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Record W2810852943 · doi:10.1073/pnas.1722401115

Kinetics teach that electronic coupling lowers the free-energy change that accompanies electron transfer

2018· article· en· W2810852943 on OpenAlexafffund
Renato N. Sampaio, Eric J. Piechota, Ludovic Troian‐Gautier, Andrew B. Maurer, Ke Hu, Phil A. Schauer, Amber D. Blair, Curtis P. Berlinguette, Gerald J. Meyer

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsUniversity of British Columbia
FundersBasic Energy SciencesCanada Research ChairsCanadian Institute for Advanced ResearchBelgian American Educational Foundation
KeywordsChemistryElectron transferAdiabatic processEquilibrium constantMarcus theoryKinetic energyAcceptorElectronAtomic physicsThermodynamicsComputational chemistryPhysical chemistryReaction rate constantKineticsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Significance Nature’s use of electronic coupling (H DA ) and free-energy (ΔG o ) gradients to vectorially control electron transport provides inspiration for artificial photosynthesis. Theoretical predictions indicate that H DA and ΔG o are not independent parameters, and are instead linked. Reported here is a broadly applicable kinetic approach that was utilized to demonstrate such behavior for four acceptor–bridge–donor compounds. When the electronic coupling was large and electron transfer was adiabatic, the free energy of the reaction |ΔG o | was less than that for nonadiabatic transfer. This finding should be taken into account in the design of hybrid materials for solar energy conversion and has broad implications to the many classes of electron-transfer reactions in biology and chemistry.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.048
GPT teacher head0.283
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicPhotochemistry and Electron Transfer StudiesFrench-language works237,207