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Record W2323479698 · doi:10.1016/j.dib.2016.03.104

Washing effect on superparamagnetic iron oxide nanoparticles

2016· article· en· W2323479698 on OpenAlex
L.K. Mireles, E. Sacher, Sophie Laurent, Dimitri Stanicki

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueData in Brief · 2016
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsPolytechnique Montréal
FundersFonds de Recherche du Québec - SantéFonds de recherche du QuébecFonds Québécois de la Recherche sur la Nature et les TechnologiesFonds De La Recherche Scientifique - FNRSEuropean Cooperation in Science and TechnologyEuropean Regional Development FundAppalachian Regional Commission
KeywordsNanotechnologyNanoparticleSuperparamagnetismNanoscopic scaleMaterials scienceIron oxide nanoparticlesIron oxideChemical engineeringMetallurgyEngineeringMagnetizationPhysics

Abstract

fetched live from OpenAlex

Much recent research on nanoparticles has occurred in the biomedical area, particularly in the area of superparamagnetic iron oxide nanoparticles (SPIONs); one such area of research is in their use as magnetically directed prodrugs. It has been reported that nanoscale materials exhibit properties different from those of materials in bulk or on a macro scale [1]. Further, an understanding of the batch-to-batch reproducibility and uniformity of the SPION surface is essential to ensure safe biological applications, as noted in the accompanying article [2], because the surface is the first layer that affects the biological response of the human body. Here, we consider a comparison of the surface chemistries of a batch of SPIONs, before and after the supposedly gentle process of dialysis in water.

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.

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 score0.154
Threshold uncertainty score0.596

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 science0.0010.000
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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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