Extraction of niobium in one step from tin slag by <scp> NH <sub>4</sub> F‐HCl </scp> leaching process
Bibliographic record
Abstract
Abstract Niobium and tantalum are generally found together in nature in ores such as columbite, tantalite, and pyrochlore. These ores can occasionally associate with cassiterite, the tin ore. The pyrometallurgical processes of cassiterite produces a slag that can contain Nb and Ta from the associated ores. Cassiterite processing for tin extraction has generated a considerable quantity of residue. This work aims to study the extraction of Nb from tin slag while leaving the Ta solid. The process of separation consists in one step by leaching using a combined solution of HCl‐NH 4 F. The slag was characterized by X‐ray fluorescence (EDXRF), atomic absorption spectroscopy (AAS), inductively coupled plasma optical emission spectrometry (ICP‐OES), X‐ray diffraction (XRD), scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS), and granulometric analysis. The effect of parameters of the leaching processes was investigated, such as the time of the process, HCl concentration, liquid‐to‐solid ratio, temperature, and salt‐to‐slag ratio. It was possible to recover 100% of Nb and 5% of Ta in liquor of leaching within 4 h, at 85°C, using an L:S ratio of 20:1, NH 4 F:slag ratio of 0.12 g, and HCl concentration equal to 9.79 mol/L.
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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".