From aerosol synthesis of materials and devices to a new kinetic theory of gases?

Date: December 6, 2024; Time: 2:00 PM Location: PWEB 175

Abstract: Recent advances in understanding of combustion and aerosol formation and growth through multiscale process design, allow now inexpensive synthesis of nanoparticles with sophisticated composition, size and morphology by spray combustion at kg/h even at academic institutions with such units now all over the world (UK, Spain, India etc.). These have led to synthesis of single noble atom heterogeneous catalysts, biomaterials and highly porous sensing films. These advances and community’s keen interest on nanoscale phenomena have motivated a closer look to the fundamentals of aerosol particles in the free molecule regime.

For eons, the kinetic theory of gases has been assuming elastic collisions between spherical gas molecules [1]. However, is this so with what we know about molecular shape and force fields today? Having reached a state of maturity now, molecular dynamics (MD) simulations can elucidate the fundamentals of basic aerosol phenomena that lead to better understanding of natural phenomena and accelerate process design and scale-up [2].

Here the mechanics of gas collisions are elucidated for plain air at room temperature by thoroughly-validated atomistic MD treating O₂ and N₂ as true diatomic molecules accounting for their shape and force field, for the first time to our knowledge. So it is revealed that their trajectories are no longer just straight (or ballistic) while collision frequencies are much higher due to the attractive component of the force field and the diatomic shape of N₂ and O₂ as will be shown by the respective videos. Frequently, colliding molecules were split from each other but soon return to collide again and again without interacting with any other molecule in between resulting in orbiting collisions as had been envisioned 60 years ago [3].

A direct result of the enhanced interactions between air molecules when treated as true diatomic ones is that their mean free path (MFP) comes out to be considerably smaller than that from the classic kinetic theory. The new MFP for air is 38.5 nm, almost 43% smaller than that in textbooks of 67.3 nm at ambient conditions [4]. Such a result is significant in aerosol synthesis of tiny (< 5 nm) nanoparticles where asymptotic (self-preserving) particle size distributions and (fractal-like) structures have not been attained yet to simplify the corresponding process design as with carbon blacks and fumed oxides today.

Most importantly, this motivates a renewed examination of aerosol dynamics in the free molecular regime. If time permits, it will be shown that accounting for the gas molecule shape and force field (in addition to that of particles) drastically decreases the diffusivity of tiny aerosol nanoparticles, up to an order of magnitude lower than that given by Epstein’s equation in all aerosol textbooks as their size approaches that of surrounding gas molecules.

1. Maxwell JCMA, The London, Edinburgh, Dublin Philos. Mag. J. Sci., 19-32 (1860).
2. Mavrantzas VG & Pratsinis SE, Curr. Opinion Chem. Eng., 23 174 – 183 (2019).
3. Hirschfelder, JO, Curtiss, CF, Bird, RB, Molecular Theory of Gases & Liquids, Wiley, 1964.
4. Tsalikis D, Mavrantzas VG, Pratsinis SE, Aerosol Sci. Technol. 58, 930 – 941 (2024).

Biographical Sketch: Dr. Pratsinis has a 1977 Diploma in Chemical Engineering from Aristotle Univ. of Thessaloniki, Greece and a 1985 PhD from Univ. of California, Los Angeles. He was in the faculty and head of ChE at the Univ. of Cincinnati, USA until 1998 when he was elected Professor of Process Engineering & Materials Science at ETH Zurich, Switzerland. He has graduated 46 PhDs, published 400+ refereed articles, filed 20+ patent families that are licensed to industry and have contributed to creation of four spinoffs. One of them (HeiQ Materials AG) was the first ever from ETH Zurich to enter the London Stock Exchange in December 2020. Another one (Alivion AG) was launched in 2022 and has sold already its devices for detection of adulterated alcohol and methanol poisoning in 26 countries. For more details on him you may glance at https://ptl.ethz.ch/people/person-detail.html?persid=79969

Data-Driven Multi-Scale Design of Engineering Materials under Uncertainty

Date: November 1, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: The area of data-driven materials design has been garnering considerable interest due to the increasing need for high-performance materials in electronics, energy and structural applications, and extreme environments. The research on engineering materials and their manufacturing will potentially extend in the future to the development of new-generation composites, alloys, ceramics, and other materials for extreme environments such as hypersonics applications, fabrication of adaptive thermal response materials, energetic composites in fuel cells, thermal energy harvesting in satellites, and materials for green energy applications with the use of computational and data-driven design strategies.

