Evidence map›Paper›PMID 42073709›Full record

ArticleMaterials (Basel, Switzerland)2026

Effect of Different Carbon-Supported Catalysts on the Thermal Decomposition of Energetic Thermoplastic Elastomers.

Zhu Wang, Wenhao Liu, Haoyu Yu, Tianqi Li, Yunjun Luo, Yonghao Xiao

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 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

6 authors.

Zhu WangSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Wenhao LiuSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Haoyu YuAVIC Manufacturing Technology Institute, Beijing 100024, China.ORCID 0000-0001-8589-7299
Tianqi LiSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Yunjun LuoSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0001-6927-2635
Yonghao XiaoCollege of Light Industry Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China.ORCID 0000-0002-7402-9012

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To enhance the thermal decomposition properties of glycidyl azide polymer energetic thermoplastic elastomer (GAP-ETPE), the effects of nano-CuO supported on different carbon carriers (GO and CNT) were systematically investigated in this study. The structural characteristics and catalytic performances were comprehensively analyzed using XRD, Raman, XPS, UPS, BET, SEM, and TEM, coupled with thermal analysis techniques including TG-DSC and TG-MS. The results indicate that the catalytic performance follows the descending order of CuO/CNT > CuO/GO > CuO. Notably, CuO/CNT exhibits the optimal catalytic activity, advancing the exothermic peak temperature of the azide groups by approximately 33 °C and resulting in a more concentrated heat release process. The superior synergistic catalytic effect of CuO/CNT is attributed to the following: the three-dimensional network constructed by CNT effectively overcomes the agglomeration of CuO nanoparticles and the restacking defects typical of GO nanosheets, thereby significantly reducing the gas-solid mass transfer resistance. Simultaneously, the highly graphitized sp

Indexed as

carbon-based composite catalystGAP-ETPEthermal decomposition

Identifiers

PMID42073709
PMCPMC13118000

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.