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Record W2948867098 · doi:10.1002/anie.201904445

Intrinsic Time‐Tunable Emissions in Core–Shell Upconverting Nanoparticle Systems

2019· review· en· W2948867098 on OpenAlexafffund
Gabriella Tessitore, Steven L. Maurizio, Tarek Sabri, John A. Capobianco

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typereview
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExcited stateLuminescenceIonDopantMaterials scienceNanoparticleExcitationWavelengthPopulationPhoton upconversionFluorescenceOptoelectronicsPhotochemistryAtomic physicsDopingChemistryNanotechnologyOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Color‐tunable luminescence has been extensively investigated in upconverting nanoparticles for diverse applications, each exploiting emissions in different spectral regions. Manipulation of the emission wavelength is accomplished by varying the composition of the luminescent material or the characteristics of the excitation source. Herein, we propose core–shell β‐NaGdF 4 : Tm 3+ , Yb 3+ /β‐NaGdF 4 : Tb 3+ nanoparticles as intrinsic time‐tunable luminescent materials. The time dependency of the emission wavelength only depends on the different decay time of the two emitters, without additional variation of the dopant concentration or pumping source. The time‐tunable emission was recorded with a commercially available camera. The dynamics of the emissions is thoroughly investigated, and we established that the energy transfer from the 1 D 2 excited state of Tm 3+ ions to the higher energy excited states of Tb 3+ ions to be the principal mechanism to the population of the 5 D 4 level for the Tb 3+ ions.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.003

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 head0.057
GPT teacher head0.317
Teacher spread0.259 · 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
GenreReview

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

Citations36
Published2019
Admission routes2
Has abstractyes

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