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Record W3174553951 · doi:10.1002/aenm.202100552

Inhibiting Oxygen Release from Li‐rich, Mn‐rich Layered Oxides at the Surface with a Solution Processable Oxygen Scavenger Polymer

2021· article· en· W3174553951 on OpenAlexafffund
Se Young Kim, Chan Sun Park, Shahrzad Hosseini, Jordan Lampert, Young Jin Kim, Linda F. Nazar

Bibliographic record

VenueAdvanced Energy Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaBASF
KeywordsMaterials scienceCathodeElectrolyteOxygenChemical engineeringOxideSurface modificationCoatingPolymerScavengerRedoxInterphaseInorganic chemistryElectrodeRadicalNanotechnologyChemistryComposite materialOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Li‐rich Mn‐rich layered oxides (LRLO) are considered promising cathode materials for high energy density storage because of their very high capacities that owe to the reversible redox of oxide anions. However, LRLO cathodes also evolve reactive oxygen species on charge, especially in the first formation cycles, which leads to reactivity with the electrolyte at the surface, reconstruction of surface layers, and deleterious impedance growth. Here, a strategy to enhance the cycle performance of a Li‐rich Mn‐rich layered cathode is demonstrated by scavenging the evolved oxygen species with a polydopamine (PDA) surface coating. PDA, a well‐known oxygen radical scavenger, provides a chemically protective layer that diminishes not only the growth of the undesirable cathode electrolyte interphase but also results in less oxygen gas release compared to an unprotected surface, and significantly suppressed phase transformation at the surface. These factors lead to improved rate capability and diminished capacity fading on cycling; namely a capacity fade of 82% over 200 cycles at a C rate for the PDA‐coated LRLO, compared to 70% for the bare LRLO material.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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.

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

Citations120
Published2021
Admission routes2
Has abstractyes

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