A computational study of biological and optical \nmaterials
Bibliographic record
Abstract
This thesis reports computational studies about protein-ion binding in part I, and \noptical properties of organic materials in part II. \nIt is very difficult to investigate a whole protein computationally. So here I proposed \nsmaller models to probe protein-ion binding: a short triple helix (triple chain), \na short peptide chain, and individual amino acids. The binding energies, and particularly \nthe differences in binding energy between Na⁺ and K⁺ ions, do depend on the \nmodel and the constraints. I have applied these models to understand experimental \nobservations about the distinct roles of Na⁺ and K⁺ in collagen aggregation and fibrillogenesis. \nI have calculated the binding energies for the Na⁺ and K⁺ with several \nkey amino acids in collagen, selected by analysis of collagen sequence, using density \nfunctional theory (DFT). \nIn part II, I have focused on first and second hyperpolarizabilities of anthraquinoidtype \nπ-extended tetrathiafulvalene, referred to as TTFAQ, and its analogues. This \nproject has employed a wide range of functional groups to exploit the electron donor \ncapability of TTFAQ in order to explore the hyperpolarizabilities of its derivatives. I \nhave assessed size and charge distribution metrics as predictors for NLO response of \nthe TTFAQ derivatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".