Phase Transitions in the α–γ–β Spodumene Thermodynamic System and Impact of γ-Spodumene on the Efficiency of Lithium Extraction by Acid Leaching
Bibliographic record
Abstract
Heat-treatment of spodumene concentrate at 1323 K (1050 °C) for 30 min in a rotary kiln yielded a successful decrepitation. Particle size decreased from 2 cm to less than 425 µm for 80% of the initial mass. X-ray analysis of both fractions did not reveal the presence of α-spodumene or γ-spodumene. The coarse fraction was ground to less than 425 µm with minimal mechanical energy and mixed with the finer fraction to perform lithium extraction. The lithium extraction efficiency reached 98 wt% without the need for flotation. Some aspects of the thermodynamic behavior of the spodumene system were assessed. Results show that metastable γ-spodumene may hinder the formation of β-spodumene at lower heat treatment temperatures. Some heat-treated samples presented non-negligible γ-spodumene content and lithium extraction efficiency decreases as the γ content increases. Finally, the assumed irreversibility of the transformations was studied by analyzing heat-treated samples following long controlled-storage periods. The results show that concentrate composition is not static over the studied time. This suggests that the β formation is not as irreversible as claimed. It is recommended to avoid long periods between heat-treatment and extraction to avoid the slow conversion of β-spodumene to other allotropes, which are less susceptible to lithium extraction.
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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".