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Record W3134630809 · doi:10.1063/5.0039263

Physics applied to electrochemistry: Tunneling reactions

2021· article· en· W3134630809 on OpenAlexafffund
Kirk H. Bevan, Yee Wei Foong, Javad Shirani, Shuaishuai Yuan, Sinan Abi Farraj

Bibliographic record

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsQuantum tunnellingSolid-stateQuantization (signal processing)ElectrochemistryElectron transferElectronChemical physicsRepresentation (politics)PhysicsMaterials scienceNanotechnologyChemistryQuantum mechanicsEngineering physicsPhysical chemistryElectrodeComputer science

Abstract

fetched live from OpenAlex

In this work, we explore how electrochemical tunneling reactions can be understood within the single-particle picture. That is, the formal approach in which band diagrams are typically utilized to understand electronic processes in solid-state materials and devices. This single-particle perspective is based upon the Gerischer–Hopfield description of electron transfer at solid–liquid interfaces. Both single and multiple electron tunneling reactions are discussed, as are related voltammetric concepts and trends. The impact of nuclear quantization on the Gerischer–Hopfield description is also addressed, as well as its compact representation of two probe electrochemical phenomena at low temperatures (often referred to as Franck–Condon blockade). In this manner, a perspective linking solid-state phenomena and tunneling electrochemical reactions is presented.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 teacher head, 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

Citations15
Published2021
Admission routes2
Has abstractyes

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