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DTSTART;TZID=America/New_York:20240618T100000
DTEND;TZID=America/New_York:20240618T113000
DTSTAMP:20260403T172610
CREATED:20240604T154627Z
LAST-MODIFIED:20240604T154627Z
UID:10007979-1718704800-1718710200@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Controlling Contact Transitions for Dynamic Robots"
DESCRIPTION:Legged robots\, robotic manipulators\, and their combined embodiment as humanoid robots have received considerable attention across both academia and industry. However\, with few notable exceptions\, state-of-the-art demonstrations are significantly less dynamic than their biological counterparts. A considerable challenge for performing more dynamic tasks for both legged robots and robotics manipulators lies within controlling contact interactions with their environment. Legged robots are sensitive to impacts with the ground when executing dynamic motions because they undergo large changes in their velocities in a short amount of time with uncertainty in both the impact model and timing. Robotics manipulators often focus on quasistatic models or static contacts to avoid the underactuation that comes with sliding. First\, we will propose a general framework for reducing sensitivity to uncertainty to the impact event\, which we demonstrate on dynamic jumping and running controllers on the 3D bipedal robot\, Cassie. Next\, we explore a dynamic non-prehensile manipulation task that requires the consideration of the full spectrum of hybrid contact modes. We leverage recent methods in contact-implicit MPC to handle the multi-modal planning aspect of the task. We demonstrate\, with careful consideration of integration between the simple model used for MPC and the low-level tracking controller\, how contact-implicit MPC can be adapted to dynamic tasks. Finally\, I propose small modifications to the MPC framework to add a dual-sided margin to the stick-slip boundary.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-controlling-contact-transitions-for-dynamic-robots/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240618T120000
DTEND;TZID=America/New_York:20240618T120000
DTSTAMP:20260403T172610
CREATED:20240611T122503Z
LAST-MODIFIED:20240611T122503Z
UID:10007985-1718712000-1718712000@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Engineering copper-vacancy color centers in zinc sulfide for quantum defect discovery"
DESCRIPTION:Photoluminescent point defects\, or color centers\, in wide-bandgap semiconductors are important platforms for quantum information science because they can be operated as solid-state quantum spin-light interfaces. Implementing so-called defect qubits in an expanded variety of materials systems is beneficial for applications\, since the host-defect material properties determine operating parameters such as emission wavelength\, spin coherence time\, and pathways for device integration. A key challenge is obtaining materials that contain defects of interest\, and at sufficiently low concentrations to allow observation of quantum emission. This thesis concerns the creation of copper-vacancy complexes for quantum defect studies in zinc sulfide\, a material in which there is no known defect qubit. Zinc sulfide\, as the host material\, possesses a wide bandgap and a low concentration of nuclear spins\, enabling the operation of an electronically isolated spin-light interface with low magnetic background noise. The copper-vacancy center\, as the point defect of interest\, has been shown to exhibit favorable characteristics including radiative transitions between isolated states inside the zinc sulfide bandgap\, a paramagnetic ground state\, and a C3V-symmetric impurity-vacancy structure which results in favorable orbital and spin characteristics for several known defect qubits. We use both chemical synthesis and focused ion beam (FIB) implantation to obtain copper-vacancy color centers in zinc sulfide. FIB implantation of copper followed by annealing creates localized arrays of copper-vacancy color centers in single-crystal zinc sulfide. Studies of copper-vacancy center activation in bulk zinc sulfide reveal new evidence regarding the origins of the associated emission\, and provide bright ensembles of centers sharing a single crystal lattice for field-dependent measurements. However\, the background emission in commercially available zinc sulfide poses a barrier to observing quantum emission from copper-vacancy color centers. This barrier is overcome by the successful activation of copper-vacancy centers in colloidal nanocrystals of zinc sulfide\, which we can sufficiently dilute using solution processing methods to the extent that we are able to measure photon antibunching from copper-vacancy centers. We discuss the templated assembly and isolation of colloidal nanocrystals of zinc sulfide containing copper-vacancy color centers\, which can withstand liftoff and ligand-exchange procedures without quenching of the copper-vacancy luminescence. We further discuss techniques uniquely developed for the spin-optical characterization of these copper-vacancy centers as potential defect qubits. These include time-gating photoluminescence scans to improve the visibility of copper-vacancy centers based on the long-lived emission components we measure in ensemble studies\, and 2D\, room-temperature optically-detected magnetic resonance spectroscopy capabilities compatible with time-gating. Prior to the work presented here to gain access to red-emitting copper-vacancy color centers for their attractive properties as a defect qubit candidates\, there has not been an intensive effort to create or understand red-emitting copper-vacancy color centers (R-Cu centers) in zinc sulfide since the mid-20th century. As a result\, they have never been created using ion beam implantation\, and there is only one report of copper-doped zinc sulfide nanocrystals which emit a red peak assigned to these color centers. In providing routes for obtaining arrays of localized emission from copper-vacancy color centers in both bulk and colloidal nanocrystal zinc sulfide\, this thesis provides new understanding of the red emission from the copper-vacancy color centers and proposes a solution to inconsistencies in reports of their emission mechanism and peak energy. We find that the R-Cu emission arises from thermally activated carrier transfer between two radiative manifolds\, producing an anomalous plateau in the thermal quenching profile and blueshifted luminescence upon increasing temperature. Understanding of these characteristics and their relationship to the charge and spin states of the R-Cu center can inform the development of protocols for operating the center as a quantum spin-light interface. We further demonstrate the powerful advantages of quantum defect exploration using colloidal nanocrystals in place of bulk single-crystals or powders.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-engineering-copper-vacancy-color-centers-in-zinc-sulfide-for-quantum-defect-discovery/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240619T100000
DTEND;TZID=America/New_York:20240619T110000
DTSTAMP:20260403T172610
CREATED:20240605T173156Z
LAST-MODIFIED:20240605T173156Z
UID:10007982-1718791200-1718794800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Computational Fluid-Structure Interaction Modeling of the Cardiovascular System"
