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Record W4410500114 · doi:10.3390/cryst15050474

Tailoring the Luminescence Properties of Strontium Aluminate Phosphors for Unique Smartphone Detectable Optical Tags

2025· article· en· W4410500114 on OpenAlex
Virgīnija Vitola, Miļena Dile, Katrīna Križmane, Ernests Einbergs, Tinko Eftimov, Kristian Nikolov, Samia Fouzar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrystals · 2025
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsPhosphorStrontiumPersistent luminescenceLuminescenceAluminateStrontium oxideMaterials scienceOptoelectronicsChemistryMetallurgyThermoluminescence

Abstract

fetched live from OpenAlex

In this work, a precursor-driven tailoring of strontium aluminate phosphors doped with Eu2+ and Dy3+ to generate unique, batch-specific luminescent signatures suitable for smartphone-detectable anti-counterfeiting tags was developed. A microwave-assisted hydrothermal synthesis approach was employed to explore the impact of a wide range of alkaline hydroxide and carbonate precursors on the structure of strontium aluminate. The resulting materials exhibited distinct differences in crystalline phase composition, morphology, and trap depth distribution. A smartphone-based detection system was developed, enabling rapid identification of spectral fingerprints. This study demonstrates a viable strategy for embedding unique luminescent identifiers, offering a scalable solution for robust, low-cost anti-counterfeiting applications in both the spectral and the time domain.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.232
Teacher spread0.213 · 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