University of Texas at Dallas

NSF Awards · FY 2025 · 2025-09

Many solar energy projects are installed in agricultural lands, creating land competition with crops, orchards, vineyards, and pastures. However, relatively little is known about how these solar installations affect surrounding communities, landscapes, and agriculture. Moreover, careful design and siting of these installations can yield a variety of benefits, including increasing farm income, enhancing water resources, improving plant and animal habitat, and enriching soil. To address these issues, this project will bring together an interdisciplinary team of scientists and engineers with agricultural extension specialists, landscape designers, community members, and industry and nonprofit partners. The project will focus on two questions: 1) how is solar energy affecting the landscape and surrounding communities?; and 2) how can the U.S. build a stronger, more productive, and more resilient agroenergy landscape? The project explores practices that will improve outcomes of solar energy in agricultural landscapes. To do so, the project will collect novel data at existing solar facilities and launch a first-of-its-kind scientific research facility to collect data on how solar installations affect agricultural land and communities. Using these data, the research team will study how solar facilities change soil and habitat conditions, the water cycle, crop production, economic returns, and surrounding communities. Throughout the project, an advisory team of farmers, stakeholders, policymakers, and community members will help shape the research and focus the project’s efforts on the needs of farmers, utilities, and the public. This approach will bring together new forms of biogeophysical data collection, modeling, and life cycle assessment with community co-creation. The project’s findings will be used to create decision-support tools, design new solar installations, conduct workforce training, and develop educational workshops and programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Moho is a first-order feature of the Earth, the boundary where the Earth’s crust meets the mantle. Since the lower crust and upper mantle are difficult to directly sample, debate on the composition of the lower continental crust continues. In particular, questions remain on how its composition might vary across a continent and the physical state of the mantle underneath the Moho. Seismic techniques provide an opportunity to study the deep crust by analyzing the world-class seismic datasets that span the United States. The researchers will map this boundary on a continental scale to find new insights into the origin of the continents, the drivers of high topography, and the tectonic history of North America. This project will support a post-doctoral scholar and graduate student who will be trained to apply novel computational approaches for constraining the detailed structure of the crust-to-mantle transition. Comparing and contrasting results from the distinct tectonic domains that make up North America can provide a better understanding of the Moho discontinuity. This project will constrain seismic velocities above, within, and below the Moho transition zone across the continental United States from the joint analysis of converted phases and surface wave velocities. A novel inverse scheme will incorporate the highest frequency data possible to obtain the most detailed model that existing data allows. With this tool, the researchers can investigate how the continental crust is stratified, and how this stratification may or may not relate to age. By pushing the limits of resolution with the relevant data, the team will explore potential processes that may modulate the sharpness of the Moho transition zone and ultimately target the role of variations in lower crustal composition in supporting modern high topography. A key parameter of interest is the shear-wave velocities in the lower crust, which correlates with compositional variations (ranging from quartz-rich to quartz-poor) that control crustal density. The good match between observed shear-wave velocities at some test sites and predicted shear-wave velocities for the Kapuskasing uplift – a site in southeastern Canada with one of the few known exposures of lower crust at the surface – implies more quartz-rich compositions that would be inconsistent with formation directly from mantle melting processes. Constraints on crustal composition can also help explain regions where high topography and crustal thickness are out of balance; first in the Appalachian Mountains where very high shear-wave velocities would be consistent with the hypothesis that metamorphic reactions densify the lower crust and thus drag down the height of the mountains, and then in the Basin and Range, where high elevation appears to require a reduced density for the lower crust. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Most robots today work in factories where people need to program every step of a task. This project explores how to make robots easier to use by helping them learn from watching people. This project uses artificial intelligence using reinforcement and imitation learning approaches. The goal is to teach robots how to see and handle objects by using video demonstrations of humans performing different actions. This is especially helpful with learning to grasp and move objects that have never been seen by the robot before. This way, robots can copy human tasks without needing detailed 3D models of every object they need to touch. If successful, the project could help bring robots into homes, hospitals, and other everyday places. It could also make robots more useful for people without the need for highly technical training. The project will support STEM education by building tools for teaching robotics and by including students in hands-on learning experiences. This project develops a new learning framework that allows robots to understand and imitate object-based manipulation tasks. The research team will design models that enable robots to recognize and segment objects from visual structured memory. Next the robots will learn where and how to grasp objects by observing