Optical Metamaterials and Metasurfaces: Design, Fabrication, and Applications

Date: October 16, 2026; Time: 2:30 PM Location: PWEB 175

 

Abstract: Optical metamaterials are artificially engineered structures whose properties are primarily governed by their geometry rather than their chemical composition. By designing subwavelength building blocks—called meta-atoms—and engineering their spatial arrangement, we can realize exotic optical properties that are difficult or impossible to achieve with natural materials. Metasurfaces, the two-dimensional counterparts of metamaterials, can manipulate light on an ultrathin platform. This capability opens the door to extremely compact optical components, which could potentially replace conventional bulky lenses, waveplates, and holographic elements.

In this talk, I will begin by introducing the fundamentals of optical metamaterials [1] and metasurfaces [2], including the underlying physical mechanisms, the key principles that guide their design, and the state of the art in nanofabrication. In particular, machine learning and deep learning have emerged as powerful tools to significantly accelerate the design of sophisticated metamaterials and metasurfaces, overcoming the limitations of conventional numerical or optimization methods [3]. I will then focus on our recent work on metasurface-based applications, including high-capacity holograms [4, 5] and nanoprinting images [6], demonstrating how a single ultrathin device can encode and display rich optical information through multiple channels such as wavelength and polarization.

References: [1] Y. M. Liu and X. Zhang, Chemical Society Review 40, 2494 (2011). [2] N. Yu and F. Capasso, Nature Materials 13, 139 (2014). [3] W. Ma et al., Nature Photonics 15, 77 (2021). [4] B. Xiong et al., Science 379, 294 (2023). [5] W. Ma et al. Advanced Materials 34, 2110022 (2022). [6] L. Deng et al., Advanced Functional Materials 36, e16059 (2026).

Biographical Sketch: Dr. Yongmin Liu obtained his Ph.D. from the University of California, Berkeley in 2009. He joined the faculty of Northeastern University in Boston in Fall 2012, and currently he is currently a full professor with a joint appointment in the Department of Mechanical & Industrial Engineering and the Department of Electrical & Computer Engineering. Dr. Liu’s research interests include nano optics, nanoscale materials and engineering, plasmonics, metamaterials, biophotonics, and artificial intelligence. He has authored and co-authored more than 150 journal papers, including Science, Nature, Nature Photonics, Nature Nanotechnology, Advanced Materials, Physical Review Letters, and Nano Letters. Dr. Liu was a recipient of the Søren Buus Outstanding Research Award at Northeastern University (2024), the Faculty Fellow of College of Engineering at Northeastern University (2019), NSF CAREER Award (2017), Office of Naval Research Young Investigator Award (2016), SPIE DCS Rising Researcher Award (2016), and 3M Non-Tenured Faculty Award (2016). He has served as an associate editor or editorial board member for Nano Convergence, Nanophotonics,   PhotoniX, IEEE Journal of Selected Topics in Quantum Electronics, and EPJ Applied Metamaterials. He is a Fellow of both OPTICA (formerly OSA) and SPIE.

Measuring, Modeling, and Controlling Blue-Laser Directed Energy Deposition of Copper and Multi-Materials

Date: October 2, 2026; Time: 2:30 PM Location: PWEB 175

 

Abstract: Blue laser directed energy deposition (DED) offers a breakthrough approach for additive manufacturing of highly reflective metals such as copper and its alloys. Unlike traditional infrared lasers with poor absorptivity (<10%), blue lasers achieve absorption rates above 60%, enabling efficient melting and stable deposition. Yet, producing consistent, defect-free components remains challenging due to the sensitivity of melt pool dynamics, fluctuations in powder delivery, and complex laser–material interactions. This talk presents a multi-sensor monitoring and process control framework specifically developed for blue-laser DED. The approach integrates in-situ multispectral imaging (MSI), real-time photodiode sensing, and global thermal history mapping via infrared (IR) imaging to capture complementary process signatures across spatial and temporal scales. Through data fusion, these signals are correlated with part-quality indicators, including track geometry, surface morphology, and porosity. Building on these insights, we demonstrate closed-loop control strategies capable of actively regulating melt pool temperature and geometry in real time. The results highlight the promise of sensor fusion in advancing defect-minimized additive manufacturing of copper and other reflective metals, with broader implications for multi-material builds and functionally graded structures.

