MétaCan
Menu
Back to cohort
Record W4309766466 · doi:10.1002/anie.202213228

Impact of the Chlorination of Lithium Argyrodites on the Electrolyte/Cathode Interface in Solid‐State Batteries

2022· article· en· W4309766466 on OpenAlexafffund
Tong‐Tong Zuo, Felix Walther, Jun Hao Teo, Raffael Rueß, Yübo Wang, Marcus Rohnke, Daniel Schröder, Linda F. Nazar, Jürgen Janek

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaInstitut National Du CancerBundesministerium für Bildung und Forschung
KeywordsElectrolyteIonic conductivityLithium (medication)ConductivityCathodeMaterials scienceFast ion conductorIonic bondingInorganic chemistryWork (physics)Chemical engineeringChemistryIonElectrodeThermodynamicsOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Lithium argyrodite‐type electrolytes are regarded as promising electrolytes due to their high ionic conductivity and good processability. Chemical modifications to increase ionic conductivity have already been demonstrated, but the influence of these modifications on interfacial stability remains so far unknown. In this work, we study Li 6 PS 5 Cl and Li 5.5 PS 4.5 Cl 1.5 to investigate the influence of halogenation on the electrochemical decomposition of the solid electrolyte and the chemical degradation mechanism at the cathode interface in depth. Electrochemical measurements, gas analysis and time‐of‐flight secondary ion mass spectrometry indicate that the Li 5.5 PS 4.5 Cl 1.5 shows pronounced electrochemical decomposition at lower potentials. The chemical reaction at higher voltages leads to more gaseous degradation products, but a lower fraction of solid oxygenated phosphorous and sulfur species. This in turn leads to a decreased interfacial resistance and thus a higher cell performance.

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 score0.045
Threshold uncertainty score0.261

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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

Citations74
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207