The Water–Amorphous Calcium Carbonate Interface and Its Interactions with Amino Acids
Bibliographic record
Abstract
Abstract Amorphous calcium carbonate is often the first phase to precipitate from solution during the mineralization of calcium carbonate, before the formation of one of the crystalline polymorphs. In vivo, this phase is believed to be essential for the manufacture of minerals displaying nonequilibrium morphologies. The precipitation of this, usually transient, phase and its subsequent transformation into one of the crystalline polymorphs can be controlled by organic molecules. Here, we present a series of molecular dynamics simulations that explore the amorphous calcium carbonate?water interface, the attachment of amino acids onto both hydrous and anhydrous amorphous calcium carbonate, and their effect on the surface. The results show that surface ions have a different coordination number distribution from bulk ions and can diffuse up to two orders of magnitude faster than their bulk counterparts, suggesting that crystallization is much more likely to occur in this region. All the amino acids investigated bind to the amorphous calcium carbonate surfaces. However, acidic amino acids have a clear preference for the surface of amorphous CaCO3?H2O. The favored mode of interaction of the amino acids is through amine and/or guanidine moieties. The important ramifications of the results for our understanding of protein?mineral interactions are discussed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".