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Record W3040795836 · doi:10.1039/c9cs00636b

Developing high safety Li-metal anodes for future high-energy Li-metal batteries: strategies and perspectives

2020· review· en· W3040795836 on OpenAlexafffund
Dai‐Huo Liu, Zhengyu Bai, Matthew Li, Aiping Yu, Dan Luo, Wenwen Liu, Lin Yang, Jun Lü, Khalil Amine, Zhongwei Chen

Bibliographic record

VenueChemical Society Reviews · 2020
Typereview
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsRegional Municipality of WaterlooNational Institute for NanotechnologyUniversity of Waterloo
FundersOffice of Energy EfficiencyWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaOffice of ScienceUniversity of WaterlooChina Postdoctoral Science FoundationUniversity of ChicagoNational Natural Science Foundation of ChinaHigher Education Discipline Innovation ProjectHenan Normal UniversityArgonne National LaboratoryOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsLithium metalMetalHigh energyAnodeEnergy densityMaterials scienceChemistryEngineering physicsMetallurgyEngineeringElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

battery systems. However, the notorious Li-dendrite growth problem results in serious safety concerns for any energy storage application. Through a recent combination of interface-based science, nanotechnology-based solutions and characterization methods, the LMA is now primed for a technological boom. In this review, the recent emerging strategies and perspectives on LMAs are summarized, following which the current huge evolution in interfacial chemistry regulation, optimizing electrolyte components, designing a rational 'host' for lithium metal, optimizing "solid-state electrolytes" and other emerging strategies for developing high-safety LMAs is highlighted. Furthermore, several state-of-the-art in situ/operando synchrotron-based X-ray techniques for high safety LMB research are introduced. With the further development of LMAs in the future, subsequent application in high energy LMBs is to be expected.

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 score0.984
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.044
GPT teacher head0.295
Teacher spread0.252 · 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

Citations470
Published2020
Admission routes2
Has abstractyes

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