MétaCan
Menu
Back to cohort
Record W4395465926 · doi:10.1002/anie.202401373

A Universal Self‐Propagating Synthesis of Aluminum‐Based Oxyhalide Solid‐State Electrolytes

2024· article· en· W4395465926 on OpenAlexaff
Simeng Zhang, Yang Xu, Han Wu, Tianlu Pang, Nian Zhang, Changtai Zhao, Junyi Yue, Jiamin Fu, Shengjie Xia, Xiangzhen Zhu, Guanzhi Wang, Hui Duan, Biwei Xiao, Tao Mei, Jianwen Liang, Xueliang Sun, Xiaona Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsSolid-stateElectrolyteAluminiumMaterials scienceChemical engineeringChemistryInorganic chemistryMetallurgyPhysical chemistryEngineeringElectrode

Abstract

fetched live from OpenAlex

Abstract Inorganic solid‐state electrolytes (SSEs) play a vital role in high‐energy all‐solid‐state batteries (ASSBs). However, the current method of SSE preparation usually involves high‐energy mechanical ball milling and/or a high‐temperature annealing process, which is not suitable for practical application. Here, a facile strategy is developed to realize the scalable synthesis of cost‐effective aluminum‐based oxyhalide SSEs, which involves a self‐propagating method by the exothermic reaction of the raw materials. This strategy enables the synthesis of various aluminum‐based oxyhalide SSEs with tunable components and high ionic conductivities (over 10 −3 S cm −1 at 25 °C) for different cations (Li + , Na + , Ag + ). It is elucidated that the amorphous matrix, which mainly consists of various oxidized chloroaluminate species that provide numerous sites for smooth ion migration, is actually the key factor for the achieved high conductivities. Benefit from their easy synthesis, low cost, and low weight, the aluminum‐based oxyhalide SSEs synthesized by our approach could further promote practical application of high‐energy‐density ASSBs.

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 score0.052
Threshold uncertainty score0.475

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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

Citations46
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207