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The Influence of Chromium Oxide on the Sintering Behavior of Silicon Nitride

2012· article· en· W2165623591 on OpenAlexaff
Ali Alem, Martin Pugh

Bibliographic record

VenueInternational Journal of Applied Ceramic Technology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceChromiumSinteringSilicon nitrideWettingOxideChromium oxideChemical engineeringDecompositionPhase (matter)SiliconSilicateMetallurgyNitrideComposite materialLayer (electronics)Organic chemistry

Abstract

fetched live from OpenAlex

In this study, pressureless sintering of silicon nitride via the addition of chromium oxide was investigated. Silicon nitride samples containing additives from the Cr – Cr 2 O 3 – SiO 2 system were sintered under different conditions. The phase transformation, the degree of densification and the in situ reactions between Si 3 N 4 and chromium compounds were investigated and the reactions were validated thermodynamically. It was found that Si 3 N 4 reacts with Cr 2 O 3 and Cr and these in situ reactions lead to Si 3 N 4 decomposition and consequently formation of a series of chromium silicides including Cr 3 Si , Cr 5 Si , and CrSi 2 . Due to the presence of chromium silicides as liquid phases during sintering, the α‐ Si 3 N 4 to β‐ Si 3 N 4 phase transformation started around 1400°C and was completed around 1800°C. On the other hand, densification of Si 3 N 4 samples with chromium oxide addition was not observed. The participation of Cr 2 O 3 in these in situ reactions prevents sufficient formation of chromium silicate and leads to insufficient liquid phase with poor wettability during sintering, resulting in poor densification.

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 score0.002

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 head0.254
Teacher spread0.246 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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