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Record W4382652910 · doi:10.1002/9781119535775.ch6

Nano‐Layered Films and Foams

2023· other· en· W4382652910 on OpenAlexaff
Troy Su, Abdullah Al Faysal, Jianxiang Zhao, Patrick Lee

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceStack (abstract data type)NanometreNano-PolymerComposite materialLayer (electronics)NanotechnologyComputer science

Abstract

fetched live from OpenAlex

Micro-/nano-layered (MNL) coextrusion is a processing technique used to form multilayered composites using passive elements called layer multipliers to iteratively divide, stack, and recombine extruded streams of molten polymers. MNL coextrusion is a simple, yet effective way of creating layered composites that exhibit enhanced, or completely novel properties compared to their constituent polymers via precise spatial control of each phase on the micro-/nanometer scales. MNL films have a wide variety of applications in packaging, biomedical components, and dielectric elements, whereas MNL foam/film structures have usage mostly in thermal and sound insulation. Due to the many advantages of multilayered films, many processing methods have been proposed for preparing such films. These methods can be divided into two categories: self-assembly and forced assembly. Barrier films can be found in a variety of applications ranging from food packaging to pharmaceutical products, and filters.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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)0.0040.001

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 head0.230
Teacher spread0.219 · 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
GenreOther

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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same topicPolymer Foaming and CompositesFrench-language works237,207