Evidence map›Paper›PMID 30966376›Full record

ArticlePolymers2018

Lignocellulose-Chitosan-Multiwalled Carbon Nanotube Composites with Improved Mechanical Strength, Dimensional Stability and Fire Retardancy.

Zhe Wang, Yutao Yan, Xiaoping Shen, Temeng Qian, Junjie Wang, Qingfeng Sun, Chunde Jin

Abstract read
In one paragraph

Article in Polymers, 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

7 authors.

Zhe WangSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. donjade@163.com.
Yutao YanSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. yytao1988@163.com.
Xiaoping ShenSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. sxp1031@hotmail.com.
Temeng QianSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. teemeeng@163.com.
Junjie WangSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. jjwang7475@163.com.
Qingfeng SunSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. qfsun@zafu.edu.cn.
Chunde JinSchool of Engineering, Zhejiang A&F University, Hangzhou 311300, China. jincd@zafu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A novel composite composed of lignocellulose (LC), glutaraldehyde crosslinked chitosan (GC) and multiwalled carbon nanotube (MWCNT) was fabricated by the hot-pressing process. The effect of the additional GC and MWCNT on the mechanical strength, dimensional stability and fire retardancy of lignocellulose composites was investigated. The results showed that LC/GC/MWCNT composite exhibited the maximum modulus of rupture (MOR) of 35.3 MPa, modulus of elasticity (MOE) of 2789.1 MPa and internal bonding (IB) strength of 1.2 MPa. Meanwhile, the LC/GC/MWCNT composite displayed improved dimensional stability with a thickness swelling (TS) value of 22.4%. Besides, the LC/GC/MWCNT composite exhibited improved fire retardancy with a limiting oxygen index of 29.0%. The peak heat release rate, the total heat release, the total smoke production and the maximum smoke production ratio of LC/GC/MWCNT composite decreased by 15.9%, 10.7%, 45.5% and 20.7% compared with those of LC composite, respectively. Therefore, the LC/GC/MWCNT composite may be a promising candidate for green wood based composites.

Indexed as

chitosandimensional stabilityfire retardancylignocellulose compositesmechanical strengthmultiwalled carbon nanotube

Identifiers

PMID30966376
PMCPMC6415154

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.