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Record W2030235507 · doi:10.1021/la902260j

Surface Modification of Upconverting NaYF<sub>4</sub> Nanoparticles with PEG−Phosphate Ligands for NIR (800 nm) Biolabeling within the Biological Window

2009· article· en· W2030235507 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLangmuir · 2009
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsPEG ratioSurface modificationNanoparticleChemistryPhosphateNanotechnologyMaterials scienceChemical engineeringBiochemistry

Abstract

fetched live from OpenAlex

We present a technique for the replacement of oleate with a PEG-phosphate ligand [PEG = poly(ethylene glycol)] as an efficient method for the generation of water-dispersible NaYF(4) nanoparticles (NPs). The PEG-phosphate ligands are shown to exchange with the original oleate ligands on the surface of the NPs, resulting in water-dispersible NPs. The upconversion intensity of the NPs in aqueous environments was found to be severely quenched when compared to the original NPs in organic solvents. This is attributed to an increase in the multiphonon relaxations of the lanthanide excited state in aqueous environments due to high energy vibrational modes of water molecules. This problem could be overcome partially by the synthesis of core/shell NPs which demonstrated improved photophysical properties in water over the original core NPs. The PEG-phosphate coated upconverting NPs were then used to image a line of ovarian cancer cells (CaOV3) to demonstrate their promise in biological application.

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.

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 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 score0.004
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.033
GPT teacher head0.256
Teacher spread0.223 · 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