Modeling chirality in vaterite crystals
Bibliographic record
Abstract
Chirality, the inability of a structure to be superimposed onto its mirror image, is exhibited in almost all biological systems.Vaterite (calcium carbonate) crystals are found in various organisms in which biomineralization occurs, ranging from skeletons of marine organisms to the inner ears of humans.The crystals are made up of nano pseudo-hexagonal structures and the presence of specific chiral amino acids induces chirality, which is either right-handed or left-handed, due to a nano-particle tilting growth mechanism.In the absence of amino acids, however, the particles form a packed crystalline structure.This thesis looks at simulating and modeling the tilting mechanisms that give rise to chirality, to better understand this phenomenon as it occurs in nature.Two mechanisms for chirality proposed in the crystal growth literature have been explored in particular, which are inter-particle rotations between an existing particle and a new daughter particle and inter-platelet tilts.We have developed algorithms to simulate these rotations that play a key role in the crystal growth evolution over time, from the initial substrate surface to their final culmination to a hierarchical structure of layers upon layers of nano-particles.We have studied the attachment of daughter particles to the vertices of a mother or existing daughter particle and determined that selective or restricted vertex particle attachment is critical for regular patterns that are consistent with the actual experiments in crystal growth.We have raised fundamental questions regarding locations and frequencies of nucleation of mother particles which determine the result of the simulation, since these issues have not been explored in detail in the existing literature.We have provided feasible solutions such as ion diffusion modeling, that could potentially be used to computationally drive the nucleation process.i discussions on the thesis which helped me learn more about the subject in particular and also helped me be more organized in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".