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Record W4389476810 · doi:10.1002/advs.202307011

Boosting Energy Storage Performance of Glass Ceramics via Modulating Defect Formation During Crystallization

2023· article· en· W4389476810 on OpenAlexafffund
Fei Shang, Juwen Wei, Jiwen Xu, Haibo Zhang, Yang Xia, Guisheng Zhu, Kunpeng Jiang, Guohua Chen, Zuo‐Guang Ye, Huarui Xu

Bibliographic record

VenueAdvanced Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaGuilin University of Electronic TechnologyNational Natural Science Foundation of ChinaGuangxi Key Laboratory of Information Materials
KeywordsMaterials scienceEnergy storageMiniaturizationPulsed powerAmorphous solidCrystallizationCeramicDielectricMicrostructureEngineering physicsPower densityOptoelectronicsNanotechnologyElectrical engineeringPower (physics)Composite materialChemical engineeringVoltageEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Along with the demand for further miniaturization of high and pulsed power devices, it becomes more and more important to realize ultrahigh recoverable energy storage density (W rec ) with high energy storage efficiency (η) and ultrahigh discharge energy storage density (W d ) accompanied by high power density (P d ) in dielectrics. To date, it remains, however, a big challenge to achieve high W rec or W d in glass ceramics compared to other dielectric energy storage materials. Herein, a strategy of defect formation modulation is applied to form “amorphous‐disordered‐ordered” microstructure in BaTiO 3 ‐based glass ceramics so as to achieve a high W rec of 12.04 J cm −3 with a high η of 81.1% and an ultrahigh W d of 11.98 J cm −3 with a superb P d of 973 MW cm −3 . This work demonstrates a feasible route to obtain glass ceramics with an outstanding energy storage performance and proves the enormous potential of glass ceramics in high and pulsed power applications.

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 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 score0.274
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations22
Published2023
Admission routes2
Has abstractyes

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