Quantitative SHINERS Analysis of Temporal Changes in the Passive Layer at a Gold Electrode Surface in a Thiosulfate Solution
Bibliographic record
Abstract
Shell-isolated gold nanoparticles (SHINs) were employed to record shell-isolated nanoparticle-enhanced Raman spectra (SHINERS) of a passive layer formed at a gold surface during gold leaching from thiosulfate solutions. The (3-aminopropyl)triethoxysilane (APTES) and a sodium silicate solution were used to coat gold nanoparticles with a protective silica layer. This protective silica layer prevented interactions between the thiosulfate electrolyte and the gold core of the SHINs when the SHINs-modified gold electrode was immersed into the thiosulfate lixiviant. The SHINERS spectra of the passive layer, formed from thiosulfate decomposition, contained bands indicative of hydrolyzed APTES. We have demonstrated how to exploit the presence of these APTES bands as an internal standard to compensate for fluctuations of the surface enhancement of the electric field of the photon. We have also developed a procedure that allows for removal of the interfering APTES bands from the SHINERS spectra. These methodological advancements have enabled us to identify the species forming the passive layer and to determine that the formation of elemental sulfur, cyclo-S8, and polymeric sulfur chains is responsible for inhibition of gold dissolution in oxygen rich thiosulfate solutions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".