Green Modification of Maple Leaf-Derived Lignin-Containing Nanocellulose through Mechanochemistry for 3D Printable Pickering Emulsions
Bibliographic record
Abstract
Mechanochemistry offers a simple and sustainable approach to chemical modification. In this work, lignin-containing cellulose nanocrystals (LCNCs) and lignin-containing cellulose nanofibrils (LCNFs) derived from fallen maple leaves are modified with isoleucine by ball milling, and their potential as sustainable stabilizers of high internal phase Pickering emulsions (HIPPEs) for extrusion-based 3D printing is investigated. For the first time, it has been observed that LCNCs swell significantly after ball milling, with length and diameter increases of approximately 4.3 times and 30 times, respectively, while LCNFs undergo defibrillation and size reduction by about 61% without noticeable swelling. Isoleucine is selected as a model amino acid to modify LCNCs and LCNFs and successfully improves their wettability, which increases the contact angle from 74.6° to 94.1° for LCNCs and from 63.9° to 82.6° for LCNFs, resulting in improved emulsifying performance. Despite different swelling ratios, HIPPEs stabilized by isoleucine-modified LCNCs exhibit superior rheological properties and 3D printing performance (printing precision of about 96%) compared to isoleucine-modified LCNFs and other biopolymer stabilizers, enabling high shape fidelity and structural integrity of printed items. Therefore, this work demonstrates the feasibility of ball milling-assisted amino acid modification of nanocellulose for the development of sustainable and high-performance all biobased HIPPEs.
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".