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Record W4411078380 · doi:10.1021/acs.iecr.5c00735

Maximizing Lithium Adsorption and Selectivity on Manganese-Based Ion Sieves: Effects of Thermal Treatment, Acid Content, and Operating Conditions

2025· article· en· W4411078380 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsManganeseSelectivityAdsorptionLithium (medication)ChemistryInorganic chemistryMolecular sieveIonChemical engineeringCatalysisOrganic chemistry

Abstract

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High Resolution Image Download MS PowerPoint Slide Lithium has been proven to be a critical metal for energy transition due to its application as high-grade energy storage. Its extraction has long been limited to conventional sources (i.e., mines and salt lakes), which use solar ponds and chemical treatment methods. However, to respond to the ever-increasing exponential demand for Li +, unconventional resources such as subsurface brines from geothermal and oilfields have lately been considered, with technological extraction means being the main challenge. The present research focuses on maximizing lithium adsorption and selectivity on manganese-based ion sieves by optimizing key factors such as the thermal treatment condition for powder calcination, acid content, and batch experiment operating conditions. Thus, it aims to enhance the efficiency of the ion-sieve synthesis process while minimizing energy and reagent consumption, addressing both the performance and scalability challenges of existing methods. It was observed that the lithium excess spinel cubic structure (i.e., Li 1.6 Mn 1.6 O 4 ) ion sieve was optimum for lithium recovery when LiMnO 2 was heat treated at 400–450 °C for 4 h at a ramping rate of 10 °C/min. The precursor was then treated with various acids to remove the template Li + from the structure without compromising it, whereby HCl-treated powder registered the highest desorption (95%), CH 3 COOH the lowest Mn 2+ dissolution (9%), and H 3 PO 4 the highest adsorption (29.5 mg/g). Hence, CH 3 COOH was the best delithiation medium when the material recyclability was the main focus, while HCl serves well to enhance the final recovery efficiency of lithium ions from the sieve structure. The adsorption of the optimum Li 1.6 Mn 1.6 O 4 spinel cubic structure treated with 0.5 M HCl acid solution was described as a Langmuir monolayer model with an equilibrium retention capacity of 34.25 mg/g and dynamic pseudo-second-order chemisorption. The powder selectivity performance, Li + ≫ Mg 2+ > Fe 2+ > Na + > Ca 2+ > Sr 2+ > Ba 2+ > K + was primarily a function of the structure’s memory effect, with a secondary dependence on size and charge. When applied to synthetic lithium-rich Oilfield brine from Buchan (U.K.), Leduc (Canada), and Somerset, the extraction performance was recorded to be 20, 23, and 27 mg/g, respectively, at an S/L ratio of 1 g/L. The effects of operating conditions were also evaluated, with adsorption increasing with pH and brine temperature while decreasing moderately with stirring rate, Mg, and Na/Li ratio.

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.

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 score0.011
Threshold uncertainty score0.625

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 communication0.0000.000
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 head0.071
GPT teacher head0.325
Teacher spread0.254 · 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