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Record W2851897062 · doi:10.1063/1.5039849

Femtosecond laser-induced porosity on poly(ethylene) surfaces—A crystallographic and rheological study

2018· article· en· W2851897062 on OpenAlexafffund
Youssef Assaf, Mark Zhao, Anne‐Marie Kietzig

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Council
KeywordsMaterials sciencePorosityComposite materialFemtosecondCrystallinityHigh-density polyethylenePolyethyleneLaserOptics

Abstract

fetched live from OpenAlex

In recent years, femtosecond (fs) laser irradiation of polymer surfaces has been shown to produce novel porous topologies that present a multi-scale roughness. In our study, grazing incidence x-ray diffractometry results showed the appearance of a monoclinic crystalline phase on fs laser machined high density polyethylene (HDPE) which is a deformation of the orthorhombic phase typically observed in pristine HDPE. This was accompanied by a local decrease in crystallinity. These findings confirmed that the induced porosity was a consequence of the rapid quenching of a superheated melt layer undergoing phase explosion. In addition, several poly(ethylene) samples with different average molecular weights were machined under the same conditions. Scanning electron micrography, along with small angle oscillatory shear tests, indicated that the final pore size decreases with increasing average molecular weight or increasing melt viscosity since these parameters act against bubble growth during phase explosion. In addition, through computed tomography, the internal structure of the porous layer was investigated. Through this technique, the average pore size and pore size distribution can be computed by chord length analysis. In conclusion, in this study, the mechanisms behind the formation of femtosecond laser-induced porous structures and the parameters that control their dimensions have been established.

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 score0.037
Threshold uncertainty score0.632

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 head0.017
GPT teacher head0.237
Teacher spread0.220 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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