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Record W2621427505 · doi:10.1002/pssa.201700081

Powder selection for hydrothermally processed sol–gel composite barium strontium titanate capacitors

2017· article· en· W2621427505 on OpenAlexaff
Justin Po, M. Sayer, Alois P. Freundorfer, Eric Simpson

Bibliographic record

Venuephysica status solidi (a) · 2017
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceSol-gelComposite numberCeramicCapacitorDielectricComposite materialRelative permittivityBarium titanatePermittivityCeramic capacitorChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Ba 0.7 Sr 0.3 TiO 3 (BST) capacitors for high frequency circuits have been fabricated at 175 °C on epoxy‐based copper‐clad printed circuit boards, having a glass transition temperature of ∼200 ° C by hydrothermal processing of dried BST acetate‐based sol–gel powder slurries. The film thickness was 2–10 μm. BST powders were prepared respectively as ceramic derived powders processed from oxide mixtures fired to 1100 °C, sol–gel derived powders fired to 750 and 1100 °C, and a low temperature solution process dried at 500 °C. The performance of the capacitor was determined by both the dielectric properties of the powder and the interaction between the crystallizing gel and the powder surface. High relative permittivity is obtained for sol–gel derived powders fired to 750 °C, while high voltage tunability is a characteristic of the ceramic derived powders. Low temperature powders are ineffective. The difference is attributed to the nature of the crystalline interface formed between the surface of the powder particles and the hydrothermally crystallized BST formed from the dried gel within the composite. The optimum molar concentration of the hydrothermal solution was 0.1 M with a Ba content larger than that of the powder. Capacitor composites having a relative permittivity of up to 300 at 20 MHz were demonstrated.

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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.018
GPT teacher head0.275
Teacher spread0.258 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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