Characterization of a Self-Assembled Monolayer of 1-Thio-β-<scp>d</scp>-Glucose with Electrochemical Surface Enhanced Raman Spectroscopy Using a Nanoparticle Modified Gold Electrode
Bibliographic record
Abstract
Preparation of a nanoparticle modified gold substrate designed for characterization of hydrophilic self-assembled monolayers (SAMs) of 1-thio-β-D-glucose (TG) with electrochemical surface-enhanced Raman spectroscopy (EC-SERS) is presented. Citrate stabilized gold nanoparticles were deposited on a polycrystalline gold electrode and subjected to an electrochemical desorption procedure to completely remove all traces of adsorbed citrate. Complete desorption of citrate was confirmed by recording cyclic voltammetry curves and SERS spectra. The citrate-free nanoparticle modified gold electrode was then incubated in a 1 mg mL(-1) aqueous solution of TG for 16 h prior to being characterized by EC-SERS. The SERS spectra confirmed that at potentials more negative than -0.10 V vs SCE thioglucose forms a monolayer in which the majority of the molecules preserve their lactol ring structure and only a small fraction of molecules appear to be oxidized. At potentials more positive than -0.10 V, the oxidation of TG molecules becomes prominent, and at potentials more positive than 0.20 V vs SCE, the monolayer of TG consists chiefly of oxidized product. The SERS spectra collected in the double layer region suggest the SAM of TG is well hydrated and hence can be used for hydrophilic modifications of a gold surface.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".