Evidence map›Paper›PMID 41174034›Full record

ArticleScientific reports2025

Multi-scale influence mechanism of fine coal gangue on the mechanical properties of concrete.

Dawei Hu, Deyu Lou, Yugen Li, Peng He, Xiaoli Ma, Yuning Kang, Feng Wei

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

7 authors.

Dawei HuSchool of Architectural Engineering, Yulin University, Yulin, 719000, China.
Deyu LouSchool of Architectural Engineering, Yulin University, Yulin, 719000, China.
Yugen LiSchool of Architectural Engineering, Yulin University, Yulin, 719000, China. liyugen@yulinu.edu.cn.
Peng HeYulin High-tech Construction Engineering Quality Testing Co., LTD, Yulin, 719000, Shaanxi, China.
Xiaoli MaSchool of Architectural Engineering, Yulin University, Yulin, 719000, China.
Yuning KangNetwork information center, Yulin University, Yulin, 719000, China.
Feng WeiSchool of Architectural Engineering, Yulin University, Yulin, 719000, China.

Funding

National Natural Science Foundation of China 52268047, 51868075, 12302490National Natural Science Foundation of China 52368041Yulin High-tech Zone Science and Technology Bureau YGXKG-2023-106Yulin Science and Technology Bureau of Shaanxi Province 2023-CXY-145
6 · The paper itself

Abstract

The aim of this study is to explore the feasibility of coal gangue (CG) fine aggregate replacing natural sand, and realize the resource utilization of solid waste in concrete materials. The coal gangue concrete (CGC) is prepared with the CG replacement of 0%, 5%, 10%, 15%, 20%, 50% and 100%, the influence of CG replacement on the slump, bulk density, compressive strength and pore structure of concrete is analyzed, and the influence mechanism is revealed combining with the scanning electron microscope (SEM), nuclear magnetic resonance (NMR) and X-ray diffractometer (XRD). The results shows that the optimal mechanical property is achieved with 10% CG replacement, which is 10.1% higher than that of the ordinary concrete. The influence mechanism is that it accelerates the hydration reaction of cement, improves the interfacial transition zone (ITZ), increases the harmless pores percentage and reduces the more harmful pores percentage. The research provides theoretical support for the high value utilization of solid waste in mining area.

Indexed as

Coal gangue fine aggregateCompressive strengthInterfacial transition zonePore structure

Identifiers

PMID41174034
PMCPMC12578931

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.