I am a multidisciplinary computational scientist who combines material science, physical chemistry, and mechanical engineering ideas with sophisticated simulations, data analysis, and machine learning to solve complex scientific problems. My research journey began with exploring fracture mechanics of materials under tensile loading using continuum and molecular simulations. In my graduate study, I focused on polyelectrolytes and their impact on flow reversal behavior. I discovered that grafted polyelectrolyte can act as an ion reservoir and effectively control the direction of electroosmotic flow. During my earlier graduate studies, I researched carbon nanotubes and discovered that a liquid's slower relaxation affects the nanotube's energy dissipation. I'm working on discovering a new way to purify human antibodies, leveraging the self-assembly of peptide amphiphiles and protein-ligand interactions.
Research Areas
The global Monoclonal Antibody market is projected to reach around USD 500 billion by 2030. These therapeutic biomacromolecules require a better method to purify them than the current column chromatography method, which is limited in its capacity and high media cost. Our research project aims to develop a more efficient method that utilizes the self-assembly behavior of peptide amphiphiles for the selective capture and precipitation of monoclonal antibodies. To achieve this, I am utilizing both all-atom and coarse-grained molecular dynamics simulations to understand better the self-assembly process, protein-ligand binding, and phase separation. I am also utilizing docking and free energy calculations. My work is guiding our collaborators' experimental efforts and answering difficult questions in soft matter physics.
Polyelectrolytes are charged polymers found in biology as DNA/RNA, etc. Scientists can also synthesize them for applications in energy, nanofluidic systems, lubrications, etc. My works were some of the pioneering works using all-atom molecular dynamics simulations to understand interactions among polyelectrolytes, ions, waters, and their combined effort to modify energy generation, flow modifications, etc. Most notably, I discovered that nanochannel grafted polyelectrolytes could introduce overscreening-like behavior, which can be used to control electroosmotic flow direction by changing electric field strength (with changing electric field direction). The Department of Energy acknowledges my research by awarding a grant for future research on this topic (currently benefiting junior graduate students in my former PhD lab).
The behavior of liquids in a nanoconfinement created by a nanotube or nanochannel is critical as they are central in energy harvesting, sensing, and nanofluidics. I explored the energy dissipation of a fluid-coupled carbon nanotube resonator. I found that fluid density inside carbon nanotubes can control energy dissipation by transitioning to a solid like strcuture in higher density. My work also sheds light on how two dissimilar liquids interact in a nanochannel, which can be critical in harvesting one liquid from a mixture of liquids.
I used continuum, atomistic simulations, and theory to understand the fracture mechanism of nano and micro-scale materials. I showed that InP nanowires show brittle failure under tensile loading using molecular dynamics simulations. What is interesting is that, depending on the temperature, the cleavage plane for brittle failure changes. I found the relations with temperature, electrostatic interaction, and applied loading direction for this surprising behavior. I have also studied how the stress intensity factor changes when adding circular or elliptical holes in a material using finite element analysis.
Frequently Used Methods
In material science and chemistry, molecular dynamics simulation is a valuable tool to understand the interactions between atoms and molecules better. This involves using Newton's equation of motion combined with advanced sampling techniques. To perform all-atom simulations, one must define all interactions at the atomic level, which can be computationally intensive depending on the size of the system and simulation time. To simplify the process, coarse-graining is often utilized to average a few atom interactions into one. While all-atom simulations remain my primary method, I also use coarse-grained simulations to explore long length and time scale results. During my research at Northwestern University, I have gained extensive experience in various coarse-graining techniques to represent atomic details better. Additionally, I use free-energy calculations when necessary to gain a more detailed understanding of crucial interaction
My fav simulation tools LAMMPS, GROMACS.
Molecular dynamics simulations produce a lot of raw data. To elaborate, one simulation generally has 100k+ atoms, and simulations are run for millions of timesteps. After each simulation, one gets the simulation results as each atom coordinates, velocity, and forces. I spend a lot of time clearing these vast amounts of coordinates and velocity data to get meaningful publishable results like spatial distributions, time correlations, and neighbor analysis, to name a few. I am also proud of producing good 2D and 3D visualizations using my edited data.
I have also used machine learning techniques in my research and projects. I have initiated and published 1st research work of my graduate lab using machine learning methods, where I used cluster analysis to understand hydrogen bonds inside a polyelectrolyte layer. Using NASA Landsat data, I have also used convolutional neural networks in another research work to understand deforestation in Sundarbans.
I mainly use MATLAB and Python for these.
Computer-aided design (CAD) plays a significant role in today's engineering. I used CAD for my project in my undergraduate study. Along with doing well in class, I used it to model our car team's chassis and body. This design helped us to get accepted into the Shell Eco-Marathon Asia region.
Finite element analysis is one of the most critical engineering tools currently available. I used it to understand the fracture mechanics of a hole containing a crack. I used different methods to estimate the stress intensity factor near cracks, which gives a good comparison with analytical projection.
Primary software: SOLIDWORKS, Ansys
Selected Journal Publications (total 15)
TH Pial, HS Sachar, PR Desai, S Das; Overscreening, co-ion-dominated electroosmosis, and electric field strength mediated flow reversal in polyelectrolyte brush functionalized nanochannels. ACS nano 15 (4), 6507-6516, 2021
TH Pial, S Das; Machine learning enabled quantification of the hydrogen bonds inside the polyelectrolyte brush layer probed using all-atom molecular dynamics simulations. Soft Matter 18 (47), 8945-8951, 2022
TH Pial, HS Sachar, S Das; Quantification of Mono-and Multivalent Counterion-Mediated Bridging in Polyelectrolyte Brushes, Macromolecules 54 (9), 4154-4163, 2021
TH Pial, Y Wang, S Das; Non-monotonic dependence of fluid dissipation on fluid density in fluid-coupled nanoresonators, Applied Physics Letters 115 (25), 2019
TH Pial, T Rakib, S Mojumder, M Motalab, S Akanda, Atomistic investigation on mechanical properties and fracture mechanism of Indium Phosphide nanowire, Physical Chemistry Chemical Physics, 20, 8647-8657, 2018
HS Sachar, TH Pial, VS Sivasankar, S Das, Simultaneous Energy Generation and Flow Enhancement (Electroslippage Effect) in Polyelectrolyte Brush Functionalized Nanochannels, ACS nano 15 (11), 17337-17347, 2021
Y. Li, M. Kim, TH Pial, Y. Lin, H. Cui, and M. Olvera de la Cruz, Aggregation-Induced Asymmetric Charge States of Amino Acids in Supramolecular Nanofibers, The Journal of Physical Chemistry B
Please see my Google Scholar profile for full publication list