Penn Engineering Commencement 2023: Undergraduate Ceremony
2023-05-13 14:00 - 16:00 (America/New_York) | Palestra, 223 South 33rd Street, Philadelphia, PA, 19104, United States
Celebrate the Penn Engineering Undergraduate Class of 2023. Additional information is available on the Penn Engineering Commencement website.

Penn Engineering 2023 Commencement Open House
2023-05-15 12:00 - 14:00 | PA
Immediately following the University Ceremony, Penn Engineering will host an Open House for the School’s returning graduates and their families throughout the first floors of the main Engineering complex.
Included are photo booth opportunities, a t-shirt giveaway (including a live t-shirt press!), and light refreshments. Department offices will also be open as a location for graduates and their loved ones to connect one last time with staff and faculty. Please join us!
More information, including a map of departmental locations, is available here.

Physics PhD Thesis Defense: "Characterization of Solid State Defect Systems For Quantum Computing, Communication, and Sensing"
2023-05-16 10:00 | Towne 319, 220 S. 33rd Street, Philadelphia, 19104, United States
Solid state defects have emerged as a leading candidate for platforms in quantum computing, communication, and sensing. The electronic spins localized around these defects have many advantages such as room temperature coherence, spin dependent optical transitions which enable visible-wavelength initialization and readout, and resonant frequencies compatible with widely available off-the-shelf microwave hardware. Furthermore, the nuclear spins coupled to these electronic spins provide additional quantum registers which can be used as long-lived memories, ancilla qubits to enhance sensing and communication schemes based on the electronic spin, or for general purpose computation. However, unlike systems which are all identical, such as trapped atoms, or systems which are man-made, such as superconducting circuits, the formation and structure of defects, as well as their coupled nuclear spins, is stochastic and difficult to model using textit{ab initio} methods. This thesis focuses on methods to efficiently and precisely characterize the properties of these systems. After presenting sufficient background for a general scientific audience, a method is outlined for robustly and efficiently quantifying the optical properties of defect-based emitters, even if the emitters are heterogeneous. We show how this method can be used to study treatment effects in novel systems, such as hexagonal Boron Nitride (hBN), where a new class of defect-based emitters has been identified but proven difficult to characterize – largely due to the widely varying optical properties of emitters which are believed to arise from the same defect. We then describe the infrastructure developed within the laboratory to generate, run, and simulate the results of experiments such as those used in the remainder of the thesis. This is followed by an explanation of some of the state of the art techniques used to identify, control, and map the nuclear spins coupled to an electronic spin within a defect system. We then show how these techniques can be expanded to precisely measure the Hamiltonian parameters of a single nuclear spin – which opens the door to a new era where individual nuclear spins can be used to measure their environment and reveal information about the local molecular and crystal structure. Finally, this thesis concludes with a brief discussion of the ongoing work and future directions inspired by the ideas and work presented in this thesis.

MEAM Seminar: "Addressing Sensitivity-Induced Challenges in Modeling Rigid-Body Systems with Frictional Impacts"
2023-05-23 10:00 - 11:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Imperfect but useful physical models play a crucial role in planning and controlling robot locomotion and manipulation. However, as the field shifts toward complex real-world applications, model accuracy requirements are increasing while calibration data is becoming scarcer. Consequently, model inaccuracy or the sim2real gap often hinders the development of performant robotics algorithms. Frictional contact, the physical process driving these robotic tasks, exacerbates this issue. The motion of robots touching their surroundings is highly sensitive to initial conditions and material properties. Thus, even slight errors in state estimation and parameter identification can lead to significant prediction discrepancies. These challenges are amplified by the use of coarse rigid-body models, rather than high-fidelity solid mechanics, due to the faster-than-real-time computational requirements of robotics.
In this talk, we present two advancements towards accurate modeling of robotics undergoing contact. In the former, we discover how this sensitivity leads to poor training-time optimization landscapes and test-time generalization in common robotics modeling methods. Our method, ContactNets, circumvents both of these issues respectively with a novel statistics- and mechanics-inspired loss and implicit learning representations. ContactNets is capable of identifying geometric and frictional properties of an object rolling, pivoting, and sliding against its environment from just a handful of motion-captured trajectories. In the latter, we attribute sensitivity to the rapid ordering or sequencing of impact forces in nearly-simultaneous collisions—such as heel and toe strikes during a foot step. As existing rigid-body models lack the fidelity and accuracy to predict this ordering, we develop a set-valued rigid-body model which captures the set of all impact orderings as a continuous-time differential inclusion. We rigorously prove key theoretical properties of this model, including existence of solutions.

BE Doctoral Dissertation Defense: "Design, Modeling and Optimization of a Tape Spring Steerable Needle" (Omar Abdoun)
2023-05-26 11:30 - 13:30 | Towne 337
The Department of Bioengineering at the University of Pennsylvania and Dr. Mark Yim are pleased to announce the doctoral dissertation defense of Omar Abdoun.

