Functionalization of Cellulose Nanocrystals Powder by Non-Thermal Atmospheric-Pressure Plasmas
Bibliographic record
Abstract
Abstract Despite promising characteristics such as the biodegradability and the environmentally-benign nature of cellulose nanocrystal (CNC) based composites, their poor dispersion and agglomeration in thermoplastic matrix during the melting process is a “bottleneck” in the development of these composites. In this work, a cylindrical atmospheric pressure dielectric barrier discharge (AP-DBD) was employed to functionalize CNCs to reduce their surface hydrophilicity and improve their dispersion in polar organic solvents. Three different gas mixtures were used for plasma treatment, argon/methane, argon/silane and an argon/methane followed by argon/silane. In all cases, the plasma treatment was conducted below 90°C as determined from optical emission spectroscopy (OES) analysis. The x-ray diffraction (XRD) analysis of both raw and plasma treated CNC powders confirms that the CNC crystallographic properties remain unchanged after plasma treatment. Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) analysis reveal the presence of hydrophobic C-Hx moieties on the CNC granular surface after argon/methane plasma treatment whereas SiHx, Si-O-Si, SiC bonds were formed after argon/silane plasma treatment. Under these experimental conditions, water wettability tests reveal some significant water repellency of the naturally hydrophilic cellulosic raw material. Moreover, the formation the SiHx moieties in silane-treated CNCs clearly enhances the hydrophobicity of the CNC powder in contrast to the sole C-Hx moieties synthetized by argon/methane plasma. High-resolution SEM images indicate the presence of agglomerated granules with 5-10 µm diameters in size. The surface functionalities of CNC powder enhance its dispersibility in polar solvents. Overall, this study emphasizes that AP-DBDs are suitable to process thermo-sensitive CNCs.
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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".