Datasets on the elastic and mechanical properties of hydroxyapatite: A first principle investigation, experiments, and pedagogical perspective
Bibliographic record
Abstract
The purpose of this data article is to report the quantum mechanical analysis by generalized gradient approximation (GGA) exchange-correlation functional using density functional theory (DFT). The predictions were based on the elastic constants and mechanical properties of stoichiometric hydroxyapatite (HAp) crystal. The elastic stiffness constants in hexagonal symmetry were obtained by fitting the Hookes' law for the energy-strain and stress-stain relations. Some of the theoretical datasets were compared to measured mechanical properties of produced HAp pellets obtained through micro and nanoindentation experiments. The datasets show considerable anisotropy in the stress-strain behaviour and are discussed in the context of the mechanical properties of HAp which are useful for tissue engineering. We also provide a pedagogical snapshot on the use of the datasets herein to teach and interpret DFT based atomistic simulations in a typical blended online teaching set-up for engineering students using a new pedagogy, CACPLA (Communicate, Active, Collaborate, Practice, Learning and Assessment).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".