Title: DESIGN, MODELING, AND OPTIMIZATION OF A TAPE SPRING STEERABLE NEEDLE

Date: 5/26
Time: 11:30am
Location: 337 Towne Building

Zoom is also an option. The Link Is below:

Omar Abdoun is inviting you to a scheduled Zoom meeting.

Topic: Omar Abdoun’s Thesis Defense
Time: May 26, 2023 11:30 AM Eastern Time (US and Canada)

Join Zoom Meeting
https://pennmedicine.zoom.us/j/93855309212

Meeting ID: 938 5530 9212

MEAM Seminar: "Progress on Templates for Spined and Tailed Legged Robots"
2023-05-30 10:00 - 11:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
By mirroring the success of biological systems, legged robots have the potential to be successful in almost every terrestrial environment. While legged machines have made significant advancements in the past 20 years, there still exists a considerable gap between what they can achieve and the abilities of animals. In this talk I’ll discuss some of my recent work on exploring how internal degrees of freedom – spines and tails – can help to create more agile robots. First I’ll discuss my recent results showing how an internal degree of freedom can enable gravity to energize a simple hopping robot using a novel stepping strategy. I’ll present my formal analysis of the system which explains how the strategy controls the distribution of energy in an intuitive manner and present simulation and hardware results showing our robot hopping at speeds of up to 8.85 leg lengths per second. Next I’ll present some new work where I show how a coupled oscillator model, traditionally used for studying vibrations, can explain the role of a spine in trotting and bounding.

ESE PhD Thesis Defense: "Compute-In-Memory on Emerging Memory Technology: From Device to Algorithm"
2023-05-30 14:00 - 16:00 | Room 313, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Current computing systems are mainly constructed on the von Neumann architecture, where data needs to be transferred to a processing unit from memory components. The latency associated with accessing data from the memory units is a key performance bottleneck for a range of data-intensive applications in the convergence of big data and AI. Several solutions have been proposed to mitigate and overcome this bottleneck, with a prominent one being placing memory and logic units in close physical proximity. While significant progress has been made along those lines at both technology and architecture levels, a transformative approach would be to implement arithmetic kernels precisely where the data are stored using memory devices. This is known as compute-in-memory (CIM).
In this dissertation, I will begin by presenting the most recent advancements in the CMOS-compatible ferroelectric memory technologies on aluminum nitride platform. Second, I will present a reconfigurable CIM system on field-programmable ferroelectric diodes in a transistor-free architecture, allowing for multiple essential data operations. Last, I will discuss the conceptualization and demonstration of a programmable parallel search architecture – analog content-addressable memory (ACAM) on complementary Si-CMOS ferroelectric field-effect-transistor memory. The deployment and acceleration of deep neural network and kernel regression on ACAM will also be presented.

MEAM Ph.D. Thesis Defense: "Transport Modeling and Design of Electrode Architectures for High Energy Density Batteries"
2023-05-31 09:00 - 10:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
With the ever-increasing production of portable electronics, internet of things devices, electric vehicles, unmanned aerial vehicles, and other autonomous robotic systems comes an increasing demand for reliable, long-lasting, portable power sources. Portable electronic systems are often limited by the energy density of the batteries that power them, and these batteries typically take up a large fraction of the overall device weight and volume. Higher energy density batteries are needed to effectively power current and future devices.
One strategy for increasing energy density is to increase the volume fraction of active materials in the battery by increasing the thickness and decreasing the porosity of the electrodes. However, existing electrode architectures cannot simultaneously enable thick, high-density electrodes because electrolyte pathways are necessary for ion transport. Creating high solid volume fraction electrodes requires new electrode architectures which enable ion transport even when electrolyte volume is limited.
To achieve high energy, most battery research focuses on making packaged batteries as small or as light as possible, irrespective of the systems that such batteries will power. In biology, multifunctional interconnected subsystems work together to create a more efficient full system. Incorporating multifunctionality into energy storage for robotics will lead to similar improvements in system-level efficiency.
This work demonstrates multiple approaches toward electrode architecture design for high energy density batteries. First, we demonstrate how continuous electrode architectures enabled by the electrodeposition of lithium cobalt oxide (LCO) can overcome electrolyte transport limitations via fast solid-state diffusion. These electrodeposited LCO cathodes create a large opportunity space for improved energy density by enabling thick, high solid volume fraction electrodes. We also present a novel catholyte architecture with the ability to store and extract energy from dissolved oxygen in silicone oil emulsions. This electrolyte is a promising candidate for multifunctional power systems and presents new design opportunities for flow batteries by removing the need for the challenging gas-liquid-solid interfaces and semi-open boundaries in conventional oxygen reduction reaction (ORR) cathodes.

