Evidence map›Paper›PMID 36987274›Full record

ArticlePolymers2023

Optimal Design of Carbon-Based Polymer Nanocomposites Preparation Based on Response Surface Methodology.

Shaoqiu Yan, Ying Tang, Gangping Bi, Bowen Xiao, Guotian He, Yuanchang Lin

Abstract read
In one paragraph

Article in Polymers, 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.

Shaoqiu YanChongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Ying TangChongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Gangping BiChongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Bowen XiaoChongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Guotian HeChongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
Yuanchang LinChongqing Institute of Green Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.

Funding

Chongqing Cooperation Project between Chongqing Universities and CAS Affiliated Institutes HZ2021011Chongqing Special Project for Technology Innovation and Application Development cstc2021jscx-cylhX0009National Key R&D Program 2018YFB1306601"Western Light" Talent Training and Introduction Program of Chinese Academy of Sciences 2019-90
6 · The paper itself

Abstract

Response surface methodology (RSM) and central composite design (CCD) were used to improve the preparation of carbon nanotube and graphene (CNT-GN)-sensing unit composite materials in this study. Four independent variable factors (CNT content, GN content, mixing time, and curing temperature) were controlled at five levels, and 30 samples were generated using the multivariate control analysis technique. On the basis of the experimental design, semi-empirical equations were developed and utilized to predict the sensitivity and compression modulus of the generated samples. The results reveal a strong correlation between the experimental and expected values of sensitivity and the compression modulus for the CNT-GN/RTV (room-temperature-vulcanized silicone rubber) polymer nanocomposites fabricated using different design strategies. The correlation coefficients for the sensitivity and compression modulus are R2 =0.9634 and R2=0.9115, respectively. The ideal preparation parameters of the composite in the experimental range include a CNT content of 1.1 g, a GN content of 1.0 g, a mixing time of 15 min, and a curing temperature of 68.6 °C, according to theoretical predictions and experimental findings. At 0~30 kPa, the CNT-GN/RTV-sensing unit composite materials may reach a sensitivity of 0.385 kPa

Indexed as

central composite designCNT/GNcompressive modulusresponse surfacesensitivity

Identifiers

PMID36987274
PMCPMC10052088

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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.