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Record W2965256323 · doi:10.3390/nano9081127

Optically Stimulated Nanodosimeters with High Storage Capacity

2019· article· en· W2965256323 on OpenAlexafffund
David Van der Heggen, Daniel R. Cooper, Madeleine Tesson, Jonas Joos, Jan Seuntjens, John A. Capobianco, Philippe F. Smet

Bibliographic record

VenueNanomaterials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsConcordia UniversityMcGill University Health Centre
FundersNatural Sciences and Engineering Research Council of CanadaUniversiteit GentConcordia University
KeywordsRadioluminescenceAfterglowThermoluminescencePhosphorOptically stimulated luminescenceMaterials scienceLuminescenceCoprecipitationDosimeterDosimetryOptoelectronicsPersistent luminescenceRadiationOpticsScintillationChemistryPhysicsNuclear medicine

Abstract

fetched live from OpenAlex

In this work we report on the thermoluminescence (TL) and optically stimulated luminescence (OSL) properties of β-Na(Gd,Lu)F4:Tb3+ nanophosphors prepared via a standard high-temperature coprecipitation route. Irradiating this phosphor with X-rays not only produces radioluminescence but also leads to a bright green afterglow that is detectable up to hours after excitation has stopped. The storage capacity of the phosphor was found to be (2.83 ± 0.05) × 1016 photons/gram, which is extraordinarily high for nano-sized particles and comparable to the benchmark bulk phosphor SrAl2O4:Eu2+,Dy3+. By combining TL with OSL, we show that the relatively shallow traps, which dominate the TL glow curves and are responsible for the bright afterglow, can also be emptied optically using 808 or 980 nm infrared light while the deeper traps can only be emptied thermally. This OSL at therapeutically relevant radiation doses is of high interest to the medical dosimetry community, and is demonstrated here in uniform, solution-processable nanocrystals.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.004

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 head0.009
GPT teacher head0.200
Teacher spread0.190 · 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; both teacher heads agree on what is shown here.

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

Citations34
Published2019
Admission routes2
Has abstractyes

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