Physicochemical properties of cellulose nanocrystals treated by photo‐initiated chemical vapour deposition (PICVD)
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are novel nanomaterials produced by the pulp and paper industry. The surface properties of CNCs are key factors for their dispersion in solvents. These polar materials disperse readily in water, but not in organic solvents. The ability to disperse into typically non‐polar organic matrices is an indispensable requirement to exploit the growing market for nanocomposite materials. We present an innovative approach for modifying the surface of CNCs through scalable, gas‐phase photo‐initiated chemical vapour deposition (PICVD). Using syngas as a treatment precursor, we demonstrate the effectiveness of this technique to render the surface of the CNCs compatible with mildly polar and non‐polar solvents, evidenced by contact angle measurements. Further proof of this successful modification is given through dispersion assays, showing for example the ability to disperse treated CNC in toluene (whereas untreated samples do not disperse). Suspensions in organic solvents remain stable in excess of two weeks. Chemical characterization through XPS and FTIR confirms the presence of an oxygen‐containing coating on the CNC surface.
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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".