(3ck) Imaging, Learning, and Engineering of Complex Fluids at the Nanoscale

Understanding, and ultimately engineering, nanostructured complex fluids is crucial in order to rationally design materials from nanoscale components and to decode the complex behavior of biological systems at the nanoscale. The physics of nanostructured complex fluids is particularly challenging to decipher because of the combination of the small size of the nanoparticles, incomplete knowledge of the forces between nanoparticles, and the enormous changes of material properties at the nanoscale compared to the bulk. It is only now that with the advent of advanced in-situ microscopy instruments, high throughput imaging and acquisition techniques, data-driven machine learning tools, and physically-inspired models, we are able to directly image, study, and interpret the motions and interactions of nanoparticles in complex fluids with an unprecedented spatial (nanometer) and temporal (milliseconds) resolution. My research uses experimental, theoretical, and computational tools such as in-situ liquid cell electron microscopy, rheology, statistical and colloidal thermodynamics, and machine learning to study the nanostructure and dynamics of complex fluids. As a faculty, my research program will be focused on three thrusts:

• Harnessing the power of in-situ liquid cell electron microscopy to study rheology and dynamics of nanostructured complex fluids using high-throughput imaging data, machine learning, and physics-based statistical thermodynamic models
• Engineering active nanomachines to study out-of-equilibrium self-organization and collective behavior of matter
• Augmenting in-situ liquid cell electron microscopy techniques under nanoconfinement with engineered surfaces to enable the study of biological systems at the nanoscale

Research Experience:

In my postdoc (Paul Alivisatos’ Lab, UC Berkeley), I have used in-situ liquid cell transmission electron microscopy in combination with deep learning and theoretical tools from Langevin dynamics to study the complex anomalous diffusive motions of a model system of nanoparticles in boundary layers and how interactions influence that. This opens up the path to use this technique for studying more sophisticated soft matter systems including biological media and polymeric solutions to extract material properties expressed at nanometer length scales. Prior to that, I studied the effect of the viscoelasticity of the polymer solutions in the crystal growth in nanocomposites, which governs the optoelectronic properties of semiconducting crystals and determines the final device performance. This work led to development a new form factor for light emitting devices.

During my PhD (Matteo Pasquali’s Lab, Rice University), I developed deep understating of colloidal complex fluid systems and interfaces as well as material processing. My main area of research as a PhD student has been the thermodynamic study of the phase behavior and morphology of liquid crystal solutions of anisotropic nanomaterial building blocks (carbon nanotubes). I combined techniques including optical microscopy, small angle X-ray and neutron scattering, rheology, cryogenic transmission electron microscopy, and mean-field theory to study the formation of nematic liquid crystal droplets, their wetting behavior, and formation of higher order liquid crystal phases in presence of flexibility and size polydispersity of constitutive particles.

Teaching Experience:

Dean’s Teaching Assistant and Guest Lecturer: Thermodynamics I (CHBE 411), Rice University Fall 2014
Teaching Assistant: Colloidal & Interfacial Phenomena (CHBE 560), Rice University Spring 2014

Teaching Assistant and Guest Lecturer: Transport Phenomena I (CHBE 401), Rice University Fall 2012

Teaching Assistant: Chemical Engineering Lab II (CHBE 433), Rice University Fall 2011

Teaching Interests: I am interested to teach chemical engineering core courses including thermodynamics, transport phenomena, fluid dynamics, and applied mathematics for engineers at both undergraduate and graduate levels. Additionally, I plan to develop a research-based course for undergraduate students who do not have the opportunity to obtain one-on-one mentorships in research due to the limited number of undergraduate research positions available. Throughout this course students from diverse backgrounds will be engaged in STEM research. The type of data typically collected in my research (videos and images) is suitable for involving undergraduate students in research through learning cutting-edge machine learning and data science tools. I am also interested in developing an advanced interdisciplinary course in soft matter, which will include concepts from colloidal and interfacial science, rheology, statistical physics and its application in nanotechnology. In addition to graduate and undergraduate level courses, I hope to initiate a new reading group or a special topic course that primarily addresses the frontier of nanotechnology, while encouraging the exchange of new ideas from various disciplines.