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Record W2418521523 · doi:10.1039/c6cp02094a

Manganese doped-iron oxide nanoparticle clusters and their potential as agents for magnetic resonance imaging and hyperthermia

2016· article· en· W2418521523 on OpenAlexaff
Maria Francesca Casula, Erika Conca, Ioanna Bakaimi, Ayyappan Sathya, Maria Elena Materia, Alberto Casu, Andrea Falqui, Elisa Sogne, Teresa Pellegrino, Antonios G. Kanaras

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsManganeseMagnetic resonance imagingNanoparticleDopingIron oxideManganese oxideNuclear magnetic resonanceMaterials scienceMagnetic nanoparticlesMagnetic hyperthermiaIron oxide nanoparticlesHyperthermiaNanotechnologyChemistryOptoelectronicsMedicineMetallurgyRadiologyPhysicsInternal medicine

Abstract

fetched live from OpenAlex

A simple, one pot method to synthesize water-dispersible Mn doped iron oxide colloidal clusters constructed of nanoparticles arranged into secondary flower-like structures was developed. This method allows the successful incorporation and homogeneous distribution of Mn within the nanoparticle iron oxide clusters. The formed clusters retain the desired morphological and structural features observed for pure iron oxide clusters, but possess intrinsic magnetic properties that arise from Mn doping. They show distinct performance as imaging contrast agents and excellent characteristics as heating mediators in magnetic fluid hyperthermia. It is expected that the outcomes of this study will open up new avenues for the exploitation of doped magnetic nanoparticle assemblies in biomedicine.

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 score0.030
Threshold uncertainty score0.931

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 head0.222
Teacher spread0.215 · 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

Citations77
Published2016
Admission routes1
Has abstractyes

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