Brownian dynamics simulation of the diffusion of rod-like nanoparticles in polymeric gels
Bibliographic record
Abstract
Rod-like nanoparticles (RNPs) have been shown to diffuse faster than spherical ones in polymeric hydrogels, but the underlying physics is not well understood. We develop a 3D Brownian dynamics model to investigate this phenomenon, representing the gel as a random network of rigid fibers in water and incorporating both steric repulsion and adhesive interactions. In non-adhesive gels, RNP diffusivity increases monotonically with the aspect ratio while its hydrodynamic diameter is kept constant, in agreement with the predictions of an obstruction scaling (OS) model. However, our model predicts a much higher diffusivity than the OS model, by up to 5 times for higher aspect ratios. To rationalize this discrepancy, we demonstrate that RNPs experience a skewed pore-size distribution in favor of the larger pores; they spend more time in coarser regions of the gel than in denser regions. Moreover, the RNPs execute a meandering motion in the coarser regions with pronounced rotational and transverse diffusion. In contrast, in denser regions, restricted rotation results in predominantly longitudinal diffusion. This anisotropy in diffusion further elevates the translational diffusivity of RNPs. Our model also reveals a competition between the steric and adhesive interactions, where steric repulsion limits access to adhesion sites, and produces a diffusivity intermediate between the purely steric and purely adhesive cases. Overall, our results show an even greater advantage for RNPs, in terms of rapid diffusion in hydrogels, than previously anticipated by using the OS model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".