Niobium and tantalum recovery from the primary source and from tin slag, an industrial challenge: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Niobium (Nb) and Tantalum (Ta) are used to increase materials' mechanical resistance and produce lighter alloys. Worldwide Nb production reached 78 000 t in 2020. The reduced ore offer justifies the recycling of these metals from tin slag, contributing to the circular economy. Nb 2 O 5 and Ta 2 O 5 extraction either from the primary source or the tin slag is an industrial challenge. Nb and Ta dissolution processes already implemented are fluoride leaching, sulphuric leaching, alkaline leaching, and alkaline roasting. The fluoride process raises environmental concerns about waste control. The sulphuric method can be managed to have higher Nb and Ta extraction in a less aggressive process, if some changes are implemented, such as increasing the number of extraction steps, decreasing the pulp density, or increasing the temperature; however, the efficiency of this methodology must be tested for tin slag. The alkaline method seems to be more selective to Nb and Ta by reactants and temperature control. Despite those well‐established Nb and Ta treatments, they must be adapted to recover Nb and Ta from slag. The slag has low Nb and Ta content, while high Si and Ca concentrations exist in the matrix. This paper brings the main methods used to extract the Nb and Ta from the primary resources and an overview of Nb and Ta recovery from the slag. This investigation comes as a tool to guide the development of new methods to recover Nb and Ta from low‐grade sources such as tin slag.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| 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 it