Selective Crystal Growth Regulation by Chiral α-Hydroxycarboxylic Acids Improves the Strength and Toughness of Calcium Sulfate Cements
Bibliographic record
Abstract
Natural biominerals, such as bones and teeth, use acidic matrix biomolecules to control growth, morphology, and organization of the brittle hydroxyapatite crystals. This interplay provides biominerals with outstanding mechanical properties. Recently, we reported that the l-enantiomer of chiral tartaric acid has a potent regulatory effect on the crystal structure and mechanical performance of brushite cement, a mineral with a monoclinic crystal system. We hypothesized that this strategy could be applied using various chiral α-hydroxycarboxylic acids to enhance the mechanical performance of calcium sulfate dihydrate cements, another mineral belonging to the monoclinic crystal system. Calcium sulfate cements are widely used in dentistry, medicine, and construction, but these cements have low mechanical properties. In this work, we first determined the impact of different chiral α-hydroxycarboxylic acids on the properties of calcium sulfate cements. After that, we focused on identifying the regulation effect of chiral tartaric acid on gypsum crystals precipitated in a supersaturated solution. Here, we show that the selective effect of α-hydroxycarboxylic acid l-enantiomers on calcium sulfate crystals improved the mechanical performance of gypsum cements, while d-enantiomer had a weak impact. Compare to the calcium sulfate cements prepared without additives, the presence of l-enantiomer enhanced the compressive strength and the fracture toughness of gypsum cements by 40 and 70%, respectively. Thus, these results prove the generalizability of this approach and help us to fabricate high-strength cements.
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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".