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Record W2095057358 · doi:10.1021/jp711319a

Preparation of Well-Dispersed Superparamagnetic Iron Oxide Nanoparticles in Aqueous Solution with Biocompatible<i>N</i>-Succinyl-<i>O</i>-carboxymethylchitosan

2008· article· en· W2095057358 on OpenAlexaff
Aiping Zhu, Lanhua Yuan, Sheng Dai

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2008
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsNational Research Council CanadaNational Institute for Nanotechnology
Fundersnot available
KeywordsBiocompatibilitySuperparamagnetismNanoparticleChemical engineeringMaterials scienceAdsorptionAqueous solutionIron oxide nanoparticlesCopolymerAmphiphilePolyelectrolyteChitosanNanotechnologyFerrofluidMagnetic nanoparticlesChemistryOrganic chemistryMagnetizationPolymerComposite material

Abstract

fetched live from OpenAlex

N -succinyl- O -carboxymethylchitosan (NSOCMCS), an amphiphilic polyelectrolyte with the property of biocompatibility and functional carboxyl groups, was used as a stabilizer to prepare a well-dispersed suspension of superparamagnetic Fe 3 O 4 nanoparticles, which were composed of a magnetite Fe 3 O 4 core and a NSOCMCS shell. The carboxyl groups of NSOCMCS can coordinate with Fe 3 O 4, which makes NSOCMCS chemically adsorb onto the surface of Fe 3 O 4 nanoparticles. The stabilizing mechanisms were proven to be both the steric hindrance and electrostatic repulsion arisen from the NSOCMCS. Transmission electron microscopy showed that the resulting Fe 3 O 4 particles were in spherical morphology with the diameter ranging from 12 to 18 nm. Magnetic property measurements indicated that NSOCMCS/Fe 3 O 4 nanoparticles preserved superparamagnetic behavior. In vitro biocompatibility studies showed that the NSOCMCS/Fe 3 O 4 nanoparticles had good cytocompatibility.

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 score0.002

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)0.0000.000

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 head0.009
GPT teacher head0.233
Teacher spread0.225 · 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

Citations55
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207