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Record W4386158391 · doi:10.1021/acsomega.3c01202

Effect of Photoinitiators Doped in PDMS for Femtosecond-Laser Writing: Characterization and Outcomes

2023· article· en· W4386158391 on OpenAlexafffund
A. R. Hlil, Jean-Sébastien Boisvert, Hatem M. Titi, Yalina García‐Puente, Wagner Correr, Sébastien Loranger, Jyothis Thomas, Ali Riaz, Younès Messaddeq, Raman Kashyap

Bibliographic record

VenueACS Omega · 2023
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsMcGill UniversityPolytechnique MontréalUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolydimethylsiloxaneFemtosecondPhotopolymerPhotoinitiatorLaserPolymerRefractive indexComposite materialBenzophenonePolymer chemistryOptoelectronicsOpticsPolymerization

Abstract

fetched live from OpenAlex

Herein, we have characterized in depth the effect of femtosecond (fs)-laser writing on various polydimethylsiloxane (PDMS)-based composites. The study combines systematic and nanoscale characterizations for the PDMS blends that include various photoinitiators (organic and inorganic agents) before and after fs-laser writing. The results exhibit that the photoinitiators can dictate the mechanical properties of the PDMS, in which Young's modulus of PDMS composites has higher elasticity. The study illustrates a major improvement in refractive index change by 15 times higher in the case of PDMS/BP-Ge [benzophenone (BP) allytriethylgermane] and Irgacure 184. Additional enhancement was achieved in the optical performance levels of the PDMS composites (the PDMS composites of Irgacure 184/500, BP-Ge, and Ge-ATEG have a relative difference of less than 5% in comparison with pristine PDMS), which are on par with glasses. This insightful study can guide future investigators in choosing photoinitiators for particular applications in photonics and polymer chemistry.

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 score0.026
Threshold uncertainty score0.384

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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