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Record W2018959999 · doi:10.1021/ma060467f

Self-Assembly of Latex Particles into Proton-Conductive Membranes

2006· article· en· W2018959999 on OpenAlexaff
Jun Gao, Yunsong Yang, David Lee, Steven Holdcroft, Barbara J. Frisken

Bibliographic record

VenueMacromolecules · 2006
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBC Innovation CouncilSimon Fraser University
Fundersnot available
KeywordsMembraneProtonElectrical conductorPolymer chemistryPolymer scienceMaterials scienceChemical engineeringChemistryComposite materialPhysicsNuclear physicsEngineering

Abstract

fetched live from OpenAlex

We present results from our investigation of the concept and practice of using surface-charged latex nanoparticles as building blocks for conductive membranes. Nanoparticles were synthesized in water by free-radical copolymerization of two hydrophobic monomers, butyl acrylate (BA) and methyl methacrylate (MMA), a cross-linker, N, N ‘-methylenebis(acrylamide) (BIS), and a charged monomer, sulfonate styrene sodium salt (NaSS). The resultant nanospheres were characterized with static and dynamic laser light scattering. Thin films were cast from dispersions of particles, followed by incubation at ∼110 °C to yield free-standing membranes. Conductivity, water uptake, and ion exchange capacity were measured. The membranes possess higher conductivities than both amorphous films cast from sulfonated poly(BA−MMA−styrene) ionomers and poly(BA−BIS−MMA−NaSS) gel films. TEM images provide visual evidence of particle and film structure and suggest the existence of continuous hydrophilic channels formed naturally through close-packing of surface-charged nanospheres. Neutron scattering confirms the particulate structure of the membranes.

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 score0.012
Threshold uncertainty score0.510

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 head0.193
Teacher spread0.189 · 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

Citations25
Published2006
Admission routes1
Has abstractyes

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