Conversion of Aluminosilicate Residue Generated from Lithium Extraction Process to NaX Zeolite
Bibliographic record
Abstract
The production of lithium from spodumene ores generates huge amounts of residues mainly composed of aluminosilicates. The main objective of the present study was to synthesize NaX zeolites with good ion-exchange capacity from these aluminosilicate residues, without using the fusion step or chemically modifying their initial Si/Al ratio. A physico-chemical (chemical composition, sorption capacity) and mineralogical (XRD, SEM) characterization of the zeolite synthesized using the conventional hydrothermal process (Process_1) was performed and compared with zeolite produced using a fusion step followed by a hydrothermal treatment process (Process_2) and commercial zeolite 13X. Then, the effect of operating parameters such as aging time and temperature, crystallization time and solid/liquid ratio on the sorption capacities of the synthesized zeolites using the conventional hydrothermal process was assessed. Initial aluminosilicate residues were mainly composed of Al2O3 (24.6%) and SiO2 (74.0%), while containing low amounts of potential contaminants (<1.6%). Based on its chemical composition, the fine fraction (<53 µm) was identified as the most suitable fraction to produce zeolites, while coarser fractions which contained higher Li content can be used to produce glass and ceramics. Physico-chemical and mineralogical characterization results show that zeolite produced using the conventional hydrothermal process (Process_1) had similar properties compared to zeolites 13X. Therefore, Process_1 was identified as the most performant while reducing operating costs related to alkaline fusion pre-treatments, which did not significantly improve zeolite properties. Finally, the optimum conditions for converting the residues into zeolite NaX, which had an ion-exchange capacity of 58 mg Ca/g were 8 h of aging at 75 °C and 16 h of crystallization at 100 °C, with a solid/liquid ratio of 1/10 (w/v).
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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".