Molecular Design and Structural Optimization of Nanocellulose-Based Functional Films
Bibliographic record
Abstract
The present work is focused on molecular design and structural optimization of nanocellulose-based functional films. Since nanocellulose can be modulated and designed at the molecular level, tunable chemistry and functionalization of nanocellulose can offer much richer materials properties and more possibilities to construct nanomaterials with numerous features for advanced applications. Herein, the grafting of carbazole units on allyl-functionalized nanofibrillated cellulose (NFC) enabled photoluminescence activity. Additionally, flexible, strong, and electrically conductive nanocellulose-based polythiophene nanofilms were fabricated. The results reveal that cellulose nanofibers changed their nature from insulator to semiconductor. Finally, regioselective functionalization of NFC was conducted to investigate whether precise control of the positioning of functional groups can enhance the electroactivity properties of nanocellulose-based films. Regioselective tuning for the design and configuration of flexible nano-substrates as demonstrated in this study can be replicated by other researchers for other cellulose derivatives. The synergetic effect of two or even three different moieties grafted on anhydroglucose units may create limitless possibilities for the design of advanced engineered nanomaterials.
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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".