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Record W2343980947 · doi:10.3390/photonics3020023

Aluminum Nitride Ceramic as an Optically Stimulable Luminescence Dosimeter Plate

2016· article· en· W2343980947 on OpenAlexaff
Go Okada, Kentaro Fukuda, Safa Kasap, Takayuki Yanagida

Bibliographic record

VenuePhotonics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceOptically stimulated luminescenceDosimeterCeramicLuminescenceOptoelectronicsPhotostimulated luminescenceIrradiationOpticsDosimetryNitridePhosphorRadiationAluminiumComposite materialNuclear medicinePhysics

Abstract

fetched live from OpenAlex

Photostimulable storage phosphors have been used in a wide range of applications including radiation measurements in one- and two-dimensional spaces, called point dosimetry and radiography. In this work, we report that an aluminum nitride (AlN) ceramic plate, which is practically used as a heat sink (SHAPAL®, Tokuyama Corp., Yamaguchi, Japan), shows good optically-stimulated luminescence (OSL) properties with sufficiently large signal and capability for imaging applications, and we have characterized the AlN plate for OSL applications. Upon interaction with X-rays, the sample color turns yellowish, due to a radiation-induced photoabsorption band in the UV-blue range below ~500 nm. After irradiating the sample with X-rays, an intense OSL emission can be observed in the UV (360 nm) spectral region during stimulation by red light. Although our measurement setup is not optimized, dose detection was confirmed as low as ~3 mGy to over 20 Gy. Furthermore, we have successfully demonstrated that the SHAPAL® AlN ceramic plate has great potential to be used as an imaging plate in radiography.

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 score0.003

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 science0.0010.000
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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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