Effect of recycled process water on spodumene flotation surface chemistry and collector interactions
Bibliographic record
Abstract
Abstract This study investigates the surface chemical mechanisms that influence spodumene flotation under fresh and recycled water conditions using advanced Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and quantitative mineralogical analysis. A six-cycle flotation experiment was conducted to simulate progressive water reuse and evaluate its effects on lithium recovery, mineral surface chemistry, and collector performance. Results indicate that water recycling leads to significant accumulation of inorganic ions such as Al 3+ , Fe 3+ , and Mg 2+ , as well as organic reagents, which collectively impart adverse chemical characteristics to the mineral surface environment. These changes correlate with a marked decline in Li 2 O recovery and flotation efficiency. Surface analyses reveal that the presence of hydrated aluminum and iron species on spodumene grains suppresses collector adsorption, while elevated organic content and collector accumulation render the mineral surfaces hydrophilic, thereby hindering bubble–particle attachment and reducing flotation performance. The study demonstrates the importance of targeted water treatment to manage organic and inorganic buildup and preserve flotation selectivity. The findings provide mechanistic insight essential for optimizing lithium beneficiation under water-limited and environmentally regulated operations.
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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 | 0.001 |
| 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 | 0.001 | 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".