Evidence map›Paper›PMID 42737052›Full record

ArticlePolymers2026

Study on CMC-Based Suppressant for Coal Dust and Spontaneous Combustion Control.

Jianguo Wang, Tianle Jia, Zhenzhen Zhang, Xinni He

Abstract read
In one paragraph

Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jianguo WangCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Tianle JiaCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Zhenzhen ZhangCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Xinni HeCollege of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.

Funding

National Natural Science Foundation of China 51974232
6 · The paper itself

Abstract

Underground coal mining faces coupled hazards from respirable coal dust and spontaneous coal combustion. This study developed a dual-function flame-retardant dust suppressant comprising carboxymethyl cellulose (CMC), polycarbodiimide (PCDI), ammonium polyphosphate (APP), and zinc borate (ZB). A four-factor, three-level orthogonal design was used to screen formulations by penetration depth, followed by rheological, water-scour, simulated-roadway, temperature-programmed oxidation, contact-angle, Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscope (SEM) analyses. CMC and PCDI significantly affected penetration, whereas APP and ZB showed no significant effects within the tested ranges. The selected formulation (1% CMC, 12% APP, 3.5% ZB, and 1.5% PCDI) showed stable viscosity development and the lowest mass loss under repeated water scour. In simulated-roadway tests, the stock solution achieved an average dust-suppression efficiency of 59.7%. At 170 °C, a 10% treatment reduced CO release by 40.0% and increased the mean apparent activation energy of coal oxidation by 29.73%. Rapid wetting, intermolecular interactions, and formation of a continuous porous crosslinked film supported dust consolidation and oxidation inhibition. The developed material therefore offers a potential integrated approach for controlling coal dust and spontaneous combustion risks in underground mines.

Indexed as

apparent activation energycarboxymethyl cellulosecoal spontaneous combustiondust suppressionflame-retardant dust suppressantpolycarbodiimide

Identifiers

PMID42737052
PMCPMC13567926

What OpenQuestion holds

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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.