Evidence map›Paper›PMID 32443850›Full record

ArticleSensors (Basel, Switzerland)2020

A New Route to Enhance the Packing Density of Buckypaper for Superior Piezoresistive Sensor Characteristics.

Mustafa Danish, Sida Luo

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
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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

2 authors at 1 institution in 1 country.

Mustafa DanishSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.ORCID 0000-0002-3242-5033
Sida LuoSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.ORCID 0000-0002-5673-6100
Beihang University · CN

Funding

Beijing Natural Science Foundation 3202017Chinese Aeronautical Establishment 2017ZF51077Commercial Aircraft Corporation of China, Ltd. COMAC-SFGS-2017-36740National Natural Science Foundation of China 61701015
6 · The paper itself

Abstract

Transforming individual carbon nanotubes (CNTs) into bulk form is necessary for the utilization of the extraordinary properties of CNTs in sensor applications. Individual CNTs are randomly arranged when transformed into the bulk structure in the form of buckypaper. The random arrangement has many pores among individual CNTs, which can be treated as gaps or defects contributing to the degradation of CNT properties in the bulk form. A novel technique of filling these gaps is successfully developed in this study and termed as a gap-filling technique (GFT). The GFT is implemented on SWCNT-based buckypaper in which the pores are filled through small-size MWCNTs, resulting in a ~45.9% improvement in packing density. The GFT is validated through the analysis of packing density along with characterization and surface morphological study of buckypaper using Raman spectrum, particle size analysis, scanning electron microscopy, atomic force microscopy and optical microscopy. The sensor characteristics parameters of buckypaper are investigated using a dynamic mechanical analyzer attached with a digital multimeter. The percentage improvement in the electrical conductivity, tensile gauge factor, tensile strength and failure strain of a GFT-implemented buckypaper sensor are calculated as 4.11 ± 0.61, 44.81 ± 1.72, 49.82 ± 8.21 and 113.36 ± 28.74, respectively.

Indexed as

buckypapercarbon nanotubespiezoresistive sensorsstructural characterization

Identifiers

PMID32443850
PMCPMC7287720
OpenAlexW3026400716

What OpenQuestion holds

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LicenceCC BY
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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.