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Record W3184420605 · doi:10.1039/d1nr03335b

Unveiling the role of surface, size, shape and defects of iron oxide nanoparticles for theranostic applications

2021· article· en· W3184420605 on OpenAlexaff
Geoffrey Cotin, Cristina Blanco-Andujar, Francis Perton, Laura Asín, Jesús M. de la Fuente, Wilfried Reichardt, Denise Schaffner, Dinh-Vu Ngyen, Damien Mertz, Céline Kiefer, Florent Meyer, Simo Spassov, Ovidiu Ersen, Michael Chatzidakis, Gianluigi A. Botton, Céline Henoumont, Sophie Laurent, Jean−Marc Grenèche, Francisco J. Terán, Daniel Ortega, Delphine Felder‐Flesch, Sylvie Bégin‐Colin

Bibliographic record

VenueNanoscale · 2021
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsMcMaster University
FundersEuropean Social FundInterregHorizon 2020 Framework ProgrammeConseil Régional d'AlsaceMinisterio de Ciencia e InnovaciónUniversidad de ZaragozaUniversité de StrasbourgInstitut National Du CancerMinisterio de Economía y CompetitividadFédération Wallonie-BruxellesWaalse GewestComunidad de MadridAgence Nationale de la RechercheDivision of Civil, Mechanical and Manufacturing InnovationEuropean Regional Development FundLabEx Chimie des Systèmes ComplexesEuropean CommissionFonds De La Recherche Scientifique - FNRSChina Scholarship Council
KeywordsNanoparticleMaterials scienceNanotechnologyCoatingDendrimerIron oxide nanoparticlesOxideIron oxideChemical engineeringMetallurgyPolymer chemistry

Abstract

fetched live from OpenAlex

experiments demonstrated that the magnetic heating capability of octopods occurs especially at low frequencies. The coupling of a small amount of glucose on dendronized octopods succeeded in internalizing them and showing an effect of MH on tumor growth. All measurements evidenced a particular signature of octopods, which is attributed to higher anisotropy, surface effects and/or magnetic field inhomogeneity induced by tips. This approach aiming at an analysis of the structure-property relationships is important to design efficient theranostic 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)0.000
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 score0.013
Threshold uncertainty score0.258

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 head0.228
Teacher spread0.221 · 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

Citations54
Published2021
Admission routes1
Has abstractyes

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