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Record W4408523354 · doi:10.46427/gold2024.23734

Enhanced Stability of Lithium Manganese Oxide Ion-sieves by Magnesium doping for Lithium Recovery from Flowback and Produced Water

2024· article· en· W4408523354 on OpenAlex
Fangshuai Wu, Karthik Ramachandran Shivakumar, Daniel S. Alessi, Kurt O. Konhauser, Yuhao Li

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLithium (medication)ManganeseMagnesiumDopingInorganic chemistryMaterials scienceManganese oxideIonChemistryMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

The increased demand for lithium (Li), driven in part by the use of Li-ion batteries, poses a crisis in its future supply.To meet demand, the exploration of alternative Li sources is imperative.Flowback and produced water (FPW), a by-product of oil and gas exploration, is a potential resource that often contains tens to hundreds of parts-per-million Li.Among the various direct Li extraction approaches applicable to FPW, spinel lithium manganese oxide (LMO) ion-sieves have emerged as a highly promising material due to their high Li uptake and rapid adsorption kinetics.However, LMO sorbents face challenges such as mass loss due to the reductive dissolution of manganese (Mn) caused by organic compounds present in FPW, which impairs its recyclability.In this study, we doped a pristine LMO (Li 1.6 Mn 1.6 O 4 ) with 4 different concentrations of magnesium (Mg) to synthesize Mg-doped lithium manganese oxides, Li 1.6 Mg x Mn 1.6-x O 4 or LMMO-x (where x = 0.1, 0.2, 0.3, 0.4).Li recovery tests conducted using FPW produced from the Duvernay Formation in Alberta, as an example, demonstrate that both Li uptake and Mn dissolution decrease with increased Mg doping.Specifically, Li uptake decreased by 53% for LMMO-0.4,while the average Mn dissolution during subsequent acid desorption was reduced by 80% compared to pristine LMO.Cycling tests show that LMMOs retain 95% of their initial Li uptake after the 5th cycle of use, compared to only 90% for LMO under the same experimental condition, demonstrating that LMMOs have a better recyclability.Extended X-ray absorption fine structure (EXAFS) analyses further confirm the improved stability of LMMOs, as irreversible structural contraction occurred in LMO after 5 cycles of use, but not found in the LMMOs.LMMOs, when further combined with polyvinylidene fluoride membrane, exhibit negligible Mn dissolution (<0.3%).This study demonstrates that Mg doping enhances the stability of LMMOs, making them promising candidates for Li recovery from FPW.

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 score0.029
Threshold uncertainty score0.472

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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