Evidence map›Paper›PMID 35890718›Full record

ArticlePolymers2022

Fabrication of Multi-Vacancy-Defect MWCNTs by the Removal of Metal Oxide Nanoparticles.

Tae Hyeong Kim, Dong Hwan Nam, Do-Hyun Kim, Gyu Leem, Seunghyun Lee

Open access · goldAbstract read
In one paragraph

Article in Polymers, 2022. 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
0.7field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 9 citations in OpenAlex.

  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

5 authors at 3 institutions in 2 countries.

Tae Hyeong KimDepartment of Applied Chemistry, Hanyang University ERICA, Ansan 15588, Korea.ORCID 0000-0002-6926-064X
Dong Hwan NamDepartment of Applied Chemistry, Hanyang University ERICA, Ansan 15588, Korea.
Do-Hyun KimSchool of Electrical Engineering, Korea University, Seoul 02841, Korea.
Gyu LeemDepartment of Chemistry, State University of New York, College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, NY 13210, USA.ORCID 0000-0003-0169-1096
Seunghyun LeeDepartment of Applied Chemistry, Hanyang University ERICA, Ansan 15588, Korea.
Hanyang University · KRKorea University · KRState University of New York · US

Funding

GRRC program of Gyeonggi province GRRC 2020-B03Nanomedical Devices Development Project of NNFC 1711160154National Research Foundation of Korea 2019R1C1C1009427Technology Innovation Program by the Ministry of Trade, Industry & Energy of Korea 20015991
6 · The paper itself

Abstract

This study aims to increase the specific surface area of multi-walled carbon nanotubes (MWCNTs) by forming and subsequently removing various metal oxide nanoparticles on them. We used facile methods, such as forming the particles without using a vacuum or gas and removing these particles through simple acid treatment. The shapes of the composite structures on which the metal oxide particles were formed and the formation of multi-vacancy-defect MWCNTs were confirmed via transmission electron microscopy and scanning electron microscopy. The crystallinity of the formed metal oxide particles was confirmed using X-ray diffraction analysis. Through specific surface area analysis and Raman spectroscopy, the number of defects formed and the degree and tendency of defect-formation in each metal were determined. In all the cases where the metal oxide particles were removed, the specific surface area increased, and the metal inducing the highest specific surface area was determined.

Indexed as

metal oxide NPsmulti-vacancy-defectmulti-walled carbon nanotubes

Identifiers

PMID35890718
PMCPMC9319261
OpenAlexW4286208799

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.