Electron transfer mechanism and potential applications of α-helical peptides
Bibliographic record
Abstract
Understanding long range electron transfer (ET) in proteins is of fundamental interest to elucidate the complex nature of many biological processes.The mechanistic discussion is highly debated in the literature and the factors that control this process are still not clear.Because of the structural complexity and dynamic nature, it is very difficult to correctly evaluate long range ET in proteins.The study of simple model peptides having specific secondary structures is useful for a systematic and accurate evaluation.The polypeptide matrix in the photosynthetic reaction centre is rich in helices and this particular structural motif is believed to play an important role in ET in nature.In this thesis, ET study through some synthetic α-helical model peptides is described.The model peptides studied herein contain the redox-active ferrocene at one end and the thiol-functionalised Cys residue at the other.Films of these peptides were formed on the surface of gold electrodes via the Au-S bond, and by employing cyclic voltammetry, the
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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".