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Record W1965214515 · doi:10.1039/c2jm31749d

Fe3O4–silica core–shell nanoporous particles for high-capacity pH-triggered drug delivery

2012· article· en· W1965214515 on OpenAlexafffund
Xuefeng Zhang, Said Mansouri, Liviu Clime, Hung Q. Ly, L’H. Yahia, Teodor Veres

Bibliographic record

VenueJournal of Materials Chemistry · 2012
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsMontreal Heart InstituteUniversité de MontréalMcGill UniversityPolytechnique MontréalNational Research Council Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMcMaster University
KeywordsNanoporousNanoparticleAqueous solutionDrug deliveryChemical engineeringHomogeneousMaterials scienceNanotechnologyAnnealing (glass)Amine gas treatingChemistryCombinatorial chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

We demonstrate a one-step procedure for the synthesis of Fe3O4–silica core–shell nanoparticles with hierarchically ultra-large pores independent of any post-treatment such as annealing and template-molecule removal. The nanoporous silica shells with available amine groups were functionalized by clickable linkers to produce pH-sensitive amides for regulating the release of an anti-cancer drug, doxorubicin (DOX). The loading amount of DOX reached up to 13.2 mg per 100 mg nanoparticles, 74.2% of which can be effectively released after 63 h at body temperature and pH 5 with decreased side effects. Such excellent features of these nanoparticles appear to arise from the integrated hierarchically ultra-large open-porosities and a homogeneous dispersibility in aqueous solution that has a great potential for their use as drug delivery systems.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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.

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

Citations50
Published2012
Admission routes2
Has abstractyes

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