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Enregistrement W4405594076 · doi:10.1149/ma2024-022259mtgabs

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

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

Notice bibliographique

RevueECS Meeting Abstracts · 2024
Typearticle
Langueen
DomaineMaterials Science
ThématiqueElectron and X-Ray Spectroscopy Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGraphiteAnodeMaterials scienceProcess engineeringMetallurgyEngineeringChemistryElectrode

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,226

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,275
Écart entre enseignants0,261 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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