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Record W2572721878 · doi:10.1021/acs.jpcc.6b11555

A Mechanistic Study of the Oxidative Reaction of Hydrogen-Terminated Si(111) Surfaces with Liquid Methanol

2017· article· en· W2572721878 on OpenAlexfundno aff
Noah T. Plymale, Mita Dasog, Bruce S. Brunschwig, Nathan S. Lewis

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsnot available
FundersDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsChemistryMethanolHydrogenFermi levelRedoxSiliconReactivity (psychology)PhotochemistryInorganic chemistryElectronOrganic chemistry

Abstract

fetched live from OpenAlex

H–Si(111) surfaces have been reacted with liquid methanol (CH 3 OH) in the absence or presence of a series of oxidants and/or illumination. Oxidant-activated methoxylation of H–Si(111) surfaces was observed in the dark after exposure to CH 3 OH solutions that contained the one-electron oxidants acetylferrocenium, ferrocenium, or 1,1′-dimethylferrocenium. The oxidant-activated reactivity toward CH 3 OH of intrinsic and n-type H–Si(111) surfaces increased upon exposure to ambient light. The results suggest that oxidant-activated methoxylation requires that two conditions be met: (1) the position of the quasi-Fermi levels must energetically favor oxidation of the H–Si(111) surface and (2) the position of the quasi-Fermi levels must energetically favor reduction of an oxidant in solution. Consistently, illuminated n-type H–Si(111) surfaces underwent methoxylation under applied external bias more rapidly and at more negative potentials than p-type H–Si(111) surfaces. The results under potentiostatic control indicate that only conditions that favor oxidation of the H–Si(111) surface need be met, with charge balance at the surface maintained by current flow at the back of the electrode. The results are described by a mechanistic framework that analyzes the positions of the quasi-Fermi levels relative to the energy levels relevant for each system.

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 score0.001
Threshold uncertainty score0.212

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 integrity0.0000.000
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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