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Record W3034263551 · doi:10.1002/aenm.202001139

Tailoring the Mechanical and Electrochemical Properties of an Artificial Interphase for High‐Performance Metallic Lithium Anode

2020· article· en· W3034263551 on OpenAlexafffund
Yipeng Sun, Maedeh Amirmaleki, Yang Zhao, Changtai Zhao, Jianneng Liang, Changhong Wang, Keegan R. Adair, Junjie Li, Teng Cui, Guorui Wang, Ruying Li, Tobin Filleter, Mei Cai, Tsun‐Kong Sham, Xueliang Sun

Bibliographic record

VenueAdvanced Energy Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversity of TorontoWestern University
FundersWestern UniversityNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsMaterials scienceFaraday efficiencyOverpotentialAnodeElectrolyteLithium metalElectrochemistryLithium (medication)NanotechnologyInterphaseChemical engineeringCoatingElectrode

Abstract

fetched live from OpenAlex

Abstract Lithium metal is regarded as the “Holy Grail” of anode materials due to its low electrochemical potential and high theoretical capacity. Unfortunately, its unstable solid electrolyte interphase (SEI) leads to low Coulombic efficiency (CE) and serious safety issues. Herein, a hybrid nanoscale polymeric protective film with tunable composition and improved stiffness is developed by incorporating aluminum crosslinkers into the polymer chains. The Li plating/stripping process is regulated through the protective coating and the dendrite growth is effectively suppressed. Promisingly, the protected Li can deliver stable performance for more than 350 h with a cycling capacity of 2 mAh cm −2 without a notable increase in overpotential. Moreover, a stable charge/discharge cycling in Li–O 2 batteries with the protected Li can be maintained for more than 600 h. This work provides guidance on the rational design of electrode interfaces and opens up new opportunities for the fabrication of next‐generation energy storage systems.

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 score0.007
Threshold uncertainty score0.565

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 head0.017
GPT teacher head0.208
Teacher spread0.191 · 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

Citations54
Published2020
Admission routes2
Has abstractyes

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