Research

Active matter-like systems, exemplified by microswimmers like bacteria, viruses, and algae, exhibit dynamic behaviors driven by microscopic entities (motors) that consume energy, whether from their own resources or externally provided, thereby generating forces that influence the system's dynamics. These systems operate as thermodynamically open, far-from-equilibrium entities, constantly exchanging energy and matter with their surroundings. 

Drying droplets containing active biological particles present a heightened level of complexity compared to passive particles (A & B: EXTERNAL & INTERNAL FACTORS), owing to the additional dynamics (C: EXTRA FACTORS) of motility and micro-environmental influences on fluid flows, alongside the inherent self-aggregation observed in bio-colloidal droplets. Remarkably, the classic 'coffee-ring' effect can be modified by bio-surfactants produced within the microenvironment, while various factors, including substrate properties, strains, nutrient availability, particle characteristics (size, shape), drying rates, etc., intricately influence the resulting deposited patterns. 

This research seeks to establish the correlation between the local nutrient availability, motility, and other traits of the active droplets in a sessile drying setting.   

Liquid crystals (LCs) represent a distinct class of anisotropic materials renowned for their ability to manifest a diverse spectrum of ordered phases. Recent advancements in experimental techniques have positioned LC droplets as a promising avenue for sensing biological and chemical phenomena, owing to their distinctive attributes encompassing label-free detection, phase separation dynamics, and straightforward visual representation. 

The intricate interplay between the drying process, the self-assembly of proteins, and the phase separation of thermotropic LCs within initial aqueous solutions constitute a fascinating realm of investigation. 

Central to this study is the comparative analysis of two distinct proteins, bovine serum albumin [BSA] and lysozyme [Lys], within a ternary system, employing optical microscopy as the primary investigative tool. 

Notably, a captivating discovery emerges in the form of an umbilical defect of [+1] strength observed near the edge of the BSA drop, while each domain within the dried Lys drop exhibits a central dark region encircled by a bright region, with no defects of LCs was observed. 

The research also delves into the intricate dynamics of drops containing a globular protein and LC in the presence of different salts, scrutinizing the drying kinetics, morphological crack patterns, and textural parameters via high-resolution microscopy, textural image analysis, and statistical methodologies. 

The presence of phase-separated ring formations in Lys drops, with and without LCs, signifies the formation of a film containing Lys and salts atop these LCs in the central region, consequently reducing the optical response (birefringence) of the LCs. 

In our ongoing research, we have turned our attention to studying and predicting the dynamics of liquid crystal textures that are formed during the drying process. To address this, we are currently employing traditional and simple neural network-based supervised machine learning algorithms. Our foray into the synergy of machine learning and liquid crystal research continues to yield promising results, and we look forward to further advancements in this captivating field.