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Record W4226156356 · doi:10.1111/jace.18480

Tetragonal reconstruction and monoclinic variants rearrangement during heat treatment in zirconia

2022· article· en· W4226156356 on OpenAlexaff
Yongzhe Wang, Nicolas Brodusch, Raynald Gauvin, Chucheng Lin, Yi Zeng

Bibliographic record

VenueJournal of the American Ceramic Society · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsMcGill University
Fundersnot available
KeywordsMonoclinic crystal systemTetragonal crystal systemCrystallographyMaterials scienceDiffusionless transformationMartensiteMicrostructureChemistryCrystal structure

Abstract

fetched live from OpenAlex

Abstract An in situ monoclinic variants selection and rearrangement study on Y 2 O 3 –ZrO 2 coatings was conducted using electron backscattered diffraction. The sequence growth of variants with the correspondence of (100) m ∼// (010) t , [010] m ∼// [001] t , and [001] m ∼// [100] t (CAB‐OR2) was found initially and converted to the 90°‐rotated sequence growth with (100) m ∼// (100) t , [001] m ∼// [001] t , and [010] m ∼// [010] t (ABC‐OR2) after heat treatment. In another coating, most of the variants first exhibited interleaving growth and changed to typically fourfold growth. This in situ evolution of both variants orientation relationship and arrangement revealed the effects of stress and stress relief on the martensitic transformation. Moreover, the variants selection might be diversified during the cyclic transformation, and the classical phenomenological theory could mainly be proved by the transformation triggered by temperature, instead of external stress. Furthermore, the variants orientation relationship was exactly confirmed by the tetragonal grain reconstructions. In particular, the orientation variation of reconstructed tetragonal grains was also found and could mainly be explained by the behavior of stress relief and dynamic recrystallization.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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 teacher head, 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

Citations3
Published2022
Admission routes1
Has abstractyes

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