Seminars

Binghan Liu Virginia Tech

“Physical Mechanism and Control of Stratification in Drying Films of Colloidal Mixtures”

Stratification is an interesting and potentially useful non-equilibrium phenomenon that occurs in a film containing mixtures of colloids and polymers when it is rapidly dried. It is the outcome of complex interplay between a range of processes including evaporation, diffusion, absorption, phoresies, and osmosis that take place at the liquid-vapor interface, in the bulk of the film, and at the surface of the substrate. This dissertation employs large-scale molecular dynamics simulations to reveal the physical mechanisms driving stratification in drying films of polydisperse colloidal mixtures, based on the concept of Diffusiophoresis, where a colloidal particle migrates in response to the concentration gradient of other types of solute particles in the suspension. The results support the notion that the asymmetric diffusiophoretic response of small and large particles plays a key role in yielding a stratified colloidal film after rapid solvent evaporation. Further simulations show that the shape of particles can also influence their diffusiophoretic behavior. Therefore, stratification can occur in drying films containing particles with various shapes. Next, the dissertation demonstrates that a binary mixture of solvents possessing different volatilities can be used to drive a binary blend of colloidal articles of the same size to stratify through contrasting couplings between the particle and the solvent components. Finally, the dissertation explores the drying behavior of colloidal films in two dimensions and the results are compared with those in there dimensions. The results in this dissertation offer new insights into the physical mechanisms and control strategies of stratification in drying films of colloidal mixtures.

Host: Shengfeng Cheng

Prof. Pham Thang Virginia Tech, Material Engineering

“Nanostructured Quantum Materials: From Deterministic Synthesis to Multimodal Characterization”

Development of qubits in recent years has been underlined by advances through circuit design, device fabrication, and standardized metrology to systematically compare qubit performance created by different platforms. However, further improvement and especially, scalable manufacturing of qubits require a bottom-up approach in which one can control the assembly and interaction of atoms across different interfaces. This quest calls for advances in materials synthesis, characterization, processing, in addition to materials selection. In my talk, I will identify problems in quantum information science (QIS) which materials science and engineering can uniquely address. First, I will show my recent results in understanding the coherence-structure relationship in superconducting materials and qubits using multimodal electron microscopy. In the second part, I will discuss my efforts in designing a new quantum material platform, namely one-dimensional van der Waals materials with controlled morphology, chemistry and interface. Finally, I will introduce my research group at VT and how we are planning to develop data-driven materials synthesis and characterization for applications in QIS.

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No Seminar (President Day)

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No Seminar (APS March Meeting)

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Mesfin Tsige University of Akron

"Computational Approaches to Understanding and Predicting Polymer Surface Properties"

The behavior of polymer surfaces and interfaces is crucial to the performance of advanced materials in coatings, adhesives, flexible electronics, and biomedical applications. Precise control over interfacial properties requires a molecular-level understanding of adhesion, wetting, and surface energetics. In this talk, I will present our computational efforts to investigate the wetting behavior of polymer surfaces using molecular dynamics (MD) simulations. By analyzing molecular structure, functional group orientations, and interfacial energetics, we gain key insights into the mechanisms that dictate polymer surface properties. Building on this foundation, I will discuss our recent integration of machine learning (ML) techniques to enhance the predictive capabilities of polymer surface properties. While MD simulations provide detailed molecular insights, their computational cost limits high-throughput screening. To address this, we have developed ML models trained on simulation and experimental data to efficiently estimate polymer surface tensions and interfacial characteristics. This hybrid approach, combining physics-based simulations with data-driven methods, provides a powerful framework for accelerating the design and optimization of polymeric interfaces.

Host: Shengfeng Cheng

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Easter Monday

Burcu Gurbuz Johannes Gutenberg University Mainz

Host: Uwe Tauber

Chris Stanton University of Florida

Host:Giti Khodaparast

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No Seminar (Thanksgiving Break)

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Prof. Marija Vucelija University of Virginia

Host:Giti Khodaparast

Joint Seminar

Prof. Elham Ghadiri Wake Forest University

Host: Giti Khodaparast

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Commencement Week (No Seminar)

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