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Record W4298119344 · doi:10.1002/aenm.202201652

Aging‐Driven Composition and Distribution Changes of Electrolyte and Graphite Anode in 18650‐Type Li‐Ion Batteries

2022· article· en· W4298119344 on OpenAlexaff
Dominik Petz, Volodymyr Baran, Christoph Peschel, Martin Winter, Sascha Nowak, M. Hofmann, Robert Kostecki, Rainer Niewa, Michael Bauer, Peter Müller‐Buschbaum, Anatoliy Senyshyn

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersBayerisch-Kalifornischen HochschulzentrumTechnische Universität MünchenBundesministerium für Bildung und Forschung
KeywordsElectrolyteMaterials scienceAnodeLithium (medication)GraphiteCalorimetryNeutron diffractionDifferential scanning calorimetryThermal decompositionIonDecompositionChemical engineeringAnalytical Chemistry (journal)ElectrodeComposite materialDiffractionThermodynamicsChromatographyChemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A series of low‐temperature studies on LiNi 0.80 Co 0.15 Al 0.05 O 2 18650‐type batteries of high‐energy type with different stabilized states of fatigue is carried out using spatially resolved neutron powder diffraction, infrared/thermal imaging, and quasi‐adiabatic calorimetry. In‐plane distribution of lithium in the graphite anode and frozen electrolyte in fully charged state is determined non‐destructively with neutron diffraction and correlated to the introduced state of fatigue. An independent electrolyte characterization is performed via calorimetry studies on variously aged 18650‐type lithium‐ion batteries, where the shape of the thermodynamic signal is evolving with the state of fatigue of the cells. Analyzing the liquid electrolyte extracted/harvested from the studied cells reveals the decomposition of conducting salt to be the main driving factor for fatigue in the electrolyte degradation.

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 score0.012
Threshold uncertainty score0.833

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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