A cross‐linking model for estimating Young's modulus of artificial bone tissue grown on carbon nanotube scaffold
Bibliographic record
Abstract
Carbon nanotubes (CNTs) provide a suitable environment for growth and proliferation of bone cells. The elastic properties exhibited by CNTs can enhance mechanical characteristics of bone mineral phase, hydroxyapatite (HAp), precipitated on such a scaffold. In this article, a simplified model for estimating the axial Young's modulus of a representative volume element (RVE) of CNT-HAp composite is presented. The model is based on the idea of HAp formation on functionalized sites on CNTs as cross-links between HAp matrix and CNT. Modeling results show that the reinforcement role contributed by CNT in the RVE causes a significant increase in the Young's modulus of the composite material which is a direct consequence of transferring stresses from the HAp matrix to the CNT through the cross-links. Similar conclusions may be suggested regarding the improvement of overall mechanical properties of the material. The prediction made by the model lies reasonably well within the limits proposed by conventional Rule-of-Mixtures, and sliding below Voigt's model. The Young's modulus predicted by the model lies adjacent to the Hashin-Shtrikman upper bound as a function of the RVE length (or equivalently CNT aspect ratio). The model simulation indicates that an increase in the CNT aspect ratio and/or number of cross-links in the RVE, results in the prediction to move closer to the estimation made by Voigt as the assumption of perfect bonding between composite phases is approached.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".