Enhanced Extraction of Valuable Metals from Copper Slags by Disrupting Fayalite and Spinel Structures Using Sodium Sulfate
Bibliographic record
Abstract
This study investigates the effects of sodium sulfate (Na2SO4) dosage, reaction temperature, and processing time on the structural decomposition of complex compounds in copper slag. Experimental results demonstrated that applying 20% Na2SO4 achieves an impressive decomposition rate of 89%, highlighting its effectiveness in liberating valuable metals from the slag matrix. The optimal temperature for maximizing fayalite decomposition is determined to be 900 °C, which significantly enhances reaction kinetics and efficiency. Furthermore, extending the reaction time to 90 min resulted in the highest observed decomposition efficiency. Subsequent leaching experiments in sulfuric acid confirmed that the liberated metal transitioned into the solution phase was very effective, ensuring high metal recovery rates. The treated samples demonstrated metal recovery rates of 97% for copper (Cu), 96% for iron (Fe), and 93% for zinc (Zn). In contrast, the untreated samples exhibited considerably lower recovery rates, with copper at 61%, iron at 59%, and zinc at 65%. Additionally, this approach mitigates filtration challenges by preventing the formation of silica gel. These findings provide key operational parameters for optimizing metal recovery from copper slag and establish a solid foundation for advancing sustainable and efficient resource extraction research.
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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) | 0.000 | 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 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".