DESCRIPTION:Patient-specific computational modeling and simulation has become a routine part of cardiovascular clinical research. These techniques leverage medical imaging to construct subject-specific models that can be used to study disease processes\, design and evaluate medical devices\, perform predictive surgery\, and aid in clinical decision-making. Modern cardiovascular simulations often require millions of elements and tens of thousands of time steps. Incorporation of additional physics only contributes to these costs and increases model complexity. Due to the presence of complex pulsatile hemodynamics potentially coupled with deformable vessel walls or heart valves\, development of accurate\, robust\, and efficient cardiovascular simulation tools remains a challenging task. In this thesis\, I present several improvements to existing finite element solver technologies for computational modeling of the cardiovascular system\, all of which were implemented in a new computational FSI framework I developed in the Modular Finite Elements Methods (MFEM) C++ library. First\, I describe a block preconditioning technique for implicit time discretization of the Navier-Stokes equations monolithically coupled to reduced dimension models of the cardiovascular system (e.g. Windkessel model). Mass conservation properties of various solution algorithms are investigated in a patient-specific aorta model. Next\, I show how these improved techniques can be leveraged to simulate FSI problems\, such as blood flow through deformable vessels\, using the arbitrary Lagrangian-Eulerian method combined with a quasi-Newton solution procedure. Lastly\, I present an immersed approach for computational modeling of fluid-structure interaction. A fully implicit monolithic coupling method is described\, as well as several discretization improvements targeted for immersed thin structures. I demonstrate the potential of the method to simulate heart valves dynamics over the cardiac cycle using an idealized problem and two extensions: heterogeneous valves as a simplified model for calcification\, as well as an anisotropic Fung type constitutive model for the leaflets.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-computational-fluid-structure-interaction-modeling-of-the-cardiovascular-system/
LOCATION:Towne 319\, 220 S. 33rd Street\, Philadelphia\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240625T100000
DTEND;TZID=America/New_York:20240625T113000
DTSTAMP:20260403T172610
CREATED:20240617T192157Z
LAST-MODIFIED:20240617T192157Z
UID:10007990-1719309600-1719315000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Rapidly Understanding Novel Object Dynamics for Robotic Manipulation"
DESCRIPTION:The field of robotic manipulation is attempting to expand from structured warehouse and lab environments to in-the-wild applications in human-centric settings. When deployed in novel settings\, a robot may see and need to interact with a new object for the first time. Model-based control approaches rely on a priori dynamics models\, an assumption that does not extend beyond highly constrained lab settings. Data-driven control approaches can require immense amounts of data and may only perform well on scenarios in their training data distribution. An alternative that boasts the interpretability of model-based approaches without requiring a model or inordinate amounts of data beforehand is to rapidly build a model of a newly encountered object. In this talk\, I will present our work on data efficient dynamics model building through contact-rich interactions. The talk will cover learning dynamics models of single and multi-link objects from state information\, theoretical insights into why our loss formulation is more data efficient than naïve alternatives\, and how we combine dynamics learning with pose estimation from computer vision.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-rapidly-understanding-novel-object-dynamics-for-robotic-manipulation/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240625T110000
DTEND;TZID=America/New_York:20240625T120000
DTSTAMP:20260403T172610
CREATED:20240612T175942Z
LAST-MODIFIED:20240612T175942Z
UID:10007987-1719313200-1719316800@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Harnessing the Regenerative Characteristics of Meniscus Superficial Zone Cells" (Sereen Assi)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Yale Cohen are pleased to announce the Doctoral Dissertation Defense of Sereen Assi.\n\n\nTitle: Harnessing the Regenerative Characteristics of Meniscus Superficial Zone Cells\nDate: June 25\, 2024\nTime: 11:00 AM\nLocation: Berger Auditorium\, Skirkanich Hall\nZoom option: https://upenn.zoom.us/j/93851147060?pwd=NEh0QmN4Z2sxVkhIb2l0d3c3cWlaZz09\n\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-harnessing-the-regenerative-characteristics-of-meniscus-superficial-zone-cells-sereen-assi/
LOCATION:Berger Auditorium (Room 13)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240626T113000
DTEND;TZID=America/New_York:20240626T133000
DTSTAMP:20260403T172610
CREATED:20240625T130540Z
LAST-MODIFIED:20240625T130540Z
UID:10007995-1719401400-1719408600@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Lipid Nanoparticles Allow Intracellular Protein Delivery for Modulation of Difficult Therapeutic Targets" (Rebecca Haley)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Mike Mitchell are pleased to announce the Doctoral Dissertation Defense of Rebecca Haley.\n\nTitle: Lipid Nanoparticles allow Intracellular Protein Delivery for Modulation of Difficult Therapeutic Targets\nDate: June 26\, 2024\nTime: 11:30am\nLocation: Levine Hall\, Wu & Chen Auditorium\n\nZoom option: https://upenn.zoom.us/j/93908532827\, Meeting ID: 939 0853 2827\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-lipid-nanoparticles-allow-intracellular-protein-delivery-for-modulation-of-difficult-therapeutic-targets-rebecca-haley/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240626T130000
DTEND;TZID=America/New_York:20240626T130000
DTSTAMP:20260403T172610
CREATED:20240621T123059Z
LAST-MODIFIED:20240621T123059Z
UID:10007992-1719406800-1719406800@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Mobile Mission Planning in Uncertain Environments"
DESCRIPTION:Robotic mission planning in uncertain environments remains a formidable challenge due to environmental complexity and sensor noise. This dissertation introduces new methodologies to tackle these issues. We develop a method for Linear Temporal Logic (LTL) planning\, extending it to completely unknown environments to enable dynamic understanding and task fulfillment. Large language models (LLMs) are also leveraged to make task expression more user-friendly and to exploit their reasoning capabilities to improve mission planning while maintaining the guarantees from traditional LTL planning. For multi-robot systems\, I focus on enhancing scalability and coordination using Graph Neural Networks (GNNs) for distributed\, non-myopic active information gathering. I validate the resulting algorithms with several numerical simulations.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-mobile-mission-planning-in-uncertain-environments/
LOCATION:Berger Auditorium (Room 13)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240627T133000
DTEND;TZID=America/New_York:20240627T150000
DTSTAMP:20260403T172610
CREATED:20240612T190421Z
LAST-MODIFIED:20240612T190421Z
UID:10007986-1719495000-1719500400@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Fluorite and Wurtzite Structure Ferroelectrics. From Fundamentals to Semiconductor Applications"
DESCRIPTION:Ferroelectric properties were initially discovered in perovskite-structured materials over a century ago. However\, it was only in the last two decades that these properties were confirmed in fluorite-structured doped HfO2 and wurtzite-structured AlN films\, respectively[1][2]. The ferroelectricity in doped HfO2 or ZrO2 has been attributed to a previously unknown non-centrosymmetric orthorhombic Pca21 phase\, while it relates to the hexagonal P63mc phase in wurtzite-structured ferroelectrics. In addition to different dopants in HfO2\, it was found that a certain dopant content\, oxygen vacancies\, surface and bulk effects\, and quenching are beneficial for the formation of the polar phase. All effects indicate that strain and stress contribute to the ferroelectric phase formation. Similarly\, strain and bond ionicity are discussed for doped AlN\, GaN\, and ZnO to influence the properties strongly[3\,12]. \nSince ferroelectric properties were first found for nanometer-scale doped HfO2 films\, processes had to be optimized to extend the occurrence of the polar phase to the bulk material [4]. For wurtzite-structured layers\, properties were found above 100 nm and needed to be scaled down to thinner films. Both material systems are compatible with semiconductor processing\, including excellent temperature stability above 200°C. Depending on the doped HfO2 