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Record W4387186608 · doi:10.3390/ma16196483

Fe3O4 Magnetic Nanoparticles Obtained by the Novel Aerosol-Based Technique for Theranostic Applications

2023· article· en· W4387186608 on OpenAlexaff
P. Pawlik, Barbara Błasiak, M. Pruba, Arkadiusz Miaskowski, Oskar Moraczyński, Justyna Miszczyk, Bogusław Tomanek, Joanna Depciuch

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Alberta
FundersNarodowe Centrum Nauki
KeywordsMaterials scienceNanoparticleAerosolMagnetiteDispersion (optics)NanotechnologyMagnetic nanoparticlesMagnetic hyperthermiaAgglomerateDispersion stabilityChemical engineeringChemistryComposite materialOpticsMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

This work is aimed at presenting a novel aerosol-based technique for the synthesis of magnetite nanoparticles (Fe3O4 NPs) and to assess the potential medical application of their dispersions after being coated with TEA-oleate. Refinement of the processing conditions led to the formation of monodispersed NPs with average sizes of ∼5–6 nm and narrow size distribution (FWHM of ∼3 nm). The NPs were coated with Triethanolammonium oleate (TEA-oleate) to stabilize them in water dispersion. This allowed obtaining the dispersion, which does not sediment for months, although TEM and DLS studies have shown the formation of small agglomerates of NPs. The different behaviors of cancer and normal cell lines in contact with NPs indicated the diverse mechanisms of their interactions with Fe3O4 NPs. Furthermore, the studies allowed assessment of the prospective theranostic application of magnetite NPs obtained using the aerosol-based technique, particularly magnetic hyperthermia and magnetic resonance imaging (MRI).

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 categoriesInsufficient payload (model declined to judge)
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 score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.253
Teacher spread0.236 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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