Cellulose Nanofibers (CNF) and Nanocrystals (CNC)
Bibliographic record
Abstract
The development and use of eco-friendly materials are imposing a huge challenge to researchers and scientists. Studies and scientific exploration of carbohydrate-based organic materials are paving the way towards the replacement of conventional non-renewable materials. Cellulose nanomaterials, derived from widely available plant sources, are sustainable, bio-degradable, bio-compatible and cost-effective materials with multidisciplinary applications in biomedical engineering, food, sensor, packaging, and so on. Crystalline nanocellulose (CNCs) and cellulose nanofibrils (CNFs) are two types of cellulose nanomaterials possess various superior properties, such as large specific surface area, high tensile strength and stiffness, low density, and low thermal expansion coefficient. In this chapter, various methods of preparation of CNCs and CNFs are summarized, including mechanical, chemical, and biological methods of fibre extraction, purification process, sample preparation, and different drying techniques. This chapter also outlines the various physicochemical characterization methods practiced for CNCs and CNFs when used in polymer matrix composites.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".