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Record W2756320170 · doi:10.21767/2471-9838.100022

Dual Mode Atomic Force Microscopy and Interferometric Scattering Imaging of Single Au@Fe3O4 Nanoshell Synthesized for Biomedical Applications

2017· article· en· W2756320170 on OpenAlexaff
Mohammad E. Khosroshahi, Lida Ghazanfari, Zahra Hasannejad

Bibliographic record

VenueNano Research & Applications · 2017
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceMaghemiteNanoshellInterferometryNanoparticleSuperparamagnetismScatteringMicroscopyResolution (logic)Magnetic force microscopeOpticsAnalytical Chemistry (journal)MagnetizationNanotechnologyMagnetic fieldChemistryPhysics

Abstract

fetched live from OpenAlex

Imaging based on interferometric scattering (iSCAT) enhances the accuracy and temporal resolution in comparison to single-emitter-based techniques. In this research, we describe a combined method, which measures the size of individual magneto plasmonic Nano shells (MPNSs). The maghemite (Fe3O4) nanoparticles (SPIONs) with core diameter of 9.5 nm ± 1.4 nm is prepared by co-precipitation and coated by gold. The final dimension of polyvinyl pyrrolidine (PVP) stabilized MPNSs are 15.8 nm ± 3.5 nm measured by TEM. UV-Vis spectrophotometer and vibrating sample magnetometer (VSM) were used to study the optical and magnetization properties. The size of these nanoshells is determined independently by correlating their iSCAT and atomic force microscopy (AFM) images. By analyzing the number of single MPNSs, an interference intensity distribution is obtained with a nominal diameter of 15.8 nm in agreement with the size distribution recorded by TEM. It seems, the combination of iScat and AFM is capable of producing high resolution images of individual nanoparticles.

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)0.001
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 score0.427
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.350
Teacher spread0.314 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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