Evidence map›Paper›PMID 39989802›Full record

ArticleACS omega2025

Wetting Characteristics and Microscopic Synergistic Mechanism of Composite Surfactants on Coal Samples with Different Degrees of Metamorphosis.

Limin Du, Jun Nian, Jinqi Fu, Jingchi Zhu, Hongfei Yu, Xiaoxia He, Chaowei Yang, Lei Zhang

Abstract read
In one paragraph

Article in ACS omega, 2025. 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

8 authors.

Limin DuCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.ORCID https://orcid.org/0009-0008-5446-0308
Jun NianCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Jinqi FuCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Jingchi ZhuCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Hongfei YuCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Xiaoxia HeCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Chaowei YangCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Lei ZhangCollege of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the effects of composite surfactants on the wettability of different coal types using a combination of macroscopic experiments, mesoscopic experiments, and microscopic molecular dynamics simulations, with coal samples of varying degrees of metamorphism as research subjects. First, contact angle and surface tension experiments were performed at the macroscopic level to determine the optimal concentration and ratio of the composite surfactants. The results showed that the composite solution formed by mixing SLES and AEO-9 in a 3:2 ratio significantly reduced both the surface tension of the solution and the contact angle of the coal samples at a mass concentration of 0.5 wt %. Second, the effects of the composite surfactants on the wetting properties of coal samples were analyzed at the mesoscopic level using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and ζ-potential measurements. The results revealed that the total content of hydrophobic groups (-CH

Identifiers

PMID39989802
PMCPMC11840616

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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.