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Record W2164288668 · doi:10.1002/app.28296

Synthesis and characterization of emulsion polymerized mixed matrix aluminum silicate/poly(2,6‐dimethyl 1,4‐phenylene oxide) films

2008· article· en· W2164288668 on OpenAlexaff
Forouzan Sadeghi, André Y. Tremblay, Boguslaw Kruczek

Bibliographic record

VenueJournal of Applied Polymer Science · 2008
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceTetraethyl orthosilicateEmulsionEmulsion polymerizationPhenyleneChemical engineeringSilicateDifferential scanning calorimetryPolymerizationOxidePolymer chemistryPolymerComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Abstract A series of poly (2,6‐dimethyl‐1,4‐phenylene oxide) (PPO)‐based organic/inorganic films for the potential application in membrane gas separation were prepared by employing a method in which aluminum hydroxonitrate contained in a stable water‐in‐oil (W/O) emulsion, the oil phase being a solution of PPO in trichloroethylene, was mixed with a homogeneous solution of PPO in trichloroethylene containing tetraethyl orthosilicate (TEOS). Inorganic polymerization occurred in or at the surface of the aqueous droplets of the W/O emulsion. Subsequently, thin films were prepared by a spin coating technique, and they were referred to as emulsion polymerized mixed matrix (EPMM) films. Scanning electron micrographs taken from a film cross section indicated the presence of particles in the PPO matrix, and energy dispersive X‐ray measurements showed that the embedded particles contained Al and Si elements. Differential scanning calorimetry analysis showed a decrease in the glass transition of the EPMM films with increase of TEOS loading. The compatibility between aluminum silicate nanoparticles and PPO in the EPMM films was confirmed by air separation tests. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

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 score0.011
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.218
Teacher spread0.209 · 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

Citations17
Published2008
Admission routes1
Has abstractyes

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