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Record W4385665674 · doi:10.53063/synsint.2023.33129

Microwave sintering of ZrB2-based ceramics: A review

2023· review· en· W4385665674 on OpenAlexvenueno aff
Samira Savani, Mohammad Alipour, Ankur Sharma, D. Benny Karunakar

Bibliographic record

VenueSynthesis and Sintering · 2023
Typereview
Languageen
FieldChemistry
TopicMicrowave-Assisted Synthesis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsZirconium diborideMaterials scienceSinteringCeramicZirconiumMicrowaveComposite materialTitanium diborideCeramic matrix compositeMetallurgy

Abstract

fetched live from OpenAlex

Recently, microwave sintering has absorbed remarkable attention on the basis of enhanced microstructural/mechanical characteristics in comparison with conventional sintering techniques based on powder technology. This method not only can be employed for the processing of metals, alloys, and metal matrix composites but also for the manufacturing of advanced ceramics and ceramic matrix composites. Zirconium diboride (ZrB2) as an interesting member of ultrahigh temperature ceramics is one of the most undertaking candidates in modern structural ceramics applications. This paper reviews the processing-densification-mechanical properties correlations in microwave-sintered ZrB2-based ceramics and composites. The text concentrates on the microwave-assisted production of ZrB2 divided into two categories: synthesis of ZrB2 powders by microwave sintering and sintering of ZrB2-based ceramics and composites by microwave sintering. The effects of some additives and reinforcements, such as B4C, SiC, TiC, and MgO, on zirconium diboride's densification and mechanical properties are summarized.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.083
GPT teacher head0.331
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2023
Admission routes1
Has abstractyes

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