Cellulose nanocrystal interactions probed by thin film swelling to predict dispersibility
Bibliographic record
Abstract
The production of well-dispersed reinforced polymer nanocomposites has been limited due to poor understanding of the interactions between components. Measuring the cohesive particle-particle interactions and the adhesive particle-polymer interactions is challenging due to nanoscale dimensions and poor colloidal stability of nanoparticles in many solvents. We demonstrate a new cohesive interaction measurement method using cellulose nanocrystals (CNCs) as a model system; CNCs have recently gained attention in the composites community due to their mechanical strength and renewable nature. Multi-wavelength surface plasmon resonance spectroscopy (SPR) was used to monitor the swelling of CNC thin films to elucidate the primary forces between CNCs. This was achieved by measuring swelling in situ in water, acetone, methanol, acetonitrile, isopropanol, and ethanol and relating the degree of swelling to solvent properties. Films swelled the most in water where we estimate 1.2-1.6 nm spacings between CNCs (or 4-6 molecular layers of water). Furthermore, a correlation was found between film swelling and the solvent's Hildebrand solubility parameter (R(2) = 0.9068). The hydrogen bonding component of the solubility parameters was more closely linked to swelling than the polar or dispersive components. The films remained intact in all solvents, and using DLVO theory we have identified van der Waals forces as the main cohesive interaction between CNCs. The trends observed suggest that solvents (and polymers) alone are not sufficient to overcome CNC-CNC cohesion and that external energy is required to break CNC agglomerates. This work not only demonstrates that SPR can be used as a tool to measure cohesive particle-particle interactions but additionally advances our fundamental understanding of CNC interactions which is necessary for the design of cellulose nanocomposites.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".