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Record W4309360826 · doi:10.1002/adma.202209074

Progress and Prospects of Inorganic Solid‐State Electrolyte‐Based All‐Solid‐State Pouch Cells

2022· review· en· W4309360826 on OpenAlexafffund
Changhong Wang, Jung Tae Kim, Chunsheng Wang, Xueliang Sun

Bibliographic record

VenueAdvanced Materials · 2022
Typereview
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaWestern UniversityCanada Foundation for InnovationU.S. Department of Energy
KeywordsMaterials scienceSolid-stateElectrolyteChemical engineeringSolid surfaceNanotechnologyEngineering physicsChemical physicsPhysical chemistryElectrodeChemistry

Abstract

fetched live from OpenAlex

All-solid-state batteries have piqued global research interest because of their unprecedented safety and high energy density. Significant advances have been made in achieving high room-temperature ionic conductivity and good air stability of solid-state electrolytes (SSEs), mitigating the challenges at the electrode-electrolyte interface, and developing feasible manufacturing processes. Along with the advances in fundamental study, all-solid-state pouch cells using inorganic SSEs have been widely demonstrated, revealing their immense potential for industrialization. This review provides an overview of inorganic all-solid-state pouch cells, focusing on ultrathin SSE membranes, sheet-type thick solid-state electrodes, and bipolar stacking. Moreover, several critical parameters directly influencing the energy density of all-solid-state Li-ion and lithium-sulfur pouch cells are outlined. Finally, perspectives on all-solid-state pouch cells are provided and specific metrics to meet certain energy density targets are specified. This review looks to facilitate the development of inorganic all-solid-state pouch cells with high energy density and excellent safety.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.441
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.279
Teacher spread0.264 · 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
GenreReview

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

Citations227
Published2022
Admission routes2
Has abstractyes

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