Evidence map›Paper›PMID 38139068›Full record

ReviewInternational journal of molecular sciences2023

State-of-the-Art Review on Amorphous Carbon Nanotubes: Synthesis, Structure, and Application.

Xiaona Ren, Muhammad Irfan Hussain, Yue Chang, Changchun Ge

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 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. Review
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.

Xiaona RenSchool of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID 0000-0002-6457-8026
Muhammad Irfan HussainSchool of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Yue ChangSchool of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Changchun GeSchool of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Funding

Central university basic research funds FRF-TP-20-006A2
6 · The paper itself

Abstract

Carbon nanotubes (CNTs) have rapidly received increasing attention and great interest as potential materials for energy storage and catalyst fields, which is due to their unique physicochemical and electrical properties. With continuous improvements in fabrication routes, CNTs have been modified with various types of materials, opening up new perspectives for research and state-of-the-art technologies. Amorphous CNTs (aCNTs) are carbon nanostructures that are distinctively different from their well-ordered counterparts, such as single-walled and multi-walled carbon nanotubes (SWCNTs and MWCNTs, respectively), while the atoms in aCNTs are grouped in a disordered, crystalline/non-crystalline manner. Owing to their unique structure and properties, aCNTs are attractive for energy storage, catalysis, and aerospace applications. In this review, we provide an overview of the synthetic routes of aCNTs, which include chemical vapor deposition, catalytic pyrolysis, and arc discharge. Detailed morphologies of aCNTs and the systematic elucidation of tunable properties are also summarized. Finally, we discuss the future perspectives as well as associated challenges of aCNTs. With this review, we aim to encourage further research for the widespread use of aCNTs in industry.

Indexed as

Nanotubes, CarbonCatalysisGasesTechnologyGasesNanotubes, Carbonamorphous carbon nanotubeslithium-ion batterymicrowave absorptionnanohybrid compositessupercapacitor

Identifiers

PMID38139068
PMCPMC10743152

What OpenQuestion holds

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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.