composition\, a temperature-induced transition to the tetragonal and monoclinic phase is reported. In contrast\, no evidence of ferroelectric to paraelectric phase transition has emerged for AlScN below 600°C [11]. Transmission electron microscopy\, electrical characterization\, and piezoresponse force microscopy studies reveal domain nucleation limited switching kinetics for fluorite-structured films and a Kolmogorov–Avrami–Ishibashi like switching behavior for wurtzite-structured layers [9][10]. \nThe newly found properties of HfO2\, even below 10 nm film thickness\, enabled an increasing number of applications such as high aspect ratio ferroelectric capacitors (FeCap) and field effect transistors (FeFET)[5][6]. Other applications\, such as ferroelectric tunnel junctions\, neuromorphic\, piezo-\, and pyroelectric devices\, are also under discussion [7][8]. Multiple devices could be realized on smaller technology nodes and in larger memory arrays. For wurtzite-structured films\, mainly ferroelectric FeCap\, FeFET\, and piezo applications have been discussed since the properties were found more than ten years later than for the fluorite-structured case [13]. \nThis talk will\, therefore\, review and discuss fundamental aspects of the recently discovered ferroelectricity in both material structure classes and present the state-of-the-art of their material integration and final properties in working devices. \n\n\n\nReferences\n[1] T. Boescke et al.\, APL 99\, 102903 (2011)\n[2] S. Fichtner et al.\, J. Appl. Phys. 125\, 114103 (2019)\n[3] S. Yasuoka et al.\, ACS Appl. Electron. Mater. (2022)\n[4] X. Xu et al.\, Nature Materials (2021)\n[5] T. Boescke et al.\, IEEE-IEDM 2011\n[6] U. Schroeder et al. Elsevier book 2019\n[7] S. Fujii et al.\, IEEE VLSI  (2016) 148\n[8] H. Mulaosmanovic et al.\, ACS Appl. M+I 9\, 3792 (2017)\n[9] E. Grimley et al.\, Adv. Mater. Interfaces 1701258 (2018)\n[10] R. Guido et al.\, Adv. Sci. 2308797 (2024)\n[11] R. Guido et al. \, ACS Appl. Mater. Interfaces 15 (2023)\n[12] K. Yazawa et al.\, J. Mater. Chem.\, 10\, 17557 (2022)\n[13] K. Kim et al.\, Nat. Nanotechnol.\, 18 (2023)
URL:https://seasevents.nmsdev7.com/event/ese-seminar-fluorite-and-wurtzite-structure-ferroelectrics-from-fundamentals-to-semiconductor-applications/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240628T103000
DTEND;TZID=America/New_York:20240628T114500
DTSTAMP:20260403T172610
CREATED:20240618T202046Z
LAST-MODIFIED:20240618T202046Z
UID:10007991-1719570600-1719575100@seasevents.nmsdev7.com
SUMMARY:Summer 2024 GRASP on Robotics: Sami Atiya\, ABB Ltd.\, "The Evolution of Robotics/AI: an Industry Perspective"
DESCRIPTION:This seminar will be held in-person in Wu and Chen as well as virtually via Zoom. \nABSTRACT\n\nSince its inception\, Robotics has constantly used technological developments to address new customer needs. These developments have occurred in two areas: mechatronics and software & control.\nMechatronic advances made robots stronger\, faster\, and more accurate. In parallel\, software & control has made it easier to program and operate robots\, for example e.g.\, through lead through programming and wizards. The increasing ease of use has addressed a key need for the deployment of robots in industries and businesses whose employees do not have deep technical expertise\, enabling them to deploy and interact with robots.\nFrom a market perspective\, global robot density comparisons indicate further significant growth potential\, particularly across new segments and SME’s and driven by global megatrends\, including an aging workforce\, the individualized consumer\, geopolitical uncertainty\, digitalization\, and sustainability.\nTo unlock this potential\, robotics needs to continue to progress in mechatronics\, and in particular in making robots more intelligent and autonomous through software/Artificial Intelligence.\nArtificial Intelligence is already used to provide robots with autonomous skills\, like item picking. It will further enable robots to learn and acquire new sensory-motor skills efficiently\, leading to higher degrees of perception and dexterous manipulation.\nAdditionally\, prompt-based\, natural language technology and autonomous planning and decision making will allow non-expert users to deploy robotics.\nMatching customer needs for automation with new technological developments has been a key growth driver for robotics in the past. With the rapid advancement of Artificial Intelligence for robotics\, this trend will continue.
URL:https://seasevents.nmsdev7.com/event/summer-2024-grasp-on-robotics-sami-atiya/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240701T110000
DTEND;TZID=America/New_York:20240701T130000
DTSTAMP:20260403T172610
CREATED:20240626T120336Z
LAST-MODIFIED:20240626T120336Z
UID:10007997-1719831600-1719838800@seasevents.nmsdev7.com
SUMMARY:MSE Thesis Defense: "Alloy Anodes for Alkali-Metal Batteries: Elucidating Lithiation Pathways of Metal/Metal Composites and Solid Solution Alloys Using Novel Nanoporous Tri-Layer Metal/Metal/Metal Composites"
DESCRIPTION:The pressing issues of global warming and the rapid depletion of fossil fuel reserves have prompted both private and governmental organizations to seek clean\, renewable energy alternatives. Solar and wind energy have been extensively researched\, leading to significantly reduced costs for energy production. However\, to address the intermittent nature of these energy sources\, reliable methods for storing excess energy produced during peak times are necessary for use during off-peak hours\, such as nighttime for solar energy. One such method is using batteries – such as Li-ion\, Na-ion\, and Mg-ion batteries – to store this excess energy. However\, current batteries suffer from relatively low energy densities\, which make it difficult to store large amounts of energy for a prolonged period of time. One promising solution is to drastically increase the energy densities by replacing current low specific capacity graphite anodes with alloy anode materials\, specifically with nanoporous structure. Incorporating nanoporosity into alloy anodes has shown improved performance in terms of cycling stability and rate capability. Further advancements have been achieved by combining materials to exploit their synergistic effects. Typically\, though\, nanoporous metal fabrication methods require a sacrificial element to be etched from a parent alloy\, leading to significant material waste and poor sustainability — often having waste material exceeding 50 at. % of the parent material. \nIn this work\, we introduce a novel technique called “chlorine-neutral etching” for sustainably producing nanoporous metals without sacrificing materials. This technique uses oxidative and reductive pulses to convert bulk silver into nanoporous silver without any sacrificial elements. We extended this method to a gold-silver alloy precursor\, successfully separating gold and silver into distinct nanoporous gold and silver layers\, creating a tri-layer composite material. Using scanning electron microscopy (SEM) and small-angle X-ray scattering (SAXS)\, we confirmed the uniform porosity of the entire material. With this innovative nanoporous silver | gold | silver tri-layer composite structure\, we explored the electrochemical alloying reaction pathways with lithium (principles which can be extended to storage of other alkali and alkaline-earth metal ions) of both a solid solution alloy and the tri-layer metal/metal composite material. We predicted phase morphology evolution in a material combining gold and silver phases based on the equilibrium phase diagrams of each component. By comparing the voltage profiles of the solid solution alloy and the composite structure\, we assessed how each structure’s voltage profile compared to the sum of its individual components. Our conclusions suggest future research directions and present preliminary results for extending the chlorine-neutral etching method to a promising alloy anode\, lead metal. We also developed battery cells using bulk lead metal and compared the long-term cycling performance of solid solution alloys against nanoporous metal/metal composite materials.