human contact patterns. The robot will then generate motion plans to manipulate those objects for specific goals. Lastly it will plan sequences of actions to complete multi-step tasks. The robots will use video data of people performing tasks in the real world to train the models. This is helpful as it is replicable and does not rely on physical interaction with a person to train the robot. This approach aims to help robots generalize and adapt to new tasks, environments, and platforms. It combines perception and learning from demonstration, for objects that have not been modeled or seen before. The research contributes to the fields of robot learning, object perception, and manipulation planning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project aims to investigate a newly reported wave mode that interacts with energetic electrons in the Earth’s magnetosphere, potentially driving their precipitation into the atmosphere to aurora activity. This work will explore the excitation mechanism and favored magnetospheric conditions of the Electron Cyclotron Harmonic (ECH) waves and their effects on electron precipitation at the dayside under different geomagnetic conditions. This work will provide knowledge and predictive capabilities of the energetic charged particles in space, which pose a hazard to the space assets that modern society relies on and contribute to the Nation’s security and welfare. The project provides training and support for a new graduate student. The primary objective of the effort is to advance our understanding of excitation, global distribution, and scattering effect on electrons of magnetospheric ECH waves, especially at the dayside. These emissions are believed to be excited by the loss-cone instability of plasma sheet electrons and have been demonstrated to cause energetic electron precipitations that lead to diffuse and pulsating auroras. Recent observations show significant occurrences of ECH waves on the dayside magnetosphere at large L-shell, which provides a great hint of a secondary source region of ECH waves at the dayside. The team will investigate the excitation mechanism and favored magnetospheric conditions of the newly reported dayside ECH waves and their effects in the Geospace environment, which remain open questions. The team will address the following science questions: SQ1) What are the preferred conditions for dayside ECH wave excitation? SQ2) Statistically, is there a secondary source region of ECH waves on the dayside magnetosphere? SQ3) How do those dayside ECH waves affect electron precipitation? The methodology includes (1) analysis of ECH waves and electron data from MMS, Van Allen Probes, THEMIS, and Cluster, (2) instability analysis to examine the ECH wave excitation, and (3) quasi-linear diffusion theory to examine the physical link between the ECH waves and electron precipitation using conjugation with DMSP observation. Understanding the ECH wave excitation, distribution, and their effect on energetic electron scattering in the magnetosphere will improve the understanding of the ring current and radiation belt dynamics and predict the energetic particle environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Grant Opportunity for Academic Liaison with Industry (GOALI) research project aims to generate new knowledge and simulation capability for vibration problems that can severely limit productivity in the manufacture of metal strip and sheet raw materials. These materials include steel, aluminum, copper, brass and specialty metals, and are amongst the most widely used raw materials in manufactured products. Metal strip and sheet make up major components in cars, commercial/military aircraft, ships, buildings, appliances, kitchenware, medical instruments, computers, food packaging, beverage cans, and numerous other capital & consumer goods. In the last four decades, the design and construction of rolling mills has all but vanished from the US, taking with it the intellectual capital for what was once the second most important domestic industry. At the same time, production of flat-rolled metals has been significantly offshored. As the US revitalizes core manufacturing, this research represents a path to achieving metal rolling technology far superior to foreign competition—by exploiting new simulation techniques to understand, predict, and prevent vibration problems that limit the production speeds of rolling mills. Working collaboratively with metal producers and machine builders, this project could ultimately generate billions in manufacturing revenue while decreasing raw material supply costs to numerous industries. The integrated educational goals are for engineering students to gain exposure to new scientific techniques that help explain and predict vibration, as well as to provide a broad spectrum of students with exciting opportunities for success in manufacturing careers. Cold rolling mills suffer limiting threshold speeds related to what the industry terms as the 3rd octave chatter vibration. This chatter phenomenon restricts production rates and can cause catastrophic mill damage. Due to the complex underlying physics, 3rd octave chatter threshold speeds are difficult to predict for mill design, and during production trials with new alloys or rolling schedules. The scientific goals are thus to understand and predict, via a new physics-based modeling technique: 1) process conditions that unfold and lead to 3rd octave chatter during cold rolling of flat metals on multi-stand tandem mills, and 2) the influential rolling parameter interactions that either reinforce or eliminate chatter, so as to generate fundamental insights into transformative new chatter mitigation techniques, improved rolling mill design, and superior rolling schedule set-ups, all of which could manifest in significantly greater rolling speeds before 3rd octave chatter can occur. If successful, the project will ultimately allow the resurging primary metals industries in the US to realize far greater productivities through superior chatter prevention technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project aims to expand the reach of mobile sensing technologies by using satellite signals, specifically