A review on fabrication of 3D printed biomaterials using optical methodologies for tissue engineering applications
Bibliographic record
Abstract
Human body comprises of different internal and external biological components. Human organs tend to fail due to continuous or sudden stress which leads to deterioration, failure, and dislocation. The choice of selection and fabrication of materials for tissue engineering play a key role in terms of suitability, sensitivity, and functioning with other organs as a replacement for failed organs. The progressive improvement of the additive manufacturing (AM) approach in healthcare made it possible to print multi-material and customized complex/intricate geometries in a layer-by-layer fashion. The customized or patient-specific implant fabrication can be easily produced with a high success rate due to the development of AM technologies with tailorable properties. The structural behavior of 3D printed biomaterials is a crucial factor in tissue engineering as they affect the functionality of the implants. Various techniques have been developed in appraising the important features and the effects of the subsequent design of the biomaterial implants. The behavior of the AM built biomaterial implants can be understood visually by an imaging system with a high spatial and spectral resolution. This review intends to present an overview of various biomaterials used in implants, followed by a detailed description of optical 3D printing procedures and evaluation of the performance of 3D printed biomaterials using optical characterization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".