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Record W2316074314 · doi:10.1021/la402007d

One-Pot Synthesis of Iron Oxide Nanoparticles with Functional Silane Shells: A Versatile General Precursor for Conjugations and Biomedical Applications

2013· article· en· W2316074314 on OpenAlexaff
Vinith Yathindranath, Zhizhi Sun, Matthew Worden, Lynda J. Donald, James A. Thliveris, Donald W. Miller, Torsten Hegmann

Bibliographic record

VenueLangmuir · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOleylamineX-ray photoelectron spectroscopyEthylene glycolBiocompatibilityNanoparticleIron oxide nanoparticlesSurface modificationFourier transform infrared spectroscopyChemistrySelected area diffractionNuclear chemistryTransmission electron microscopySilaneBovine serum albuminMaterials scienceChemical engineeringNanotechnologyOrganic chemistryBiochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Iron oxide nanoparticles (IONPs) and their surface modifications with therapeutic or diagnostic (theranostic, TN) agents are of great interest. Here we present a novel one-pot synthesis of a versatile general TN precursor (aminosilane-coated IONPs [IONP-Sil(NH2)]) with surface amine groups. Surface functional group conversion to carboxylic acid was accomplished by conjugating poly(ethylene glycol) diacid to IONP-Sil(NH2). The NPs were characterized using powder X-ray diffraction (XRD), transmission electron microscopy (TEM), high-resolution TEM, selected area electron diffraction (SAED), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared (FT-IR) spectroscopy. Biocompatibility and cell uptake profile of the nanoparticles were evaluated in-vitro using cultured liver cells (HepG2). Oleylamine (hydrophobic) and bovine serum albumin (BSA) as model drugs were attached to IONP-Sil-PEG(COOH). The ability of IONP-Sil(NH2) to bind small interfering RNA (siRNA) is also shown.

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 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 score0.014
Threshold uncertainty score0.438

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)0.0000.000

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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.

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

Citations45
Published2013
Admission routes1
Has abstractyes

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