Abstract: Chalcogenide phase change materials (PCMs) have promising properties for photonic applications thanks to their nonvolatile and large refractive index modulation [1]. The last decade has seen a growing interest in such a combination of properties for a variety of nonvolatile programmable devices, such as metasurfaces, tunable filters, phase/amplitude modulators, color pixels, thermal camouflage, photonic memories/computing, plasmonics, etc.—giving rise to the so-called Phase-change Photonics field.[1] PCM-based devices rely on the precise switching between the amorphous and the crystalline states, which can be achieved through optical or electrical pulses via optical absorption and Joule heating, respectively. Optical pulse switching is the fastest and most precise method; however, it lacks scalability given the difficulty of on-chip pulse routing when considering many PCM cells. It is also limited to absorptive PCMs, such as Ge2Sb2Te5. As a scalable alternative, on-chip microheaters using multiple material platforms have been proposed, e.g. doped-silicon, graphene, ITO, metals, etc. Doped-silicon microheaters are particularly interesting since they are CMOS compatible and can be fabricated onto silicon-on-insulator (SOI) wafers—the same platform used for silicon photonic integrated circuits. However, this electro-thermal switching also has shortcomings. It lacks stable multi-level response due to the stochastic nature of both amorphization and crystallization processes in the typical bow-tie-like devices where the microheater heats the entire cell to an almost flat temperature [2,3]. Because of this, the focus is shifting towards the microheater’s geometry in addition to the choice of conductive material and its integration. One common goal is to control the hotspot size within the PCM cell to deterministically switch specific areas (i.e., spatially resolved amorphous domains in a crystalline cell) and, thus, achieve controllable and reproducible optical modulation [4,5]. In this talk, I will review the fundamentals of PCM for photonics and the proposed electro-thermal mechanisms. I will then focus on our current efforts to re-engineer doped-silicon microheaters for optimum performance.

[1] M. Wuttig, et, al. “Phase-change materials for non-volatile photonic applications,” Nat. Photon.11(8), 465–476 (2017).

[2] J. Feldmann, et, al. “Calculating with light using a chip-scale all-optical abacus,” Nat Commun 8(1), 1256 (2017)

[3] T. Tuma, et, al. “Stochastic phase-change neurons,” Nat. Nanotechnol.11(8), 693–699 (2016).

[4] C. Ríos, et, al “Ultra-compact nonvolatile phase shifter based on electrically reprogrammable transparent phase change materials,” PhotoniX 3(1), 26 (2022).

[5] X.  Li, et, al, “Fast and reliable storage using a 5 bit, nonvolatile photonic memory cell,” Optica 6, 1-6 (2019)

[6] Y. Zhang, et, al, “Myths and truths about optical phase change materials: A perspective.” Appl. Phys. Lett. 118 (21): 210501.

Biographical Sketch: Carlos A. Ríos Ocampo is an Assistant Professor at the University of Maryland, College Park, where he has led the Photonic Materials & Devices groups since 2021. Before joining UMD, Carlos was a Postdoctoral Associate at MIT, received a DPhil (PhD) degree in 2017 from the University of Oxford (UK), an MSc degree in Optics and Photonics in 2013 from the KIT (Germany), and a BSc in Physics in 2010 from the University of Antioquia (Colombia). Carlos’s scientific interests focus on studying and developing new on-chip technologies driven by the synergy between nanomaterials and photonics.

 

Abstract: Aerosol particles are ever-present in both natural environments, and in engineered systems.  In large part, control over aerosol particle transport hinges upon control of particle inertia, i.e. the propensity of particles to maintain a particular trajectory whilst the surrounding fluid moves in a distinct direction.  Increasing the inertia of increasingly small particles typically involves increasing particle initial velocities, such that when fluid velocities slow down, particle-fluid velocity differences are maximized.  While inertial principles have long been exploited in a wide variety of aerosol instruments, including impactors, virtual impactors, and aerodynamic particle spectrometers, application of inertially-governed aerosol systems wherein the particle Mach number (based on its velocity difference with the fluid) approaches or even exceeds unit value are much more limited.  This presentation will overview current understanding of aerosol particle behavior in high-speed systems, and subsequently discuss ongoing studies of high speed particle behavior.  Specifically, the drag force on particles as a function of both the Knudsen number and the Mach number, derived from direct simulation Monte Carlo, will first be discussed.  Subsequently, results from ongoing studies of crater formation due to microparticle impacts at up to 1 km s-1 speeds with high-speed flight-relevant materials will be presented.  Also discussed will be insights from atomistic simulations into the changes in crystallinity and defect density experienced by particles during high-speed impacts, relevant to aerosol deposition of coatings, and the optimization of virtual impactor aerosol concentrators operating near the sonic limit, capable of submicrometer particle concentration enhancement.

