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Record W2407185709 · doi:10.3390/nano6060100

Fabricating Water Dispersible Superparamagnetic Iron Oxide Nanoparticles for Biomedical Applications through Ligand Exchange and Direct Conjugation

2016· article· en· W2407185709 on OpenAlex
Tina Lam, Pramod Avti, Philippe Pouliot, Foued Maafi, Jean‐Claude Tardif, Éric Rhéaume, Frédéric Lesage, Ashok Kakkar

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.

Bibliographic record

VenueNanomaterials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversité de MontréalPolytechnique MontréalMontreal Heart InstituteMcGill University
Fundersnot available
KeywordsLigand (biochemistry)NanoparticlePhosphonateColloidMagnetic nanoparticlesMaterials scienceIron oxide nanoparticlesNanotechnologyChemistryChemical engineeringIron oxideOrganic chemistry

Abstract

fetched live from OpenAlex

Stable superparamagnetic iron oxide nanoparticles (SPIONs), which can be easily dispersed in an aqueous medium and exhibit high magnetic relaxivities, are ideal candidates for biomedical applications including contrast agents for magnetic resonance imaging. We describe a versatile methodology to render water dispersibility to SPIONs using tetraethylene glycol (TEG)-based phosphonate ligands, which are easily introduced onto SPIONs by either a ligand exchange process of surface-anchored oleic-acid (OA) molecules or via direct conjugation. Both protocols confer good colloidal stability to SPIONs at different NaCl concentrations. A detailed characterization of functionalized SPIONs suggests that the ligand exchange method leads to nanoparticles with better magnetic properties but higher toxicity and cell death, than the direct conjugation methodology.

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 score0.007
Threshold uncertainty score0.556

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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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