URL:https://seasevents.nmsdev7.com/event/mse-thesis-defense-alloy-anodes-for-alkali-metal-batteries-elucidating-lithiation-pathways-of-metal-metal-composites-and-solid-solution-alloys-using-novel-nanoporous-tri-layer/
LOCATION:LRSM Reading Room\, 3231 Walnut St.\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240702T100000
DTEND;TZID=America/New_York:20240702T113000
DTSTAMP:20260403T172610
CREATED:20240610T152227Z
LAST-MODIFIED:20240610T152227Z
UID:10007984-1719914400-1719919800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Macroscopic Ensemble Methods for Robot Team Data Collection in Dynamic Environments"
DESCRIPTION:Data is necessary to improve our understanding of dynamic environments\, like the ocean. With limited sensing resources\, the challenge lies in identifying and acquiring sensor measurements over large spatial and temporal scales. One solution is to use a team of robots equipped with sensors to collect data. However\, robot teams still require methods that effectively tell the robots where to sample informative data. Existing approaches develop plans for each individual robot which works well if the team is small (less than 10 agents) and operating in a simple environment (an open field). Unfortunately\, these solutions require careful engineering and cannot be easily adapted to changing environmental conditions. For this reason\, we want to model team-wide objectives using dynamical systems theory. Specifically\, our robot team modeling technique is called macroscopic ensemble modeling. These methods are known to easily control large robot teams (more than 50 robots) and even scale to control many different types of robots. Nevertheless\, macroscopic ensemble methods require extensions to effectively distribute robots in dynamic environments. This seminar will cover our recent results incorporating both collaborations and environmental feedback into macroscopic ensemble robot team models. Our results demonstrate novel team-wide behavior beneficial to collecting data in dynamic environments.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-macroscopic-ensemble-methods-for-robot-team-data-collection-in-dynamic-environments/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240702T120000
DTEND;TZID=America/New_York:20240702T140000
DTSTAMP:20260403T172610
CREATED:20240617T183301Z
LAST-MODIFIED:20240617T183301Z
UID:10007989-1719921600-1719928800@seasevents.nmsdev7.com
SUMMARY:MSE Thesis Defense: "High-Resolution Characterization of Solid-Liquid Interfaces in Energy Storage Materials Using Microscopy: From Lithium Metal Anodes to Liquid Sodium-Potassium"
DESCRIPTION:Battery technologies are crucial for reducing greenhouse gases by enabling the use of renewable energy and facilitating the transition to electric vehicles\, thereby lowering emissions. Lithium metal anodes are ideal for next-generation batteries in automotive applications due to their high energy density. However\, issues such as dendrite formation and solid electrolyte interphases (SEI) affect their long-term stability and reliability. Conversely\, liquid metal batteries\, like liquid Na-K anodes\, show promise for large grid storage systems due to their fast kinetics\, low cost\, and potentially long cycle life. Despite these benefits\, the development of liquid metal anodes has not been fully explored\, making the feasibility of Na-K anodes uncertain. This thesis aims to address these challenges using cryogenic electron and ion beam microscopy. We developed a technique to create electron transparent battery samples for characterization by cryogenic transmission electron microscopy (cryo-TEM). Through this method\, we discovered short-range ordering in the SEI of Li-metal batteries using cryogenic four-dimensional scanning transmission electron microscopy (4D-STEM). We propose that the structural ordering in SEIs is crucial for suppressing Li dendrites\, thereby enhancing battery performance. Although SEIs were previously thought to be a mix of inorganic precipitates and organic matrix\, our data suggest that their true morphology is largely amorphous\, indicating significant electron beam damage in earlier analyses. We also investigated the behavior of liquid Na-K anodes with Na-ion electrolytes. Characterizing liquid metals is challenging due to their fluidic properties and high surface tension. However\, cryogenic focused ion beam/scanning electron microscopy (cryo-FIB/SEM) and various characterizations revealed the dissolution of K\, making these anodes unsuitable for Na-ion electrolytes. Our findings highlight the potential of cryogenic microscopy techniques in advancing battery technology and addressing key challenges in the development of next-generation energy storage systems.
URL:https://seasevents.nmsdev7.com/event/mse-thesis-defense-high-resolution-characterization-of-solid-liquid-interfaces-in-energy-storage-materials-using-microscopy-from-lithium-metal-anodes-to-liquid-sodium-potassium/
LOCATION:LRSM Reading Room\, 3231 Walnut St.\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Student,Dissertation or Thesis Defense
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240703T101500
DTEND;TZID=America/New_York:20240703T111500
DTSTAMP:20260403T172610
CREATED:20240626T130018Z
LAST-MODIFIED:20240626T130018Z
UID:10007998-1720001700-1720005300@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Fingertip Friction\, Materials\, and Tactile Perception"
DESCRIPTION:Sliding touch is one of the key inputs for the perception of materials in our environment. We are interested in the contribution of fingertip friction to the process of tactile exploration and perception. Psychophysical studies address correlations between subjective judgements of perception and measured physical parameters of interactions\, in our case of friction on materials with systematically varied surface structure. A challenge in this approach are large variances in skin physiological parameters between the participants in our studies. I will discuss how surface structures are perceived in sliding touch\, for which materials the determination of physiological parameters is helpful to predict friction\, and if humans are able to rank friction differences correctly. Materials range from 3D printed plastic over micro-structured rubber to honey.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-fingertip-friction-materials-and-tactile-perception/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240708T150000
DTEND;TZID=America/New_York:20240708T163000
DTSTAMP:20260403T172610
CREATED:20240624T192550Z
LAST-MODIFIED:20240624T192550Z
UID:10007993-1720450800-1720456200@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Metal Oxides and Ion-Modification: A Study in Chemical Stability\, Transformations\, and Dynamics" (Bader A. Alayyoub)
DESCRIPTION:Abstract: \n\n\n\nMetal oxides\, both simple and complex\, are essential for catalysis and energy conversion due to their versatile physicochemical properties and are often enhanced through ion doping\, a well-established strategy to improve their functionality. However\, challenges remain\, particularly in accurately characterizing the surface stability of such oxides beyond idealized conditions and in understanding the thermodynamics and kinetics associated with ion doping and migration. This thesis combines first-principles methods\, including density functional theory\, with molecular dynamics and enhanced sampling techniques to provide atomic-level insights into intrinsic and dopant ionic behaviors and dynamics within metal oxides. Additionally\, in the context of heterogeneous catalysis\, it aims to establish a pathway to guide future design improvements and enhance their chemical properties. The composition and stability of metal oxide surfaces are essential in shaping their properties. This thesis focuses on SrTiO3 and examines entropic effects by incorporating phonon contributions and utilizing established thermodynamic principles. It maps the free energies of different surface structures across temperatures\, finding that phonon effects on surface morphology at higher temperatures cannot be ignored. This discovery strengthens the link between experiments and theory. Furthermore\, the interplay between titanate-based perovskite oxides and transition metal ions through doping and surface adsorption forms a central focus of this work. Although these interactions are generally examined for their bulk doping properties\, surface compositions can differ significantly. Through a systematic evaluation of the thermodynamics of 4d-transition metal doping\, migration\, and segregation with and without host strain effects\, this research provides a high-throughput initial assessment of their thermal stability and synthesis feasibility. Another aspect of ion-perovskite interaction involves the thermodynamics of Ni egress and ingress across different SrTiO3 facets. The findings reveal a pronounced egress propensity from the (110) facet\, which is uniquely significant for advancing novel catalyst synthesis methods like exsolution. Similarly\, the thermodynamic impacts of Pd\, Pt\, and Rh migrating to the surface of LaFeO3 are explored\, revealing that these metal ions can form surface alloys and alter the reducibility of surface oxygens\, thereby modifying surface properties compared to the bulk material. Beyond thermodynamic analysis\, ion surface migration and dissolution dynamics are explored through ab initio molecular dynamics and enhanced sampling methods. Modeling the exsolution of Pt from various perovskite titanate hosts reveals that Pt diffusion to the surface varies with the host material and specific exposed facets\, with distinct temperatures required to overcome the kinetic barriers for exsolution. These insights inform predictions for the synthesis of supported metal catalysts. Additionally\, ion dissolution dynamics and surface stability at oxide-solvent interfaces are investigated using metadynamics\, focusing on a CaO-water interface to provide insights into phenomena such as catalyst surface leaching contributing to catalyst degradation. Collectively\, these analyses provide a comprehensive atomistic understanding of complex metal oxides and establish a framework for furthering their strategic design.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-metal-oxides-and-ion-modification-a-study-in-chemical-stability-transformations-and-dynamics-bader-a-alayyoub/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T101500