Biographical Sketch: Dr. Jihoon Jeong is an Assistant Professor in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering at Texas A&M University. His research spans laser-based metal additive manufacturing (AM) and semiconductor packaging, with particular emphasis on blue-laser directed energy deposition of copper and multi-materials, real-time process monitoring and control, in-situ characterization using non-destructive methods such as thermography and laser spectroscopy, and physics-informed machine learning for thermal modeling. Prior to joining Texas A&M in 2024, he was a Postdoctoral Scholar at Northwestern University (2021–2023), working on metal AM. He received his B.S. and M.S. from KAIST in 2009 and 2011, respectively, and his Ph.D. from The University of Texas at Austin in 2020, all in mechanical engineering. Before his doctoral studies, he conducted research on Li-ion batteries and fuel cells at Samsung SDI and the Korea Institute of Science and Technology (KIST). Dr. Jeong has authored more than 40 journal articles and holds 12 patents. His recognitions include the Young Researcher Award at PRESM 2024, the Outstanding Paper in Manufacturing Processes Award at NAMRC 53 (2025), and the 2026 Outstanding Early Career Award from the IISE Manufacturing and Design Division.

Understanding Atomistic Transport and Interfacial Evolution to Design Electrochemical Devices

Date: April 17, 2026; Time: 2:30 PM Location: PWEB 175

 

Abstract: Electrochemical devices rely on coupled transport within materials and charge transfer across interfaces involving both ions and electrons. Their performance and durability are often limited by interfacial reaction mechanisms, where sluggish charge transfer, parasitic reactions, and defect formation drive energy conversion and degradation and ultimately determine device efficiency and lifetime. Despite significant advances in materials and interface design, fundamental questions remain regarding how local chemistry and defects control reaction kinetics and the failure mechanisms that emerge under operating conditions. In this talk, the speaker will introduce the fundamental operating principles of electrochemical devices, with an emphasis on atomic-scale transport and interfacial processes that govern performance and degradation in lithium-ion battery cathode/electrolyte systems and proton-exchange membrane electrolyzers. The presentation will highlight research efforts from the speaker’s recently established group on identifying atomic-scale mechanisms of defect formation and electrochemical dissolution, and on designing improved electrochemical interfaces for enhanced stability and performance. Two Examples include protective coatings for battery materials and strategies to tune electronic structure of catalysts through chemical interactions with supports. Finally, the talk will discuss how a combined understanding of transport and interfacial chemistry enables the identification of physically grounded descriptors, opening pathways for the discovery of new materials and design strategies for next-generation electrochemical devices.

Biographical Sketch: Daniele Vivona is an Assistant Professor in the School of Mechanical, Aerospace, and Manufacturing Engineering at the University of Connecticut. He received his Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology (MIT), where he was a MathWorks Mechanical Engineering Fellow, Rohsenow Graduate Fellow, and member of the MIT Society of Energy Fellows. He holds B.Sc. and M.S. degrees in Energy Engineering from the Polytechnic University of Milan and an M.S. in Mechanical Engineering from the University of Connecticut.

His research focuses on the mechanisms governing degradation, dissolution, and interfacial reactivity in electrochemical energy systems, including batteries and catalysts. His group combines first-principles simulations, atomistic modeling, and electrochemical experiments to establish quantitative links between surface chemistry, reaction environments, and material stability. By integrating physics-based models with data-driven approaches, his work aims to develop predictive frameworks, and digital and experimental “twins” for the design and lifetime control of electrochemical materials and devices.

