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Record W1992181493 · doi:10.1166/jnn.2006.432

Physical Stability and Lyophilization of Poly(<i>ε</i>-caprolactone)-b-Poly(ethyleneglycol)-b-Poly(<i>ε</i>-caprolactone) Micelles

2006· article· en· W1992181493 on OpenAlexaff
Yong Hu, Yin Ding, Yuan Li, Xiqun Jiang, Changzheng Yang, Yonghua Yang

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2006
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsMicelleCaprolactoneMaterials sciencePEG ratioChemical engineeringAqueous solutionHydrogen bondPolymer chemistryCopolymerNuclear chemistryPolymerOrganic chemistryChemistryMoleculeComposite material

Abstract

fetched live from OpenAlex

The stability and lyophilization of core-shell PCL-PEG-PCL micelles were investigated by fluorescence spectra, DLS, DSC, WAXD, and FT-IR. The prepared micelles were not stable when they were stored in aqueous dispersion under different condition. Their size increased in the first 20 days and decreased gradually when the storage period was extended. Lyophilization experiment showed that the cryoprotective agent (glucose) was an essential additive to protect the micelles from aggregating during the lyophilization process. After lyophilizing and re-dispersion, the PCL-PEG-PCL micelles became larger in size compared to as-prepared ones. DSC, WAXD, and IR measurements indicated the hydrogen bonding was formed between the hydroxyl group in glucose and the carbonyl group in PCL-PEG-PCL micelles. The effect of added glucose on protection of micelles from aggregation can be explained by the formation of hydrogen bonding with PCL-PEG-PCL micelles and the formation of solid glucose matrix.

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)0.001
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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.015
GPT teacher head0.222
Teacher spread0.208 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

Explore more

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