Reef Sweeney | Searcharxiv

arXiv2026

Mechanics of heterogeneous fiber networks

Internally generated active stresses drive soft materials into architectures inaccessible to thermal self-assembly. We use a microtubule-based active fluid to assemble and irreversibly restructure actin-fascin networks. Subsequently, we probe the mesoscale mechanics of such networks by combining active microrheology with fluorescence imaging of the strain field around the probe. Increasing motor concentration broadens the pore-size distribution and thickens load-bearing bundles, raising the mean local elastic modulus and its spatial variability. Displacement fields of actively-processed networks propagate over longer range when compared to unprocessed networks. At large strains, both networks strain soften and plastically restructure. The combined microrheology and strain-imaging approach show that tunable active stresses reprogram the structure and viscoelastic response of fiber networks at the scale of their structural heterogeneity.

cond-mat.soft↗

arXiv2024

Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics

Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motions of objects at the pixel level. In this article, we evaluate the ability of optical flow to quantify the spontaneous flows of MT-based active nematics under different labeling conditions. We compare DLOF against the commonly used technique, particle imaging velocimetry (PIV). We obtain flow velocity ground truths either by performing semi-automated particle tracking on samples with sparsely labeled filaments, or from passive tracer beads. We find that DLOF produces significantly more accurate velocity fields than PIV for densely labeled samples. We show that the breakdown of PIV arises because the algorithm cannot reliably distinguish contrast variations at high densities, particularly in directions parallel to the nematic director. DLOF overcomes this limitation. For sparsely labeled samples, DLOF and PIV produce results with similar accuracy, but DLOF gives higher-resolution fields. Our work establishes DLOF as a versatile tool for measuring fluid flows in a broad class of active, soft, and biophysical systems.

cond-mat.soft↗