Effect of Surface Energy on Dispersion and Mechanical Properties of Polymer/Nanocrystalline Cellulose Nanocomposites
Bibliographic record
Abstract
Dispersion quality and polymer-filler interaction are important factors in determining the final properties of polymer nanocomposites. Surface energy of nanocrystalline cellulose (NCC) and some polymers (polypropylene, PP, and polylactic acid, PLA) was measured at room and high temperatures. NCC had higher polarity and surface energy than PP and PLA at room temperature but had a lower surface energy at higher temperatures. The effect of surface modification with alkenyl succinic anhydride (ASA) on NCC surface energy at room and high temperature was studied. Total surface energy of NCC was lowered after surface modification. Thermodynamic work of adhesion for PP/NCC and PLA/NCC was lowered by NCC surface modification. A thermodynamic analysis is proposed to estimate the dispersion energy, based on surface energy measurements at room and high temperatures. Also, a dispersion factor is defined to provide a quantitative indication of the dispersibility of nanoparticles in a polymer matrix under various conditions. The required dispersion energy was reduced by lowering the interfacial tension. On the other hand, it increased as the quality of NCC dispersion (i.e., the nanoparticle surface area) in the system was improved. Surface modification of NCC with ASA had a negative effect on the compatibility between NCC and PLA, whereas it had a positive influence on compatibility between PP and NCC.
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".