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Record W4366760304 · doi:10.1002/adma.202301837

Thermally Stable Red‐Emitting Oxide Ceramics for Laser Lighting

2023· article· en· W4366760304 on OpenAlexafffund
Zhiyu Yang, Tristan de Boer, Patrick M. Braun, Binbin Su, Qinyuan Zhang, A. Moewes, Zhiguo Xia

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsUniversity of Saskatchewan
FundersGovernment of SaskatchewanNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlliance de recherche numérique du CanadaSiberian Branch, Russian Academy of SciencesGuangdong Provincial Pearl River Talents ProgramCanadian Light Source
KeywordsMaterials sciencePhosphorColor rendering indexPhotoluminescenceLaserCeramicLuminous fluxOxideOptoelectronicsLuminescenceLuminous efficacyRed lightOpticsNanotechnologyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Laser‐driven phosphor‐converted white light sources are highly desirable for their unprecedented energy efficiency and lighting quality. However, important challenges remain due to a lack of efficient and stable red‐emitting materials. Here Eu 2+ ‐activated oxide‐based double perovskites are explored as red emitters with thermally stable photoluminescence. Sr 3 TaO 5.5 :Eu 2+ ceramics exhibit a red emission band peaking at 620 nm upon blue laser pumping owing to the Eu 2+ occupation at highly ordered substitutional lattice sites. A constructed laser‐driven white light wheel under an incident power density of 19.2 W mm −2 presents a record luminous flux of 1115 lm with an excellent color rendering index of 90. This study invigorates the development of Eu 2+ ‐activated oxide‐based ceramics with thermally stable luminescence for laser‐pumped lighting and display 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.265
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations150
Published2023
Admission routes2
Has abstractyes

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