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Preliminary study of laminated glass with nanocellulose and poly(vinyl butyral) for safety glazing

2019· article· en· W2940731016 on OpenAlexaff
Chloé Maury, Frank Crispino, Éric Loranger

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNanocelluloseGlazingMaterials scienceLaminated glassComposite materialPolyvinyl butyralCelluloseEngineeringLayer (electronics)Chemical engineering

Abstract

fetched live from OpenAlex

The increase in fatal road accidents, natural disasters, and even terrorist attacks around the world have contributed to the improvement of public security. Windows can be particularly hazardous because of cutting fragments expelled during breakage or an explosion, which may induce injury. It is becoming essential to develop a resistant daily security glazing for houses to prevent damage in earthquakes and tornado areas, for utility cars, etc. Nanocellulose was used, which has a low ecological footprint, to improve safety glazing properties and was based on poly (vinyl butyral) (PVB). Following the processing of many different recipes for layers based on both PVB and nanocellulose polymers, intercalary films were assembled with glass using hot pressing. The results of the three-point bending experiments were promising. Breaking loads were approximately 8000 N for the two nanocellulose samples, which were close to the results of the sample with PVB only. Furthermore, the obtained composites possessed a transparency near that of PVB only. Finally, nanocellulose overtop PVB had a surface mass as low as one eighth of that of the PVB.

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 score0.114
Threshold uncertainty score0.575

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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