arXiv subjects

Sara M. Hashmi

Publications and source records attributed to Sara M. Hashmi.

4 recordsLinked to original sources

arXiv2025

Subtleties of UV-crosslinking in microfluidic particle fabrication: UV dosage and intensity matter

Curable hydrogels have tunable properties that make them well-suited for applications in drug delivery, cell therapies, and 3D bioprinting. Advances in microfluidic droplet generation enable rapid fabrication of polymer-filled droplets. UV-curable polymers offer a clear path toward using fluidic generation to produce monodisperse microgels with uniform properties. In flow, polymer concentration and UV exposure both control the degree of crosslinking. High UV intensity is often used to ensure complete gelation and avoid complications that may arise from partial curing. Optical microscopy can assess droplet and particle sizes in flow. However, optimizing formulations for mechanical properties usually requires removal of generated material and external measurement outside of flow. In this study, we couple droplet generation, microgel fabrication, and mechanics assessment within a single fluidic device. We make and measure soft polyethylene glycol diacrylate (PEGDA) microgels by curing polymer-filled water drops in mineral oil. Crosslinking is tuned by varying UV dosage, allowing us to study how gelation degree influences microgel properties. Within the device, we use shape deformation in flow to measure the restoring stress of both droplets and particles. Our results suggest that PEGDA droplets gel from the inside out. If gelation is incomplete, a particle resides within a fluid drop. Independent measurements outside of flow corroborate this observation. Crosslinking PEGDA-filled droplets in a pendant drop geometry, with dye, suggests the persistence of an aqueous shell around the gel. Similarly, microparticles in PEGDA-filled drops undergoing gelation exhibit diffusive arrest near the drop center, while maintaining mobility in an outer region. Together, these results suggest the importance of considering the extent of gelation when fabricating microgels using fluidics.

physics.flu-dyn↗

arXiv2025

Rigid body rotation and chiral reorientation combine in filamentous E. coli swimming in low-Re flows

When treated with antibiotics below the minimum inhibitory concentration, bacterial cell division turns off, but cell growth does not. Thus, rod-like bacteria, including E. coli, can elongate many times their length without increasing their width. The swimming of these filamentous bacteria through small channels may provide insights into how bacteria that survive antibiotic treatment can reach channel walls. Such swimming behaviors in settings like hospital tubing may signal precursors to adhesion, biofilm formation, and infection. Despite the importance of understanding the behavior of bacteria not killed by antibiotics, the swimming of filamentous bacteria in external flows has not received much attention. We study the swimming behavior of stressed, filamentous E. coli. In quiescence, highly elongated E. coli swim with a sinusoidal undulating motion, suggesting rigid body rotation of long, rigid, buckled cell bodies. In low-Re pressure-driven microchannel flow, the undulation becomes irregular; it may even stop and start within a particular trajectory. We refer to this behavior in flow as "wiggling". Rigid body rotation persists in flow, appearing as a high-frequency change in body orientation on top of a slower one that can be explained by chiral reorientation. We quantify swimming behaviors in two different flow rates and observe rheotaxis in addition to preferential orientation of bacterial bodies. Faster flow constrains wiggling bacteria trajectories and orientations compared to those observed in slower flow, with rheotaxis taking bacteria toward the wall. But not all bacteria in flow wiggle. Populations of non-motile "non-wiggling" filamentous E. coli follow streamlines, without preferential orientation of their bodies. Non-motile bacteria do not behave like chiral rods propelled by rotating flagellar bundles, but like rigid rods. Motility slows swimmers in comparison.

cond-mat.soft↗

arXiv2024

Mixing soft and rigid particles in a hopper: soft particles induce flow intermittency and avalanches

Instabilities and avalanches in granular flows represent hallmarks of failure: they can both disrupt industrial process flows and signal dangerous conditions, like those in grain silos and snowy mountaintops. We investigate intermittency and avalanches in the gravity-driven flow of granular materials through a quasi-2D hopper. Mixtures of rigid polypropylene and soft polyacrylamide particles flow through a hopper constriction. A combination of high-speed imaging, particle identification and tracking analyses enable us to measure quantities including particle velocities, outflow rates, the time intervals between consecutive particle exits, and the geometric properties of any temporary arches that form during a flow test. As the fraction of rigid particles increases in the mixture, the velocity of exiting particles increases. So too, however, does the probability of complete clogging. While soft particles exhibit slower velocities at the hopper exit, they also facilitate greater overall discharge rates. Simultaneously, the presence of soft particles induces both intermittency and avalanches in the flow. In this case, arches that could permanently block the flow are more likely to be temporary in nature. Interestingly, the identity of the particle that falls first from a temporary arch correlates linearly with the mixing fraction in the overall sample. That is, soft particles, despite their correlation with flow instabilities, are not significantly more likely to fall first from a temporary arch. Investigating the arch geometries suggests that the particle forming the largest bond angle with its neighbors is the one that causes an arch to fail, regardless of being soft or rigid.

cond-mat.soft↗

arXiv2022

A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation

Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions. It is analytically intractable to compute the necessary control parameter values of a droplet-generating device that produces optimized droplets. Furthermore, as the length scale of the fluid flow changes, the formation physics and optimized conditions that induce flow decomposition into droplets also change. Hence, a single proportional integral derivative controller is too inflexible to optimize devices of different length scales or different control parameters, while classification machine learning techniques take days to train and require millions of droplet images. Therefore, the question is posed, can a single method be created that universally optimizes multiple length-scale droplets using only a few data points and is faster than previous approaches? In this paper, a Bayesian optimization and computer vision feedback loop is designed to quickly and reliably discover the control parameter values that generate optimized droplets within different length-scale devices. This method is demonstrated to converge on optimum parameter values using 60 images in only 2.3 hours, 30x faster than previous approaches. Model implementation is demonstrated for two different length-scale devices: a milliscale inkjet device and a microfluidics device.

cs.LG↗