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Record W4200254491 · doi:10.1039/d1tb01644j

Polymer brush coated upconverting nanoparticles with improved colloidal stability and cellular labeling

2021· article· en· W4200254491 on OpenAlexafffund
Vaidas Klimkevičius, Evelina Voronovič, Greta Jarockytė, Artiom Skripka, Fiorenzo Vetrone, Ričardas Rotomskis, Artūras Katelnikovas, Vitalijus Karabanovas

Bibliographic record

VenueJournal of Materials Chemistry B · 2021
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersLietuvos Mokslo TarybaFonds de recherche du Québec – Nature et technologiesCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsBrushMaterials scienceColloidPolymerNanoparticleNanotechnologyPolymer brushChemical engineeringComposite materialPolymerization

Abstract

fetched live from OpenAlex

the reversible addition-fragmentation chain transfer (RAFT) polymerization method). Polymers containing different amounts of PEGylated and carboxylic groups were studied. Coating with both polymers increased the upconversion (UC) emission intensity and photoluminescence lifetime values of the UCNPs, which directly translates to more efficient cancer cell labeling nanoprobes. The polymer composition plays a crucial role in the modification of UCNPs, not only with respect to their colloidal stability, but also with respect to the cellular uptake. Colloidally unstable bare UCNPs aggregate in cell culture media and precipitate, rendering themselves unsuitable for any biomedical use. However, stabilization with polymers prevents UCNPs from aggregation, increases their uptake in cells, and improves the quality of cellular labeling. This investigation sheds light on the appropriate coating for UCNPs and provides relevant insights for the rational development of imaging and therapeutic tools.

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 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 score0.665

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 science0.0000.000
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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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