 

Challenges and Achievements in Translation of Sensory Neuroprosthetics

Date: March 27, 2026; Time: 2:30 PM Location: PWEB 175

Abstract: Cochlear implants have now been used to treat >1 million patients[Zeng 2022], making this technology an exemplar for neuroprosthetic devices. Vision prosthetics, devices that restore a sense of touch, and brain-computer interfaces (BCIs) are also actively being developed by both researchers and companies. Materials research to achieve stable and high-performance microelectrodes is one critical challenge, and achieving controlled and stable electrophysiological interfaces to neural substrates is a second. Concrete examples highlighting these challenges will be introduced. The first part of my presentation will highlight materials processes to improve the electrode metallization and encapsulation materials for Utah slanted electrode arrays (USEAs), and identification of failure modes observed from long-term implantation. I start by highlighting work under the DARPA HAPTIX program where the impedances of electrodes was decreased and their stimulation stability was increased, while identifying additional materials degradation processes that still need to be resolved. This leads into outcomes from pre-clinical bench studies to evaluate the stimulation stability of fully integrated auditory nerve interface (ANI) devices. Fully integrated devices were stimulated with 6.3 billion pulses at 100 and 1200 µA collecting periodic impedance measurements, where no strong increases in impedances for electrodes were observed. This is consistent with optical and electron microscopy taken before and after stimulation, which did not show electrode damage. This transitions to electrophysiological evaluation of auditory nerve stimulation in pre-clinical large animal and human intraoperative studies. Devices maintained impedances of <140 kΩ, the functional limit we’ve imposed, for in-dwelling times up to 9 months. Evoked auditory brainstem responses and evoked compound action potentials were measured, and suggest reasonable stimulation thresholds are possible, stimulation and nerve health can be maintained, and electrodes are recruiting distinct populations of auditory nerve fibers. These data support the ANI device is sufficiently safe and effective to initiate critical perceptual testing as part of a clinical study, which starts soon.

Biographical Sketch: Loren Rieth is an Associate Professor of Mechanical, Materials and Aerospace Engineering in the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University (WVU). He leads the Microsystems and Neural Technologies Lab (MANTL) to develop neural interface technologies spanning from basic neuroscience tools to translation of technologies for human subjects. His work focuses on research, development, design, fabrication, testing, and translation of neural interface systems and implantable electronics. This includes work on electrical and optical interfaces, their underlying mechanisms, and leveraging this to improve their safety and efficacy for treating conditions and diseases. This work has been supported by grants and contracts from DARPA, NIH, DOE, NSF, and Industry, and has led to >100 publications, invited presentations, and securing IDE approvals from the FDA and German Ethics Committees for clinical investigations.

He received his BS (’94) and PhD (’01) in Materials Science and Engineering from Johns-Hopkins and University of Florida, respectively, focusing on electronic materials. He then joined the University of Utah as a post-doc to investigate epitaxial growth of III/V semiconductors. Dr. Rieth advanced to Research Associate Professor in Electrical and Computer Engineering at Utah, with research focused on basic and translational neural interfaces. He also investigates semiconductor-based sensors, (Bio)MEMS, materials and surface analysis, and microfabrication. He then became Associate Professor in the Institute for Bioelectronic Medicine at the Feinstein Institutes for Medical Research in Manhasset, NY, where he developed advanced vagus nerve stimulation devices, prior to his current appointment at WVU.

Deep Learning Enabled Modeling and Condition-Based Maintenance of Smart and Connected Systems

Date: April 24, 2026; Time: 2:30 PM Location: PWEB 175

Abstract: Due to the fast development of sensing and information technology, many modern engineering systems, such as manufacturing and logistics systems, have become data-rich. The unprecedented data availability, combined with ever-growing computational power, creates unprecedented opportunities for system modeling and decision-making. In this presentation, new deep learning enabled modeling approaches for system degradation will be introduced. The approach features an integration of deep neural networks and the classical hidden Markov structure.  As a result, the proposed modeling approach has excellent interpretability, scalability, and flexibility. In addition, a condition-based maintenance strategy for a large-scale multiple-component system is presented. The advantageous features of the developed methods are demonstrated through numerical studies and real-world case studies. Thoughts on potential research opportunities exploiting the ever-growing data-rich engineering environment will be shared as well.

