MétaCan
Menu
Back to cohort
Record W4285397995 · doi:10.1149/ma2022-015593mtgabs

(Invited) Recycling and Upcycling Electrode Materials from Spent Lithium-Ion and Alkaline Batteries

2022· article· en· W4285397995 on OpenAlexaff
Xiaolei Wang

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAlkaline batteryMaterials scienceHydrometallurgyPyrometallurgyCoatingSinteringCathodeMetallurgyEnergy storageNanotechnologyWaste managementElectrodeElectrolyteChemistryEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Lithium-ion batteries (LIBs) and alkaline batteries have been widely used in electric vehicles and portable electronics, dominating the energy storage market for decades, [1] but their recycling/upcycling has lagged far behind. Re/up-cycling batteries is urgently needed because it can not only preserve raw materials such as Li, Co, Ni, Mn, Al, and Cu for LIBs as well as Zn, Mn, Fe for alkaline batteries but also reduce hazardous wastes towards the environment. [2] Despite previous efforts made on re/up-cycling LIBs and alkaline batteries, there remains an urgent demand for simple, economic, environmentally benign, and energy-saving approaches. For LIB recycling, the dominated recycling methods including pyrometallurgy and hydrometallurgy are destructive and criticized of high energy consumption, undesirable economic outputs, and water contamination. By contrast, non-destructive methods such as solid-state sintering [3] and hydrothermal treatment coupled with short annealing (HT-SA) [4-5] are more promising. However, these emerged direct recycling approaches are deficient at regenerating outdated cathode materials to meet current market need. As such, upcycling (i.e., upgraded regeneration of cathodes) through surface engineering (coating [6] and doping [7-9] ) or bulk reconstruction [10] has been developed. In our work, we demonstrate one upcycling approach to regenerate LiCoO 2 cathode through an improved HT-SA approach in which a coating layer is prepared accompanied with the regeneration process. The upcycled cathode material shows improved electrochemical performance surpassing the pristine electrode materials. For alkaline battery recycling, the major recycling still focuses on hydrometallurgy and pyrometallurgy. Hydrometallurgical processes generally follow different steps of pre-treatment and subsequent leaching and separation of different metals by electrolysis, extraction or precipitation. [11] Another approach for treating spent alkaline battery materials is upcycling towards other applications such as supercapacitors, [12] catalysis [13] , micronutrient fertilizer, [14] Mn alloy fabrication. [14] In our work, for the first time, we directly upcycle both zinc anode and Mn-based cathode for their use in rechargeable Zn-MnO 2 batteries by a simple yet efficient annealing procedure. Zn was regenerated in a reductive atmosphere and the regenerated Zn shows high Coulombic efficiency and long life in symmetric cells while the regenerated Mn-based cathode shows superior performance than fresh MnO 2 with regards to capacity, rate, and life. Under optimized N/P ratio, the regenerated Zn and MnO 2 was paired to make rechargeable Zn-MnO 2 batteries delivering excellent performance, comparable or even superior to state-of-the-art Zn-MnO 2 batteries. Reference [1] Li, M.; Lu, J.; Chen, Z.; Amine, K. Advanced Materials 2018, 30 , 1800561. [2] Rarotra, S.; Sahu, S.; Kumar, P.; Kim, K.-H.; Tsang, Y. F.; Kumar, V.; Kumar, P.; Srinivasan, M.; Veksha, A.; Lisak, G. ChemistrySelect 2020, 5 , 6182. [3] Fan, M.; Chang, X.; Guo, Y.-J.; Chen, W.-P.; Yin, Y.-X.; Yang, X.; Meng, Q.; Wan, L.-J.; Guo, Y.-G., Energy Environ. Sci. 2021, 14 (3), 1461-1468. [4] Xu, P.; Dai, Q.; Gao, H.; Liu, H.; Zhang, M.; Li, M.; Chen, Y.; An, K.; Meng, Y. S.; Liu, P.; Li, Y.; Spangenberger, J. S.; Gaines, L.; Lu, J.; Chen, Z., Joule 2020, 4 (12), 2609-2626. [5] Shi, Y.; Chen, G.; Liu, F.; Yue, X.; Chen, Z., ACS Energy Lett. 2018, 3 (7), 1683-1692. [6] Meng, X.; Cao, H.; Hao, J.; Ning, P.; Xu, G.; Sun, Z., ACS Sustain. Chem. Eng. 2018, 6 (5), 5797-5805. [7] Wu, J.; Lin, J.; Fan, E.; Chen, R.; Wu, F.; Li, L., ACS Appl. Energy Mater. 2021, 4 (3), 2607-2615. [8] Fan, X.; Tan, C.; Li, Y.; Chen, Z.; Li, Y.; Huang, Y.; Pan, Q.; Zheng, F.; Wang, H.; Li, Q. J. Hazard. Mater. 2021, 410 , 124610. [9] Xu, B.; Dong, P.; Duan, J.; Wang, D.; Huang, X.; Zhang, Y., Ceram. Int. 2019, 45 (9), 11792-11801. [10] Gaines, L.; Dai, Q.; Vaughey, J. T.; Gillard, S. Recycling 2021, 6 (2). [11] Ferella, F.; De Michelis, I.; Vegliò, F. Journal of Power Sources 2008, 183 , 805. [12] Farzana, R.; Hassan, K.; Sahajwalla, V. Scientific Reports 2019, 9 , 8982. [13] Gallegos, M. V.; Falco, L. R.; Peluso, M. A.; Sambeth, J. E.; Thomas, H. J. Waste Management 2013, 33 , 1483. [14] Hu, X.; Robles, A.; Vikström, T.; Väänänen, P.; Zackrisson, M.; Ye, G., Journal of Hazardous Materials 2021, 411 , 124928.

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)0.000
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 score0.063
Threshold uncertainty score0.718

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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicExtraction and Separation ProcessesFrench-language works237,207