DTEND;TZID=America/New_York:20240709T111500
DTSTAMP:20260403T172610
CREATED:20240625T190811Z
LAST-MODIFIED:20240625T190811Z
UID:10007996-1720520100-1720523700@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Metallivore Robots Powered by Aluminum-air Batteries"
DESCRIPTION:Mobile robots have shown significant advancements in agility\, intelligence\, and efficiency over the past few decades. However\, their endurance and overall performance remain limited by the onboard power supplies. Current power sources typically restrict mobile robots to areas close to the electrical grid and necessitate heavier batteries for extended range. Energy refueling could be significantly challenging in remote and inaccessible regions\, and traditional energy harvesting methods have also proven inadequate for sustaining continuous operations. To address this challenge\, we propose an alternative approach: enabling robots to “digest” energy-dense metals to generate power\, analogous to how animals consume food. This concept is based on the working principle of an aluminum-air battery\, which operates like a small chemical plant converting aluminum into electricity. This method could dramatically reduce the trade-off between endurance and battery weight\, thus unleashing new potential for mobile robotics. This talk will begin by quantifying the energy gap between mobile robots and their biological counterparts based on a comparative analysis of their energy density and power density. I will then highlight our recent progress in developing metallivore robots\, particularly addressing how we have overcome various challenges associated with conventional aluminum-air batteries\, such as byproduct accumulation\, hydrogen production\, and water consumption. Lastly\, I will showcase our development of a highly stretchable metal-air battery using sliding electrodes\, making it ideal for integration into soft robotics.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-metallivore-robots-powered-by-aluminum-air-batteries/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T130000
DTEND;TZID=America/New_York:20240709T150000
DTSTAMP:20260403T172610
CREATED:20240507T173053Z
LAST-MODIFIED:20240507T173053Z
UID:10007965-1720530000-1720537200@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Path-Sampling and Machine Learning for Rare Abnormal Safety and Reliability Events" (Vikram Sudarshan)
DESCRIPTION:Abstract: \nIt is crucial for chemical and manufacturing industries to ensure safe and reliable operation of their plants and processes\, by mitigating safety issues (e.g.\, extreme operating conditions) and reliability issues (e.g.\, production losses). But\, a significant challenge faced by these industries is that such events are rare and undesirable\, with little occurrence data available from process historians. Extensive control and alarm systems\, with Safety Instrumented Systems (SIS) and reliability risk assessment methods\, are often successful in mitigating postulated abnormal events anticipated in HAZOPs. However\, it is very challenging to consider the effects of highly infrequent unpostulated abnormal events (i.e.\, non-specific\, randomly-occurring events)\, which cannot be anticipated in process design\, and lead to severe consequences. Hence\, in this thesis\, novel\, improved multivariate alarm systems are developed using path-sampling and machine learning\, for handling rare unpostulated abnormal events resulting from random perturbations in one or more process variables. \nAs a first application of path-sampling to analyze rare abnormal events for chemical process safety\, Moskowitz (2016 ) introduced transition path-sampling (TPS) to locate rare safety pathways for an exothermic CSTR and an air separation unit (ASU). In this thesis\, to circumvent the computational limitations posed by TPS\, forward-flux sampling (FFS) is introduced. It simulates rare unpostulated abnormal events more-efficiently in a piecewise manner\, moving from desirable to undesirable operating regions\, with valuable key process-variable data stored during the simulations\, followed by calculations of committer probabilities to reach undesirable regions (i.e.\, p B ). \nGiven the process variable-p B data\, accurate predictive models are developed using machine learning (ML) – one of the cornerstones of Industry 4.0’s vision for increased automation. Using predictions provided by the ML-based models\, initial multivariate alarm systems are developed\, which are improved significantly by introducing an alarm rationalization- dynamic risk analyses (DRAn) integrated framework. Such improved systems\, when implemented alongside widely-used HAZOP studies\, aid operators in handling both postulated and unpostulated abnormal events to improve overall safety and reliability.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-path-sampling-and-machine-learning-for-rare-abnormal-safety-and-reliability-events-vikram-sudarshan/
LOCATION:Towne 336\, 220 S 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T130000
DTEND;TZID=America/New_York:20240709T150000
DTSTAMP:20260403T172610
CREATED:20240705T134714Z
LAST-MODIFIED:20240705T134714Z
UID:10008005-1720530000-1720537200@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Fair and Generalizable Machine Learning for Neuroimaging"
DESCRIPTION:Machine learning has been widely adopted to medical imaging research\, yet it suffers from domain shift for real world applications. Due to the heterogeneity of medical data\, machine learning-based diagnostic models are also prone to biases. In this thesis\, we start from arguing the necessity of domain adaptation to achieve the optimal performance for each subcategory. We develop an adaptation algorithm which doesn’t require any ground-truth labels from the unseen domain. We also discuss the value of handcrafted imaging features in the representation learning era for brain imaging application. Next\, we show that machine learning-based diagnostic models can be unbiased if they are trained using rigorous data pre-processing techniques and well-constructed models. We find that multi-source data is helpful sometimes in elevating both model performance consistency and precision. Finally\, we introduce a weighted-empirical risk minimization algorithm to further boost the model’s performance on unseen data distribution by only using a few samples. We demonstrate the empirical results on large-scale diverse-population brain imaging datasets for rich clinical tasks.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-fair-and-generalizable-machine-learning-for-neuroimaging/
LOCATION:Zoom – Meeting ID 3394168579
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240709T150000
DTEND;TZID=America/New_York:20240709T180000
DTSTAMP:20260403T172610
CREATED:20240702T124633Z
LAST-MODIFIED:20240702T124633Z
UID:10008002-1720537200-1720548000@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense: "Graph Machine Learning under Requirements"