Biographical Sketch: Dr. Chris Hogan is the Carl and Janet Kuhrmeyer Chair Professor and the Associate Department Head in the Department of Mechanical Engineering at the University of Minnesota, Twin Cities, where has been a faculty member since 2009.  His research group focuses largely on fundamental and applied research in aerosols, including the development of theories to describe transport and reactions in aerosols, the design of new measurement principles and instruments for aerosols, and the evaluation of HVAC control technologies.  He has published more than 150 peer reviewed papers focusing in these areas, and is the editor-in-chief of the Journal of Aerosol Science.

Abstract: In this talk, we’ll discuss how engineering students can utilize KeyShot to elevate the aesthetics and presentation of their concepts, increasing the likelihood their ideas will garner attention and be valued. Through an in-person demonstration, the Education Program Manager from KeyShot will illustrate just how quick and painless learning the industry-leading 3D-visualization software can be.

Biographical Sketch: Nick Abbott is a seasoned Industrial Designer with a rich five-year journey in the field. He has contributed to numerous projects for prominent clients, constantly expanding his creative horizons and refining his product design skills. Nick’s versatility led him to co-teach a design course at ASU, which paved the way for his latest endeavor at KeyShot. His bold decision to leave Purdue for startup adventures in the music industry reflects his risk-taking spirit and innovative mindset. Outside of work, Nick is an avid photographer, often found capturing the stunning landscapes of the mountains.

Abstract: Multiscale modeling methods are typically envisioned as precise and predictive simulation tools to solve complex science and engineering problems. However, even conventional atomistic models often lack computational efficiency and accuracy, making them inadequate for providing reliable information for large-scale continuum models. In this seminar, I will discuss the developments aimed at overcoming these critical limitations. 

At the beginning of the talk, I will introduce how atomistic models can enhance our understanding of the experimental observations of crystal growth in 2D materials using empirical reactive forcefield (FF). Although the models offer valuable insights at the atomic scale, the development of reliable FFs is severely limited due to the fixed potential expressions. Recently, neural network potentials (NNPs) have emerged to surpass the longstanding limitations of empirical potentials.

While NNPs can provide higher accuracy than other empirical FFs and lower computational costs compared to quantum calculations, efficient sampling or data generation for training becomes increasingly critical. I will present recent advancements in an automated active learning (AL) framework for NNPs, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling with trained NNPs and assessing model transferability to other alkane chains.

In the end, I will introduce one of the sampling approaches, the multiorder-multithermal (MOMT) ensemble, to capture a broad range of liquid- and solid-phase configurations in a metallic system, nickel. Data distillation through active learning can significantly reduce sampled data without losing much accuracy. The NNP, trained from the distilled data, can predict different energy-minimized closed-pack crystal structures even though those structures were not explicitly part of the initial data. Moreover, this data can be applied to other metallic systems (aluminum and niobium) without repeating the sampling and distillation processes.

The capabilities developed through the research will provide valuable tools for a fundamental understanding of the chemical process and mechanistic insights into the predictive design and interpretive simulations of materials processes and properties.

Biographical Sketch: GS Jung is a Research Staff at Oak Ridge National Laboratory. His research interests are in the multiscale modeling of materials to understand their fundamental properties from synthesis and growth to performance and failures. Before joining ORNL, he earned his Ph.D. in multiscale modeling for 2D materials from the Massachusetts Institute of Technology.

