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Record W1979252879 · doi:10.1021/bm034458f

Characterization of Nanostructure of Stimuli-Responsive Polymeric Composite Membranes

2004· article· en· W1979252879 on OpenAlexaff
Kai Zhang, Huiyu Huang, Guocheng Yang, James E. Shaw, Christopher M. Yip, Xiao Yu Wu

Bibliographic record

VenueBiomacromolecules · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMembranePolyelectrolyteChemical engineeringNanoparticleScanning electron microscopeMaterials scienceNanostructureX-ray photoelectron spectroscopyCoatingComposite numberChemistryPolymer chemistryNanotechnologyPolymerComposite material

Abstract

fetched live from OpenAlex

To elucidate the mechanism of stimuli-responsive permeability and to optimize the design, the nanostructure of polymeric composite membranes, developed in our laboratory, was characterized. The membranes were prepared to contain various amounts of stimuli-responsive nanoparticles of poly(N-isopropylacrylamide-co-methacrylic acid), with or without polyelectrolyte coating. Scanning electron microscopy and X-ray photoelectron spectroscopy were used respectively to examine the morphology and surface chemical composition, whereas atomic force microscopy and laser scanning confocal microscopy were employed to characterize the in situ surface and internal structure of the membranes in aqueous media of various pHs. The porous structure was evidenced in the presence of the nanoparticles. The surface content of the nanoparticles increased with increasing particle concentration while the polyelectrolyte coating was nearly undetectable. AFM images revealed that the particles in the membranes shrank with a concomitant increase in pore size as the buffer pH decreased. LSCM results indicated that particles were distributed through the membrane as interconnected clusters.

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 score0.626

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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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

Citations46
Published2004
Admission routes1
Has abstractyes

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