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Record W4412510439 · doi:10.1149/ma2025-0111mtgabs

Direct Re-Functionalization of Spent LFP Cathodes of Lithium-Ion Batteries by Aqueous Electrochemical Process

2025· article· en· W4412510439 on OpenAlexaboutno aff
François Larouche, Kamyab Amouzegar, Ashok K. Vijh, George P. Demopoulos

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
Fundersnot available
KeywordsSurface modificationElectrochemistryLithium (medication)Aqueous solutionCathodeIonMaterials scienceProcess (computing)Inorganic chemistryChemical engineeringChemistryComputer scienceElectrodeEngineeringOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Lithium-ion batteries (LIBs) find many applications from powering multitudes of portable electronics, to automotive, and stationary energy storage. The current rapid market growth, more specifically in mobility and stationary energy storage, has resulted in an exponential increase in the usage of LIBs since early 2000. It is predicted that this market may reach 6.3 TWh by 2030. Inevitably, the quantity of spent LIBs will follow the same trend, causing important challenges to the waste management system. Consequently, we expect an important increase of spent lithium batteries available for recovery during the next decade raising the pressure on the emerging LIBs recycling industry. In addition to this rapid increase in volume of spent batteries, the recent migration from cobalt and nickel rich cathodic materials to lithium iron phosphate (LiFePO4, LFP) will impact significantly the current hydrometallurgical processes both on their efficiency and on their profitability. Indeed, the industry has focused until now on recovering the most valuable elements like lithium, cobalt, and nickel while iron and phosphorus from LFP batteries ending to waste. In this context, new low-cost processes that enable the recovery of LFP as highly valuable products are needed. Direct recycling, that aims to re-functionalize the spent LFP as new active material by keeping its initial orthorhombic structure, responds perfectly to these criteria. Hydro-Québec has developed such a process that includes a hydrometallurgical step extracting selectively Li ions as lithium bicarbonate from LFP black mass followed by the relithiation of the obtained iron (III) phosphate (FePO4, FP). Research in collaboration with McGill University has elucidated the chemistry of the process [1,2] and determined the relithiation step to be crucial to the re-functionalization of LFP since it restores the spent cathode’s Li-ion storage capacity [3]. While most relithiation processes are performed by high-temperature treatments or hydrothermal methods, Hydro-Québec’s process is an ambient temperature aqueous solution electrochemical relithiation method that has been recognized for its potential for industrial applications [4]. The results show that FP can be efficiently relithiated using either Li2SO4 or LiHCO3 electrolyte. The FP reduction reaction follows mostly a Cottrellian behavior, determined from potentiostatic experiments, indicating that diffusion in the solution is a critical mechanism. The application of this electrochemical relithiation process to FP originating from the delithiation of black mass from spent LFP batteries, resulted in a 96% relithiation yield at a current efficiency of 91%. The overall recycling process successfully re-functionalized deeply damaged spent LFP recovering up to 99% of the LFP’s original discharge capacity, achieving up to 153 mAh g-1 at C/12. Meanwhile, the initial discharge capacity of the re-functionalized LFP at 1C attained 119 mAh g-1, which interestingly increased upon cycling, reaching 131 mAh g-1 after 225 cycles. This increase could be associated with electrochemical activation promoted by cycling-induced LFP crystal annealing and/or by electrochemical milling. Hence, the final recycled LFP is a re-functionalized cathodic active material suitable for reuse in new LFP battery manufacturing. [1] F. Larouche, et al.; J. Electrochem. Soc. 169 (2022) 73509. [2] F. Larouche, et al.; Ind. Eng. Chem. Res. 62 (2023) 903–915. [3] F. Larouche, et al.; J. Power Sources 624 (2024) 235533. [4] E. Beletskii, et al.; Energy Environ. Mater. (2024) 1–17.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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