MétaCan
Menu
Back to cohort
Record W1997107977 · doi:10.1039/c4tb01601g

Tunable stellate mesoporous silica nanoparticles for intracellular drug delivery

2014· article· en· W1997107977 on OpenAlex
Lin Xiong, Xin Du, Bingyang Shi, Jingxiu Bi, Freddy Kleitz, Shi‐Zhang Qiao

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.

Bibliographic record

VenueJournal of Materials Chemistry B · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceNanoparticleChemical engineeringNucleationParticle sizeDrug deliveryMesoporous silicaReagentMesoporous materialTriethanolamineNanotechnologyImineCatalysisOrganic chemistryChemistryAnalytical Chemistry (journal)

Abstract

fetched live from OpenAlex

Stellate mesoporous silica nanoparticles with special radial pore morphology were easily synthesized using triethanolamine as the base catalyst in a wide range of synthesis conditions. By adjusting the surfactant composition, reaction temperature and time, and reagent ratio, the particle size of the material could be tailored continuously ranging from 50 to 140 nm and the pore size from 2 to 20 nm. By analyzing the effects of different synthesis parameters, it is concluded that the particles are formed following a nucleation-growth mechanism and the reaction kinetics play an important role in determining the particle size and pore structure. These stellate MSNs can be conveniently functionalized with a nontoxic low molecular weight poly(ethylene imine) (PEI, 800 Da) by a delayed condensation method. The resulting nanocomposites not only possess auto-fluorescence for suitable particle tracking but also demonstrate good potential for intracellular delivery of the anticancer doxorubicin drug.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.012
Threshold uncertainty score0.997

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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