Evidence map›Paper›PMID 40077392›Full record

ArticleMaterials (Basel, Switzerland)2025

A Study on the Environmental Performance of an Asphalt Mixture Modified with Directly Added Waste Plastic.

Liting Yu, Haoyi Kang, Rui Li, Jianzhong Pei, Yizhi Du

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 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

5 authors.

Liting YuSchool of Traffic and Transportation Engineering, Xinjiang University, Urumqi 830017, China.
Haoyi KangSchool of Highway, Chang'an University, Middle Section of South Erhuan Road, Xi'an 710064, China.
Rui LiSchool of Highway, Chang'an University, Middle Section of South Erhuan Road, Xi'an 710064, China.ORCID 0000-0002-9736-6468
Jianzhong PeiSchool of Highway, Chang'an University, Middle Section of South Erhuan Road, Xi'an 710064, China.
Yizhi DuSchool of Highway, Chang'an University, Middle Section of South Erhuan Road, Xi'an 710064, China.

Funding

the National Natural Science Foundation of China Grant No. 52178408the "Tianchi Talent"Introduction Plan Leading Innovative Talents Project of Xinjiang none
6 · The paper itself

Abstract

The environmental pollution caused by waste plastics has raised widespread concern within the global academic community. The use of waste plastic in road construction is seen as a future trend for road materials, offering benefits such as energy conservation, pollution reduction, and the enhanced high-temperature performance of asphalt mixtures. However, conventional testing methods have limited the scope of performance measurements for modified asphalt mixtures, and fewer studies have explored the pavement performance of such mixtures. This study evaluated the environmental performance of asphalt mixtures modified with waste plastics. A series of experiments, including rutting tests, low-temperature bending tests, water stability tests, and aging tests, demonstrated that the use of waste plastic-modified asphalt significantly improved high-temperature performance. Notably, with transition dispersants, the rutting resistance improved by 24.5%, and the low-temperature bending strength increased by 15.8%, demonstrating excellent anti-aging properties. Statistical analysis indicated that waste plastic-modified asphalt has superior high-temperature stability and good low-temperature crack resistance.

Indexed as

environmentLDPE (low-density polyethylene)modified asphaltpavementwaste plastic

Identifiers

PMID40077392
PMCPMC11902065

What OpenQuestion holds

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Registered trials

None linked

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.