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Record W2257182333 · doi:10.1149/ma2014-02/1/12

Lithium Extraction in Aqueous Solutions through the Use of Heterosite FePO<sub>4</sub>

2014· article· en· W2257182333 on OpenAlexaboutno aff
Noramon Intaranont, Nuria Garcı́a-Aráez, Andrew L. Hector, James A. Milton, John R. Owen

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBrineLithium (medication)ChemistryLithium carbonateLimeInorganic chemistryEvaporationMaterials scienceMetallurgyIonOrganic chemistry

Abstract

fetched live from OpenAlex

The global resources of lithium, essential for electric vehicle batteries, are a subject of some debate. Lithium resources are mainly from two types of deposit; brine (21.6 Mt of Li) and pegmatite (3.9 Mt of Li) 1 . The largest deposit of lithium reserves are the brine deposits in South America. Although existing lithium resources may be sufficient to support demand until the year 2100 (assuming that lithium batteries are recycled 1, 2) , lithium extraction is currently too complex, slow and inefficient to be economical 3, 4 . In current lithium industries, one of the most economical ways to extract lithium is by lime soda evaporation ; a solar evaporation and chemical plant process, which takes between 12 to 24 months. After the solar evaporation, the concentrated brine is pre-treated to a pH of 2 in order to remove Boron by a solvent extraction. Lime (CaO) is then added to remove magnesium, sulphate, and borate. Calcium left in the brine is removed by a small amount of soda ash or sodium carbonate (Na 2 CO 3 ). Then, the brine is heated to about 90°C and more soda ash is added to precipitate lithium carbonate 4, 5 . This lime soda evaporation is considered as a long-process of lithium extraction. Therefore, the objective of this work is to develop a new, fast and inexpensive approach to recover lithium chemically, from the lithium sources that contain other metal cations. Heterosite iron phosphate (FePO 4 ) is a discharged product of lithium iron phosphate (LiFePO 4 ). There are a few studies about the structure of heterosite FePO 4 that report that it is more selective for lithium ions (Li + ) over sodium ions (Na + ). This structure displays excellent reversibility charge/discharge properties 6,7 . There small potential differences of the redox couple and the stability of LiFePO 4 over a wide range of pH in an aqueous solution are the main advantages of this structure to this application 6 . This work investigates a novel process that may prove to be more economical than the lime soda evaporation process for lithium extraction. Heterosite FePO 4 was used to selectively remove Li + from the brine with the aid of a reducing agent (sodium thiosulfate; Na 2 S 2 O 3 ). The resulting LiFePO 4 can be directly sent to lithium battery industries. In principle, the other cations could be retrieved back into the brine as shown in figure 1. We have examined and demonstrated the process of lithium insertion into a heterosite FePO 4 framework in aqueous salt solutions. In this process, the amount of Li + uptake can take up to 46 mg Li + /g solid and other cations (i.e. sodium, potassium, and magnesium) can take less than 3 mg/g solid . Furthermore, the re-lithiated FePO 4 performed satisfactorily as a positive electrode. This work could also be developed for future lithium recycling processes. References 1 S. E. Kesler, P. W. Gruber, P. a. Medina, G. a. Keoleian, M. P. Everson, and T. J. Wallington, Ore Geol. Rev. , 2012, 48 , 55–69 2 P. W. Gruber, P. a. Medina, G. a. Keoleian, S. E. Kesler, M. P. Everson, and T. J. Wallington, J. Ind. Ecol. , 2011, 15 , 760–775. 3 L. T. Peiro, G. V. Mendez and R. U. Ayres, Jom , 2013, 65 , 986-996. 4 D. E. Garrett, Handbook of lithium and natural calcium chloride: their deposits, processing, uses and properties , Elsevier Academic Press, Amsterdam, Boston, 2004 5 L. Moreno, Lithium Industry; A Strategic Energy Metal , Euro Pacific Canada Inc., 2013. 6 7. Z. Zhao, X. Si, X. Liu, L. He, and X. Liang, Hydrometallurgy , 2013, 133 , 75–83. 7 8. C. V. Ramana, a. Mauger, F. Gendron, C. M. Julien, and K. Zaghib, J. Power Sources , 2009, 187 , 555–564

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.535

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 head0.040
GPT teacher head0.254
Teacher spread0.214 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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