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Record W1136966190 · doi:10.1021/acs.langmuir.5b02670

Electrochemical Investigations of 4-Methoxypyridine Adsorption on Au(111) Predict Its Suitability for Stabilizing Au Nanoparticles

2015· article· en· W1136966190 on OpenAlexafffund
Bipinlal Unni, Sajna Simon, Ian J. Burgess

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdsorptionChemistryElectrochemistryPyridineNanoparticleMetalLangmuir adsorption modelLangmuirInorganic chemistryPhysical chemistryNanotechnologyOrganic chemistryMaterials scienceElectrode

Abstract

fetched live from OpenAlex

A thermodynamic analysis of the adsorption of 4-methoxypyridine (MOP) on Au(111) surfaces is presented in an effort to determine its propensity to stabilize metal nanoparticles. The adsorption of MOP is compared and contrasted to the adsorption of 4-dimethylaminopyridine (DMAP), the latter of which is well-known to form stable Au nanoparticles. Electrochemical studies show that MOP, like most pyridine derivatives, can exhibit two different adsorption states. The electrical state of the metal, the pH of the solution, and the surface crystallography determine whether MOP adopts a low-coverage, π-bonded orientation or a high-coverage, σ-type orientation. A modified Langmuir adsorption isotherm is used to extract free energies of adsorption which are roughly 10% stronger for DMAP compared to MOP at equivalent conditions when expressed on a rational basis. The higher adsorption strength is attributed to DMAP's greater Lewis basicity. Qualitatively, MOP and DMAP adsorption are found to be completely analogous, implying that MOP-protected gold particles should be stable under conditions that favor the high-coverage adsorption state. Using a previously reported, single-phase synthesis, this is shown to be the case.

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 score0.011
Threshold uncertainty score0.376

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 head0.028
GPT teacher head0.249
Teacher spread0.221 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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