DESCRIPTION:Graphs are powerful mathematical tools that enable modeling of complex systems. Graph machine learning exploits possibly unknown data structures\, which provides a unified approach to tackle a wide variety of problems. However useful in practice\, graph machine learning solutions tend to suffer from three main limitations: they do not scale with the size of the graph\, they are not robust to changes of the graph\, and they require an homogeneous underlying graph. In this thesis we address all three of these requirements. In terms of scalability\, we show that Graph Neural Networks (GNNs) improve their generalization capabilities with the number of nodes. This positive result with ample empirical evidence\, motivates the need for scalable training solutions. To this end\, I developed two strategies to train GNNs on large scale graphs\, first by growing the graphs in time as we train\, and second by distributing the graph in a set of machines and growing the graph in space. These two methods alleviate the computational costs required to train GNNs on large scale graphs\, without compromising the accuracy. In terms of robustness\, even though many data modalities reside in a very high-dimensional space\, their dynamics can be assumed to belong to a lower-dimensional structure. We can model the low dimensional space using a graph Laplacian\, and show that the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem. Heterogeneity is a fundamental property of networks. Even if a network is composed of homogeneous agents\, every agent will have different interactions with the environment\, which will translate into heterogeneous data acquisition. Graph machine learning solutions that do not address the heterogeneous nature of the data tend to only allocate the needs of some of the nodes in the graph. I propose two ways to tackle heterogeneity\, a single solution to improve the outcome of all the individual components in the network\, and a node-specific solution such that every agent in the network improves upon working with their individual data.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-graph-machine-learning-under-requirements/
LOCATION:Greenberg Lounge (Room 114)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240710T110000
DTEND;TZID=America/New_York:20240710T120000
DTSTAMP:20260403T172610
CREATED:20240625T125853Z
LAST-MODIFIED:20240625T125853Z
UID:10007994-1720609200-1720612800@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Designing emerging computing systems with ferroelectric devices"
DESCRIPTION:This talk will present a brief overview of advances in ferroelectric devices and their integration into computing systems to provide novel functionality and energy efficiency in various data intensive applications. The talk will emphasize on cross-stack design opportunities in designing stacked intelligent 3D memory systems.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-designing-emerging-computing-systems-with-ferroelectric-devices/
LOCATION:Greenberg Lounge (Room 114)\, Skirkanich Hall\, 210 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240711T120000
DTEND;TZID=America/New_York:20240711T130000
DTSTAMP:20260403T172610
CREATED:20240614T134645Z
LAST-MODIFIED:20240614T134645Z
UID:10007988-1720699200-1720702800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Examples of Elasto-Capillary Mediated Interactions on Thin Elastic Film"
DESCRIPTION:I plan to tell two short stories. The first story is about a new drop fluidics that uses a deformable and stretchable elastomeric film as the platform. Such a soft film decorated with magnetic particles can be deformed locally with an external magnetic field. When these deformations are coupled with capillary force\, small liquid drops can be moved over the platform over long distances efficiently and accurately. The resulting drop fluidics could be suitable for the digital control of drop motion by simply switching on and off the local magnetic fields applied at different positions underneath the elastomeric film in a Boolean sequence. \nThe second story is about the interaction of two beads on the surface of a thin elastic film supported on a pool of liquid. Here\, the excess energy of the surface due to the curvature of the stretched film induces attraction of the beads. It is found that the hysteresis of adhesion between the beads and the elastic film retard the motion of the objects\, which\, however\, can be reduced by introducing a thin hydrogel layer atop the elastic film. Morphological instabilities in the gel create corrugated paths to the motion\, thus causing the particles to aggregate along the patterns thus developed. These observations suggest that a deformable elastic film affords an effective model system to study elasticity and mediated interaction of particles on its surface.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-examples-of-elasto-capillary-mediated-interactions-on-thin-elastic-film/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240716T101500
DTEND;TZID=America/New_York:20240716T111500
DTSTAMP:20260403T172610
CREATED:20240703T171655Z
LAST-MODIFIED:20240703T171655Z
UID:10008004-1721124900-1721128500@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Tribosintering of Metal Oxide Nanocrystal Antiwear Additives: Growth\, Wear\, and Modeling"
DESCRIPTION:Machinery relies on lubrication to regulate the friction and wear at contacting interfaces. As new lubricants become less viscous to comply with environmental and cost-saving standards\, the risk of surface-initiated machine failure grows. We show that metal oxide additives in low-viscosity lubricants will form protective coatings\, or tribofilms\, in situ at contacting interfaces. Compared to state-of-the-art surface coatings and anti-wear additives\, metal oxide coatings have several practical benefits and environmental advantages\, but their formation mechanism is not well-understood. We will first discuss the mechanistic and practical implications of the coatings’ cooperativity of with S- and P-based co-additives\, then the interplay between the coatings’ growth on and their removal from contacting machinery. Finally\, we will discuss data-driven and traditional modeling efforts to develop predictive models for these antiwear coatings.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-tribosintering-of-metal-oxide-nanocrystal-antiwear-additives-growth-wear-and-modeling/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240717T100000
DTEND;TZID=America/New_York:20240717T110000
DTSTAMP:20260403T172610
CREATED:20240709T153759Z
LAST-MODIFIED:20240709T153759Z
UID:10008006-1721210400-1721214000@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "INVESTIGATION OF IMAGE QUALITY SPATIAL DEPENDENCY IN RELATION TO ACQUISITION GEOMETRY IN DIGITAL BREAST TOMOSYNTHESIS" (Chloe Jeongin Choi)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Andrew Maidment are pleased to announce the Doctoral Dissertation Defense of Chloe Jeongin Choi.\n\nTitle: INVESTIGATION OF IMAGE QUALITY SPATIAL DEPENDENCY IN RELATION TO ACQUISITION GEOMETRY IN DIGITAL BREAST TOMOSYNTHESIS\n \nDate: Wednesday\, July 17\, 2024\nTime: 10:00am\nLocation: Reunion auditorium\, JMB.  
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-investigation-of-image-quality-spatial-dependency-in-relation-to-acquisition-geometry-in-digital-breast-tomosynthesis-chloe-jeongin-choi/
LOCATION:JMB Reunion Auditorium\, 3620 Hamilton Walk\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240717T130000
DTEND;TZID=America/New_York:20240717T140000
DTSTAMP:20260403T172610
CREATED:20240702T175343Z
LAST-MODIFIED:20240702T175343Z
UID:10008003-1721221200-1721224800@seasevents.nmsdev7.com
SUMMARY:IC2S2 Conference Virtual Keynote: "Societies are complex systems. So\, how to manage them?"
DESCRIPTION:To kick off the IC2S2 conference being held right here at the University of Pennsylvania\, we warmly invite you to join us for Dirk Helbing’s (ETH Zurich) Virtual Keynote address “Societies are complex systems. So\, how to manage them?\, to be given at 1 pm ET / 7 pm CET on Wednesday\, July 17th. The talk will be at this Zoom link: \nhttps://upenn.zoom.us/j/97394867827?pwd=a3jRWgWJT56JFL6edF9DyAQjEbPYEr.1 \nYou do not have to register to attend the virtual keynote. However\, if you wish to attend the in-person sessions\, please register by Wednesday\, July 3rd at: https://ic2s2-2024.org/register
URL:https://seasevents.nmsdev7.com/event/ic2s2-conference-virtual-keynote-societies-are-complex-systems-so-how-to-manage-them/
LOCATION:Zoom
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240717T143000
DTEND;TZID=America/New_York:20240717T153000
DTSTAMP:20260403T172610
CREATED:20240711T174508Z
LAST-MODIFIED:20240711T174508Z
UID:10008017-1721226600-1721230200@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Knowledge-Integrated Learning for AI in Medical Image Analysis" (Yifan Wu)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Jim Gee are pleased to announce the Doctoral Dissertation Defense of Yifan Wu.\n\nTitle: Knowledge-Integrated Learning for AI in Medical Image Analysis\nDate: July 17\, 2024\, Wednesday\nTime: 2:30 PM to 4:40 PM\nLocation: Towne Room 337 \nZoom option: https://upenn.zoom.us/j/96378457553?pwd=Zf5UWdZmA87iVawEPnGmvMOOURbNDo.1\n\nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-knowledge-integrated-learning-for-ai-in-medical-image-analysis-yifan-wu/
LOCATION:Towne 337
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240718T110000
DTEND;TZID=America/New_York:20240718T120000
DTSTAMP:20260403T172610
CREATED:20240627T185835Z
LAST-MODIFIED:20240627T185835Z
UID:10008001-1721300400-1721304000@seasevents.nmsdev7.com
SUMMARY:BE/PICS Joint Seminar: Uma Balakrishnan and Kunal Poorey\, Sandia National Laboratories
