Polylactic acid reinforced with nanocellulose: current applications and future trends
Bibliographic record
Abstract
In recent years renewed interest on the development of biopolymers, based on constituents obtained from natural resources is gaining much attention. Reinforced biopolymer with natural fibres is the future of ‘‘green composites’’ addressing many sustainability issues. Among the available biopolymer, Polylactic acid (PLA) is the only natural resource polymer produced at a large scale of over 140,000 tonnes per year. PLA is a biodegradable thermoplastic aliphatic polyester derived from renewable resources, such as corn starch (in the United States and Canada), tapioca roots, chips or starch (mostly in Asia), or sugarcane (in the rest of the world). In 2010, PLA had the second highest consumption volume of any bioplastic of the world. Natural fibre reinforced PLA based biocomposites are widely investigated by the polymer scientists in the last decade to compete with non renewable petroleum based products. The type of fibre used plays an important role in fibre/matrix adhesion and thereby affects the mechanical performance of the biocomposites. For the processing of polymer nanocomposites, cellulose nanoparticles are an ideal candidate, because of their mechanical properties, reinforcing capabilities, abundance, low density, and biodegradability. Cellulose is probably the most used and well-known renewable and sustainable raw materialA comprehensive and exhaustive review was carried out based on the combination of nanocellulose with PLA to produce nanocomposite materials. The processing conditions to obtain the nanoscale materials are summarized and discussed. The main advantages and limitations of these nanomaterials are addressed. The addition of cellulose nanocrystals (CNC) to the biopolymers such as PLA, is pioneer of a new potential to create innovative bio-nanocomposite materials with improved properties and performance. However, safety issues of nanocellulose should be precisely monitored and controlled in order to confirm whether it has no harmful effects on human´s health and on environment.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".