CBE PhD Thesis Defense: "Modeling Diverse Processes at Oxide Interfaces"
2023-06-01 12:00 - 13:30 | Towne 225
In this thesis, ab initio methods including density functional theory are used in concert with molecular dynamics, enhanced sampling techniques, and microkinetic modeling to study oxide materials as applied to electrochemical ammonia synthesis, carbon mineralization, and the oxygen evolution reaction. Special attention is directed towards discussion of model selection and its relationship to the experimental system.

Perovskite oxide BaZrO3-based ceramic electrolytes are shown to favor migration of protons from the bulk to the surface followed by hydrogen evolution without the need for a recombination catalyst. Using this same BaZrO3 electrolyte model surface, microkinetic modeling of ammonia synthesis leads to the proposal of a new experimental methodology for improving selectivity to ammonia at elevated temperatures. The dissolution rates of Mg- and Ca-containing mineral oxides are shown to be surface dependent. Methods for calculating the lowest energy facets and terminations at different water chemical potentials are presented and discussed. Similarly, transition metal oxides under acidic oxygen evolution reaction conditions expose different facets and have different adsorbate coverages depending on material and reaction conditions. Activity and stability are intimately related to these condition-dependent changes. Multi-surface Pourbaix diagrams are presented that allow for targeting model systems that are most likely to compare well with experiment. To help reduce the inherent complexity of these materials, a model for predicting the hybridization energy due to interactions between adsorbates and metal oxide surfaces is presented pointing out the key electronic structure features dictating the strength of this adsorption interaction.

Agreement between theory and experiment for metal oxides depends strongly on model selection subject to the reaction environment constraints. Further insight can be gleaned from physical models such as the generalized concerted coupling model, which offers insights into how the oxide electronic structure can be tuned for an application.

MEAM Seminar: "Wall-modeled LES of 3-D Turbulent Boundary Layer with Emphasis on Grid Independency"
2023-06-05 10:00 - 11:00 | Room 2C4, David Rittenhouse Laboratory Building, 209 S. 33rd Street, Philadelphia, PA, 19143, United States
The capability to predict high-Reynolds-number turbulent flows is essential for many natural and engineering flows such as external aerodynamics (wind turbines, aircraft wings), hydrodynamics (the hull of marine vehicles) and atmospheric boundary-layer flow over complex landscapes and cityscapes. However, due to extreme disparity of scales present in high-Reynolds-number wall-bounded turbulent flows, any attempt to simulate these flows directly without resorting to modeling of some sort results in prohibitively large computational cost. Among various modeling paradigms, wall-modeled large-eddy simulation (WMLES) has the potential to be both predictive and affordable, capturing more of the complex flow physics in the outer portion of boundary layers while modeling the expensive inner layer.
In this talk, I will first present a comparative study of WMLES of a turbulent boundary layer with mean-flow three-dimensionality developing on the floor of a bent square duct, which mimics the flow over the swept wing of the aircraft. It is demonstrated that more complex wall models better predict the near-wall three-dimensionality. The wall-stress direction from the wall models is shown to have separable contributions from the equilibrium stress part and the integrated nonequilibrium effects, where how the latter is modeled differs among the wall models. Second, I will discuss the ill-defined notion of grid convergence in WMLES. I propose that the convergence rate of WMLES is controlled by the extent of the wall-modeled region. A new perspective here is that one may attempt to converge WMLES at the desired grid resolution, while the accuracy of the converged solution can be attributed primarily to wall models. Lastly, I will talk about a new perspective on the dynamic LES models for subgrid-scale closure. I will show that there are dynamically important directions along which dynamic models can be further optimized.

MEAM Seminar: “Computational Study on the Influence of Roughness at Low and Very-High Reynolds Numbers”
2023-06-06 10:00 - 11:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Many fluid problems of interest, such as turbulent flow over an airplane or transport processes in geophysical flows, contain wall-bounded regions that form boundary layers. Oftentimes, both numerical and experimental studies are simplified by using smooth surfaces. This simplification has allowed us to gain a greater understanding of near-wall processes for flows of engineering interest, yet most surfaces are inherently rough. In many cases, especially at higher Reynolds numbers, the roughness protrudes far enough into the boundary layer to disrupt the flow. This roughness can induce form drag, reducing efficiency for ships, planes, and turbines, or alter transport of particles in atmospheric flows. For lower Reynolds number flows, roughness may produce the opposite effect, reducing drag or enhancing lift capabilities. This is with the addition of dimples on a golf ball or the alula on a bird wing.
In this talk, I present numerical results on the effects of roughness at two opposite ends of the spectrum. At low Reynolds number (O(103)), roughness elements displayed an ability to decrease drag, reducing power required, and augment lift capabilities for a propeller designed for use with a micro-unmanned aerial vehicle. I discuss the physical mechanisms at play which influence near-wall vortical structures, enhancing the aerodynamics of the propeller in a particularly viscous regime. The results of the simulations are corroborated with an in-house experimental study. Next, I present preliminary simulation results to a very-high Reynolds number flow (O(106)) over a real topography featuring a step-change in roughness. An atmospheric boundary layer flow over the dunes at White Sands National Park in New Mexico is studied to discern the mechanism behind reduced sediment transport downstream of the step-change. I then discuss current efforts to explore how the internal boundary layer formed by the new wall condition may influence small near-wall scale interaction with large-scales in the outer portion of the flow.

