Zeolite Synthesis from Spodumene Leach Residue and Its Application to Heavy Metal Removal from Aqueous Solutions
Bibliographic record
Abstract
This study presents an approach to synthesizing LTA-type zeolite from spodumene residue generated during a lithium extraction process. A residue was obtained after leaching β-spodumene with 2 mol/L phosphoric acid. After solid–liquid separation, the delithiated residue was first treated with 2 mol/L sodium hydroxide and then subjected to hydrothermal synthesis using sodium aluminate as an additional aluminum source. The resulting material was characterized by XRD, SEM-EDS, XPS, and FTIR, which collectively confirmed the formation of a crystalline material exhibiting the structural features, elemental composition, and morphological characteristics consistent with LTA-type zeolite. Additional analyses, including BET surface area, particle size distribution, and zeta potential measurements, were performed to further evaluate the physicochemical properties of the synthesized zeolite. The spodumene leach residue (SLR)-derived zeolite was further tested for its adsorption performance in heavy metal ions removal from a mixed ion solution containing Pb2+, Cu2+, Zn2+, and Ni2+ ions. The zeolite demonstrated a high selectivity for Pb2+, followed by moderate uptake of Cu2+, while Zn2+ and Ni2+ adsorption was minimal. These findings demonstrate that spodumene residue, a waste by-product of lithium processing, can be effectively upcycled into LTA zeolite suitable for heavy metal remediation in water treatment applications.
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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) | 0.000 | 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 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".