SERS of <i>β</i>‐Thioglucose Adsorbed on Nanostructured Silver Electrodes
Bibliographic record
Abstract
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.
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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".