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Record W2483520781 · doi:10.1063/1.4959094

Negative stiffness in ZrW2O8 inclusions as a result of thermal stress

2016· article· en· W2483520781 on OpenAlexafffund
Carl P. Romao, Mary Anne White

Bibliographic record

VenueApplied Physics Letters · 2016
Typearticle
Languageen
FieldMaterials Science
TopicThermal Expansion and Ionic Conductivity
Canadian institutionsDalhousie University
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of Canada
KeywordsNegative thermal expansionThermal expansionThermoelastic dampingMaterials scienceStiffnessHyperelastic materialComposite materialStress (linguistics)Stiffness matrixThermalOrthorhombic crystal systemBulk modulusThermodynamicsFinite element methodCrystal structureChemistryCrystallographyPhysics

Abstract

fetched live from OpenAlex

Materials with negative stiffness, although inherently unstable in isolation, can be stabilized by external constraints, for example, by inclusion within a material with positive stiffness. We have identified ZrW2O8, a material with negative thermal expansion, as a candidate negative-stiffness material arising from its negative bulk modulus during a ferroelastic cubic–orthorhombic pressure-induced phase transition (PIPT). A hyperelastic constituent equation for this transition was developed and implemented in a finite-element model of ZrW2O8 inclusions in positive stiffness, positive thermal expansion matrices. In these matrices, thermal stress during cooling, originating from thermal expansion mismatch, would be sufficient to initiate the PIPT after small temperature drops. The subsequent progress of the PIPT depends strongly on the thermoelastic properties of the matrix, with stiff, low thermal expansion matrices stabilizing the transition state over broad temperature ranges, indicating that ZrW2O8 or materials with similar properties could be used as versatile negative-stiffness inclusion materials. The models were used to understand previous experiments on composites that include ZrW2O8.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.386

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)0.0000.000

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 head0.015
GPT teacher head0.245
Teacher spread0.231 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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