In this talk, Dr. Acar will present an overview of the multi-scale computational methods developed by her research group to design metallic microstructures and mechanical metamaterials for enhanced mechanical performance. The talk will also discuss the impact of manufacturing-related uncertainty arising from the imperfections and defects during processing on the reliability and performance of these engineering materials. Additional topics will cover the integration of Artificial Intelligence (AI)/Machine Learning (ML) techniques into physics-informed material models to accelerate the design of material systems processed with conventional and additive manufacturing techniques.

Biographical Sketch: Dr. Pinar Acar is an Associate Professor at the Mechanical Engineering Department of Virginia Tech. Her research interests focus on multi-scale materials modeling, materials design, design optimization, uncertainty quantification, and machine learning. She received her Ph.D. degree in 2017 from the Aerospace Engineering Department of the University of Michigan. During her Ph.D., she developed various computational methods for studying the multi-scale modeling and design of metals under uncertainty.

Dr. Acar is the winner of the National Science Foundation (NSF) Career Award, the Air Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP) Award, the Dean’s Awards of Excellence: Faculty Fellow and Outstanding New Assistant Professor Awards at Virginia Tech, Frontiers of Materials Award by The Minerals, Metals and Materials Society (TMS), and the International Amelia Earhart Fellowship, as well as the recipient of the best paper award in Non-Deterministic Approaches field in AIAA SciTech Forum 2022.  She is an elected member of technical committees in various professional societies, including the American Society of Mechanical Engineers (ASME), The Minerals, Metals & Materials Society (TMS), The U.S. Association for Computational Mechanics (USACM), and The American Institute of Aeronautics and Astronautics (AIAA).

 Nanotechnology-enabled Manufacturing Processes

Date: November 22, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: The advances in technology and intelligent components, driving innovation in engineering systems or processes, enable expectations to meet the growing demand for enhanced performance and a deeper understanding of mechanisms in a range of applications. While research activities in nanotechnology have exploded over the past decades, the infusion of nanotechnology into practical engineering systems or processes, especially manufacturing processes, has been limited due to the intricate barriers in various manufacturing processes. Appropriate integration of nanodevices into manufacturing processes is crucial for retaining the advanced functionality and performance of the devices in harsh environments. Furthermore, scale-up production of functional materials with uniform incorporation of nanoelements, such as nanoparticles, nanotubes, nanofibers, nanorods, and so on, is essential to leverage the distinctive physical, chemical, and mechanical properties of nanoelements for a wide range of industrial applications. This talk will present two aspects of nanotechnology-enabled manufacturing processes: nanodevice-aided manufacturing and scalable manufacturing of functional materials with nanoelements. In-situ monitoring of several manufacturing processes, particularly friction element welding, an advanced joining process for aluminum alloy to high strength steel, has been successfully achieved with embedded nano-thin-film sensors. The nano-thin-film sensors (embedded or not) would be powerful tools for in-situ sensing at critical locations, thus advancing fundamental understanding of manufacturing processes. In addition, a novel methodology for uniformly incorporating nanoelements into functional materials has been successfully developed for large-scale production of high-performance materials. This nanotechnology-enabled manufacturing process promises to be a transformative technology for further advancing manufacturing processes and economically producing high-performance functional materials for the energy and sustainability challenges facing today’s manufacturing sectors.

