Bibliographic record
Abstract
Two major effects of pressure in nanomaterials are discussed in this chapter. First, the pressure-induced phase transitions in nanomaterials often differ significantly from those of their bulk counterparts. This opens the opportunity of including surface energy contributions in thermodynamic models. The importance or even the dominance of surface atoms and the underlying surface state (point defects, ligands, etc.) greatly influence the thermodynamics and kinetics of phase transformations. This can lead to a competition between pressure-induced polymorphic transitions and amorphization depending on the defect density in the nanomaterials. Second, the mechanical properties of 2D materials such as graphene are difficult to apprehend because of their dimensionality. The variation over a wide range of pressure allows a better understanding of the stress transfer through the substrate and its role in the production of biaxial strain conditions. The mechanism of stress transmission to an atomically-thin material will be discussed, and a practical example that aims to determine the coupling between components in graphene-based nanocomposites will be presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.015 | 0.010 |
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; both teacher heads agree on what is shown here.
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".