arXiv subjects

Harsh Jain

Publications and source records attributed to Harsh Jain.

4 recordsLinked to original sources

arXiv2026

Multi-particle gates on driven one-dimensional paths: probing deep traps

We study single-file transport of driven overdamped colloidal particles on a periodic path with deep potential wells. In the small trap limit (i.e., trap size smaller than particle size), the particle current transitions from zero to finite as the number of particles on the path exceeds a critical number $n_c$. Beyond this threshold, $n_c$ particles cluster behind the trap, demonstrating collective correlated motion. The remaining `extra' particles circulate, giving a finite current. We study this phenomenon numerically using overdamped Brownian dynamics simulations, and present an experimental realization of this behaviour for micron-scale colloidal particles driven in an optical vortex. Using our experimental observations, we present results characterizing potential wells as deep as several hundred $k_BT$.

cond-mat.soft↗

arXiv2023

Emergent reprogrammable mechanical memory in soft rods network via friction tuning

We present emergent mechanical memory storage behavior in soft cellular materials. The cellular materials are a network of soft hyperelastic rods which store shape changes, specifically local indentation. This happens under an applied global compressive strain on the material. The material transits under strain from an elastic state (capable of `forgetting' any applied indentation after un-indentation) to plastic state (indefinitely storing the shape change due to indentation). The memory can be erased via removal of applied global strains and is therefore re-programmable. We characterise this behaviour experimentally and present a simple model that makes use of friction for understanding this behavior.

cond-mat.soft↗

arXiv2020

Recharging and rejuvenation of decontaminated N95 masks

N95 respirators comprise a critical part of the personal protective equipment used by frontline health-care workers, and are typically meant for one-time usage. However, the recent COVID-19 pandemic has resulted in a serious shortage of these masks leading to a worldwide effort to develop decontamination and re-use procedures. A major factor contributing to the filtration efficiency of N95 masks is the presence of an intermediate layer of charged polypropylene electret fibers that trap particles through electrostatic or electrophoretic effects. This charge can degrade when the mask is used. Moreover, simple decontamination procedures (e.g. use of alcohol) can degrade any remaining charge from the polypropylene, thus severely impacting the filtration efficiency post decontamination. In this report, we summarize our results on the development of a simple laboratory setup allowing measurement of charge and filtration efficiency in N95 masks. In particular, we propose and show that it is possible to recharge the masks post-decontamination and recover filtration efficiency.

physics.med-ph↗

arXiv2018

Molding 3D curved structures by selective heating

It is of interest to fabricate curved surfaces in three dimensions from easily available homogeneous material in the form of flat sheets. The aim is not just to obtain a surface $M$ in $\mathbb{R}^3$ which has a desired intrinsic Riemannian metric, but to get the desired embedding $M \subset \mathbb{R}^3$ up to translations and rotations (the Riemannian metric alone need not uniquely determine this). In this paper, we demonstrate three generic methods of molding a flat sheet of thermo-responsive plastic by selective contraction induced by targeted heating. These methods do not involve any cutting and gluing, which is a property they share with origami. The first method is inspired by tailoring, which is the usual method for making garments out of plain pieces of cloth. Unlike usual tailoring, this method produces the desired embedding in $\mathbb{R}^3$, and in particular, we get the desired intrinsic Riemannian metric. The second method just aims to bring about the desired new Riemannian metric via an appropriate pattern of local contractions, without directly controlling the embedding. The third method is based on triangulation, and seeks to induce the desired local distances. This results in getting the desired embedding in $\mathbb{R}^3$, in particular, it also gives us the target Riemannian metric. The second and the third methods, and also the first method for the special case of surfaces of revolution, are algorithmic in nature. We give a theoretical account of these methods, followed by illustrated examples of different shapes that were physically molded by these methods.

cond-mat.soft↗