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Record W2914724732 · doi:10.3390/ceramics2010011

Near-Zero Thermal Expansion in Freeze-Cast Composite Materials

2019· article· en· W2914724732 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCeramics · 2019
Typearticle
Languageen
FieldMaterials Science
TopicThermal Expansion and Ionic Conductivity
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal expansionMaterials scienceComposite materialComposite numberCeramicThermalPerpendicularCastingTape castingPolymerThermodynamics

Abstract

fetched live from OpenAlex

Most materials expand when heated, which can lead to thermal stress and even failure. Whereas thermomiotic materials exhibit negative thermal expansion, the creation of materials with near-zero thermal expansion presents an ongoing challenge due to the need to optimize thermal and mechanical properties simultaneously. The present work describes the preparation and properties of polymer–ceramic composites with low thermal expansion. Ceramic scaffolds, prepared by freeze-casting of low-thermal-expansion Al2W3O12, were impregnated with poly(methylmethacrylate) (PMMA). The resulting composites can have a coefficient of thermal expansion as low as 2 × 10−6 K−1, and hardness values of 4.0 ± 0.3 HV/5 (39 ± 3 MPa) and 16 ± 3 HV/5 (160 ± 30 MPa) parallel and perpendicular to the ice growth, respectively. The higher hardness perpendicular to the ice growth direction indicates that the PMMA is acting to improve the mechanical properties of the composite.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.063
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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)0.0050.002

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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