Cellulose Nanocrystals Examined by Atomic Force Microscopy: Applications and Fundamentals
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are nanoscale particles with huge surface areas, excellent mechanical properties, and the ability to develop tunable surface chemistry, thus allowing them to be mixed into a wide range of matrices. Using atomic force microscopy (AFM), we highlight recent developments in the microstructural characterization of CNC particles in various shapes at both particle and organization scales. Considering new uses for CNC suspensions and gels and given the considerable potential of CNC-based products in medicinal, energy, cosmetics, filtration, and food applications, leveraging existing state-of-the-art characterization technologies such as AFM to improve CNC-produced properties cannot be ruled out. AFM may be used as a probe to disclose more intimate information about CNCs and can show modulus, size, and morphology in comparison to other characterization tools. AFM based on the literature review here was found to be a good assessment tool to verify the state of interaction, the adhesion strength of certain particles or chemicals, and the mechanical properties of CNCs. Thus, in tandem with other technologically advanced characterization tools, knowledge provided here for proper assessment of CNCs is necessary. Based on the AFM application currently widespread on CNCs, it appears that more research efforts are needed to provide additional cues regarding CNC individual states or the organized state (isotropic or liquid crystalline) for future sustainable, ecofriendly product designs based on 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".