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A comparative study on the mechanical, thermal, and water barrier properties of PLA nanocomposite films prepared with bacterial nanocellulose and cellulose nanofibrils

2019· article· en· W2914601082 on OpenAlexafffund
Baraka Gitari, Boon Peng Chang, Manjusri Misra, Alireza Navabi, Amar K. Mohanty

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsNanocelluloseNanocompositeMaterials scienceCrystallinityCelluloseBacterial celluloseUltimate tensile strengthComposite materialLactic acidChemical engineering

Abstract

fetched live from OpenAlex

Mechanical, thermal, and water barrier properties of poly(lactic acid) (PLA) film reinforced with cellulose nanofibrils (CNF) and bacterial nanocellulose (BNC) were studied and compared. The in-situ formation of sodium carbonate (Na2CO3) on the BNC surface may aid in reducing the interchain hydrogen bonding and agglomeration of BNC fibers. At optimum loading, both CNF/PLA and BNC/PLA nanocomposite films exhibited higher tensile strength and Young’s modulus than the neat PLA without sacrificing its toughness. The BNC/PLA nanocomposite films displayed lower water vapor transmission rate (WVTR) as compared to neat PLA and CNF/PLA films at 0.5 and 1.0 wt%. BNC was found to induce imperfect crystal structures and exhibited higher overall crystallinity than neat PLA and CNF/PLA composites at 0.5 wt%. The BNC/PLA showed higher mechanical properties than CNF/PLA nanocomposites. Nanocellulose derived from plants and bacteria could provide promising solutions to develop high performance biobased-nanocomposites film for packaging application.

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 communication0.0000.000
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 head0.025
GPT teacher head0.208
Teacher spread0.183 · 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

Citations45
Published2019
Admission routes2
Has abstractyes

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