Biographical Sketch: Shiyu Zhou is the David H. Gustafson Department Chair and Vilas Distinguished Achievement Professor of the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on data-driven modeling, monitoring, diagnosis, and prognosis for engineering systems with particular emphasis on manufacturing and after-sales service systems. He has established methods for modeling, analysis, and control of Internet-of-Things (IoT) enabled smart and connected systems, variation modeling, analysis, and reduction for complex manufacturing processes, and process control methodologies for emerging nano-manufacturing processes. He is a recipient of CAREER Award from the National Science Foundation and multiple Best Paper Awards. He is a fellow of IISE, ASME, and SME.

Breaking the one-material-per-part paradigm in engineering design

Date: March 6, 2026; Time: 2:30 PM Location: PWEB 175

 

Abstract: Today’s one-material-per-part paradigm leads to vulnerabilities when highly engineered components demand locally optimized properties. Design, manufacturing, and qualification of parts made of functionally-graded materials, on the other hand, are hindered by the lack of reliable methods for on-demand discovery and high-throughput testing of new materials. The conventional test methods, based on decades-old techniques, are slow, costly, and not scalable to graded materials. Moreover, the existing part design tools are unable to explore material design spaces as material selection is often treated as a fixed input. In this talk, I will show how we bridge this divide using a novel material-integrated part design framework, informed by material feasibility, criticality, and performance criteria provided by data-driven materials informatics. To collect data on a range of advanced mechanical properties, we use high-resolution digital image correlation (HR-DIC) and AI-based methods to predict long-term macroscopic behavior (e.g., fatigue and creep properties) from short-term microscopic observables, with a current focus on metallic alloys. Together, these capabilities unlock breaking new grounds in co-design of geometry and materials, qualification for advanced manufacturing, and optimization for supply chain sustainability.

Biographical Sketch: Morad Behandish joined our school as an Associate Professor in Spring 2026. Before returning to academia, he spent several years leading cross-disciplinary research in computational design and digital manufacturing in industry, serving in research manager and director roles at SRI and PARC. Prior to joining PARC, he did a Postdoc on cyber-manufacturing at ICSI of UC Berkeley in 2017 and received a Ph.D. in Mechanical Engineering and a M.Sc. in Computer Science and Engineering from UConn in 2016. His research has been funded by DARPA, DoD, DoE, and commercial partners with a focus on geometric and physical modeling to support design automation, digital materials, and advanced manufacturing. This talk is based on his recent research as a PI on the DARPA METALS program in collaboration with UIUC and UCSD.

AI to Enable Better Designs and Better Designing

Date: January 23, 2026; Time: 2:30 PM Location: PWEB 175

Abstract: AI as a means toward better design became an active area of research in the mid-1980s.  Yet today new understandings of how people design, coupled with new approaches to AI (through agents, optimization and deep learning), and more capable computing technologies have enabled better design tools and a better understanding of how AI can help people themselves design.  This talk will look at how AI can accelerate design as a tool, but also how AI can accelerate human teams in the act of designing.  The talk will examine the role of generative design, digital twins of designers, psychological constructs, and the confidence of humans with AI as a guide to new AI-based design methods.

Biographical Sketch: Jonathan Cagan is the David and Susan Coulter Head of Mechanical Engineering and George Tallman and Florence Barrett Ladd Professor at Carnegie Mellon University, with an appointment in Design.  Cagan also served as Interim Dean of the College of Engineering and Special Advisor to the Provost. Cagan co-founded the Integrated Innovation Institute for interdisciplinary design education at CMU, bringing engineering, design and business together to create new products and services.  Cagan’s research focuses on design automation and methods, problem solving, and medical technologies.  His work merges AI, machine learning, and optimization methods with cognitive- and neuro-science problem solving.  A Fellow of the American Society of Mechanical Engineers, Cagan has been awarded the ASME Design Theory and Methodology, Design Automation, Ruth and Joel Spira Outstanding Design Educator, and Computers and Information in Engineering Lifetime Achievement Awards.  He is also a Fellow of the American Association for the Advancement of Science.  A registered Professional Engineer, Cagan received his PhD from UC Berkeley, and has been on the faculty at CMU since 1990.