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Record W2158188690 · doi:10.1002/cphc.200900930

SERS of <i>β</i>‐Thioglucose Adsorbed on Nanostructured Silver Electrodes

2010· article· en· W2158188690 on OpenAlexaff
Mansoor Vezvaie, Christa L. Brosseau, John D. Goddard, Jacek Lipkowski

Bibliographic record

VenueChemPhysChem · 2010
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsSaint Mary's UniversityUniversity of Guelph
Fundersnot available
KeywordsRaman spectroscopyElectrodeElectrochemistryMicroprobeMoleculeMaterials scienceElectrolyteAnalytical Chemistry (journal)ChemistryChemical engineeringPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Highly ordered microporous films of silver-containing regular arrays of spherical pores of differing diameters were prepared by electrochemical deposition into the interstitial spaces of a template formed by nanosphere lithography. These nanostructured electrodes in conjunction with a Raman microprobe spectrometer were used to obtain surface-enhanced Raman spectra (SERS) of beta-thioglucose (beta-TG) under potential control. The SERS results were compared with SERS of beta-TG on an electrochemically roughened silver electrode surface. The bands in the experimental spectra were assigned to particular vibrations with the help of ab initio predictions of the spectra. The results of this study show that beta-TG self-assembled at a silver electrode forms a hydrophilic film that may be used in biomimetic research to enhance interactions between the electrode and the hydrophilic portion of a model membrane, and to prevent interactions of proteins inserted into this membrane with the metal surface. However, in contact with the aqueous electrolyte an anomerization reaction takes place and the beta-TG film is a mixture of the alpha- and beta-anomers of thioglucose. A partial oxidation of the self-assembled molecules was also observed. In addition, the orientation of the adsorbed molecules changes as a function of the applied potential.

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 score0.006
Threshold uncertainty score0.507

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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
Published2010
Admission routes1
Has abstractyes

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