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Record W2996416976 · doi:10.1063/1.5129944

Shape-controlled MnO nanoparticles as <i>T</i>1 MRI contrast agents

2019· article· en· W2996416976 on OpenAlexafffund
Abhinandan Banerjee, Weiwei Zeng, M. Taheri, Barbara Błasiak, Bogusław Tomanek, Simon Trudel

Bibliographic record

VenueAIP Advances · 2019
Typearticle
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationAlberta Innovates - Health SolutionsUniversity of Calgary
KeywordsNanoparticleMagnetic resonance imagingContrast (vision)Materials scienceAnisotropyMagnetic nanoparticlesNuclear magnetic resonanceNanotechnologyVisualizationBiomedical engineeringComputer scienceOpticsRadiologyPhysicsComputer vision

Abstract

fetched live from OpenAlex

Magnetic resonance imaging is a non-invasive imaging method that offers high-resolution, high quality in vivo visualization for medical diagnostics. Magnetic nanoparticles (NPs) containing Mn2+ offer an attractive alternative to Gd-based molecular contrast agents for T1 MRI. In this work, we show that highly anisotropic MnO NPs can be generated from a single precursor using simple synthetic protocols. These anisotropic morphologies offer better contrast augmentation when compared to spherical MnO NPs of similar sizes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.062
Threshold uncertainty score0.995

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.006

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 head0.011
GPT teacher head0.257
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2019
Admission routes2
Has abstractyes

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