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Record W3024557899 · doi:10.1149/ma2020-02453809mtgabs

Impact of Nickel on Next-Generation Li-Rich Oxide Cathodes for Li-Ion Batteries

2020· article· en· W3024557899 on OpenAlexaff
Michelle Ting, Eric McCalla

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectrochemistryNickelLithium (medication)RedoxMaterials scienceCathodeOxygenChemical engineeringOxideOxygen evolutionElectrodeInorganic chemistryNanotechnologyChemistryMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

As developments in lithium-ion batteries have focused on improving energy density, cycle life, and reducing cost, an important class of layered oxides with excess lithium, Li[Li,Ni,Mn,Co]O2, has been heavily studied as a next-generation electrode material. However, our limited understanding of the underlying electrochemical mechanisms hinders our ability to mitigate the negative impacts of these mechanisms, and has to date prevented commercialization. Previous studies have shown that these materials show a myriad of simultaneous mechanisms including changes in the nickel oxidation state, reversible oxygen redox accounting for capacities exceeding the theoretical limit1, oxygen gas release decreasing cycle efficiency2, as well as structural transformations leading to voltage fade3. Due to this complex mixture of electrochemical processes, model systems have been used extensively to observe these processes one at a time. This work on model systems is continued here by studying materials that show both nickel and oxygen redox but no structural transformations during cycling. This builds on work performed on Li-Fe-Sb-O and Li-Fe-Te-O where oxygen gas release was found to reduce Fe during charging of the electrode, an unexpected observation that has not been seen in other materials until the present study4. Given that nickel, and not iron, is of interest for next-generation materials, herein we use Li-Ni-Sb-O and Li-Ni-Te-O as model systems to better understand the electrochemical consequences of combining Ni and O redox processes during cycling. Herein, we show extensive electrochemical tests, ex-situ powder X-ray diffraction, online electrochemical mass spectrometry, X-ray absorption near-edge spectroscopy (XANES) and X-ray photoemission spectroscopy (XPS). Results show limited oxygen release takes place from the particle surfaces only, coupled with a marked contrast between the nickel oxidation states at the surface (as seen by XPS) and those of the bulk of the particles (seen with XANES). XPS data also demonstrates a reduction of surface nickel during oxygen gas release, confirming that the mechanism identified with Li-Fe-Sb-O does in fact occur in Ni-containing materials, though this is limited to the particle surface. Despite this surface effect, the reversibility of the nickel in the bulk is lost resulting in poor long-term cycling. Given that a great deal of work is currently being done to maximize the amount of nickel in next-generation electrode material, the consequences of nickel use in Li-rich oxides observed here are of high importance to further our understanding of these important materials. References 1. McCalla, E. et al, Science 2015, 350 (6267), 1516. 2. Jung, R. et al, Journal of The Electrochemical Society 2017, 164 (7), A1361-A1377. 3. Gallagher, K. G et al, Electrochemistry Communications 2013, 33, 96-98. 4. McCalla, E. et al, Journal of the American Chemical Society 2015, 137 (14), 4804-4814.

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)0.000
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 score0.001
Threshold uncertainty score0.004

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.058
GPT teacher head0.291
Teacher spread0.233 · 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
Published2020
Admission routes1
Has abstractyes

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