Modification of cellulose nanocrystals with epoxidized canola oil for enhancing interfacial compatibility with poly(lactic acid)
Bibliographic record
Abstract
Herein, we investigate a sustainable hydrophobic modification of cellulose nanocrystals (CNCs) with epoxidized canola oil (ECO) via an oxirane ring-opening reaction, in order to improve compatibility with poly(lactic acid) (PLA). The modified CNCs (CNC-g-ECO) exhibited improved hydrophobicity, as evidenced by their lack of colloidal stability in water and an increase in water contact angle from 33° to 73°. Importantly, XRD analysis indicated that the modification did not disrupt the crystalline structure of the CNCs. Neat and modified CNCs were compounded with PLA at 1 wt% using multiple processing protocols. Polarized optical microscopy revealed that the modified CNCs exhibited significantly improved dispersion and distribution within the PLA matrix across all processing methods. Additionally, the modified CNCs acted as effective nucleating agents for PLA, leading to an increase in its degree of crystallinity. Notably, we found that CNC modification with ECO enhanced thermal stability and completely mitigated discoloration or thermal aging, which is a common occurrence in CNC-based composites, typically caused by CNC degradation and oxidation of carboxyl groups during processing. Overall, our approach gives CNC an expanded thermal processing window and improves dispersion in polymer matrices, facilitating the development of high-performance composites for a number of different 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".