Resorbable Dicalcium Phosphate Bone Substitutes Prepared by 3D Powder Printing
Bibliographic record
Abstract
Abstract Bioceramic bone substitutes with programmed architecture were manufactured at room temperature in this study using a novel 3D printing process that combined 3D powder printing with calcium phosphate cement chemistry. During printing, biphasic α/β‐tricalcium phosphate (Ca 3 (PO 4 ) 2 , TCP) powder reacted with a liquid component consisting of phosphoric acid solution to form a matrix of dicalcium phosphate dihydrate (CaHPO 4 ·H 2 O, DCPD, brushite) and unreacted TCP. Printed samples showed compressive strengths between 0.9–8.7 MPa after printing depending on the acid concentration. A further strength improvement to a maximum of 22 MPa could be obtained by additional hardening of the samples in phosphoric acid for three one minute washes. After this treatment, the samples mainly consisted of brushite with minor phases of unreacted TCP and a lesser amount of dicalcium phosphate anhydrate (CaHPO 4 , DCPA, monetite). Hydrothermal conversion of brushite to DCPA resulted in an increase of porosity of approximately 13 % and a decrease of strength to 15 MPa, however the resorption rate in vivo was increased as demonstrated after intramuscular implantation over 56 weeks. Major advantages compared with commonly used sintering techniques are the low processing temperature, which enables the fabrication of thermally instable and degradable matrices of secondary calcium phosphates.
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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) | 0.001 | 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".