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Record W1980155485 · doi:10.1063/1.2165915

One-step synthesis of magnetic hollow silica and their application for nanomedicine

2006· article· en· W1980155485 on OpenAlexaff
Wei Wu, Mark A. DeCoster, Bron Daniel, J. F. Chen, Ming Yu, D. Cruntu, C.J. O’Connor, Weilie Zhou

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsNanomedicineNanoparticleMaterials scienceScanning electron microscopeMagnetic nanoparticlesTransmission electron microscopyChemical engineeringSuperparamagnetismNanotechnologyNuclear chemistryMagnetizationChemistryComposite materialMagnetic field

Abstract

fetched live from OpenAlex

Magnetic nanoparticles are usually present in the form of magnetic carriers and used in nanomedicine and biosystem. In this paper, magnetic hollow silica (MHS) nanoparticles were fabricated by a one-step synthesis of Fe3O4 nanoparticles and then coating of silica on nanosized spherical calcium carbonate under alkaline conditions, in which nanosized calcium carbonate (CaCO3, 25–60nm) was used as a scarified template, tetraethoxysilane as a precursor, and Fe3O4 nanoparticles (∼5nm), formed in the initial reaction stage, as magnetic agents. The as-synthesized nanoparticles were immersed in a weak acetic acidic solution to remove CaCO3, forming MHS carriers. The nanostructures of the MHS carriers were characterized by scanning electron microscope, transmission electron microscope, and x-ray diffraction. Superconducting quantum interference device measurement exhibited that the MHS nanoparticles were superparamagnetic. Toxicity was tested for MHS carriers using rat brain microvascular endothelial cells. The cells treated with concentration lower than 50μg∕ml of the MHS nanoparticles showed no significant toxicity. After modification by silane coupling agent, the MHS carriers have strong absorption for ibuprofen in nanomedicine field.

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 score0.021
Threshold uncertainty score0.358

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations24
Published2006
Admission routes1
Has abstractyes

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