Negative stiffness in ZrW2O8 inclusions as a result of thermal stress
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".