DESCRIPTION:These talks will be held jointly\, with each talk 30 minutes each (1 hour total). \nTalk 1: “Optimizing Anomaly Detection for GenAI based Digital Twins of Wearable Data” (Uma Balakrishnan) \nIn this presentation\, we introduce a methodology that utilizes both real and synthetic datasets (digital twins) to address the uncertainties associated with anomaly detection thresholds in health data from wearables. By integrating state-of-the-art wearables using generative AI\, and sophisticated anomaly detection techniques\, our approach offers a precise and comprehensive understanding of potential health issues\, significantly reducing the false negative rate. Enhancing real datasets with generative AI-based digital twins increases population size and achieves strong concordance in uncertainty analysis with results obtained from real data alone. This robust concordance is consistent even when applied to small village populations\, showcasing the scalability and reliability of our generative algorithm. Validating synthetic users (digital twins) by comparing their statistical signatures with real datasets confirms the effectiveness of our approach. Our methodology promises to revolutionize healthcare data collection and address privacy concerns by providing a more comprehensive and reliable health assessment tool for early detection of biothreats or pandemics. Moreover\, we have developed a versatile anomaly detection method based on the fourth-order moments of physiological parameters\, applicable to a wide range of datasets and compatible with various healthcare data sources\, including wearables. Our goal is to empower individuals and healthcare systems with advanced tools for real-time anomaly detection and enhanced health assessment\, paving the way for improved public health outcomes. \nTalk 2: “AI-aided Computational Methods to Overcome Challenges in Biology and Engineering” (Kunal Poorey) \nEarly detection of an emerging biothreat and robust supply of relevant medical counter measures\, coupled with the capacity for developing novel therapeutics in response to emerging diseases\, is vital for enhancing national healthcare resilience. Challenges such as supply chain disruptions\, propriety formulations\, drug resistance\, and reliance on single-source raw materials or the products itself can hinder access to proper medical countermeasures. Today\, advancements in artificial intelligence and machine learning are revolutionizing detection\, diagnostics\, and intervention strategies across all sectors of science and technology. Here we will discuss data science applications including Generative AI can early detect an emerging threat and accelerate drug discovery. \nWe have developed a cutting-edge computational strategy for the early detection of emerging biothreats such as infectious outbreaks (natural or intentional). Our approach utilizes advanced anomaly detection techniques applied to a diverse array of health datasets\, including wearable technology\, healthcare site data\, Google search terms\, and Twitter feeds. These sources are analyzed at multiple scales\, from individual health to broader population health dynamics\, to identify and notify anomalies at multiple resolutions. \nFurthermore\, we will discuss how machine learning (ML) aided drug discovery significantly reduces bottlenecks by enhancing both the speed and cost-effectiveness of the process. By using extensive datasets\, ML algorithms identify potential drugs\, predict interactions with biological targets\, and optimize chemical properties for effective manufacturing. New drug development also aids in improving treatment efficacy and reducing development costs. There are still needs in developing “explainable” machine learning (XML) methods that enhances our understanding of structure-property relationships\, aiding in the optimized design of effective and safer drugs by understanding model recommendations\, identifying new drug targets\, and predicting side effects. Additionally\, we explore the potential of generative artificial intelligence (GenAI) in leveraging this knowledge to further enhance accelerated drug development. We introduce MIRA (Machine Intelligence for Rapid Acceleration of Drug Discovery and Repurposing)\, an innovative model combining GenAI tools and XML for accelerated drug discovery. MIRA integrates state-of-the-art GenAI models capable of conditionally generating drug-like molecules. And utilizing publicly available databases\, we’ve compiled a database of drug compounds and their properties\, such as toxicity\, solubility\, and permeability. This holistic approach paves a promising future in more accurate and informed drug discovery.
URL:https://seasevents.nmsdev7.com/event/be-pics-joint-seminar-uma-balakrishnan-and-kunal-poorey-sandia-national-laboratories/
LOCATION:Room 534 A Wing\, 3401 Walnut\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240719T150000
DTEND;TZID=America/New_York:20240719T170000
DTSTAMP:20260403T172610
CREATED:20240712T171637Z
LAST-MODIFIED:20240712T171637Z
UID:10008018-1721401200-1721408400@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Fabrication of Functional Nanostructured Polymers Based on Thermotropic and Lyotropic Liquid Crystals Derived from Sustainable Resources" (Ruiqi Dong)
DESCRIPTION:Abstract: \nNanostructured materials with interconnected nanoscale structures have garnered significant attention due to the ability to precisely regulate molecular transport by tuning molecular-level properties and offering more functional interfaces for interactions compared to bulk systems. Through self-assembly\, liquid crystal (LC) mesophases offer a route to achieve nanostructured polymers\, a promising alternative to traditional nanostructured inorganic materials and block copolymers. LC mesophases can spontaneously form monodisperse nanostructures and their self-assembly can be precisely controlled to yield well-organized 1D\, 2D\, and 3D periodic nanostructured materials. \nDeveloping functional polymers from sustainable resources is crucial due to the economic and environmental benefits that result. Sustainably derived unsaturated fatty acids are of interest in this regard because their unsaturated carbon bonds allow crosslinking to form stable polymers\, and their carboxylic acid functional groups enable specific surface chemistry and chemical derivatization. \nThis presentation focuses on how useful nanostructured functional materials can be realized from thermotropic and lyotropic liquid crystals derived from unsaturated fatty acids\, and on the properties of the materials thus produced. While prior use of the so-called “molecular templating” approach has produced well-defined nanostructured membranes from thermotropic LCs\, precise tuning of pore size and functionality (e.g. for addressing different applications) remains a challenge. We developed a new approach in which changing the stoichiometry of building blocks of self-assembling supramolecular constructs led to a robust handle for controlling pore shape\, and pore size with sub-nm resolution. The approach used thermotropic LCs based on citronellol\, a plant-derived molecule. To balance selectivity and permeability\, we developed highly selective and permeable thin nanofiltration membranes based on lyotropic LCs from conjugated linoleic acid. This reproducible process can be applied to various systems for creating larger-scale nanostructured thin films. Additionally\, we explored LC materials for ion and electron transport\, presenting for the first time a nanostructured lyotropic LC membrane for cation conduction derived from conjugated linoleic acid\, which shows improved electron transport performance compared to single-ion polymeric electrolyte materials.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-fabrication-of-functional-nanostructured-polymers-based-on-thermotropic-and-lyotropic-liquid-crystals-derived-from-sustainable-resources-ruiqi-dong/
LOCATION:Towne 337
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240722T100000
DTEND;TZID=America/New_York:20240722T120000
DTSTAMP:20260403T172610
CREATED:20240712T174714Z
LAST-MODIFIED:20240712T174714Z
UID:10008019-1721642400-1721649600@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "MEMS-Based Electrochemical Power Sources for Extended Operational Duration of Micro Unmanned Vehicles” (Yanghang Huang)
DESCRIPTION:Abstract: \nMicro unmanned vehicles\, defined as 25-gram to 5-kg in weight\, are increasingly being utilized in applications such as search and rescue or agricultural monitoring. Such vehicles are typically powered with lithium-ion batteries. However\, vehicle operational efficiency and capability are often compromised by the limited energy density of these batteries\, resulting in short operational duration. This dissertation explores high-energy chemistries beyond lithium-ion batteries\, including metal-air batteries and fuel cells\, as power sources to significantly extend the operational duration of both the kilogram-scale and gram-scale micro unmanned vehicles. \nThree different power source chemistries are proposed and investigated for different types of micro unmanned vehicles: gram-scale micro unmanned-ground-vehicles; gram-scale micro unmanned-aerial-vehicles; and kilogram-scale micro unmanned-aerial-vehicles. \n1. Gram-Scale Unmanned-Ground-Vehicles: Micro zinc-air batteries are developed for extended operation durations for gram-scale unmanned-ground-vehicles. The lean electrolyte operation of the battery enhances its gravimetric performance; however\, this approach suffers from rapid electrolyte degradation\, limiting battery performance. The study identifies carbonation and slow zincate decomposition as the main degradation mechanisms. Based on these findings\, a discharge energy model is