Abstract: Mathematical and computational modelling approaches can quantify the mechanics & mechanical environment of soft biological tissues under physiological and pathological loading. These tools can quantify ‘mechanical stimuli’ inputs to algorithms that control growth and remodelling (G&R) of the tissue to simulate adaptation to altered environmental conditions. In this talk, I will overview a rate-based constrained mixture G&R modelling approach which was developed to create the first computational model of aneurysm evolution [1]; I will then summarise its sophistications and applications over the past 20 years. In general, the modelling approach is as follows: the constitutive model of the tissue accounts for the individual natural reference configurations of cells and matrix components; a model of the organ/tissue is defined/calibrated to initially be in homeostasis in the physiological loaded configuration; subsequent tissue loss/damage or changes to the mechanical environment can change the distribution of mechanical stimuli from homeostatic setpoints and drive G&R processes that lead to the progression of disease or the adaptation to a new homeostatic state. Illustrative applications of the approach will include fluid-solid-growth frameworks for modelling intracranial aneurysm evolution [2], in vivo-in vitro-in silico modelling of bladder adaption to outlet obstruction [3] and a conceptual chemo-mechano-biological model of cartilage evolving in health, disease and treatment [4]. Outlook for future research and clinical translation of the models will be discussed.

References:

[1] Watton, Hill & Heil (2004) A Mathematical Model for the Growth of the Abdominal Aortic Aneurysm, BMMB, 3:98-113.

[2] Teixeira et al. (2020) Modeling intracranial aneurysm stability and growth: an integrative mechanobiological framework for clinical cases. BMMB, 19:2413–2431.

[3] Cheng et al. (2022) A constrained mixture-micturition-growth (CMMG) model of the urinary bladder: Application to partial bladder outlet obstruction (BOO), JMBBM, 134: 105337.

[4] Rahman et al. (2023) A chemo-mechano-biological modelling framework for cartilage evolving in health, disease, injury, and treatment, CMPB, 231: 107419.

 

Biographical Sketch: Paul Watton is Head of the Complex Systems Modelling Group and Professor of Computational and Theoretical Modelling at the Dept. of Computer Science & Insigneo Institute for in silico Medicine at the University of Sheffield, UK. He also holds an adjunct position with the Dept. of Mech. Eng. and Materials Science, University of Pittsburgh, US. He has a mathematical background (BSc Pure & Applied Mathematics, MSc Mathematical Logic, PhD Applied Mathematics) and his research focuses on modelling the biomechanics & mechanobiology of soft-biological tissues with application to disease progression and treatment.

Abstract: Soot is a major air pollutant produced by incomplete combustion of hydrocarbon fuels. The contribution of soot to global warming is currently estimated with large uncertainty (partly) due to the fractal-like agglomerate structure of its constituent nanoparticles. Here, the dynamics of soot nanoparticles are investigated to advance our current understanding of particle formation during combustion. Discrete element modeling (DEM) enables the detailed description of the particle morphology (doi.org/10.1016/j.proci.2016.08.078) and optical properties (doi.org/10. 1016/j.proci.2018. 08.025) in population balance models and computational fluid dynamics (doi.org/10.1016/j.combustflame.2021.01.010). Power laws relating the optical properties of soot to its filamentary structure are derived by DEM (doi.org/10.1016/j.carbon.2017.06.004) to facilitate the accurate monitoring of soot emissions by aerosol (doi.org/10.1016/j.proci.2020. 07.055), laser (doi.org/10.1016/j.combustflame.2022.112025) diagnostics and fire detectors (doi.org/10.1016/j.powtec.2019.02.003). Most importantly, these relations enable the estimation of the soot direct radiative forcing accounting for its realistic agglomerate structure (doi.org/10.1021/acs.est.2c00428).

