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Record W3091820339 · doi:10.1186/s42252-020-00011-z

Morphological, thermal, and thermomechanical properties of cellulose nanocrystals reinforced polylactide/poly [(butylene succinate)-co-adipate] blend composite foams

2020· article· en· W3091820339 on OpenAlexfundno aff
Mpho Phillip Motloung, Simphiwe Zungu, Vincent Ojijo, Jayita Bandyopadhyay, Suprakas Sinha Ray

Bibliographic record

VenueFunctional Composite Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsnot available
FundersInnotech AlbertaDepartment of Science and Technology, Republic of South AfricaCouncil for Scientific and Industrial Research, South Africa
KeywordsMaterials scienceAdipateCrystallinityComposite numberComposite materialThermal stabilityCelluloseEnvironmentally friendlyPolybutylene succinatePolyesterChemical engineering

Abstract

fetched live from OpenAlex

Abstract This study examines the influence of cellulose nanocrystal (CN) particles on the morphological, thermal, and thermo-mechanical properties of polylactide (PLA)/poly [(butylene succinate)-co-adipate] (PBSA) blend foams prepared by casting and particulate leaching method using fructose as porogen particles. The morphological analysis showed an interconnected open-cell structure, with porosity above 80%. The crystallinity of the prepared foams was disrupted by the inclusion of CN particles as observed from XRD analyses, which showed a decrease in PLA crystal peak intensity. With regards to neat blend foam, the onset thermal degradation increased with the addition of CN particles, which also increased the thermal stability at 50% weight loss. Furthermore, CN acted as a reinforcing agent in improving the stiffness of the prepared blend foam. Overall, completely environmentally friendly foams were successfully prepared, as a potential material that can replace the current existing foam materials that pose many environmental concerns. However, there is a need to develop an environmentally friendly processing technique.

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), Insufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0020.000

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 head0.032
GPT teacher head0.220
Teacher spread0.189 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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