developed\, successfully predicting the discharge performance of the micro zinc-air battery. Insights from the degradation study are used to enhance the electrochemical performance of micro zinc-air batteries\, demonstrating their potential to significantly extend the operation duration of gram-scale unmanned ground vehicles. \n2. Gram-Scale Micro Unmanned-Aerial-Vehicles: Due to the higher power requirements of gram-scale unmanned aerial vehicles\, micro aluminum-air batteries are developed as onboard power sources. The cell design and cell packaging of the battery are optimized to achieve an energy density of 320 Wh/kg above a power density of 500 W/kg\, far surpassing the performance of equivalent commercial lithium-ion batteries. A 3D-printed small-scale quadrotor platform is used to evaluate flight duration\, with the micro aluminum-air battery delivering 13.1 minutes of flight time compared to the 4.5 minutes of the commercial micro lithium-ion battery. This demonstration also marks the first successful untethered flight of a gram-scale micro unmanned-aerial-vehicle powered by air batteries. Additionally\, cathode fabrication techniques are explored\, utilizing a Ag-based cathode developed through microfabrication techniques to maximize the electrochemically active surface area. This cathode can support the necessary power density for the small-scale quadrotor while offering a potential cost reduction of 1/1500th of that of conventional cathodes. This work highlights the potential of micro aluminum-air batteries to serve as efficient\, cost-effective\, and long-lasting power sources for gram-scale unmanned-aerial-vehicles. \n3. Kilogram-Scale Micro Unmanned-Aerial-Vehicles: For kilogram-scale micro unmanned-aerial-vehicles\, solid oxide fuel cells are investigated. A flight duration model is first developed\, showing that the solid oxide fuel cell is promising for significantly extending the flight duration. To meet the power requirement of the kilogram-scale micro unmanned-aerial-vehicle\, a microfabrication process for thin-film solid oxide fuel cells is developed\, aiming to increase its power performance. While preliminary results confirm the successful fabrication of the thin-film cell\, scaling the technology to power the kilogram-scale micro unmanned-aerial-vehicle remains a significant challenge. \nThis work demonstrates that high-energy-density electrochemical power sources with simultaneous high-power-density for micro unmanned vehicles are feasible. Such unconventional high-energy power sources\, including zinc-air batteries\, aluminum-air batteries\, and solid oxide fuel cells\, show significant potential to substantially extend operational durations of multiple micro unmanned vehicle types.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-mems-based-electrochemical-power-sources-for-extended-operational-duration-of-micro-unmanned-vehicles-yanghang-huang/
LOCATION:Room 221\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240722T103000
DTEND;TZID=America/New_York:20240722T113000
DTSTAMP:20260403T172610
CREATED:20240709T184707Z
LAST-MODIFIED:20240709T184707Z
UID:10008016-1721644200-1721647800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Multi-Robot Coordination and Cooperation via Graph-Based Computation"
DESCRIPTION:Multi-robot coordination and cooperation are critical behaviors that improve team performance and enable new tasks in application areas like autonomous construction\, agriculture\, and extended operation in large unknown regions. This dissertation examines these behaviors in the context of the multi-robot resource allocation problem\, where robots must be allocated to regions of demand. In particular\, we are interested in uncertainty-tolerant approaches that apply to large multi-robot teams. We introduce graph-based computation via Graph Neural Networks applied to the distributed multi-agent coverage control problem\, which boasts improved performance and scalability by leveraging learned inter-agent communication strategies. We also introduce a graph-based modeling and planning approach to multi-robot task allocation in complex multi-task missions where coordination and cooperation are explicitly required. We expand this approach into an online setting that re-plans around task failures and unexpected observations. We demonstrate empirically that these modeling approaches and algorithms bring performance improvements that further the state of the art by leveraging the fundamental graph structure present in some multi-robot problems.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-multi-robot-coordination-and-cooperation-via-graph-based-computation/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240722T130000
DTEND;TZID=America/New_York:20240722T150000
DTSTAMP:20260403T172610
CREATED:20240722T125810Z
LAST-MODIFIED:20240722T125810Z
UID:10008029-1721653200-1721660400@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Aluminum Scandium Nitride Ultra-Wideband Resonator and Filter Systems"
DESCRIPTION:Aluminum scandium nitride (AlScN)\, a ternary alloy by doping scandium into aluminum nitride (AlN)\, has circumspectly gained recognition through the last decade as one of the promised materials in forming the next generation radio frequency (RF) filters and resonators – core components in the wireless telecommunication systems that interconnect today’s world. This\, coupled with the recent discovery of its ferroelectricity\, has led to unexpected possibilities and prospects. Woefully\, while Sc-alloying has gifted AlScN remarkable properties\, it took great effort to make these properties come into realization. Namely\, AlScN is difficult to deposit\, hard to etch\, and arduous to be made useful. In this dissertation\, techniques and methodologies were developed to handle these specific challenges\, and approaches for new actuation principles were proposed and validated.  Utilizing an AlN seed and a gradient AlScN seed layer\, the sputtering deposition of high quality AlScN films on commercial substrates was demonstrated. Through exploring the wet and dry etching methods\, a variety of etching rates\, selectivity and sidewall profile were calibrated and therefore made selectable. Via the implementation of an AlN buffer layer\, a surface acoustic wave (SAW) resonator built directly on a low-cost silicon substrate with a high electromechanical coupling coefficient (kt2) is shown. Furthermore\, by adopting and adapting a method known as apodization\, Lamb wave resonators (LWRs) and filters with record-breaking figure of merits (FOM) exhibiting spurious free operation characteristics were achieved. Second harmonic generation (SHG)\, a powerful tool in laser optics for the purpose of frequency doubling was examined and the ability of periodic poling to a domain width as small as 220 nm with tailored 50 % duty cycle has been attained\, allowing quasi phase matching (QPM) for the generation of deep ultraviolet (DUV) in photonic integrated circuits. Finally\, by leveraging this ability\, a brand-new actuation mechanism for periodically poled LWR (PPLWR) was introduced and investigated. Acoustic resonances were realized at all target frequencies\, with the highest one providing double-digit kt2 when working in the cross-sectional Lamé mode. With all roadblocks removed\, such devices working in the X-band or above have great potential of serving the goal of frequency selection in the coming 6 G era.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-aluminum-scandium-nitride-ultra-wideband-resonator-and-filter-systems/
LOCATION:Room 35\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240722T133000
DTEND;TZID=America/New_York:20240722T143000
DTSTAMP:20260403T172610
CREATED:20240709T183405Z
LAST-MODIFIED:20240709T183405Z
UID:10008015-1721655000-1721658600@seasevents.nmsdev7.com
SUMMARY:MEAM MSE Thesis Defense: "Algorithmic Design of an Origami Squirrel-Inspired Paw for Quadrupedal Locomotion in Bar-like Terrains"
DESCRIPTION:The Dynamic Origami Quadruped (DOQ)\, an origami quadrupedal robot\, has been primarily utilized for hopping and walking on flat ground. However\, adapting it walking on bar-like terrains presents significant challenges. Inspired by the Squirrel-inspired Rapid Tenodesis (SQRT) foot which is capable of passively grasping onto curved surfaces during perching\, this thesis explores the integration of the DOQ and SQRT designed through origami fabrication techniques. Origami fabrication can offer a parametric approach to robot design\, enabling researchers to rapidly prototype by inputting serval constraint parameters. On the other hand\, the inherent compliance of origami bodies further enhances the passivity of the foot\, reducing the restoring time during takeoff from the bar. The origami SQRT demonstrates promising performance\, showcasing its potential in closely grasping bars and assisting the DOQ in maintaining stability.
URL:https://seasevents.nmsdev7.com/event/meam-mse-thesis-defense-algorithmic-design-of-an-origami-squirrel-inspired-paw-for-quadrupedal-locomotion-in-bar-like-terrains/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense,Master's
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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END:VCALENDAR