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Record W3089075793 · doi:10.1016/j.jhazmat.2020.124073

Transparent films by ionic liquid welding of cellulose nanofibers and polylactide: Enhanced biodegradability in marine environments

2020· article· en· W3089075793 on OpenAlexafffund
Xun Niu, Siqi Huan, Haiming Li, Hui Pan, Orlando J. Rojas

Bibliographic record

VenueJournal of Hazardous Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersEuropean Research CouncilHorizon 2020 Framework ProgrammeNational Natural Science Foundation of ChinaCanada Foundation for Innovation
KeywordsNanocelluloseBiodegradationCrystallinityCelluloseMaterials scienceUltimate tensile strengthIonic liquidNanofiberWeldingChemical engineeringDegradation (telecommunications)Polylactic acidComposite materialPolymerOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

We introduce a green and facile method to compatibilize hydrophobic polylactide (PLA) with hydrophilic cellulose nanofibers (CNF) by using ionic liquid ([DBNH][OAc]) welding with a cosolvent system (gamma-valerolactone). Such welding affords strong (230 MPa tensile strength), flexible (13% elongation at break), transparent (>90%) and defect-free CNF/PLA films. The films are biodegradable in marine environments (70% degradation in 7 weeks), facilitating the otherwise slow PLA decomposition. Physical, chemical and structural features of the films before and after welding are compared and factored in the trends observed for degradation in seawater. The results point to the possibility of PLA-based films forming a co-continuous system with nanocellulose to achieve an improved performance. The role of film morphology, hydrophobicity, and crystallinity is discussed to add to the prospects for packaging materials that simultaneously display accelerated degradability in marine environments.

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)0.001
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 score0.002
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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

Citations41
Published2020
Admission routes2
Has abstractyes

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