Evidence map›Paper›PMID 41339416›Full record

ArticleScientific reports2025

Preparation of CNT/PEEK porous composites via NaCl leaching and FDM for bearing cage applications.

Baorun Yang, Zhuangya Zhang, Xiaoqiang Wang, Ruijie Gu, Ming de Duan

Abstract read
In one paragraph

Article in Scientific reports, 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.

Baorun YangSchool of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, GA, China.
Zhuangya ZhangSchool of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, GA, China.
Xiaoqiang WangSchool of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, GA, China.
Ruijie GuSchool of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, GA, China. IAPTE@haust.edu.cn.
Ming de DuanSchool of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, GA, China.

Funding

Natural Science Foundation of Henan Province 252300421327
6 · The paper itself

Abstract

Porous composites can store and release lubricants via their unique pore structures, enabling continuous lubrication for self-lubricating bearing cages. In this study, carbon nanotube (CNT)/polyether ether ketone (PEEK) porous self-lubricating bearing cage materials were fabricated using the fused deposition molding (FDM) technique. Their preparation process, microstructure, and properties were systematically investigated. The results demonstrate the initial feasibility of preparing CNT-reinforced PEEK porous composites via FDM with optimized parameters. The incorporation of CNTs effectively modulated the material's structure, increasing the average pore size from 0.08 μm to 11.62 μm and optimizing the porosity within a specific content range. However, higher CNT content resulted in poor extrusion flowability and nozzle clogging. Nevertheless, CNTs significantly enhanced the material's tribological performance while maintaining good mechanical properties. This study provides a theoretical basis and process reference for preparing self-lubricating bearing cages, while also identifying that process optimization and CNT dispersion require further in-depth investigation.

Indexed as

Bearing cageCNT/PEEKFDMMechanical propertyPorous compositesTribological

Identifiers

PMID41339416
PMCPMC12675489

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.