Evidence map›Paper›PMID 41872267›Full record

ArticleScientific reports2026

Defining synergy for three-phase polymer nanocomposites: a volume-weighted quantitative framework.

Sherif Araby, Umut Bakhbergen, Sensen Han, Roubi Zaied

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. 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.

Sherif ArabyDepartment of Mechanical and Aerospace Engineering, Nazarbayev University, Astana, 010000, Kazakhstan. Sherif.gouda@nu.edu.kz.
Umut BakhbergenDepartment of Mechanical and Aerospace Engineering, Nazarbayev University, Astana, 010000, Kazakhstan.
Sensen HanCollege of Aerospace Engineering, Shenyang Aerospace University, Shenyang, 110136, China.
Roubi ZaiedDepartment of Mechanical Engineering, Benha Faculty of Engineering, Benha University, Banha, 13511, Egypt.

Funding

The Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan AP26100209
6 · The paper itself

Abstract

When two types of fillers are incorporated into a polymer matrix, their interaction can be classified as additive, synergistic, or antagonistic. Although synergistic effects are frequently reported in three-phase polymer nanocomposites, their quantification remains inconsistent due to the absence of a unified and physically meaningful methodology. In this work, existing synergy formulations are critically reviewed, and their limitations in capturing nanoscale effects and filler functionality are identified. Modified equations are then developed by incorporating volume-weighted contributions and explicitly accounting for the functional roles of individual fillers. The proposed framework enables quantitative distinction between cooperative, asymmetric and inhibiting interactions in three-phase systems. The applicability of new formulations is demonstrated through comprehensive analysis of reported polymer nanocomposites systems, covering mechanical, thermal, electrical and functional properties. The results show that cases previously classified as antagonistic using classical equations are consistently identified as synergistic using the modified formulations. This work provides a robust quantitative framework for evaluating synergy in polymer nanocomposites and offers a rational basis for the design of multifunctional hybrid filler systems.

Indexed as

Hybrid materialsPolymer matrix-compositesPolymer nanocompositesSynergyThree-phase composites

Identifiers

PMID41872267
PMCPMC13153417

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.