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Record W4406199848 · doi:10.1016/j.fufo.2025.100540

Development of a new sustainable packaging paper based on cellulose filaments and refined kraft pulp

2025· article· en· W4406199848 on OpenAlexafffund
Lahbib Abenghal, Julien Bley, Balázs Tolnai, Guy Njamen, Bruno Chabot

Bibliographic record

VenueFuture Foods · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsKruger (Canada)Université du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Trois-Rivières
KeywordsKraft processPulp (tooth)Pulp and paper industryCelluloseKraft paperPolymer scienceMaterials scienceComposite materialChemistryEngineeringOrganic chemistryDentistry

Abstract

fetched live from OpenAlex

The traditional use of single-use plastics in the packaging sector is limited by pollution, non-recyclability, and non-biodegradability. Replacing it with recyclable, biodegradable, and compostable materials such as cellulose has become vital. In this study, mixtures of refined kraft pulp and cellulose filaments were used to produce paper with good barrier properties. The results showed that cellulose filaments significantly improved the barrier properties of the handsheets without the use of other chemical agents. The water vapor transmission rate (WVTR) of samples produced from refined kraft pulp alone was 177 g/m 2 . day, which significantly decreased to 77 g/m 2 . day by the addition of 80 % cellulose filaments owing to the formation of a complex network of physical and chemical bonds between the fibers. The water barrier also improved significantly as the Cobb60 value decreased from 87 to 57 g/m 2 when 80 % cellulose filaments were used. Furthermore, all the samples produced had a recyclability percentage of more than 89 %, which is an essential requirement in the packaging industry.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.285
Teacher spread0.273 · 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

Citations14
Published2025
Admission routes2
Has abstractyes

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