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Record W2345038446 · doi:10.1021/acsami.6b01460

Bright YAG:Ce Nanorod Phosphors Prepared via a Partial Wet Chemical Route and Biolabeling Applications

2016· article· en· W2345038446 on OpenAlexaff
Daidong Guo, Baojin Ma, Lili Zhao, Jichuan Qiu, Wei Liu, Yuanhua Sang, Jérôme P. Claverie, Hong Liu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsUniversité du Québec à Montréal
FundersGovernment of Shandong ProvinceMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMaterials sciencePhosphorNanorodNanotechnologyChemical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

Cerium-doped yttrium aluminum garnet (YAG:Ce) nanorods were prepared via a partial wet chemical route followed by a calcination process by using Al2O3 nanorods as both templates and the reactant. These novel well-crystallized YAG:Ce phosphors with a 200-300 nm diameter and a 2-3 μm length have a high specific surface area while being virtually devoid of surface defects. The YAG:Ce nanorod phosphors possess good luminescent properties compared with granular YAG:Ce phosphors. Photoluminescence quantum yields of YAG:Ce nanorod phosphors are higher than those of granular ones. The YAG:Ce nanorod phosphors exhibit two luminescent decay times due to their unique morphology. The YAG:Ce nanorods exhibited good cytocompatibility with bone marrow mesenchymal stem cells and can be used as biolabel nanoparticles in bioimaging.

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 science0.0000.000
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations29
Published2016
Admission routes1
Has abstractyes

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Same venueACS Applied Materials & InterfacesSame topicLuminescence Properties of Advanced MaterialsFrench-language works237,207