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Record W2034406095 · doi:10.1021/jp070354d

Electrochemical Characterization of Si(111) Modified with Linear and Branched Alkyl Chains

2007· article· en· W2034406095 on OpenAlexaff
Xiaomin Bin, T. Mischki, Chaoyang Fan, Gregory P. Lopinski, Danial D. M. Wayner

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2007
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsCarleton UniversitySteacie Institute for Molecular Sciences
Fundersnot available
KeywordsAlkylElectrolyteMonolayerSiliconChemistryDielectric spectroscopyElectrochemistryAqueous solutionSubstrate (aquarium)Chemical engineeringAnalytical Chemistry (journal)ElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A simple chemical strategy is described to produce branched alkyl chains on Si(111) from the reaction of an ester-terminated silicon surface. The stability of the silicon surfaces with linear and branched monolayers is characterized by electrochemical impedance, Kelvin probe, and high-resolution electron energy loss spectroscopy (HREELS). The direct observation of surface states in capacitance−voltage plots can be used to monitor the growth of electrically active defects associated with oxidation of the silicon substrate. We find that the total surface state density of the freshly made surfaces increases in the following order: Si−B < Si−UDE < Si−C10 (where B is the branched structure, UDE is ethyl undecanoate, and C10 is decyl) in aqueous and organic solvent/electrolyte systems. After 24 h in the electrolyte solution, the surface state densities increase but the order remains the same. The branched structure is significantly more resistant to oxidation. These observations are consistent with the results of other characterization techniques including HREELS and surface photovoltage and indicate that the branched alkyl chain-modified surfaces are considerably more stable, especially in aqueous electrolytes, making them suitable for future use in biological sensor applications.

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 score0.022
Threshold uncertainty score0.209

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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