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Record W4405594076 · doi:10.1149/ma2024-022259mtgabs

On the Influence of Recycled Graphite Properties for Anode Preparation and Efficiency in LIBs

2024· article· en· W4405594076 on OpenAlexaboutno aff
Elsa Briqueleur, Brunilda Rica, Antonin Bogaert, Karen C. Waldron, Mickaël Dollé

Bibliographic record

VenueECS Meeting Abstracts · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGraphiteAnodeMaterials scienceProcess engineeringMetallurgyEngineeringChemistryElectrode

Abstract

fetched live from OpenAlex

The consumption of rechargeable batteries, and especially lithium-ion batteries (LIBs) has exponentially grown since their first commercialization. They are currently dominating the market, both for stationary and mobile applications: from one LIB to power a cellphone to a pack of six cells in a laptop or thousands in an electric vehicle (E.V.). In short, the number of LIBs currently in use and in need for end-of-life management in the coming years is tremendous. Furthermore, considering the new regulations following commitments to allow for the energy transition, the E.V. market is expected to continue to grow, which on one hand implies a surge in the international battery demand, which incidentally puts pressure on the stock and on the availability of the valuable elements composing the battery, and on the other hands, requires solving their end-of-life management. For both these reasons, the LIBs recycling became a necessity as it offers several advantages, including: (i) providing a sustainable feedstock of battery components; (ii) avoiding mining of raw limited minerals; (iii) adding value to a system (i.e. the battery pack) that was meant to be discarded; (iv) avoiding the creation of waste. At the end of life, the LIBs are usually crushed to obtain a “black mass” from which minerals must be extracted. Nowadays, most of the spent batteries actually end their life in China, where pyrometallurgy is used (i.e. heating up the batteries to high temperature (e.g. 1000°C)) to recover cobalt, and nickel. However, lithium can not be recovered using the pyro-metallurgical process as it will be lost in the slag, as well as aluminium. Graphite is also destroyed in this process. Another technique is hydrometallurgy to recover the valuable metals in solution as well as the graphite as a solid. Moreover, lithium can also be recovered in this recycling process, by precipitation of lithium carbonate. However, the currently used hydrometallurgy processes often imply the use of H 2 SO 4 /H 2 O 2 mixture which is detrimental to the graphitic structure and lead to acidic wastewater generation. Even though graphite was recently classified as “critical” by Canada, so far, the focus in the LIBs recycling field wad mainly placed on the recovery and regeneration of the critical minerals that compose the cathode (Lithium, Cobalt, Nickel), due to their high value on the market. This explains the currently developed methods that were detrimental to this long forgotten critical mineral. Therefore, we hereby present a recycling process that, in addition to being efficient for the recovery of transition metals, takes into account the regeneration of graphite. As demonstrated with XRD, Raman and analytical results, by developing and tailoring a soft hydrometallurgy leaching treatment of black mass, the graphitic structure of the residues was preserved while still being purified from their contaminants. In addition, the large acidic wastewater usually generated by hydrometallurgy was avoided. It was also demonstrated that thanks to the preservation of the graphitic structure during its purification, the material only needed low temperature and soft conditions for its refinement. The physico-chemical properties of this new graphite feedstock were thoroughly evaluated and compared to battery-grade graphite to understand their implications in both the electrode making and LIB efficiency.

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)0.001
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 score0.005
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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