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Record W2078962730 · doi:10.1021/cm052067x

Multifunctional Nano-Architecture for Biomedical Applications

2006· article· en· W2078962730 on OpenAlexaff
Dongling Ma, Jingwen Guan, Franςois Normandin, Stéphane Dénommée, G.D. Enright, Teodor Veres, Benoît Simard

Bibliographic record

VenueChemistry of Materials · 2006
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsSuperparamagnetismMaterials scienceLuminescenceNanoparticleNanotechnologyTransmission electron microscopySpectroscopySurface modificationNanomaterialsChemical engineeringNano-OptoelectronicsMagnetizationComposite material

Abstract

fetched live from OpenAlex

A multifunctional architecture for biomedical applications has been developed by deliberately combining the useful functions of superparamagnetism, luminescence, and surface functionality into one material. Good control of the core−shell architecture has been achieved by employing a sol−gel synthesis. Superparamagnetic iron oxide nanoparticles are first coated with silica to isolate the magnetic core from the surrounding. Subsequently, the dye molecules are doped inside a second silica shell to improve photostability and allow for versatile surface functionalities. The architecture has been characterized by transmission electron microscopy, UV−vis absorption and emission spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and magnetometry. The hybrid nanoparticles exhibit improved superparamagnetic behavior over the as-received nanoparticles with a significant decrease in the blocking temperature. The architecture shows emission properties similar to those of the free dye molecules, suggesting that the first silica shell successfully prevents luminescence quenching by minimizing dye−magnetic core interactions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations191
Published2006
Admission routes1
Has abstractyes

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