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Record W4399585181 · doi:10.1021/acssuschemeng.4c03117

Active Food Packaging Composite Films from Bast Fibers-Derived Cellulose Nanofibrils

2024· article· en· W4399585181 on OpenAlexaff
Yu Wang, Liru Luo, Yongjian Yi, Xing Chen, Yuanru Yang, Zhonghai Tang, Xuhong Guo, Zhijian Tan, Kam Chiu Tam

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
FundersEarmarked Fund for China Agriculture Research SystemChinese Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsBast fibreCelluloseComposite numberFood packagingMaterials scienceNanocelluloseComposite materialPolymer scienceCellulose fiberChemical engineeringPulp and paper industryFiberChemistryFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The increasing concerns about food waste and environmental pollution call for highly efficient food packaging materials from sustainable sources. Cellulose nanofibrils (CNFs) are emerging sustainable materials, and more nonwoody biomasses should be used to prepare CNF-based food packaging materials due to the commercial applications of wood pulp. Herein, we explored the use of four bast fibers to produce CNFs via ball milling and high-pressure homogenization to produce CNF suspensions that were spray-coated onto model fruits (e.g., banana and mango) for food preservation. Unlike other CNF coatings derived from ramie, flax, and kenaf fibers, jute-derived CNF coating significantly prolonged the shelf life of fruits, due to its improved homogeneity, higher oxygen and UV barrier properties, and higher antioxidant activity. This work offers a new strategy to prepare sustainable active packaging materials from natural biomass without chemical modification or additive, boosting related applications in the fields of food and agriculture.

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 categoriesMeta-epidemiology (narrow)
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 score0.009
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.001
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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

Citations26
Published2024
Admission routes1
Has abstractyes

Explore more

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