material for high-performance electronics. The periodically distributed vacancy defects in PNTs result in novel semiconducting properties, but may also compromise their mechanical properties. However, the role of these defects in modifying the structural and mechanical properties is not yet well understood. To address this, we conducted systematic molecular dynamics simulations investigating the structural evolution and mechanical responses of PNTs under various conditions. Our results demonstrated that the twisting of linear carbon chains in both armchair and zigzag PNTs led to interesting structural transitions, which were sensitive to chiralities and diameters. Additionally, when subjected to tensile and compressive loading, PNTs' cross-sectional geometry and untwisting of linear carbon chains resulted in distinct mechanical properties compared to carbon nanotubes. Our findings provide comprehensive insights into the fundamental properties of these new structures while uncovering a new mechanism for modifying the mechanical properties of one-dimensional nanostructures through the twisting-untwisting of linear carbon chains.

Indexed as

bond twistingmechanical propertiesmolecular dynamicsphenine nanotubes

Identifiers

PMID37445019
PMCPMC10342797

What OpenQuestion holds

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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.