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Record W4399608983 · doi:10.1002/marc.202400205

Water‐Soluble Reversible Photo‐Cross‐Linking Polymer Dielectrics

2024· article· en· W4399608983 on OpenAlexafffund
Menandro Cruz, Sophia P. McKillop, Vanessa Tischler, Benoît H. Lessard

Bibliographic record

VenueMacromolecular Rapid Communications · 2024
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsMethacrylateMaterials sciencePolymerMethacrylic acidCopolymerPolymer chemistryMonomerSulfonateChemical engineeringStyrenePhotopolymerComposite material

Abstract

fetched live from OpenAlex

Effective recycling of mixed materials requires the separation of the different components without the need for toxic solvents. One approach involves utilizing a water-soluble coating with reversible photo-cross-linkers, making it robust until end of life where it can then be dissolved in water after de-cross-linking. Here, a novel coumarin methacrylate monomer and its nitroxide-mediated copolymerization to create poly((methacrylic acid)-co-(styrene sulfonate)-co-(coumarin methacrylate)) for water-soluble thin films are reported. Under exposure to light, the coumarin functional groups produce reversible [2+2] cycloadditions which cross-link the resulting polymer films, making them no longer water soluble. Characterization of reversible cross-linking behavior is reported through changes in contact angle and in situ rheological characterization. The resulting polymers are successfully integrated into metal-insulator-metal capacitors, demonstrating the potential use for water-soluble reversible photo-cross-linkable dielectric materials for organic electronics.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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