Evidence map›Paper›PMID 41480233›Full record

ArticleSmart molecules : open access2025

Multifunctional polyimide performance prediction based on explainable machine learning.

Suisui Wang, Tianyong Zhang, Han Zhang, Wenxuan Zhu, Zixu Gu, Xufeng Huang, Hande Zhang, Bin Li, Jianhua Zhang

Abstract read
In one paragraph

Article in Smart molecules : open access, 2025. 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

9 authors.

Suisui WangSchool of Chemical Engineering and Technology Tianjin University Tianjin China.
Tianyong ZhangSchool of Chemical Engineering and Technology Tianjin University Tianjin China.
Han ZhangSchool of Microelectronics Shanghai University Shanghai China.
Wenxuan ZhuSchool of Chemical Engineering and Technology Tianjin University Tianjin China.
Zixu GuSchool of Chemical Engineering and Technology Tianjin University Tianjin China.
Xufeng HuangSchool of Chemical Engineering and Technology Tianjin University Tianjin China.
Hande ZhangSchool of Chemical Engineering and Technology Tianjin University Tianjin China.
Bin LiSchool of Chemical Engineering and Technology Tianjin University Tianjin China.ORCID https://orcid.org/0000-0002-8509-9833
Jianhua ZhangSchool of Microelectronics Shanghai University Shanghai China.ORCID https://orcid.org/0000-0001-8061-1861

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polyimides (PIs) are widely used in the microelectronics field due to their excellent comprehensive performance and the diversity and designability of their structures. In flexible substrate applications, designing the molecular structure to balance thermodynamic and optical properties is the most critical part of the PI design process. To accelerate the discovery of high-performance PIs, we established predictive models for glass transition temperature (T

Indexed as

cut‐off wavelengthmachine learningperformance predictionpolyimidesthermodynamics

Identifiers

PMID41480233
PMCPMC12755225

What OpenQuestion holds

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LicenceCC BY
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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.