Evidence map›Paper›PMID 36234298›Full record

ArticleMaterials (Basel, Switzerland)2022

Mechanical Property Evaluation and Prediction of Cementing Composites Blended with MK and UFA under High-Temperature Steam Curing.

Chao Liang, Yongming Xing, Xiaohu Hou

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. 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

3 authors.

Chao LiangCollege of Science, Inner Mongolia University of Technology, Hohhot 010051, China.
Yongming XingCollege of Science, Inner Mongolia University of Technology, Hohhot 010051, China.
Xiaohu HouCollege of Materials Science and Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.

Funding

National Natural Science Foundation of China 11762014
6 · The paper itself

Abstract

In this paper, the influence of the substitution rate of metakaolin (MK) and ultrafine fly ash (UFA) on the hydration degree, the micromechanical properties, the pore size distribution, and the corresponding fractal dimension of composite cement-based material was investigated under high-temperature steam curing. Furthermore, Thermogravimetric, Nanoindentation, and low-field nuclear magnetic resonance tests were used to explore the influencing factors of pore size distribution and its corresponding multi-fractal dimension. Finally, the correlations among the pore size distribution, related fractal dimensions, and compression strength were analyzed. Results indicate that the MK-UFA cement ternary cementation system (TCS) can improve the compressive strength and fluidity of samples and enhance the hydration degree and micromechanical properties of the cementitious system. TCS effectively refines the pore size and increases microporosity. In addition, micropore and its fractal dimension have a stronger correlation with the compressive strength of composite cement-based materials. Furthermore, the micro-fractal dimensions can better reflect the essential characteristics of the composite cementitious system. The higher the degree of hydration of the cementitious system and the nanomechanical properties of the C-(A)-S-H gel, the lower the micro-fractal dimension. Finally, the GM (1,3) prediction model of compressive strength, micro-fractal dimension, and microporosity are established based on the grey relational theory.

Indexed as

composite cement-based materialcompressive strengthgrey relevance predictionhigh-temperature steam curinglow-field nuclear magnetic resonancemulti-fractal dimensionnanoindentation

Identifiers

PMID36234298
PMCPMC9573449

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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.