Stabilization of Amorphous Calcium Carbonate with Nanofibrillar Biopolymers
Bibliographic record
Abstract
Abstract Calcium carbonate is the most abundant biomineral that is biogenically formed with a vast array of nano and microscale features. Among the less stable polymorphs present in mineralized organisms, the most soluble, amorphous calcium carbonate (ACC), formed in chitin exoskeletons of some crustacea, is of particular interest since aqueous stability of isolated ACC is limited to a few hours in the absence of polyanions or magnesium. Here the influence of a selection of biopolymer gels on the mineralization of calcium carbonate is investigated. Mineralization is achieved in all biopolymers tested, but is particularly abundant in collagen hydrogels, in which a significant proportion of the calcium carbonate (≈18%) is found to be amorphous. In dense collagen gels, this amorphous fraction does not crystallize for up to six weeks in deionized water at room temperature. The reason why collagen in particular should stabilize this phase remains obscure, although the results suggest that the fiber diameter, fiber spacing, and the amphoteric nature of collagen fibers are important. Upon immersion in phosphate containing solutions, the calcium carbonate present within the collagen hydrogels is readily converted to carbonated hydroxyapatite, enabling the formation of a stiff bone‐like composite containing 78 wt% mineral, essentially equivalent to cortical bone.
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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".