Green Recycling of Goethite and Gypsum Residues in Hydrometallurgy with α-Fe<sub>3</sub>O<sub>4</sub> and γ-Fe<sub>2</sub>O<sub>3</sub> Nanoparticles: Application, Characterization, and DFT Calculation
Bibliographic record
Abstract
Millions of tons of hazardous iron oxide residues are produced during the iron purification process of sulfuric acid leaching solutions in the nonferrous metals hydrometallurgy industry per year. The generated iron oxide residues, which mainly contain goethite and gypsum precipitates, pose great threats to the local ecological environment and human health. We proposed a novel method, separation and recovery of goethite and gypsum by the synthetic magnetic nanoparticles (MNPs) such as α-Fe 3 O 4 and γ-Fe 2 O 3, to treat the residues efficiently and cost-effectively. MNPs served as the magnetic crystal nuclei of the goethite precipitates during the iron purification process, and the goethite and gypsum precipitates formed under this condition can be separated in a magnetic field for recycling purposes. The separation efficiency of the goethite and gypsum precipitates was much higher when γ-Fe 2 O 3 was used as the crystal nuclei, indicating that the surface of γ-Fe 2 O 3 was more favorable for the formation of goethite particles than α-Fe 3 O 4, which has also been verified by SEM, FBRM, XRD, TEM, and XPS analysis. DFT calculations suggested that the binding energy between the MNPs and iron hydroxide plays a critical role and is responsible for the distinguished collecting efficiencies of α-Fe 3 O 4 and γ-Fe 2 O 3 toward goethite.
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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".