those from global navigation satellite systems (GNSS), as a new source of wireless sensing. Unlike conventional approaches that rely on local infrastructure, such as Wi-Fi or Bluetooth, satellite signals offer truly global coverage, enabling low-cost, infrastructure-free sensing even in remote or rural areas. The project explores how satellite signals can be utilized alone for sensing human activity and other physical behaviors, as well as be combined with other signals of opportunity, if they are available, to improve sensing performance through multi-modal multi-task learning techniques. By advancing new sensing methods that do not depend on high-density networks, this research is expected to enhance the understanding of satellite signal-based sensing systems and enable new sensing applications with broad societal benefits. The project also involves graduate and undergraduate students developing open-source software tools, fostering a future workforce in the field of wireless mobile sensing technology. The project investigates a sensing framework that leverages satellite signals for real-time, mobile wireless sensing. It develops a novel signal model to extract activity-relevant variations from GNSS carrier-phase measurements, supported by multi-stage interference cancellation and signal diversity exploitation. A key component of the project is the design of a data-driven sensing model that integrates cross-modal knowledge from other sensing domains, such as inertial or Wi-Fi signals, through pretraining and multi-task learning. To ensure practical deployment, the project also includes system-level optimization for memory usage, energy efficiency, and processing latency on mobile devices. This integrated research effort advances the foundational theory, algorithm design, and system deployment of satellite signal-based sensing, creating a scalable and efficient solution for applications such as health monitoring, activity recognition, and situational awareness in both connected and infrastructure-sparse environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Zinc-ion batteries offer a promising alternative to lithium-based systems for large-scale or grid-scale energy storage owing to their safety, low cost, and use of abundant materials. However, key challenges such as zinc dendrite growth, electrode degradation, and limited electrolyte stability hinder their widespread application. This project introduces a novel approach to electrolyte design by exploring the effects of molecular crowding, a concept drawn from biological systems, to engineer zinc-ion electrolytes with enhanced performance. By altering the structure and transport behavior of the dissolved zinc ions in the electrolyte using large, non-reactive crowding agents, the research aims to suppress unwanted reactions and improve battery efficiency and longevity. The project will integrate research findings into teaching and outreach, including hands-on training for undergraduate and graduate students, virtual battery lab development using Minecraft for STEM education, and mentoring through programs such as community college partnerships. These efforts will contribute to building a skilled energy workforce while promoting public understanding of electrochemical energy storage. The project will establish a mechanistic understanding of how molecular crowding affects zinc-ion solvation, interfacial behavior, and electrochemical performance in aqueous battery systems. By introducing non-reactive crowding agents into the electrolyte, the research seeks to tune water activity, ion pairing, and interfacial dynamics in order to suppress side reactions and improve battery reversibility and longevity. The project will: (1) investigate how the chemistry and concentration of crowding agents and zinc salts modulate Zn2+ solvation shells, ion association, and transport in bulk electrolytes; (2) study the evolution of interfacial solvation structures and zinc deposition behavior under electrochemical conditions using in-situ Raman, FTIR, and synchrotron-based scattering techniques; and (3) evaluate the electrochemical stability and compatibility of molecularly crowded electrolytes with high-voltage cathodes in full-cell systems. The research integrates spectroscopy, electrochemical testing, synchrotron X-ray scattering (SAXS, PDF), and molecular dynamics simulations to establish composition-structure-property relationships. The resulting knowledge will enable rational electrolyte design for advanced zinc-ion batteries and provide transferable insights applicable to other multivalent and aqueous electrochemical energy storage systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Population density and growth are high in coastal regions in the US and around the globe, resulting in more and more people being affected by weather that spans the land-atmosphere-ocean interface. This award will address processes in the atmosphere near the ocean surface, including turbulence and air-sea interactions that result in sea-spray aerosol production and transport. Turbulence and sea-spray aerosol are important for air quality, storm preparedness, safeguarding and restoration planning of near-shore ecosystems, ship operations, national defense, and offshore renewable energies. As an NSF Mid-Career Advancement award, it will also allow for the lead researcher to expand scientific horizons to become well-versed in coastal meteorology and physical oceanography. This NSF award is intended to fill a knowledge gap in understanding and modeling of Marine Atmospheric Boundary Layer (MABL) turbulence and Sea Spray Aerosol (SSA) transport in coastal regions. The project will quantify limitations in current techniques that are not optimized for coastal environments, identify interactions between waves and atmospheric turbulence, and develop machine learning models of MABL wind turbulence and SSA transport. To address these goals, the researcher and collaborators will make field observations of wind and aerosols in the vicinity of Martha’s Vineyard, Massachusetts using a scanning Doppler LiDAR and existing meteorological and oceanographic data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project explores new ways to control