MSE PhD Thesis Defense: Self-Healing and Shape Memory Metal Electrodes in Magnesium-and Lithium-Ion Batteries
2023-06-06 14:00 - 15:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States

Spring 2023 GRASP on Robotics: Pauline Pounds, The University of Queensland, "Drones, Bipeds and Sensors - 10 Years of the UQ Robotics Design Lab"
2023-06-09 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance via Zoom. This week’s presenter will be in-person as well.

ABSTRACT
The Robotics Design Lab was founded in 2012 at the University of Queensland, Australia, and considers the holistic design of robots as a system. With the RDL Pauline Pounds’ work has focussed on improving robot performance, reducing cost, and developing practical solutions. Her work has sought to increase drone endurance, payload and range, as well as develop ultra-low cost self-deploying sensor systems. She has developed new sensors to allow drones to better sense and respond to aerodynamic conditions around them. Her latest work on bipeds seeks to enable affordable dynamical bipeds for commercial applications. This talk will give a broad overview of the last decade of her work in the RDL and highlight specific results within each of these areas.

MEAM Ph.D. Thesis Defense: "Manually-Operated, Slider Cassette for Multiplexed Molecular Detection at the Point of Care"
2023-06-09 13:00 - 14:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Personalized medicine requires the identification of disease-causing agents prior to prescribing therapy. Currently, molecular detection requires shipping bio samples to centralized laboratories. The time delay between sample collection and test results prevents health care givers from making timely, informed decisions. Furthermore, there is a shortage in centralized laboratories and trained technicians in resource poor settings such as rural areas and developing countries. To address this need for cost-effective, reliable, user-friendly, real-time, multiplexed detection of co-endemic pathogens at the point of need by minimally trained personnel, I propose a 3D-printed, manually operated, slider cassette for multiplexed molecular detection. All needed reagents (in dry form) and buffers are pre-stored in the cassette, refrigeration-free. Once the user introduces a raw sample such as whole blood into the cassette, the user pushes a sliding bar that contains a nucleic acid isolation membrane, comprised of chitosan coated glass fiber, through a sequence of unit operations. The slider’s motion actuates blisters that discharge, in sequence, lysis buffer, wash solution, and elution solution. The lysis process is assisted with heat incubation, wherein heat is provided by an exothermic reaction. Nucleic acids are isolated from the lysate through binding to the chitosan-coated membrane under low pH conditions. At the end of the sliding process, the nucleic acids are eluted with a high pH buffer and aliquoted by capillary forces to individual reaction chambers. Each chamber stores a pre-dried LAMP reaction mix specific to a target. The chambers are then incubated at 65C, facilitating LAMP amplification. The reaction products are detectable either in real time with fluorescent dye and/or at the end point with color change (colorimetric dye). A two-dimensional finite element (COMSOL) simulation was carried out to test for potential crosstalk among the reaction chambers and a three-dimensional finite element (FLUENT) simulation was performed to evaluate the on-cassette chemical heater’s performance used in the lysis process. I demonstrate the entire molecular diagnostic process from sample (plasma and blood) to answer by testing contrived samples spiked with various concentrations of HIV, HCV, and HBV.

MEAM Ph.D. Thesis Defense: "High Performance Electroadhesives for Materials and Robots with Electroprogrammable Stiffness"
2023-06-12 11:00 - 12:00 | Room 2C8, David Rittenhouse Laboratory Building, 209 South 33rd Street, Philadelphia, PA, 19104, United States
Materials with electroprogrammable stiffness and adhesion can enhance the performance of robotic systems, including prosthetics, medical devices, wearables, exoskeletons, and grippers. However, achieving large changes in stiffness and adhesive forces in real time is an ongoing challenge. Electroadhesive clutches can rapidly adhere high stiffness elements, although their low force capacities, high activation voltages, and inability to separate and turn off stiffness changes reliably have limited their applications.
A major challenge in realizing stronger electroadhesive clutches is that current parallel-plate models poorly predict clutch force capacity and cannot be used to design better devices. Thus, current electroadhesive clutches suffer from force capacities below that of other materials with an electrically-programmable stiffness. Furthermore, soft material interfaces have not been utilized for stronger electroadhesive clutches due to latent adhesion at the contact interface that prevents programmable release.
This work demonstrates strategies to improve electroadhesive clutch designs for high-performance applications in material systems and robots with an electrically-programmable stiffness. Using a fracture mechanics framework, we build an improved understanding of the relationship between clutch design, force capacity and contact area. This mechanics-based framework predicts clutch performance across multiple geometries and applied voltages. Based on this approach, a Coulombic electrostatic clutch with 63 times the force capacity per unit electrostatic force of state-of-the-art electroadhesive clutches is realized. By doing so with traditional dielectrics and electrode materials, we demonstrate the power of our mechanics-based design methodology to increase clutch performance without relying on expensive materials or intensive manufacturing processes, making our approach optimal for widespread adoption by robotics researchers.
Finally, this mechanics-based design methodology is applied to the design of clutches with soft material interfaces. We demonstrate that this approach in conjunction with an engineered electroadhesive surface enables the use of elastomeric materials and low-voltage ionoelastomers in electroadhesive clutches with increased force capacities, which are capable of programmable release at reduced device sizes. We utilize these high-performance clutch designs in novel applications with an electrically-programmable stiffness, including soft robotic hands with enhanced load carrying capacity, wearable haptic interfaces, and morphing fabrics.

