Bacterial Cellulose Nanofiber Reinforced Poly (lactic acid) Nanocomposite
Bibliographic record
Abstract
The objective of the present work is to investigate the impact of the modified BC nanofibers orientation in poly(lactic acid) (PLA)/ polyethylene glycol (PEG) blends (porous and nonporous) on the mechanical properties and thermal properties. A new class of PLA/PEG biocomposites containing BC nanofibers was successfully fabricated using solution casting technique followed by silane coupling agent of BC grafting into PLA/PEG chains. The mechanical properties were investigated with and without developing the porosity for PLA/PEG. The results revealed that incorporating BC nanofibers into PLA/PEG enhanced mechanical properties, but this improvement set up at BC nanofiber loading (5 wt%). The tensile strength increased from 13 MPa for PLA/PEG to 18 MPa after the addition of 5 wt% BC. The young’s modulus was significantly increased upon increased BC content. Differential Scanning Calorimetric (DSC) results revealed that the BC 5% nanofiber improved glass transition temperature (Tg) to 57°C, melting temperature (Tm) to 171°C, and crystallinity (χ%) to 43% of PLA/PEG reinforced-BC-5%. These results are of significant interest to further expand the use of PLA in biomedical applications.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".