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Record W4382626667 · doi:10.1002/aenm.202300815

In Situ Constructed 3D Lithium Anodes for Long‐Cycling All‐Solid‐State Batteries

2023· article· en· W4382626667 on OpenAlexafffund
Hui Duan, Changhong Wang, Ruizhi Yu, Weihan Li, Jiamin Fu, Xiaofei Yang, Xiaoting Lin, Matthew Zheng, Xiaona Li, Sixu Deng, Xiaoge Hao, Ruying Li, Jiantao Wang, Huan Huang, Xueliang Sun

Bibliographic record

VenueAdvanced Energy Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaMitacsChina Automotive Battery Research Institute
KeywordsMaterials scienceAnodeElectrolyteElectrochemistryNucleationHalideLithium (medication)Dendrite (mathematics)Chemical engineeringMetalDeposition (geology)NanotechnologyElectrodeInorganic chemistryMetallurgyPhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Constructing a 3D lithium metal anode has been demonstrated to be the most effective strategy to address its dendrite issue in liquid batteries. However, this promising approach has proved challenging to inherit in all‐solid‐state Li metal batteries (ASSLMBs) because of the rigidity of inorganic solid electrolytes (SEs), which constrains interfacial solid–solid ionic contact. Herein, a 3D Li anode is in situ constructed for ASSLMBs by spontaneous chemical reactions between halide SEs and Li metal. The in situ formed Li–Al alloys and well‐maintained sulfide SEs inside the 3D structure serve as continuous electron and Li + transport pathways and facilitate the homogenous distribution of charge carriers. The lithiophilic Li alloy can regulate Li deposition behavior and enable uniform Li nucleation and deposition. Both the Li||Li symmetric and full batteries exhibit good electrochemical performance at high current density. This work provides a universal strategy and new insight perspective to construct 3D Li for high‐performance ASSLMBs.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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
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

Citations87
Published2023
Admission routes2
Has abstractyes

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