Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it