Evidence map›Paper›PMID 42098327›Full record

ArticleScientific reports2026

Polypropylene fiber-reinforced geopolymer mortar from coal washing waste and recycled glass powder: mechanical and microstructural performance.

Fatemehzahra Najafitabar, Meysam Pourabbas Bilondi, Vahideh Ghaffarian

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

3 authors.

Fatemehzahra NajafitabarDepartment of Civil Engineering, Architecture and Urban Planning, Sadjad University of Technology, Mashhad, Iran.
Meysam Pourabbas BilondiDepartment of Civil Engineering, University of Gonabad, Gonabad, Iran. mpoorabbas64@gmail.com.
Vahideh GhaffarianDepartment of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, a sustainable geopolymer mortar has been developed entirely from industrial waste materials, including coal washing waste (CWW) ash, recycled glass powder (RGP), and calcium carbide residue (CCR), reinforced with polypropylene fibers (PPF). A five-stage experimental approach was employed to optimize the binder composition, alkaline activator ratio (Na2SiO3/NaOH) (NS:NH), alkali solution-to-binder ratio (S:B), fiber content, and the heat-curing process, based on mechanical performance. The optimal binder consisted of 7% CWW ash, 10% RGP, and 20% CCR, activated at an NS:NH ratio of 1.5 and an S:B ratio of 0.65. Incorporation of 0.8% PPF increased flexural strength by approximately 7% compared to the unreinforced geopolymer mortar due to effective crack-bridging and improved matrix integrity. Heat curing at 100 °C for 24 h resulted in the highest performance, yielding a 28-day compressive strength of 42.15 MPa, flexural strength of 5.23 MPa, and UPV of 4746 m·s⁻¹, representing improvements of 50%, 45.2%, and 10.1%, respectively, over the Portland cement control sample. SEM, FTIR, and XRD analyses confirmed the formation of dense aluminosilicate and calcium-rich gel phases, which correlate with the enhanced mechanical properties. The novelty of this research lies in the synergistic utilization of three industrial wastes, combined with fiber reinforcement and optimized curing, to produce a high-performance, low-carbon geopolymer mortar suitable for both structural and non-structural engineering applications.

Indexed as

Calcium carbide residueCoal washing wasteMechanical strengthPolypropylene fiberRecycled glass powder

Identifiers

PMID42098327
PMCPMC13342223

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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.