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Record W4405852519 · doi:10.1002/adma.202413499

Challenges and Strategies of Low‐Pressure All‐Solid‐State Batteries

2024· review· en· W4405852519 on OpenAlexaff
Jiaxu Zhang, Jiamin Fu, Pushun Lu, Guantai Hu, Shengjie Xia, Shutao Zhang, Ziqing Wang, Zhimin Zhou, Wenlin Yan, Wei Xia, Changhong Wang, Xueliang Sun

Bibliographic record

VenueAdvanced Materials · 2024
Typereview
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceEnergy densityNanotechnologyElectrochemical energy storageHigh pressureSolid-stateEnergy storageScale (ratio)Process engineeringSystems engineeringElectrochemistryEngineering physicsMechanical engineeringElectrodeEngineeringThermodynamicsSupercapacitor

Abstract

fetched live from OpenAlex

All-solid-state batteries (ASSBs) are regarded as promising next-generation energy storage technology owing to their inherent safety and high theoretical energy density. However, achieving and maintaining solid-solid electronic and ionic contact in ASSBs generally requires high-pressure fabrication and high-pressure operation, posing substantial challenges for large-scale production and application. In recent years, significant efforts are made to address these pressure-related challenges. In this review, the impact of pressure on ASSBs is explored. First, the categories, origins, and challenges associated with pressure in ASSBs are outlined. Second, an overview of recent advancements in addressing pressure-related issues in ASSBs is provided, focusing on electrode materials and their interfaces with various solid-state electrolytes (SSEs). Third, advanced characterizations and simulations employed to unravel the intricate electrochemical-mechanical interactions in ASSBs are examined. Finally, existing strategies and the insights on achieving low-stack-pressure ASSBs are presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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 head0.035
GPT teacher head0.312
Teacher spread0.277 · 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 designNot applicable
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

Citations94
Published2024
Admission routes1
Has abstractyes

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