5th Annual Learning for Dynamics & Control Conference
2023-06-14 - 2023-06-16 | University of Pennsylvania
Over the next decade, the biggest generator of data is expected to be devices that sense and control the physical world.
The explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking the foundations of our discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data-driven control and optimization of dynamical processes. Our overall goal is to create a new community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area. We are happy to welcome you to the University of Pennsylvania for the 5th annual L4DC. Click here for more information.
Registration is now open!

MSE PhD Defense: Effects of Size and Composition on the Performance of Dealloyed Nanoporous Metal Catalysts for CO2 Reduction, O2 Evolution, and H2 Generation
2023-06-14 10:30 - 12:00 | LRSM Reading Room, 3231 Walnut St., Philadelphia, PA, 19104, United States

Department of Bioengineering Juneteenth Address: "A White Neighbor, a Black Surgeon, and a Mormon Computer Scientist Walk into a Bar…” (Kevin B. Johnson)
2023-06-14 11:00 - 12:00 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
“A White Neighbor, a Black Surgeon, and a Mormon Computer Scientist Walk into a Bar…”
As we recognize Juneteenth, a holiday that brings awareness to what journalist Corey Mitchell calls “…a complex understanding of the nation’s past”, we also need to understand how many of our neighbors, staff, and faculty—even those born in the last 100 years—continue to navigate through the environment that made Juneteenth remarkable. Dr. Johnson will share a bit of his personal story and how this story informs his national service and passion for teaching.
This talk will be held live in person in Berger Auditorium (Skirkanich Hall basement), streamed on Zoom, and recorded.
Following the event, a limited number of box lunches will be available for in-person attendees. If you would like a box lunch, please RSVP here so we can get an accurate headcount.
Join Zoom Meeting
https://upenn.zoom.us/j/92503256013?pwd=[REDACTED]
Meeting ID: 925 0325 6013
Passcode: [REDACTED]

BE Doctoral Dissertation Defense: "Detecting and Localizing Progressive Changes In Longitudinal MRI of the Hippocampal Region in Alzheimer’s Disease" (Mengjin Dong)
2023-06-15 10:00 - 12:00 | BRB 0252
The Department of Bioengineering at the University of Pennsylvania and Dr. Paul Yushkevich are pleased to announce the Doctoral Dissertation Defense of Mengjin Dong.

Title: Detecting and Localizing Progressive Changes In Longitudinal MRI of the Hippocampal Region in Alzheimer’s Disease

Date: Thursday, June 15th, 2023
Time: 10:00 am
Location: Biomedical Research Building (BRB), Classroom 0252 and Zoom.

Google Maps of BRB:
https://www.google.com/maps/place/Biomedical+Research+Building+(BRB)/@39.9480917,-75.1985746,17z/data=!3m1!4b1!4m6!3m5!1s0x89c6c65ec9dc0937:0x276d01d621dbae62!8m2!3d39.9480917!4d-75.1963859!16s%2Fg%2F11bwnd8dc7

Zoom Meeting ID: 461 034 1415
Zoom Link: https://upenn.zoom.us/j/4610341415

The public is welcome to attend.

MEAM Ph.D. Thesis Defense: "Photophoretic Light-flyers for Mesospheric Applications"
2023-06-16 10:00 - 11:00 | Lynch Lecture Hall, Cret Wing, CHEM, 231 S. 34th Street, Philadelphia, PA, 19104, United States
The Earth’s mesosphere plays a crucial role in weather forecasting, environmental conservation, and planetary exploration. However, traditional unmanned aerial vehicles (UAVs) such as balloons or spacecraft face significant challenges when attempting to access this layer. Balloons and airplanes struggle due to the low pressure, while satellites encounter high aerodynamic drag. To overcome these limitations, we have developed a photophoresis-based UAV capable of levitating in the Earth’s mesosphere for an extended duration of days or even months. Photophoresis refers to the movement of small particles suspended in fluids when illuminated by an intense beam of light. This phenomenon can be attributed to a difference in heat exchange of a particle illuminated in gases or liquids. Our research focuses on the levitation of mylar-based light-flyers utilizing the photophoretic force driven by the difference in the thermal accommodation coefficient (TAC). These levitations ultimately occur under light irradiances comparable to natural sunlight (1.36 kW/m2).
Our work encompasses several key contributions. First, we proposed a guideline to minimize the ground effect associated with testing in a vacuum chamber, ensuring realistic performance evaluations of light-flyers. Second, we utilized germanium coatings as selective absorbers to achieve levitation at irradiances as low as 1.5 kW/m2 with minimal ground effect. Third, we revised the previous semi-empirical model that underlies the photophoretic force based on our experimental observations. Finally, we predicted successful levitations of light-flyers in a mesosphere-like environment under natural sunlight, accommodating sub-milligram payloads. Overall, our study represents a significant advancement towards launching long-life UAVs into Earth’s mesosphere, which can offer substantial benefits to atmospheric science.

