Modeling Material-Degradation-Induced Elastic Property of Tissue Engineering Scaffolds
Bibliographic record
Abstract
The mechanical properties of tissue engineering scaffolds play a critical role in the success of repairing damaged tissues/organs. Determining the mechanical properties has proven to be a challenging task as these properties are not constant but depend upon time as the scaffold degrades. In this study, the modeling of the time-dependent mechanical properties of a scaffold is performed based on the concept of finite element model updating. This modeling approach contains three steps: (1) development of a finite element model for the effective mechanical properties of the scaffold, (2) parametrizing the finite element model by selecting parameters associated with the scaffold microstructure and/or material properties, which vary with scaffold degradation, and (3) identifying selected parameters as functions of time based on measurements from the tests on the scaffold mechanical properties as they degrade. To validate the developed model, scaffolds were made from the biocompatible polymer polycaprolactone (PCL) mixed with hydroxylapatite (HA) nanoparticles and their mechanical properties were examined in terms of the Young modulus. Based on the bulk degradation exhibited by the PCL/HA scaffold, the molecular weight was selected for model updating. With the identified molecular weight, the finite element model developed was effective for predicting the time-dependent mechanical properties of PCL/HA scaffolds during degradation.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".