Biographical Sketch: Dr. Georgios Kelesidis is an Assistant Professor at Rutgers School of Public Health and Deputy Director of the Nanoscience and Advanced Materials Center of the Environmental and Occupational Health Sciences Institute at Rutgers University. Prior to this appointment, he was a Lecturer and Research Associate at the Department of Mechanical and Process Engineering of ETH Zürich, Switzerland. He received a Diploma in Chemical Engineering from the University of Patras, Greece with honors (top 3%), along with the Limmat Stiftung Award of Academic Excellence (2013). His subsequent MSc studies in Process Engineering at ETH Zürich were supported by a Particle Technology Laboratory Fellowship (2013-2015), while his MSc thesis earned the IBM research prize (2017) for computer modelling and simulations in chemistry, biology and material science. His 2019 PhD thesis on the morphology and optical properties of flame-made nanoparticles received the 2020 PhD Award from GAeF (German Association for Aerosol Research) and the ETH medal for Outstanding Doctoral Thesis (top 8 %). He received also the 1st Graduate Student Award on Carbon Nanomaterials at the 2019 AIChE Annual Meeting (Orlando, FL, USA), as well as Best Poster Awards at the European Aerosol Conference (EAC) in 2016 (Tours, France) and 2020 (Aachen, Germany), the 2019 ETH Conference on Combustion Generated Nanoparticles (Zürich, Switzerland) and the 2019 Fall Meeting of the Material Research Society (MRS). The societal impact of his PhD research was also highlighted by the Forbes Magazine by including him in the 2020 Forbes 30 under 30 Europe list for Science & Healthcare. He has co-authored 21 peer-reviewed articles so far, being the first author in 16 of them. He has organized technical sessions at MRS (2016), EAC (2019-2021), the 2020-2022 Annual Meetings of the American Association for Aerosol Research, the 11th International Aerosol Conference (2022) and the 9th World Congress on Particle Technology (2022). He has supervised so far 10 MSc and 7 BSc students. He is currently co-supervising 1 PhD student at ETH Zürich.

Abstract: Recent decades have seen rapid development in all manufacturing technologies, including additive manufacturing (AM). This has raised the need for design methods to leverage the new, increasingly complex fabrication possibilities. Topology optimization has the potential to generate new high-performing design solutions since it is a free-form design method that does not require a preconceived notion of the final layout. It uses computational mechanics and optimization tools to generate improved designs. For operating designs to perform as predicted, the used model must capture the material behavior. Additionally, the planned manufacturing process might induce material characteristics and design limitations that should be considered as the design is generated. This talk focuses on identifying and incorporating behavioral and manufacturing aspects within the design process. Different strategies for integration within topology optimization will be discussed. This includes consideration of manufacturing-induced material characteristics, which is illustrated through tailoring design to material extrusion-based AM. In material extrusion, a nozzle moves across a build plate and deposits a material bead on a 2D slice of the design. These processes typically induce some degree of anisotropy through weak(er) bonding between adjacent beads. To improve the manufacturability of large-scale designs, the application of a Mixed Integer Linear Programming formulation is discussed for highly restricted volume scenarios. Finally, a new design framework is introduced in which the interactive participation of the design engineer is enabled to resolve more complex mechanic phenomena.

Biographical Sketch: Josephine Carstensen is the Gilbert W. Winslow Career Development (Assistant) Professor in the Department of Civil and Environment Engineering (CEE) at MIT. She leads the Carstensen Group, conducting research that revolves around the engineering question of “how we design the structures of the future?” Her work spans from the development of computational design frameworks for various structural types and design scenarios to experimental investigations that are used to inform necessary algorithmic considerations.

Dr. Carstensen has received awards for both research and teaching, including the National Science Foundation CAREER award and CEE Maseeh Award for Excellence in Teaching. She joined the MIT CEE faculty in 2019 after two years as a lecturer at MIT, jointly appointed in CEE and Architecture.  She received her PhD from Johns Hopkins University in 2017 and holds a B.Sc. and a M.Sc. from the Technical University of Denmark.

Abstract: Current robots are primarily rigid machines that exist in highly constrained or open environments such as factory floors, warehouses, or fields. There is an increasing demand for more adaptable, mobile, and flexible robots that can manipulate or move through complex environments. This problem is currently being addressed in two complementary ways: (i) learning and control algorithms to enable the robot to better sense and adapt to the surrounding environment and (ii) embedded intelligence in mechanical structures. My vision is to create robots that can mechanically conform to the environment or objects that they interact with to alleviate the need for high-speed, high-accuracy, and high-precision controllers. In this talk, I will give an overview of our key challenges and contributions to developing mechanically conformable robots, including soft parallel mechanisms for dexterous manipulation, physically-coupled multi-agent systems, and dynamic origami.

