Solid freeform fabrication and characterization of porous calcium polyphosphate structures for tissue engineering purposes
Bibliographic record
Abstract
Solid freeform fabrication (SFF) enables the fabrication of anatomically shaped porous components required for formation of tissue engineered implants. This article reports on the characterization of a three-dimensional-printing method, as a powder-based SFF technique, to create reproducible porous structures composed of calcium polyphosphate (CPP). CPP powder of 75-150 microm was mixed with 10 wt % polyvinyl alcohol (PVA) polymeric binder, and used in the SFF machine with appropriate settings for powder mesh size. The PVA binder was eliminated during the annealing procedure used to sinter the CPP particles. The porous SFF fabricated components were characterized using scanning electron microscopy, micro-CT scanning, X-ray diffraction, and mercury intrusion porosimetry. In addition, mechanical testing was conducted to determine the compressive strength of the CPP cylinders. The 35 vol % porous structures displayed compressive strength on average of 33.86 MPa, a value 57% higher than CPP of equivalent volume percent porosity made through conventional gravity sintering. Dimensional deviation and shrinkage analysis was conducted to identify anisotropic factors required for dimensional compensation during SFF sample formation and subsequent sintering. Cell culture studies showed that the substrate supported cartilage formation in vitro, which was integrated with the top surface of the porous CPP similar to that observed when chondrocytes were grown on CPP formed by conventional gravity sintering methods as determined histologically and biochemically.
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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 | 0.000 |
| 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) | 0.001 | 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".