Oscillatory Shear Response of the Rigid Rod Model: Microstructural Evolution
Bibliographic record
Abstract
In this study, colloidal liquid crystals are described by the Doi–Hess model and their response to an external transient flow (oscillatory shear) is investigated. The nonlinear partial differential equation governing the probability distribution of rods is solved numerically via an expansion in spherical harmonics and by solving a set of equivalent stochastic differential equations. These approaches provide microstructural insights into the behavior of systems with broken symmetries far from equilibrium. Thanks to this level of microstructural details, we here propose a new methodology to switch between the nematic and isotropic orientation state thanks to the transient nature of the externally imposed flow. Moreover, we show that the oscillatory shear flow is more efficient than the simple shear flow to capture the full microstructural dynamics that these systems can exhibit. Specifically, when a sinusoidal shear rate with large amplitude and low frequency (compared to the relaxation time of the system) is applied, a single oscillation period is sufficient to obtain a succession of microstructural dynamics (e.g., tumbling and wagging). These states would be obtained in a simple shear flow only by applying multiple shear rates to the system in multiple experiments. Hence, this methodology can provide a new strategy for experimental characterization.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".