Extraction separation of Ir(III) and Rh(III) with Cyanex 923 from chloride media: a possible recovery from spent autocatalysts
Bibliographic record
Abstract
Abstract Liquid–liquid extraction of Ir(III) and Rh(III) with Cyanex 923 from aqueous hydrochloric acid media has been studied. Quantitative extraction of Ir(III) was observed in the range of 5.0–8.0 mol dm−3 HCl with 0.1 mol dm−3 Cyanex 923, while Rh(III) was extracted quantitatively in the range of 1.0–2.0 mol dm−3 HCl with 0.05 mol dm−3 Cyanex 923 in toluene along with 0.2 mol dm−3 SnCl2. The Ir(III) was back extracted with 4.0 mol dm−3 HNO3 quantitatively from the organic phase while Rh(III) was stripped with 3.0 mol dm−3 HNO3. The extraction of Rh(III) with Cyanex 923 was not quantitative without use of SnCl2. However in the extraction of Ir(III) a negative trend was observed in the presence of SnCl2. Varying the temperature of extraction showed that the extraction reactions of both the metal ions are exothermic in nature, and the stoichiometric ratio of Ir(III)/Rh(III) to Cyanex 923 in organic phase was found to be 1:3. The methods developed were applied to the recovery of these metal ions from a synthetic solution of similar composition to that from leaching of spent autocatalysts in 6.0 mol dm−3 HCl. © 2002 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".