Cooperative Ordering and Kinetics of Cellulose Nanocrystal Alignment in a Magnetic Field
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are emerging nanomaterials that form chiral nematic liquid crystals above a critical concentration (C*) and additionally orient within electromagnetic fields. The control over CNC alignment is significant for materials processing and end use; to date, magnetic alignment has been demonstrated using only strong fields over extended or arbitrary time scales. This work investigates the effects of comparatively weak magnetic fields (0-1.2 T) and CNC concentration (1.65-8.25 wt %) on the kinetics and degree of CNC ordering using small-angle X-ray scattering. Interparticle spacing, correlation length, and orientation order parameters (η and S) increased with time and field strength following a sigmoidal profile. In a 1.2 T magnetic field for CNC suspensions above C*, partial alignment occurred in under 2 min followed by slower cooperative ordering to achieve nearly perfect alignment in under 200 min (S = -0.499 where S = -0.5 indicates perfect antialignment). At 0.56 T, nearly perfect alignment was also achieved, yet the ordering was 36% slower. Outside of a magnetic field, the order parameter plateaued at 52% alignment (S = -0.26) after 5 h, showcasing the drastic effects of relatively weak magnetic fields on CNC alignment. For suspensions below C*, no magnetic alignment was detected.
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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 | 0.000 | 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) | 0.001 | 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".