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Record W4392139926 · doi:10.1016/j.mineng.2024.108613

Preliminary investigation into lithium extraction by phosphoric acid leaching of spodumene

2024· article· en· W4392139926 on OpenAlexafffund
Justin Paris, Shiva Mohammadi-Jam, Ronghao Li, Jingyi Liang, Hak Jun Oh, Ozan Kökkılıç, Sidney Omelon, Kristian E. Waters

Bibliographic record

VenueMinerals Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsMcGill University
FundersMcGill University
KeywordsSpodumenePhosphoric acidLeaching (pedology)Extraction (chemistry)Lithium (medication)ChemistryMetallurgyMineralogyEnvironmental chemistryEnvironmental scienceMaterials scienceChromatographySoil science

Abstract

fetched live from OpenAlex

• Leaching of lithium from β-spodumene was investigated. • At low temperature (≤100 °C) and atmospheric pressure over 40 % Li was extracted. • Low levels of Al and Si extraction imply selectivity. Lithium demand has risen significantly with the dominance of lithium-ion batteries in renewable energy technology. Spodumene is a primary lithium-bearing mineral of lithium extraction interest, due to its comparatively high grade and simple composition. However, lithium extraction processes from spodumene remain complex. In this study, phosphoric acid was investigated as a lithium leaching agent. Preliminary leach experiments were performed on 1 g of pure β-spodumene, with varying phosphoric acid concentration, temperature, residence time, and liquid-to-solid ratio. Chemical composition of the leach filtrates and residues by inductively coupled plasma optical emission spectroscopy (ICP-OES) revealed that > 40 % lithium leaching efficiency was achieved at an 8 M acid concentration, 100 °C, an 8 h residence time, and a liquid-to-solid ratio of 10 mL/g. With further optimization and understanding of the leaching mechanism, phosphoric acid could prove to be a suitable alternative for selective lithium extraction from spodumene.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.230
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes2
Has abstractyes

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