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Record W4412792186 · doi:10.1021/acsaem.5c01575

Ultrafast Laser Texturing of Ceramic Electrolytes toward Solid-State Battery Application

2025· article· en· W4412792186 on OpenAlexafffund
Shuo Yan, Deliang Guo, Elena A. Baranova, Ali Akbar Merati, Chae-Ho Yim, Yaser Abu‐Lebdeh, Arnaud Weck

Bibliographic record

VenueACS Applied Energy Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNational Research Council CanadaOffice of Energy Research and DevelopmentNatural Sciences and Engineering Research Council of Canada
KeywordsCeramicUltrashort pulseMaterials scienceElectrolyteSolid-stateLaserBattery (electricity)OptoelectronicsNanotechnologyEngineering physicsOpticsMetallurgyElectrodeChemistryEngineeringPower (physics)PhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Solid-state batteries are expected to complement liquid-based lithium-ion batteries (LIBs) with improved safety and energy density. However, point-to-point interface contact between oxide-based ceramic solid-state electrolytes (SSEs) and electrodes impedes Li-ion transfer and degrades the battery performance. Herein, micropatterns with controllable geometries were created onto perovskite-type SSE surfaces via femtosecond ultrafast laser processing. Laser-textured SSEs with two-directional (2D) perpendicular grooves can achieve a significant reduction in interfacial resistance (<1 versus 3416.81 Ω cm –2 for bare sample) attributed to increased contact area and trapping of the liquid electrolyte. This work serves as a proof-of-concept for applying ultrafast laser processing toward solid-state battery 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)0.000
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 score0.007
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 communication0.0000.000
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 head0.007
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.

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

Citations3
Published2025
Admission routes2
Has abstractyes

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