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Record W4411871542 · doi:10.1021/acs.nanolett.5c02279

Spalled Barium Titanate Single Crystal Thin Films for Functional Device Applications

2025· article· en· W4411871542 on OpenAlexfundno aff
Prachi Thureja, Andrew W. Nyholm, Martin Thomaschewski, Phillip Jahelka, Julie Belleville, Harry A. Atwater

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
FundersMetaNatural Sciences and Engineering Research Council of CanadaCalifornia Institute of TechnologyAir Force Office of Scientific ResearchMeta Research
KeywordsMaterials scienceThin filmSingle crystalBarium titanateOptoelectronicsCharacterization (materials science)MicrostructureFerroelectricityCrystal (programming language)Composite materialOpticsNanotechnologyCrystallographyComputer science

Abstract

fetched live from OpenAlex

We report a scalable approach for fabricating single-crystal barium titanate (BTO) thin films through spalling from bulk substrates. Conventional thin film growth techniques often face challenges in achieving high-quality single crystal microstructure over large areas, resulting in reduced performance in functional devices. In contrast, spalling, i.e., performing stress-induced exfoliation of bulk single crystals, enables the separation of single crystal thin films with controllable thicknesses ranging from 100 nm to 15 μm and lateral dimensions up to several millimeters. Electro-optic characterization of the spalled films yields a Pockels coefficient of r 33 = 55 pm/V in multidomain regions and 160 pm/V in single-domain regions, leading to projections up to 1980 pm/V for r 42 under conditions of unclamped excitation. Our results indicate that spalled BTO single-crystal thin films preserve bulk electro-optic properties and exceed the performance of commercially available thin film lithium niobate, making them suitable for integration in advanced photonic and optoelectronic devices.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.388

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 head0.010
GPT teacher head0.212
Teacher spread0.201 · 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 designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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