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Record W4399661210 · doi:10.1002/marc.202400200

Crosslinking Vinyl‐Addition Polynorbornenes via Difunctional Diazirines to Generate Low Dielectric‐Constant and Low Dielectric‐Loss Thermosets

2024· article· en· W4399661210 on OpenAlexaff
Pramod Kandanarachchi, Gerhard Meyer, Stefania F. Musolino, Jeremy E. Wulff, Larry F. Rhodes

Bibliographic record

VenueMacromolecular Rapid Communications · 2024
Typearticle
Languageen
FieldChemistry
TopicN-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDielectricThermosetting polymerMaterials sciencePolymer chemistryDielectric lossComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Thermosets having low dielectric constant ( D k < 3) and low dielectric dissipation factor ( D f < 0.003), high glass transition temperature ( T g > 150 °C), and good adhesion to copper are desirable for the low loss layers of the copper clad laminates (CCL) in next generation printed circuit boards. Three different difunctional diazirines are evaluated for both thermal and photochemical crosslinking of a high T g vinyl‐addition polynorbornene resin: poly(5‐hexyl‐1‐norbornene) (poly(HNB)). The substrate polymer, crosslinked by the carbenes generated from the activated diazirines, forms thermosets with D k < 2.3 and D f < 0.001 at 10 GHz depending on the identity of the diazirine and the loading. The D k and D f values for one composition are stable for 1600 h at 125 °C in air and for 1400 h at 85 °C and 85% relative humidity, suggesting good long‐term reliability of this thermoset. Adhesion of poly(HNB) to copper can be enhanced by priming the copper surface with a diazirine prior to high temperature lamination; peel strength values of greater than 7.5 N cm −1 are achieved. Negative‐tone photopatterning of poly(HNB) with diazirines upon exposure to 365 nm light is demonstrated.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.087
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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.

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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

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