The effect of chemically coated nanofiber reinforcement on biopolymer based nanocomposites
Bibliographic record
Abstract
The aim of this work was to explore how various surface treatments would change the dispersion component of surface energy and acid-base character of hemp nanofibers, using inverse gas chromatography (IGC), and to investigate the effect of the incorporation of these modified nanofibers into a biopolymer matrix on the properties of their nano-composites. Bio-nanocomposite materials were prepared from poly (lactic acid) (PLA) and polyhydroxybutyrate (PHB) as the matrix, and the cellulose nanofibers extracted from hemp fiber by chemo-mechanical treatments. Cellulose fibrils have a high density of –OH groups on the surface, which have a tendency to form hydrogen bonds with adjacent fibrils, reducing interaction with the surrounding matrix. It is necessary to reduce the entanglement of the fibrils and improve their dispersion in the matrix by surface modification of fibers without deteriorating their reinforcing capability. The IGC results indicated that styrene maleic anhydride coated and ethylene-acrylic acid coated fibers improved their potential to interact with both acidic and basic resins. From transmission electron microscopy (TEM), it was shown that the nanofibers were partially dispersed in the polymer matrix. The mechanical properties of the nanocomposites were lower than those predicted by theoretical calculations for both nanofiber-reinforced biopolymers.
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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".