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Record W2948933696 · doi:10.1039/c9ra00798a

Fe<sup>3+</sup>-codoped ultra-small NaGdF<sub>4</sub>:Nd<sup>3+</sup> nanophosphors: enhanced near-infrared luminescence, reduced particle size and bioimaging applications

2019· article· en· W2948933696 on OpenAlexaff
Yabing Li, Fujin Li, Yanan Huang, Haiyan Wu, Jian Wang, Jin Yang, Qingbo Xiao, Hongzhen Lin

Bibliographic record

VenueRSC Advances · 2019
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsL'Alliance Boviteq
FundersNational Key Research and Development Program of ChinaState Key Laboratory of Structural ChemistryChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsLuminescenceCoprecipitationNanoparticleLanthanideMaterials scienceParticle sizeDopingNanocrystalNear-infrared spectroscopyIonParticle (ecology)MoleculeInfraredAnalytical Chemistry (journal)Luminescent MeasurementsNanotechnologyChemistryOptoelectronicsInorganic chemistryPhysical chemistryOpticsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

NPs were successfully applied as luminescent probes for targeted NIR imaging of tumors in biological tissues. Moreover, they also show great potential as a high contrast agent for T2-weighted MRI imaging.

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 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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.231
Teacher spread0.222 · 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 teacher head, not a consensus.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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