Study of the Surface Chemistry and Dissolution Rate of Gold in ThiosulfateSolutions with Organic Additives
Bibliographic record
Abstract
Gold nanorods were fabricated by electrodeposition of gold in porous alumina templates. Thesesubstrates were characterized by scanning electron microscopy (SEM), specular reflectancespectroscopy, and surface enhanced Raman spectroscopy (SERS). Reflectance measurements revealedminima at 550 nm and 750 nm and these were assigned to transverse plasmon modes. SERSexperiments with 4‐aminothiophenol determined that the surface enhancement factor was 105 – 106.These SERS substrates were then used for the study of the surface chemistry of gold in solutionsof thiosulfate and its decomposition products. It was established that there is initial adsorption ofthiosulfate followed by formation of surface bound trithionate and tetrathionate. At extended times(3 hours) there is an increase in the surface concentration of gold sulfide and elemental sulfur which isthe result of thiosulfate decomposition. This passive layer prevents further interaction of gold with theleaching solution.The organic compounds 2‐thiouracil, 3‐mercaptopropionic acid, and L‐cysteine were studied foruse as additives in thiosulfate leaching solutions. It was found that 2‐thiouracil quickly formed a selfassembledmonolayer (SAM) and this minimized the interaction of thiosulfate with the gold surface. 3‐mercaptopropionic acid also showed evidence of SAM formation but thiosulfate and its decompositionproducts could also be detected with SERS. When L‐cysteine was used as an additive the SERS spectrawere largely featureless except for clear signs of gold‐sulfur bonds. It was proposed that this was due toadsorption of L‐cysteine which has a low scattering cross section.Leaching rates for thiosulfate solutions with and without the additives were determined byinductively coupled plasma with optical emission spectroscopy, ellipsometry, and UV‐Vis spectroscopy.Thiosulfate had the highest rate of 1.2 nm gold / 24 hours though the organic additives were similar.When these rates were adjusted to consider only the first 30 minutes of exposure they were similar toreported values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".