Bio‐inspired dicalcium phosphate anhydrate/poly(lactic acid) nanocomposite fibrous scaffolds for hard tissue regeneration: <i>In situ</i> synthesis and electrospinning
Bibliographic record
Abstract
The fundamental building blocks of hierarchically structured bone tissue are mineralized collagen fibrils with calcium phosphate nanocrystals that are biologically "engineered" through biomineralization. In this study, we demonstrate an original invention of dicalcium phosphate anhydrate (DCPA)/poly(lactic acid) (PLA) composite nanofibers, which mimics the mineralized collagen fibrils via biomimetic in situ synthesis and electrospinning for hard tissue regenerative medicines. The interaction of the Ca(2+) ions and the carbonyl groups in the PLA provides nucleation sites for DCPA during the in situ synthesis process. This resulted in the improved dispersion of DCPA nanocrystallites in the intrananoporous PLA nanofibers through electrospinning, compared to the severely agglomerated clusters of DCPA nanoparticles fabricated by conventional mechanical blending/electrospinning methods. The addition of poly(ethylene glycol), as a copolymer source, generated more stable and efficient electrospun jets and aided in the electrospinability of the PLA nanofibers incorporating the nanocrystallites. It is expected that the uniformly distributed DCPA nanocrystallites and its unique nanocomposite fibrous topography will enhance the biological performance and the structural stability of the scaffolds used for hard tissue reconstruction and regeneration.
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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 | 0.000 |
| 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) | 0.000 | 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".