CBE PhD Thesis Defense: "Electrochemical and Heterogeneous Catalysis for Selected Industrial Commodities"
2023-06-19 10:00 - 12:00 | Towne 225
Novel high-surface-area catalysts were synthesized for selected industrial chemical reactions, conducted in heterogeneous reactors and electrochemical reactors. Atomic layer deposition (ALD) was used to synthesize highly dispersed metal catalysts and to deposit uniform oxide thin films on porous supports.
The interaction between Cu catalyst and its oxide support was studied for the water-gas-shift reaction, a critically important reaction for maximizing the H2 production from syngas. The ALD-prepared Cu catalyst demonstrated ten times more reactivity than the impregnated Cu catalyst with the same metal loading and surface area. The selected oxide supports did not show promotion effects for the reaction, but some mild stabilization over long-term aging was observed. In another case, the selective oxidation of butane to maleic anhydride was studied on an ALD-prepared vanadium phosphate catalyst with eight times more surface area compared to the commercial bulk catalyst. Furthermore, an ALD-prepared CaO/MgAl2O4 catalyst was studied for the aldol condensation of furfural and ketones/aldehydes to form precursors of lubricant oils and surfactants. The side reaction, Cannizaro reaction, was investigated for the long-term use of this catalyst.
Lastly, ammonia synthesis was studied in a solid oxide electrochemical cell made with the state-of-the-art proton conductor BZCYYb (BaZr0.1Ce0.7Y0.1Yb0.1O3−δ). Instead of using both high temperature and high pressure in the commercial Haber-Bosch process, this reaction was carried out under ambient pressure. The proton recombination was found to be barrierless on the cell surface, greatly limiting the selectivity to ammonia. The implications of this phenomenon and its suppression were discussed.

MEAM Seminar: "Computationally-constrained Dynamically-feasible Search-based Motion Planning"
2023-06-20 10:00 - 23:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Planning fast and autonomous robotic motion in unstructured, cluttered environments remains a core challenge of the robotics community. The robot’s motion must be agile and dynamic, operating near the limit of its physical capabilities, to reach its goal location as quickly as possible. Fast motion in turn creates a need for fast plan computation over large and complex areas of the robots environment. Search-based planning, entailing search over trajectory-based graphs, offers a theoretically grounded method to plan optimal motion that is dynamically feasible. However, the computational footprint of this approach is often too burdensome, as graph search speed scales inversely with the size of the planning graph. To tackle this challenge, we concentrate on designing a sparse planning graph and dynamically determining the required graph size based on obstacle density.
We introduce a method for selecting vertices and edges in a motion primitive graph grounded in statistical dispersion, which ensures guarantees on planner completeness. By minimizing dispersion of graph vertices in the trajectory cost-induced metric space, our approach efficiently covers the space of feasible trajectories. Our motion primitive graphs outperform baseline methods, with lower dispersion, fewer iterations of graph search, and fewer tunable parameters.
While this method can generate high-quality graphs of a specified size, the selection of this size has significant effects on planner performance. Sparser graphs may miss a narrow corridor that the plan must traverse, while denser ones may result in excessive computation time. We address this tradeoff with a framework consisting of two parts: offline maximization of planner completeness for several graph sizes, and online dynamic adjustment of the graph size based on empirical planner performance. Through real world experiments in cluttered pine forests, we demonstrate the real-time adaptability of the planner to different environments, enabling flight up to 2.5 m/s in varying tree densities.
Finally, we explore the integration of trajectory optimization with search-based planning, highlighting the potential synergies between these approaches and providing design tradeoffs.

