MétaCan
Menu
Back to cohort
Record W4405436939 · doi:10.26599/jac.2024.9221019

<i>In situ</i> formation of high-entropy carbide phase in porous SiBCN ceramic for enhanced high-temperature stability

2024· article· en· W4405436939 on OpenAlexaff
Zifeng Hu, Huijie Wang, Dong Su

Bibliographic record

VenueJournal of Advanced Ceramics · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceStructural materialCeramicCarbideIn situPorosityPhase (matter)Composite materialMetallurgyChemistry

Abstract

fetched live from OpenAlex

Porous SiBCN ceramics exhibit great potential in high-tech structural and functional applications. However, nucleation-crystallization and carbothermal decomposition limit their use in high-temperature environments. Herein, high-entropy carbide (HEC) (Ti0.25Zr0.25Hf0.25Ta0.25)C-modified porous SiBCN ceramics (HEC/SiBCN) were successfully fabricated from a multi-metal (Ti,Zr,Hf,Ta) precursor containing polyborosilazane via solvothermal methods, freeze-drying, and pyrolysis. The porous HEC/SiBCN ceramic possesses tailorable porosity (63.5%–79.1%), low thermal conductivity (0.054–0.089 W/(m·K)), and good mechanical strength. The HEC phase is in situ formed by carbothermal reduction and solid solution reaction of the multicomponent precursor with highly active free carbon in the SiBCN matrix during the pyrolysis, which endows the porous HEC/SiBCN ceramics with outstanding thermal stability up to 1800 °C. The in situ formation of the HEC phase provides novel insight and a promising strategy for enhancing the overall performance of porous SiBCN ceramics, expanding their application in high-temperature environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.272
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Advanced CeramicsSame topicAdvanced ceramic materials synthesisFrench-language works237,207