Evidence map›Paper›PMID 39065361›Full record

ArticlePolymers2024

High-Value and Environmentally Friendly Recycling Method for Coal-Based Solid Waste Based on Polyurethane Composite Materials.

Xu Li, Yang Liu, Mingyi Li, Sitong Zhang, Lan Jia, Fengbo Zhu, Wenwen Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Polymers, 2024. 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

7 authors.

Xu LiCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Yang LiuCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Mingyi LiCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Sitong ZhangCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Lan JiaCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.ORCID 0000-0001-7005-5873
Fengbo ZhuCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Wenwen YuCollege of Materials Science & Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Funding

National Natural Science Foundation of China U22A20151Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering 2021SX-TD008Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering 2022SX-TD010the Key Scientific Research Project in Taiyuan City [2024]29
6 · The paper itself

Abstract

This study aims to provide a high-value and environmentally friendly method for the application of coal-based solid waste. Modified fly ash/polyurethane (MFA/PU) and modified coal gangue powder/polyurethane (MCG/PU) composites were prepared by adding different contents of MFA and MCG (10%, 20%, 30%, 40%). At the filler content of 30%, the compressive strengths of MFA/PU and MCG/PU are 84.1 MPa and 46.3 MPa, respectively, likely due to an improvement in interface compatibility, as indicated by scanning electron microscopy (SEM). The MFA/PU and MCG/PU composites present their highest limiting oxygen index (LOI) values of 29% and 23.5%, respectively, when their filler content is 30%. MFA has advantages in improving the LOIs of composites. Cone calorimetry (CCT) and SEM demonstrate that the two composites exhibit similar condensed-phase flame-retardant behaviors during combustion, which releases CO

Indexed as

coal ganguecomposite materialsfly ashpolyurethane

Identifiers

PMID39065361
PMCPMC11281150

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.