MEAM Ph.D. Thesis Defense: “Design, Characterization, and Fabrication of Low-Cost, Passive, and Biodegradable Sensors For Precision Agriculture”
2023-06-20 12:00 - 13:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
With the global population projected to reach 9.1 billion people by 2050 there is a need to develop highly efficient agricultural systems that maximize crop yield. Precision Agriculture (PA) systems enabled by the Internet of Things (IoT) offer a potential solution through improvements in labor, resource, and time efficiency to improve agricultural output. PA systems enable this by providing a detailed characterization of field environment (e.g., soil moisture, pH, temperature, etc.) so that these resources can be properly deployed spatially and temporally. To realize these systems, sensors that give information about the state of the field are required. However, for the technology to be scalable and practically implemented, these sensors must balance performance and cost. These requirements limit the materials and methods that can be used to develop the technology, including many that are common in modern sensor development. Additionally, the challenge of biocompatibility and biodegradability must be addressed.
In this work, a passive RF sensing system is presented for the detection of soil moisture. First, a fabrication process for a fully biodegradable cellulose nanofibril (CNF) based composite substrate is presented. By using small quantities of CNF, we are able to planarize the surface of a pulp-based cardstock paper to achieve smooth surfaces that are suitable for the fabrication of electrical structures. Next, the hygroscopic properties of cellulose are leveraged to develop a capacitive sensor that utilizes the substrate as the sensing mechanism. By utilizing screen printing, we repeatably produce capacitive structures with a high degree of fidelity. We demonstrate the ability to detect both humidity and soil moisture over a wide range and show the ability of the sensor to operate within the 902 – 928 MHz band. Additionally, sensor cycling and repeatability is demonstrated, a key requirement for in-field application.
Finally, integration and packaging of the sensor is investigated. Capacitive structures are integrated into a wired PCB system and are shown to exhibit good performance. Natural wax-based packagings are also explored, with mechanical characterization of various wax mixtures conducted to choose a suitable candidate. We show that the selected wax mixture can protect devices in a soil environment while still enabling capacitive sensing, demonstrating the ability for natural waxes to be used as a suitable packaging material for biodegradable sensors.

PSOC@Penn Seminar: Michael Tobin
2023-06-20 13:00 - 14:00 | Towne 327

MEAM Ph.D. Thesis Defense: "Addressing Stiffness-induced Challenges In Modeling and Identification of Rigid Body Systems with Frictional Impact"
2023-06-21 10:00 - 11:00 | Moore 216, 200 S. 33rd Street, Philadelphia, PA, 19104, United States
Imperfect but useful dynamical models have enabled significant progress in planning and controlling robotic locomotion and manipulation. Traditionally, these models have been physics-based, with accuracy relying upon manual calibration only feasible in laboratory environments. As robotics expands into complex real-world applications, models must instead be automatically fit to limited data. One major challenge is modeling frictional contact, especially during collisions involved in common robotics tasks. Rapid deformation under impact manifests as extreme sensitivity to initial conditions and material properties. Thus, even slight errors in state estimation and system identification can lead to significant prediction errors. Consequently, model inaccuracy or the sim-to-real gap often hinders the development of performant robotics algorithms.
When only a few parameters are unknown, physical models can be optimized using advanced techniques to overcome these challenges. However, even with accurately identified parameters, roboticists must make inaccurate rigid-body approximations to reduce the computational burdens of physical simulation to meet faster-than-real-time requirements. An alternative black-box approach has attempted to address these issues, in which dynamical models are learned from scratch, for instance using deep neural networks (DNN’s). While DNNs in theory can capture any dynamical behavior, they have empirically struggled with the stiff behaviors associated with contact.
The dissertation instead focuses on scaling physical model identification to the high-dimensional setting and quantifying the limited accuracy of low-fidelity physics models. We consider rigid bodies undergoing rigid contact, for which infinite stiffness is represented as constrained optimization inside the dynamics. By careful treatment of these constraints, we demonstrate that infinitely-stiff dynamics can be identified by optimizing a non-stiff objective. In conjunction, we use DNN’s in a white-box setting to model purely physical quantities, specifically reconstructing geometries from scratch. We then consider how the simplified rigid-body view of collisions lacks fidelity to correctly predict the outcomes of nearly-simultaneous collisions—such as heel and toe strikes during a foot step. We develop a theoretical basis to capture partial knowledge of such events as uncertain set-valued outcomes, and again use numerical optimization to compute approximations of such sets.

