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Record W2109850896 · doi:10.1002/ppap.201400162

Correlation Between Mechanical Properties and Cross‐Linking Degree of Ethyl Lactate Plasma Polymer Films

2015· article· en· W2109850896 on OpenAlexaff
Sylvie Ligot, Étienne Bousser, Damien Cossement, J.E. Klemberg-Sapieha, Pascal Viville, Philippe Dúbois, Rony Snyders

Bibliographic record

VenuePlasma Processes and Polymers · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNanoindentationMaterials sciencePolymerCreepComposite materialViscoelasticityCoatingPlasma

Abstract

fetched live from OpenAlex

Plasma polymer films are more and more used in numerous applications such as the day‐to‐day packaging, which requires a good mechanical resistance of the coating. Since mechanical properties are function of the cross‐linking degree of the thin films, it is important to understand the relationship between the two. In this work, plasma polymer films were studied in terms of cross‐linking degree and mechanical properties, measured by means of ToF‐SIMS coupled to PCA and depth‐sensing nanoindentation, respectively. The data reveal that when the power increases, the films are more cross‐linked and show higher hardness, higher elastic recovery, lower creep strain and an increased ability for self‐healing. As the plasma polymer films are more cross‐linked, the plasticity drops and the viscoelasticity and hardness increase.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.056
GPT teacher head0.246
Teacher spread0.190 · 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

Citations53
Published2015
Admission routes1
Has abstractyes

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