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Record W4200061164 · doi:10.1002/adma.202108079

2D Materials for All‐Solid‐State Lithium Batteries

2021· review· en· W4200061164 on OpenAlexafffund
Qianyi Ma, Yun Zheng, Dan Luo, Tyler Or, Yizhou Liu, Leixin Yang, Haozhen Dou, Jiequan Liang, Yihang Nie, Xin Wang, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueAdvanced Materials · 2021
Typereview
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersGuangdong Science and Technology DepartmentMitacsChina Postdoctoral Science Foundation
KeywordsMaterials scienceAnodeElectrolyteEnergy densityCompatibility (geochemistry)CathodeBattery (electricity)Ionic conductivityElectrochemistryFabricationSynchrotronLithium (medication)Fast ion conductorNanotechnologyEnergy storageCurrent densityLithium vanadium phosphate batteryCharacterization (materials science)Power densityEngineering physicsElectrodeElectrical engineeringComposite materialPower (physics)Engineering

Abstract

fetched live from OpenAlex

Although one of the most mature battery technologies, lithium-ion batteries still have many aspects that have not reached the desired requirements, such as energy density, current density, safety, environmental compatibility, and price. To solve these problems, all-solid-state lithium batteries (ASSLB) based on lithium metal anodes with high energy density and safety have been proposed and become a research hotpot in recent years. Due to the advanced electrochemical properties of 2D materials (2DM), they have been applied to mitigate some of the current problems of ASSLBs, such as high interface impedance and low electrolyte ionic conductivity. In this work, the background and fabrication method of 2DMs are reviewed initially. The improvement strategies of 2DMs are categorized based on their application in the three main components of ASSLBs: The anode, cathode, and electrolyte. Finally, to elucidate the mechanisms of 2DMs in ASSLBs, the role of in situ characterization, synchrotron X-ray techniques, and other advanced characterization are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.334
Teacher spread0.301 · 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; both teacher heads agree on what is shown here.

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

Citations91
Published2021
Admission routes2
Has abstractyes

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