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Record W3186613233 · doi:10.1149/ma2021-01272mtgabs

Reevaluating the Criticality of Li-Excess for Disordered-Rocksalt Li-Battery Cathodes

2021· article· en· W3186613233 on OpenAlexaff
Jinhyuk Lee, Ju Li

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsCathodeMaterials scienceLithium (medication)Battery (electricity)DiffusionChemistryPhysical chemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Disordered-rocksalt (DRX)-type lithium transition-metal (TM) oxides/oxyfluorides are a promising class of cathode materials for rechargeable Li-batteries [1]. These materials deliver high capacity (>250 mAh/g) and energy density (>800 Wh/kg), and they can be made with all kinds of inexpensive cations (e.g., V, Cr, Mn, Fe, Nb, Zr, Mo, W, Ta) and anions (e.g., O, F, S), leading to new opportunities to develop sustainable high-energy Li-batteries with a low cost [1]. The attention to DRX-cathodes grew from observations that DRX Li-TM oxides with Li-excess compositions (e.g., x=1.25 in Li x TM2-x O2) could deliver superior capacities (>250 mAh/g), while those without Li-excess (e.g., x=1.0 in Li x TM2-x O2) typically showed limited performance (<100 mA/g)[1,2]. Theoretical studies attribute the difference in performance to percolation of the so-called “0-TM channels”, promoting Li diffusion in the DRX-cathodes, only possible if the Li-excess level exceeds a critical threshold (e.g., x>1.1 in Li x TM2-x O2) [1,2]. Indeed, several new DRX materials (e.g., Li-Ni-Ti-Mo-oxides, Li-Fe-Ti-oxides) showed a large improvement in reversible capacity with the increasing Li-excess level [3,4]. Also, various Li-excess DRX oxyfluorides and sulfides were developed (e.g., Li2VO2F), achieving a very high capacity above 300 mAh/g [1,5,6]. Based on these results, the Li-excess strategy has become the primary consideration for designing the DRX cathodes. Meanwhile, introducing excess Li for 0-TM percolation sacrifices the theoretical TM-redox capacity for O-redox capacity, as demonstrated in many Li-rich cathodes shown to operate on oxygen redox as an additional electron reservoir. O-redox can provide alternative electron-capacities, but it also can trigger structural damage (e.g., O loss at the particle surface), resulting in poor cycling stability [1,3,6]. As a result, recent efforts reported in the DRX literature have concentrated on minimizing the O-redox-triggered structural damage, which is coupled with improving the Li-transport through a high-level of Li-excess. For example, F-substitution for O could improve the capacity retention of the Li-excess DRX oxides by increasing the TM-redox capacity at a given Li-excess level [7,8]. In this presentation, we will demonstrate that once particle size is sufficiently reduced, Mn-based DRX cathodes can deliver high capacity (>250 mAh/g) regardless of the Li-excess level, effectively removing the Li-excess “constraint” without sacrificing capacity and also reducing the O-redox-related side reactions. Our finding that Li-excess is not critical for cycling Mn-rich DRX-cathodes with small particles motivates addressing why other DRX cathodes with different chemistries, such as Li-Ni-Ti-Mo oxides, do require a high level of Li-excess to deliver high capacity, even with nano-sized active particles. By contextualizing our results with other reports from the DRX literature and investigating through Density-Functional-Theory (DFT) calculations, we find a strong correlation between the TM-redox potential and the measured capacity, revealing that Li-excess is more critical to cycling DRX-cathodes with high TM-redox potential than the ones with lower redox potential. Our work provides new insight into the multifaceted role of Li-excess to the performance of the DRX-cathode beyond 0-TM percolation. References R. J. Clement et al., Energy Environ. Sci. 13, 345–373 (2020). J. Lee et al., Science 343, 519–522 (2014) J. Lee et al., Energy Envion. Sci. 8, 3255 (2015). M. Yang et al., ACS Appl. Mater. Interfaces 11, 44144–44152 (2019). R. Chen et al., Adv. Energy Mater. 5, 1401814 (2015). J. Lee et al., Nature 556, 185–190 (2018). J. Lee et al., Nat. Commun. 8, 981 (2017). Z. Lun et al., Adv. Energy. Mater. 1802959 (2019).

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 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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.306
Teacher spread0.272 · 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
Published2021
Admission routes1
Has abstractyes

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