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Record W2031238897 · doi:10.1021/cm049083z

Photocuring and Photolithography of Proton-Conducting Polymers Bearing Weak and Strong Acids

2004· article· en· W2031238897 on OpenAlexaff
Jennifer Schmeisser, Steven Holdcroft, Jianfei Yu, Tran B. Ngo, Ged McLean

Bibliographic record

VenueChemistry of Materials · 2004
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSurgical Specialties (Canada)Simon Fraser University
FundersCenters for Disease Control and Prevention
KeywordsPeekPolymer chemistryPolymerPolyelectrolytePolysulfoneMaterials scienceEtherMembraneChemical engineeringProtonComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A series of novel conformable proton-conducting thin films has been prepared from photocurable liquid polyelectrolytes. Films prepared in such a fashion show promise for engineering unconventionally shaped proton-exchange membranes using photolithographic techniques. These films are semi-interpenetrating networks (semi-IPN) comprising a linear proton-conducting guest polymer, sulfonated poly[ether ether ketone] (S-PEEK), in the presence of a statistically cross-linked host polymer matrix comprising divinyl sulfone, vinylphosphonic acid, and acrylonitrile. Film properties ranging from brittle and fragile to robust and flexible have been obtained, depending on the ratio of host/guest composition used. The extent of dissociation of the weak acid component, vinylphosphonic acid, is strongly dependent on the S-PEEK content. Proton conductivity, water content, and lambda values are dependent on the membrane composition and degree of cross-linking. Proton conductivities similar to those of pure S-PEEK, ∼0.07 S/cm, have been observed for a semi-IPN containing as little as 35 wt % S-PEEK.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.203
Teacher spread0.192 · 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

Citations19
Published2004
Admission routes1
Has abstractyes

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