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Record W4214828861 · doi:10.1201/9781003160458-14

Electrospinning and Electrospraying in Polylactic Acid/Cellulose Composites

2022· book-chapter· en· W4214828861 on OpenAlexaff
Juliana Botelho Moreira, Suelen Goettems Kuntzler, Ana Gabrielle Pires Alvarenga, Jorge Alberto Vieira Costa, Michele Greque de Morais, Loong‐Tak Lim

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPolylactic acidElectrospinningMaterials scienceComposite materialCelluloseNanofiberChemical engineeringPolymerEngineering

Abstract

fetched live from OpenAlex

Poly(lactic acid) (PLA) is an aliphatic polyester produced from renewable resources that has the potential to replace petrochemical plastics. PLA has important properties such as biocompatibility and processability. However, this polymer has low mechanical strength and high cost. Thus, blends with other polymers, such as cellulose, have been explored by researchers. Cellulose is a low-cost, non-toxic, biocompatible natural polymer that is suitable for forming PLA/cellulose composites. In addition, nanocellulose has been applied as a reinforcing agent for composites developed with PLA. Some of these polymers and composite materials are being electrospun and electrosprayed into fibers and particles, respectively, which are promising for the development of composites with differentiated functionalities. This chapter discusses the electrospinning and electrospraying processes for producing PLA/cellulose composites, highlighting different technologies, the importance of cellulose as a reinforcing agent, as well as the main applications and challenges in various areas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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