MétaCan
Menu
Back to cohort
Record W4367369253 · doi:10.1002/smll.202301904

High Cathode Loading and Low‐Temperature Operating Garnet‐Based All‐Solid‐State Lithium Batteries – Material/Process/Architecture Optimization and Understanding of Cell Failure

2023· article· en· W4367369253 on OpenAlexafffund
Hirotoshi Yamada, Tomoko Ito, Tatsuya Nakamura, Raman Bekarevich, Kazutaka Mitsuishi, Sanoop Palakkathodi Kammampata, Venkataraman Thangadurai

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of Calgary
FundersAdvanced Low Carbon Technology Research and Development ProgramJapan Science and Technology AgencyNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMultiphysicsCathodeLithium (medication)SinteringElectrolyteBattery (electricity)Composite materialChemical engineeringElectrodeElectrical engineering

Abstract

fetched live from OpenAlex

Abstract All‐solid‐state lithium batteries (ASSLBs) are prepared using garnet‐type solid electrolytes by quick liquid phase sintering (Q‐LPS) without applying high pressure during the sintering. The cathode layers are quickly sintered with a heating rate of 50–100 K min −1 and a dwell time of 10 min. The battery performance is dramatically improved by simultaneously optimizing materials, processes, and architectures, and the initial discharge capacity of the cell with a LiCoO 2 ‐loading of 8.1 mg reaches 1 mAh cm −2 and 130 mAh g −1 at 25 °C. The all‐solid‐state cell exhibits capacity at a reduced temperature (10 °C) or a relatively high rate (0.1 C) compared to the previous reports. The Q‐LPS would be suitable for large‐scale manufacturing of ASSLBs. The multiphysics analyses indicate that the internal stress reaches 1 GPa during charge/discharge, which would induce several mechanical failures of the cells: broken electron networks, broken ion networks, separation of interfaces, and delamination of layers. The experimental results also support these failures.

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 score0.392
Threshold uncertainty score0.879

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 head0.012
GPT teacher head0.212
Teacher spread0.199 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueSmallSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207