Synthesis of zeolites using aluminosilicate residues from the lithium extraction
Bibliographic record
Abstract
Abstract The production of lithium from spodumene ores generates huge amounts of residue mainly composed of aluminosilicate. The main objective of this study was to compare the performances of three different processes to produce zeolites from aluminosilicates residues originating from lithium extraction. Zeolites were synthesized using: i) a conventional hydrothermal process (Process_1), ii) a conventional hydrothermal process assisted by calcination (Process_2), and iii) a conventional hydrothermal process assisted by alkaline fusion (Process_3). A physico-chemical (e.g., chemical composition, sorption capacity) and mineralogical (e.g., XRD, SEM) characterization of synthesized and commercial zeolite was done to identify the most performing synthesis route. Then, the effect of operating parameters (i.e., aging time and temperature, crystallization time, solid/liquid ratio) on the physico-chemical properties of the zeolite synthesized using the most performant process route 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 zeolite. Physico-chemical and mineralogical characterization of produced zeolite showed that conventional hydrothermal process was the most performant route to synthesize zeolite with properties like commercial zeolite 13X. Crystallization time (from 8 to 24 h), aging temperature (from 25 to 75°C) and S/L ratio (from 10 to 30% - w/v) are the main parameters affecting the properties of synthesized zeolite (i.e., ion-exchange capacity). Finally, a zeolite type X with an ion-exchange capacity of 58 mg/g, which is close to commercial zeolites (76–77 mg/g), was synthesized from the fine fraction of aluminosilicate residue using the conventional hydrothermal process after 8 h of aging at 75°C and 16 h of crystallization at 100°C, with a solid/liquid ratio of 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".