Extraction studies of platinum group metals with cyanex 925 in toluene—role of tin(II) chloride in their separation
Bibliographic record
Abstract
Extraction of platinum group metals Os(VIII), Ru(IV), and Ir(III) was carried out from aqueous chloride media with Cyanex 925 in the absence and in the presence of tin(II) chloride. In the presence of hydrochloric acid (HCl) alone, only Os(VIII) and Ru(IV) get extracted quantitatively, while extraction of Ir(III) was incomplete. Further, Os(VIII) could be extracted at lower concentrations of HCl and Ru(III) at higher concentrations (3.5–5.0 M). In the presence of tin(II) chloride, the extraction of Ir(III) increases and becomes quantitative. However, it decreases to some extent in the case of Os(VIII) and Ru(IV). The extraction conditions for all the metal ions were optimized under influence of variables such as HCl concentration, reagent concentration, tin(II)chloride concentration, equilibration time, stripping agents, and effect of diverse ions. The separation of individual metal ions Os(VIII), Ru(IV), and Ir(III) was carried out by taking advantage of differences in their extraction and stripping conditions towards Cyanex 925. The methods developed were applied to the recovery of these metal ions from some real catalysts samples.
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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 | 0.001 |
| 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) | 0.001 | 0.001 |
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".