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Record W4362607979 · doi:10.1002/pen.26321

Life cycle assessment and mechanical properties of nanocomposites based on cellulose nanocrystals

2023· article· en· W4362607979 on OpenAlexaff
Donya Ansari Movahed, Mehdi Jonoobi, Alireza Ashori, Tizazu H. Mekonnen

Bibliographic record

VenuePolymer Engineering and Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthPolyhydroxybutyratePolypropyleneMaleic anhydrideBiopolymerCelluloseComposite materialIzod impact strength testNanocompositeTensile testingInjection mouldingMolding (decorative)PolymerChemical engineeringCopolymer

Abstract

fetched live from OpenAlex

Abstract The present work focuses on the preparation of cellulose nanocrystals (CNCs) reinforced composites based on polyhydroxybutyrate (PHB) and polypropylene (PP). The effects of the CNCs content as reinforcement and the addition of maleic anhydride (MA) as a coupling agent on the mechanical and morphological properties of the composites were studied. In addition, the biodegradability of the composites was investigated. Samples were prepared using PP and PHB as matrixes and CNCs as reinforcement. Test samples were prepared using the injection molding machine. The studied parameters were microscopic analysis and tensile strength measurement. Tensile strength increased with increasing CNC, but it decreased slightly when 5 wt% of CNC was added. Tensile strength decreased with the addition of MA. Environmental impacts from the production of 100 g of PP and PHB were evaluated using the life cycle assessment (LCA) approach. The results showed that the environmental impacts of composites containing PHB biopolymer are higher than PP ones.

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 score0.196
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.273
Teacher spread0.252 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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