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Record W4360585805 · doi:10.15376/biores14.2.3168-3181

Preparation of composites from natural rubber and oil palm empty fruit bunch cellulose: Effect of cellulose morphology on properties

2019· article· en· W4360585805 on OpenAlexfundno aff
José Antonio Fiorote, Alair Pereira Freire, Dasciana de Sousa Rodrigues, Maria Alice Martins, Larissa Andreani, Leonardo Fonseca Valadares

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerEmbrapa Recursos Genéticos e Biotecnologia
KeywordsCelluloseNatural rubberMaterials scienceComposite materialFiller (materials)NanocompositePalm oilPulp (tooth)Chemical engineeringChemistry

Abstract

fetched live from OpenAlex

Rubber tree and oil palm are industrial crops cultivated in the same climate and environment. These plants are used to prepare nanocomposites of natural rubber and cellulose from empty fruit bunches, an abundant residue in the palm oil industry. For this study, the cellulose particles were extracted from the bunches and subjected to enzymatic hydrolysis or microfibrillation to produce nanostructured particles. The nanoparticles were blended with natural rubber latex in an aqueous medium, and the mixture was dried. The properties of the nanocomposites were compared to those of pure natural rubber and unprocessed cellulose composites. The mechanical properties of the natural rubber can be modified by the cellulose content and morphology. As a consequence, it is possible to modulate the material properties by changing only the filler morphology. The use of microfibrillated cellulose had stronger reinforcement effects. The thermal properties of natural rubber were not affected by the addition of cellulose.

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 score0.016
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.269
Teacher spread0.258 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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