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Record W4399054155 · doi:10.1515/npprj-2023-0067

Thermoformed products from high-density polyethylene and Softwood kraft pulp

2024· article· en· W4399054155 on OpenAlexafffund
Eric Desnoes, Pascale Deshaies, Benoît Bideau, Jorge Rubiano

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsDuPont (Canada)Cegep de Trois-Rivieres
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Trois-Rivières
KeywordsSoftwoodThermoformingKraft processPulp (tooth)Kraft paperPulp and paper industryIndustrial chemistryPolyethyleneComposite materialMaterials scienceEngineeringBiochemical engineeringDentistry

Abstract

fetched live from OpenAlex

Abstract Plastic recycling, waste minimization such as process outfall valorization promotes a circular economy. Herein, food trays have been produced in the moulded pulp thermoforming process. To this end, high-density polyethylene (HDPE) outfall has been dispersed in water via Poly vinyl alcohol (PVA) addition in a Northern Bleached Softwood Kraft Pulp (NBSKP) slurry. Samples physical and mechanical properties have been evaluated. With an increasing HDPE content, parts air permeability was drastically reduced to a minimum of 2.4 ± 0.8 mL min −1 . In addition, water and grease hold out properties have been increased with minimum water Cobb 1800 value of 10.9 ± 5.4 gm −2 and oil Cobb 1800 value of 13.18 ± 6.5 gm −2 . Samples with high HDPE content demonstrated hydrophobic surface with water contact angle value above 90°. HDPE melting and binding to wood pulp fibers was monitored by SEM images. Regarding the mechanical properties, HDPE induced plastic deformation with a reduced Young modulus by 17 %. Moreover, the addition of HDPE increased wet strength by 81 %. However, the produced food tray composites with high HDPE content demonstrated low repulpability index.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
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 score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.047
GPT teacher head0.318
Teacher spread0.271 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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