Organic solvent‐assisted crystallization of inorganic salts from acidic media
Bibliographic record
Abstract
Abstract BACKGROUND Solvent displacement crystallization ( SDC ) provides an energy‐efficient alternative to evaporative crystallization potentially leading to crystal products of superior quality, both in terms of purity and size, due to better supersaturation control. The present work investigates the SDC process in terms of appropriate organic solvent selection and application to several metal (K + , Na + , Mg 2+ Fe 2+ , Cu 2+ , Ni 2+ , Co 2+ , Zn 2+ , Fe 3+ and Al 3+ ) sulfate and chloride systems of hydrometallurgical interest. RESULTS Criteria for the screening of organic compounds with suitable physical and chemical properties have been established and 2‐propanol was selected as an effective salting out agent to precipitate crystalline metal sulfates of practical interest; differences in crystallization behaviour among the various salts were linked to the hydration energy of the cation. None of the tested metal chlorides could be successfully separated, due to enhanced metal chloride solubility in non‐aqueous solvents relative to water by formation of chloro‐complexes with larger stability constants. CONCLUSIONS The solvent displacement crystallization process was investigated and selection criteria for the organic solvent were established. 2‐propanol proved to be the most effective salting out agent for metal sulfates resulting in > 90% cation removal. By contrast none of the metal chlorides could be successfully separated due to salting‐in behaviour. © 2014 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) | 0.000 | 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 | 0.000 |
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".