MSE PhD Defense: Autonomous Stimuli-Responsive Metamaterials Based on Liquid Crystal Elastomers
2023-06-22 13:00 - 14:30 | Towne 227 (MEAM Conference Room), 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Numerous responsive materials have been developed in recent decades and applied toward engineering challenges ranging from medicine to robotics. For example, polydimethylsiloxane (PDMS), hydrogels, shape memory polymers (SMPs), liquid crystal elastomers (LCEs), and many other materials can be engineered to respond to many environmental stimuli, such as non-polar solvents, humidity, heat, light, magnetic fields, and electric fields. However, there are some major limitations with most responsive materials: they typically respond slowly to the environment, and the deformation triggered by the environmental input is often small. In this dissertation, we propose to address these limitations by integrating responsive materials with mechanical metamaterials that are governed by scale-independent nonlinear mechanisms. These mechanisms can amplify and speed up the deformation of the responsive materials beyond what is possible for the responsive material
by itself.
In this dissertation, we specifically focus on the use of nonlinear mechanisms to improve the responsiveness of LCEs to changing heat or light in the environment, enabling autonomous, large-amplitude, rapid deformation in engineered systems. We demonstrate the potential utility of this strategy in several contexts. First, we engineer a metamaterial based on rotating squares with hinges consisting of LCE-PDMS bilayers. These bilayers, combined with geometric properties, such as the hinge thickness, determine how the metamaterial deforms in response to temperature variations in the environment, including the possibility of achieving either local or global deformation changes in response to localized temperature variations. Next, we build a kirigami-inspired robot that autonomously changes its trajectory in response to the environment, without any electronic controlsystem. The LCEs govern the behavior of modular “control units” that can be placed throughout the kirigami. These, in turn, locally impose mechanical constraints that control how the kirigami bends, and thereby where the robot moves. Finally, with the goal of attaining autonomous functional changes beyond what simple mechanical constraints can achieve, we explore the idea of using LCEs, in combination with hydrogels, to regulate pneumatic circuits. We integrate LCEs with modular mechanical valves. We achieve basic logic gates and construct fluidic networks that can regulate the output pressure based on the local environment. We use this strategy to autonomously control the trajectory and function soft pneumatic robots. These prototypes illustrate the potential for designing autonomous intelligent materials that rapidly and autonomously undergo large-amplitude shape and functional transformations in response to their environment. The principles developed in this dissertation can, in principle, be implemented at smaller length scales in the future, e.g., to develop responsive/intelligent metamaterial systems for micro- or medical robotics.

PSOC@Penn Seminar: Mai Wang
2023-06-26 13:00 - 14:00 | Towne 337

BE Doctoral Dissertation Defense: “Decellularized Cartilage for Airway Repair” (Paul Gehret)
2023-06-26 14:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Riccardo Gottardi are pleased to announce the Doctoral Dissertation Defense of Paul Gehret.
Title: “Decellularized Cartilage for Airway Repair”.
Date: June 26, 2023
Time: 2:00 PM
Location: Wu and Chen Auditorium in Levine Hall
The public is welcome to attend.
Join Zoom Meeting
https://upenn.zoom.us/j/7937445851?pwd=[REDACTED]
Meeting ID: 793 744 5851
Passcode: [REDACTED]

MEAM Seminar: "Multi-robot Coordination Via Graph-based Computation"
2023-06-27 10:00 - 11:30 | Towne 337
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 talk explores the utility of graph-based modeling and computational frameworks to improve the coordination and cooperation of multi-robot teams in a variety of problems. I will introduce a graph-based modeling and solution approach to multi-robot task allocation in complex multi-task missions where coordination and cooperation are explicitly required. I will discuss preliminary results, which show a vast improvement over the state of the art, and limitations of these results and my plans to address these limitations. I will also briefly introduce graph-based computation via Graph Neural Networks applied to the distributed multi-agent coverage control problem. 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.

MEAM Ph.D. Thesis Defense: "Design and Self-Reconfiguration Planning for Variable Topology Truss Robots"
2023-06-28 12:30 - 13:30 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
A modular self-reconfigurable robot (MSRR) is a set of robotic building blocks that can be connected together in different ways. By rearranging these connections, the robot can adapt its shape to address a wider variety of tasks than a robot with a fixed morphology. However, traditional modular architectures fail to scale up to address large-scale and high-force applications. To meet these challenges, we introduce the first self-reconfigurable modular truss robot system. The Variable Topology Truss (VTT) is a synthesis of two robot paradigms: MSRRs and variable geometry truss robots (VGTs). The structural efficiency of the truss architecture alleviates scaling issues with modular robots, while the task flexibility afforded by reconfiguration expands the possible applications of parallel robots.
We first demonstrate hardware prototypes of the components necessary to build a VTT, which include a novel reconfigurable spherical joint and an improved version of a high extension ratio linear actuator. Then, we characterize the reconfiguration capability afforded by the new mechanisms and apply graph-theoretic techniques to enumerate all possible reconfigurable truss topologies up to a certain size. With these techniques, we can identify potential sequences of actions that reconfigure between desired start and goal topologies. However, the entangled nature of the truss architecture presents new challenges for collision-free motion planning. The self-collision constraints divide up the configuration space into many disconnected regions. We develop a mathematical invariant inspired by knot theory—the link-augmented graph—which serves as a test to quickly prove when certain configurations lie in disconnected regions. This invariant can be combined with more traditional planning techniques to boost their performance on truss-like robots, which we demonstrate with a new variant of RRT-Connect. Finally, we combine the topological reconfiguration planning with the link-augmented graph and geometric planner to create a multi-modal planner for VTT. This planner is capable of finding a sequence of collision-free paths and reconfiguration actions that transform a VTT from a given start configuration to a goal configuration.
