Effects of Temperature and Water Vapor Content on Microstructure, Mechanical Properties and Corrosion Behavior of C/C-SiC Composites.

Yanbin Wei, Zhiyong Ye, Yalei Wang, Xiang Xiong, Zaidong Liu, Jinming Wang, Tongqi Li

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Non-Destructive Quantitative Characterization of Constituent Content in C/C-SiC Composites Based on Multispectral Photon-Counting X-Ray Detection.Sensors (Basel, Switzerland) · 2026
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

7 authors.

Yanbin WeiNational Key Laboratory of Science and Technology on High-Strength Structural Materials, Central South University, Changsha 410083, China.

Zhiyong YeNational Key Laboratory of Science and Technology on High-Strength Structural Materials, Central South University, Changsha 410083, China.ORCID 0000-0002-9073-2576

Yalei WangNational Key Laboratory of Science and Technology on High-Strength Structural Materials, Central South University, Changsha 410083, China.

Xiang XiongNational Key Laboratory of Science and Technology on High-Strength Structural Materials, Central South University, Changsha 410083, China.

Zaidong LiuNational Key Laboratory of Science and Technology on High-Strength Structural Materials, Central South University, Changsha 410083, China.

Jinming WangAerospace Research Institute of Materials and Processing Technology, Beijing 100076, China.

Tongqi LiAerospace Research Institute of Materials and Processing Technology, Beijing 100076, China.

Funding

the Changsha Science and Technology Project kh2102018the National Key Research and Development Program of China 2022YFB3706103

6 · The paper itself

Abstract

Carbon-fiber-reinforced carbon and silicon carbide (C/C-SiC) composites were prepared using chemical vapor infiltration (CVI) combined with reactive melt infiltration (RMI). The microstructure and flexural properties of C/C-SiC composites after oxidation in different temperature water vapor environments were studied. The results indicate that the difficulty of oxidation in water vapor can be ranked from easy to difficult in the following order: carbon fiber (CF), pyrolytic carbon (PyC), and ceramic phase. The surface CFs become cone-shaped under corrosion. PyC has a slower oxidation rate and lower degree of oxidation compared to CF. The SiO

Indexed as

C/C-SiC compositesflexural propertiesmicrostructurewater vapor corrosion

Identifiers

PMID39769857

DOI10.3390/ma17246259

PMCPMC11678444

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.