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Record W2785812992 · doi:10.1039/c7cc09402g

Facile meltPEGylation of flame-made luminescent Tb<sup>3+</sup>-doped yttrium oxide particles: hemocompatibility, cellular uptake and comparison to silica

2018· article· en· W2785812992 on OpenAlexaff
Kerda Keevend, Guido Panzarasa, Fabian H. L. Starsich, Martin Zeltner, Anastasia Spyrogianni, Elena Tsolaki, Giuseppino Fortunato, Sotiris E. Pratsinis, Sérgio Bertazzo, Inge K. Herrmann

Bibliographic record

VenueChemical Communications · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsYttriumLuminescenceDopingOxideMaterials scienceTerbiumRadiochemistryChemistryNuclear chemistryChemical engineeringInorganic chemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Flame aerosol technology is a versatile method for scalable synthesis of nanoparticles. Since particles are produced and collected in a dry state, dispersibility and further functionalization could pose hurdles to their biomedical use. We report on a one-pot, scalable and robust procedure for the PEGylation of flame-made yttria and silica nanoparticles. We demonstrate improved colloidal stability, attenuated activation of blood coagulation and decreased uptake into phagocytic cells, all of which pave the way for facilitated biomedical use of flame-made oxide 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.046
GPT teacher head0.311
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2018
Admission routes1
Has abstractyes

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