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Record W1710908321 · doi:10.1139/cjc-2014-0263

Synthesis, self-assembly, and degradation of amphiphilic triblock copolymers with fully photodegradable hydrophobic blocks

2014· article· en· W1710908321 on OpenAlexafffundvenue
James T. McIntosh, Ali Nazemi, Colin Bonduelle, Sébastien Lecommandoux, Elizabeth R. Gillies

Bibliographic record

VenueCanadian Journal of Chemistry · 2014
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmphiphilePolymersomeDynamic light scatteringCopolymerChemistryEthylene glycolSelf-assemblyPolymerVesicleChemical engineeringPolymer chemistryNanotechnologyOrganic chemistryMaterials scienceNanoparticleMembrane

Abstract

fetched live from OpenAlex

The development of stimuli-responsive materials is of significant interest for many applications including drug delivery, medical imaging, sensors, and microfluidic devices. Among the available stimuli, light is particularly attractive as it can be applied with high spatial and temporal resolution. We describe here the synthesis of amphiphilic triblock copolymers composed of poly(ethylene glycol) and a hydrophobic block containing o-nitrobenzyl esters throughout the backbone using copper-catalyzed azide–alkyne cycloaddition chemistry. These materials were designed to have a high weight fraction of the hydrophobic block to favour nonmicellar aggregates. The self-assembly in water was studied using nanoprecipitation and the resulting assemblies were characterized by dynamic light scattering and transmission electron microscopy. Under optimized conditions, it was possible to prepare polymer vesicles, commonly referred to as polymersomes, with diameters of approximately 100 nm. The degradation of these materials in response to UV light was studied by spectroscopy, light scattering, and electron microscopy, demonstrating that the vesicles were broken down. These results suggest the potential of these materials for applications such as encapsulation and release.

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)0.000
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 score0.005
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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

Citations10
Published2014
Admission routes3
Has abstractyes

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