Rheological enhancement of mechanically activated α‐tricalcium phosphate cements
Bibliographic record
Abstract
Most biocements are two- or three-component acid-based systems with large differences in the component particle sizes, which occurs by virtue of the differing processing routes. This work aimed to improve injectability and strength of a single reactive component cement, that is, mechanically activated alpha-tricalcium phosphate (TCP)-based cement by adding 13-33 wt % of several fine-particle-sized (d(50) of 0.5-1.1 microm) fillers [dicalcium phosphate anhydrous (DCPA), titanium dioxide (TiO(2)), and calcium carbonate] to the monomodal alpha-TCP matrix (d(50) = 9.8 microm). A high zeta-potential was measured for all particles in trisodium citrate solution. The fraction of alpha-TCP cement "injected" through an 800-microm hypodermic needle was found to be only 35% at a powder-to-liquid ratio of 3.5 g/mL. In contrast, the use of fillers decreased cement viscosity to a point, where complete injectability could be obtained. Mechanistically, these additives disrupted alpha-TCP particle packing yet decreased the interparticle spacing by a factor of approximately 5.5 such that the electrostatic repulsion effect was enhanced. A strength improvement was found when DCPA and TiO(2) were used as fillers despite the lower degree of conversion of these cements. Compressive strengths of precompacted cement samples increased from 70 MPa for unfilled alpha-TCP cement to 140 (110) MPa for 23 wt % DCPA (or TiO(2)) fillers as a result of porosity reduction. Strength improvement for more clinically relevant uncompacted cements was achieved by higher powder-to-liquid ratio mixes for filled cements such that maximum strengths of 90 MPa were obtained for 23 wt % DCPA filler compared with 50 MPa for single-component alpha-TCP cement.
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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".