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Record W2025224346 · doi:10.1002/chem.200500143

Peptide Electron Transfer: More Questions than Answers

2005· article· en· W2025224346 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemistry - A European Journal · 2005
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Saskatchewan
FundersCanada Research Chairs
KeywordsElectron transferComputer scienceChemistryPhotochemistry

Abstract

fetched live from OpenAlex

Nature has specifically designed proteins, as opposed to DNA, for electron transfer. There is no doubt about the electron transfer within proteins compared with the uncertain and continuing debate about charge transfer through DNA. However, the exact mechanism of electron transfer within peptide systems has been a source of controversy. Two different mechanisms for electron transfer between a donor and an acceptor, electron hopping and bridge-assisted superexchange, have been proposed, and are supported by experimental evidence and theoretical calculations. Several factors were found to affect the kinetics of this process, including peptide chain length, secondary structure and hydrogen bonding. Electrochemical measurements of surface-supported peptides have contributed significantly to the debate. Here we summarize the current approaches to the study of electron transfer in peptides with a focus on surface measurements and comment on these results in light of the current and often controversial debate on electron transfer mechanisms in peptides.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.188
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it