Biographical Sketch: Professor Hongseok Choi is an associate professor in the Department of Mechanical Engineering at Clemson University, where he focuses on advanced materials processing, particularly in the realm of manufacturing with a strong emphasis on the interplay between material properties and fabrication methods. He has earned his Ph.D. in Mechanical Engineering from the University of Wisconsin-Madison (UW-Madison) in 2007 and worked as an assistant scientist in Nano-Engineered Materials Processing Center (NEMPC) until 2013, where his work laid the groundwork for various innovations in manufacturing and materials processing. Dr. Choi has authored numerous influential publications in the field, contributing significantly to the understanding and application of advanced manufacturing processes. He actively participates in interdisciplinary collaborations and serves on various committees for professional organizations, fostering growth and advancement within the fields of manufacturing and materials science. He is a recipient of the SME Distinguished Faculty Advisor Award, demonstrating his dedication to fostering the next generation of engineers and reflecting his commitment to education and mentorship. Dr. Choi also engages in active collaboration with industry partners to translate research findings into practical applications, further solidifying the bridge between academia and industry in addressing current engineering challenges.

Thermal Energy Storage for Grid Resilience

Date: October 18, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: The energy sector is undergoing a major transformation.  More renewable energy resources are being added to the energy grid to replace aging fossil fuel power plants and meet growing demand.  However, these renewable energy resources are more intermittent and rely on favorable weather conditions to produce energy often resulting in a misalignment of energy generation and demand.  Thermal energy storage (TES) is a powerful tool to combat this misalignment.  This talk will discuss how TES can improve the resilience of the evolving energy grid.  Near-ambient temperature TES can complement building technologies to reduce building energy usage for heating and cooling, shift energy usage from on-peak to off-peak times, and extend the capabilities of existing building heating and cooling systems.  At elevated temperatures, TES can serve as a peaking resource delivering power to the grid by discharging high grade heat to a thermal power cycle when renewable generation drops or demand rises.  As the energy grid continues to evolve, TES provides unique opportunities to further enable widespread renewable energy deployment.

Biographical Sketch: Jason Hirschey is a postdoctoral researcher in the Thermal Energy Systems group at the National Renewable Energy Laboratory in Golden, Colorado.  He received his PhD in mechanical engineering from the Georgia Institute of Technology in 2022 on near ambient temperature thermal energy storage for building heating and cooling.  His current research focuses on thermal energy and heat transfer with special emphasis on concentrated solar power and high temperature thermal energy storage for power generation.

Two-player zero-sum differential games with one-sided information and state constraints, aka Football

Date: October 11, 2024; Time: 2:30 PM Location: PWEB 175

Abstract: Enabling embodied intelligence requires robots to plan according to unknown and potentially adversarial intents of interacting agents. This talk will focus on one scenario where theory and methods are underdeveloped. Specifically, we study zero-sum differential games with state constraints and one-sided information, where the informed player (Player 1) has a categorical payoff type unknown to the uninformed player (Player 2). The goal of Player 1 is to minimize his payoff without violating the constraints, while Player 2 either aims to violate the state constraints or, failing that, maximize the payoff. Examples of such games include man-to-man matchup in football and missile defense scenarios. Due to the zero-sum nature, Player 1 may need to delay information release or even manipulate Player 2’s belief to take full advantage of information asymmetry, while Player 2’s strategy will need to balance all possible consequences. Existing solvers such as CFR+ (e.g., for Poker) are applicable, but are not scalable to continuous action spaces as is often the case in robotics. We will discuss efficient solvers for these games by leveraging unique structural properties of their value functions.

Biographical Sketch: Dr. Yi Ren is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Arizona State University. His research spans a range of topics at the intersection of machine learning and engineering, with recent focuses on differential game theory, GenAI model attribution, and representation learning for materials. He has published in both machine learning conferences, including ICLR and ICML, and engineering journals, such as IEEE Transactions on Robotics and Acta Materialia. Dr. Ren received his Ph.D. in Mechanical Engineering from the University of Michigan in 2012 and his Bachelor’s degree in Automotive Engineering from Tsinghua University in 2007. Outside of research, he enjoys playing soccer and spending time with his children.