Transmission Electron Microscopy for the Characterization of Cellulose Nanocrystals
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are high aspect ratio nanomaterials readily obtained from cellulose microfibrils via strong acid hydrolysis. They feature unique properties stemming from their surface chemistry, their crystallinity, and their three-dimensional structure. CNCs have been exploited in a number of applications such as optically active coatings, nanocomposite materials, or aerogels. CNC size and shape determination is an important challenge and transmission electron microscopy (TEM) is one of the most powerful tools to achieve this goal. Because of the specifics of TEM imaging, CNCs require special attention. They have a low density, are highly susceptible to electron beam damage, and easily aggregate. Specific techniques for both imaging and sampling have been developed over the past decades. In this review, we describe the CNCs, their properties, their applications, and the need for a precise characterization of their morphology and size distribution. We also describe in detail the techniques used to record quality images of CNCs. Finally, we survey the literature to provide readers with specific examples of TEM images of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".