Electron transfer in peptides
Bibliographic record
Abstract
In this review, we discuss the factors that influence electron transfer in peptides. We summarize experimental results from solution and surface studies and highlight the ongoing debate on the mechanistic aspects of this fundamental reaction. Here, we provide a balanced approach that remains unbiased and does not favor one mechanistic view over another. Support for a putative hopping mechanism in which an electron transfers in a stepwise manner is contrasted with experimental results that support electron tunneling or even some form of ballistic transfer or a pathway transfer for an electron between donor and acceptor sites. In some cases, experimental evidence suggests that a change in the electron transfer mechanism occurs as a result of donor-acceptor separation. However, this common understanding of the switch between tunneling and hopping as a function of chain length is not sufficient for explaining electron transfer in peptides. Apart from chain length, several other factors such as the extent of the secondary structure, backbone conformation, dipole orientation, the presence of special amino acids, hydrogen bonding, and the dynamic properties of a peptide also influence the rate and mode of electron transfer in peptides. Electron transfer plays a key role in physical, chemical and biological systems, so its control is a fundamental task in bioelectrochemical systems, the design of peptide based sensors and molecular junctions. Therefore, this topic is at the heart of a number of biological and technological processes and thus remains of vital interest.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".