MétaCan
Menu
Back to cohort
Record W4287218444 · doi:10.3390/polym14152999

Hemicellulose Films from Curaua Fibers (Ananas erectifolius): Extraction and Thermal and Mechanical Characterization

2022· article· en· W4287218444 on OpenAlexafffund
Mariana Roldi-Oliveira, Layse M. Diniz, Anastasia Elias, Sandra M. Luz

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaFundação de Apoio à Pesquisa do Distrito FederalCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorConselho Nacional de Desenvolvimento Científico e TecnológicoAlberta Agriculture and Forestry
KeywordsHemicelluloseBiopolymerLigninMaterials scienceThermal stabilityPolylactic acidPolymerChemical engineeringChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

With growing environmental concerns over synthetic polymers, natural polymeric materials, such as hemicellulose, are considered a good sustainable alternative. Curaua fibers could be an excellent source of biopolymer as they have a relatively high hemicellulose content (15 wt%) and only a small amount of lignin (7 wt%). In this work, hemicellulose was extracted by an alkaline medium using KOH and the influence of the alkali concentration, temperature, and time was studied. A hemicellulose film was produced by water casting and its mechanical, thermal, and morphological properties were characterized. The results show that the best method, which resulted in the highest hemicellulose yield and lowest contamination from lignin, was using 10% (w/v) KOH concentration, 25 °C, and time of 3 h. The hemicellulose film exhibited better thermal stability and elongation at break than other polymeric films. It also exhibited lower rigidity and higher flexibility than other biodegradable polymers, including polylactic acid (PLA) and polyhydroxybutyrate (PHB).

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 categoriesInsufficient 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 score0.011
Threshold uncertainty score0.999

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 communication0.0000.000
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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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

Citations13
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuePolymersSame topicAdvanced Cellulose Research StudiesFrench-language works237,207