Biographical Sketch: Zeynep Temel is an Assistant Professor with the Robotics Institute at Carnegie Mellon University. Her research focuses on developing robots that can mechanically conform to the environment or objects that they interact with. Prior to joining RI, she was a postdoctoral Fellow at the Microrobotics Lab in Harvard University. She received her Ph.D. from Sabanci University, Turkey, where her work is funded by Turkish Science Foundation. In 2020, she was selected as one of 25 members of the Young Scientists Community of World Economic Forum.

Abstract: Mixing-induced vibrational non-equilibrium was studied in the turbulent shear layer between a high-speed jet and a surrounding hot-air co-flow. The vibrational and rotational temperatures of N2 and O2 were determined by fitting measured spontaneous Raman scattering spectra to a model that allows for different vibrational and rotational temperatures. The mixing of the jet fluid with the co-flow gases occurs over microsecond time scales, which is sufficiently fast to induce vibrational non-equilibrium in the mixture of hot and cold gases. The effect of fluctuating temperatures on the time-averaged Raman measurement was quantified using single-shot Rayleigh thermometry. The Raman scattering results were found to be insensitive to fluctuations except where the flame is present intermittently. Vibrational non-equilibrium was detected in nitrogen but not in oxygen. This difference between species temperatures violates the two-temperature assumption often used in the modeling of high-temperature non-equilibrium flow. A multiple-pass cell was constructed to obtain single-shot Raman scattering measurements in the turbulent shear layer using a pulsed stretched laser. These measurements agreed with the previous time-average results and allowed us to make measurements near the fluctuating base of a lifted flame – a region where time-averaged measurements do not give meaningful results.

Biographical Sketch: Prof. Philip L. Varghese holds the Ernest H. Cockrell Centennial Chair in Engineering at The University of Texas at Austin and has an international reputation in the areas of rarefied and non-equilibrium flows and optical diagnostics for combustion and plasmas. He received his Bachelor of Technology degree from the Indian Institute of Technology in Madras in 1976, an MS from Syracuse University in 1977, and a PhD from Stanford University in 1983 all in Mechanical Engineering. He was a post-doctoral Scholar in the Molecular Physics Laboratory at SRI International and joined UT Austin in 1983 in the department of Mechanical Engineering. He was promoted to Associate Professor in 1988 and transferred to Aerospace Engineering in 1989. He was promoted to full Professor in 1995 and has been the Director of the Center for Aeromechanics Research since 1999. He served as Chair of the Department from 2009-2012.

Among numerous awards he was Fulbright Senior Scholar in France in 1993 and was awarded the Boeing-A.D. Welliver Faculty Fellowship by the Boeing Company in 1998. He received the Lockheed Martin Aeronautics Company Award for Excellence in Engineering Teaching in Spring 2003, and was elected to the Academy of Distinguished Teachers at the University of Texas in 2005. In February 2012 he was selected Professor of the Year by the Senate of College Councils at UT Austin and was awarded The University of Texas System Regents’ Outstanding Teaching Award in August 2016.

Dr. Varghese’s research focuses on understanding the basic molecular processes occurring in high speed, high temperature, and non-equilibrium flows. This is an inter-disciplinary field, requiring a synthesis of physics and chemistry with the more traditional engineering disciplines of fluid mechanics, heat transfer, and thermodynamics. He applies his work to the study of hypersonic and rarefied flows, plasmas, and combustion. He has established a laser diagnostics laboratory for experimental studies in combustion and plasma discharges. He also has an active program in planetary scale simulations of rarefied flows and has developed a novel technique for accurate solutions of the Boltzmann equation using quasi-particle simulation. His research publications have been extensively referenced and a recent search showed over 3800 citations of his work on Google Scholar. He is co-inventor on six US patents related to applications of Raman spectroscopy.