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Record W2795565315 · doi:10.1021/acsomega.8b00056

In Situ Cellulose Nanocrystal-Reinforced Glycerol-Based Biopolyester for Enhancing Poly(lactic acid) Biocomposites

2018· article· en· W2795565315 on OpenAlexafffund
Elizabeth Brown, Mohamed A. Abdelwahab, Oscar Valerio, Manjusri Misra, Amar K. Mohanty

Bibliographic record

VenueACS Omega · 2018
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 AffairsUniversity of Guelph
KeywordsCelluloseLactic acidGlycerolNanocrystalIn situMaterials scienceChemical engineeringOrganic chemistryChemistryNanotechnologyBacteriaBiology

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Biobased, elastomeric polymer poly(glycerol succinate- co -maleate) (PGSMA) was produced using a “green” synthesis with added cellulose nanocrystals (CNCs) to create a novel PGSMA–CNC material. PGSMA–CNC was synthesized with the aim of developing a new strategy for successfully dispersing CNCs within a poly(lactic acid) (PLA) matrix for optimal reinforcement of tensile strength and modulus while having the added benefit of the proven toughness enhancements of PLA/PGSMA blends. Optical microscopy and fractionation in tetrahydrofuran showed that CNCs agglomerated during PGSMA–CNC synthesis and remained in agglomerates during PLA/PGSMA–CNC reactive blending. Fourier transform infrared, differential scanning calorimetry, and dynamic mechanical analyses also showed that PGSMA–CNC inhibited the formation of PGSMA crosslinks and PLA- g -PGSMA during reactive blending. These two effects resulted in loss of impact strength and only a 4% increase in tensile modulus over PLA/PGSMA at the highest CNC content. Further work in preventing CNC aggregation could help improve mechanical properties of the final blend.

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 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 score0.010
Threshold uncertainty score0.877

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 science0.0000.000
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations19
Published2018
Admission routes2
Has abstractyes

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