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Record W2030961712 · doi:10.1002/app.41493

Characterization of cross‐linking depth for thin polymeric films using atomic force microscopy

2014· article· en· W2030961712 on OpenAlexafffund
Qiuquan Guo, Maxim Paliy, Brad Kobe, Tomáš Třebický, Natalie D. Suhan, Gilles Arsenault, Lorenzo Ferrari, Jun Yang

Bibliographic record

VenueJournal of Applied Polymer Science · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsLanxess (Canada)Western University
FundersFuel Cells and Hydrogen Joint UndertakingOntario Ministry of Economic Development and Innovation
KeywordsElastomerMaterials sciencePolymerCharacterization (materials science)Thin filmComposite materialCross section (physics)Cross-linkAtomic force microscopyNanotechnology

Abstract

fetched live from OpenAlex

ABSTRACT Thin polymeric films made with various elastomers, like polyisoprene, and elastomer composites were prepared for characterization of cross‐linking depth in this study. Various cross‐linking methods have been applied to get mechanically stronger, more thermally stable and chemically resistant polymer coatings. However, there is no existing approach that could effectively characterize the degree or depth of cross‐linking for thin polymer films. The objective of this work is to use atomic force microscopy to characterize cross‐linking depth in a precise way. Hyperthermal hydrogen bombardment‐induced cross‐linking was employed as a cross‐linking method and the depth of cross‐linking was estimated via local change of the elastic modulus along the sample cross‐section with precise force measurement and high spatial resolution. It is found that the cross‐linking depth is closely related to the chemical composition of thin films. Understanding the depth of cross‐linking is vital for a broad range of applications. It is believed that the developed technique is also applicable for studying other cross‐linkable materials. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 41493.

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 score0.269
Threshold uncertainty score0.492

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 science0.0010.000
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 head0.011
GPT teacher head0.306
Teacher spread0.295 · 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

Citations12
Published2014
Admission routes2
Has abstractyes

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