Evidence-based guidelines for the ultrasonic dispersion of cellulose nanocrystals
Bibliographic record
Abstract
Nanoparticles possess unique, size-driven properties. However, they can be challenging to use as they easily agglomerate - their high surface area-to-volume ratio induces strong interparticle forces, generating agglomerates that are difficult to break. This issue prevails in organic particles as well, such as cellulose nanocrystals (CNCs); when in their dried form, strong hydrogen bonding enhances agglomeration. Ultrasonication is widely applied to prepare CNC suspensions, but the methodology employed is non-standardized and typically under-reported, and process efficiency is unknown. This limits the ability to adapt dispersion protocols at industrial scales. Herein, numerical simulations are used in conjunction with validation experiments to define and optimize key parameters for ultrasonic dispersion of CNCs, allowing an operating window to be inferred.
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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 | 0.060 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".