Perspectives on cellulose nanofibril size measurement using scanning electron microscopy
Bibliographic record
Abstract
Abstract Cellulose nanofibril suspensions present a broad range of particle morphology and dimensions spanning from millimeters to nanometers. As a result, direct imaging and indirect scattering approaches are used to quantify the morphology and dimensions across different length scales. There is a notable gap in detailed size measurement of cellulose nanofibrils produced from the mechanical refining of woody plants, which makes the required characterization for production control, grade specification, product specifications, and compliance with safety/regulatory requirements difficult. The cellulose nanofibril particles produced by mechanical treatment have a morphology that is dominated by a hierarchical branched fibrillar structure, in which a thicker central fibril branches off into thinner fibrillar elements, which may also undergo further branching into even finer fibrillar elements. The large differences in dimensional scales between fibril length (micrometers) to that of fibril width (nanometers) within a given nanofibrillated cellulose object makes it difficult to measure, as well as to identify the relevant features to measure and report. This paper provides a perspective on scanning electron microscopy (SEM) as a method to partially address this issue. SEM imaging offers a reasonable balance between ease of use, measurement time, image quality, and versatility in magnification to enable size characterization and assessment of features across the variable length scales of the hierarchical branching. This paper also provides a summary of useful SEM techniques for CNF size measurements and practical guidelines for sample preparation, fibril diameter measurement, and methods to account for hierarchical branching. Finally, a comprehensive set of guidelines for measurement reporting is given, together with a discussion of future directions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".