and stabilize quantum systems that are not in equilibrium, which is an essential step for advancing quantum technologies. One leading technique for realizing new phenomena is periodic driving with external sources, such as lasers, which can be combined with other methods such as optimal control. This research aims to overcome current limitations in cooling and controlling these systems, which are crucial for simulating complex quantum behaviors. Robust control of quantum matter is a fundamental building block of quantum computers, sensors, and devices, with potential applications in energy, superconductivity, and precision measurement. This project tackles a major challenge in quantum simulation: how to reliably prepare and stabilize exotic quantum states that arise when systems are driven out of equilibrium. By leveraging tools like Floquet engineering—where periodic driving creates new effective Hamiltonians—and advanced control techniques, the research aims to overcome current limitations in cooling and manipulating quantum systems such as ultracold atoms and superconducting qubits. The project focuses on three key areas: understanding how topological order emerges during non-equilibrium transitions, developing optimal control strategies in open systems, and managing resonances in periodically driven systems. These efforts promise to deepen our theoretical understanding of quantum dynamics while also offering practical protocols for experimental platforms. The broader impact includes advancing technologies relevant to quantum computing, materials science, and precision measurement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Children’s language development is influenced by the everyday language environments they experience at home. Home language environments are complex and dynamic, often changing in day-to-day stress and caregiver responsivity. This project aims to examine how family stress and cohesion relate to a young child’s language environment and, in turn, their language acquisition. Child language skills have an impact on long-term academic achievement; therefore, understanding the factors that support and challenge children’s language development are important to improving the education, economic competitiveness, and lifelong well-being of children. This project consists of two studies. In the first study, the researchers conduct a secondary analysis of existing data from 100 families with 3- to 5-year-old children. By transcribing and analyzing the language of all family members, researchers can use these data to test how aspects of natural environments, including observed ratings and parent self-report of family stressors and supports, relate to a child’s language environment and language abilities. In the second study, the research team uses an intensive longitudinal design with natural home observations to collect data from 50 multigenerational families. Researchers aim to use the data from both studies to develop a generalizable theory of environmental context influences on children's language exposure and acquisition. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Robotic systems increasingly operate in critical sectors such as manufacturing, healthcare, agriculture, and defense, where security and reliability are essential. These systems combine components from the computational and physical domains that interact closely with one another, creating complex interdependencies and vulnerabilities. Traditional security approaches focus on either the computational or physical domain in isolation, leaving significant gaps in protection. This project aims to address these challenges by creating a comprehensive security framework that models and protects the intricate dependencies between computational and physical components. The project's novelties are in its cross-domain approach to vulnerability analysis, real-time mitigation, and forensic investigation. The project's broader significance and importance are in improving the safety and trustworthiness of robotic systems in high-stakes environments, advancing cybersecurity education, and broadening participation in computing through outreach and hands-on learning. This project develops a cross-domain security framework that systematically mitigates vulnerabilities in robotic systems by integrating advanced modeling, real-time detection, and post-attack analysis. The research consists of three technical thrusts. The first thrust focuses on vulnerability analysis by constructing unified models that capture the interactions between computational and physical components, using static analysis, system identification, and stateful fuzzing to detect cross-domain weaknesses. The second thrust designs real-time attack mitigation methods by leveraging these models for predictive state monitoring, enabling early detection of discrepancies between expected and actual system behavior across both domains, and facilitating recovery from corrupted physical states. The third thrust introduces a post-attack investigation technique that applies deterministic replay and causal inference to trace the root causes of attacks through both computational and physical domains. Together, these efforts contribute to a new generation of tools that enhance the resilience of robotic systems. Results from the project will improve the development and deployment of secure robotic applications, influence cybersecurity education, and foster public trust in robotic technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Shear-wave splitting is a tool that provides information about how the mantle flows. However, this technique is difficult for data collected on the seafloor which is nearly 70% of the Earth’s surface. This project implements a new approach for oceanic datasets. Hypotheses will be tested about what drives mantle flow and how magmas reach volcanoes. An undergraduate student will help with data analysis and the method will be released for wide use. Observations of shear-wave splitting (SWS) provide a first-order constraint on the geodynamics of the lithosphere-asthenosphere system. This project will investigate three science questions. 1) Do the motions of the plates on the Earth’s surface or internal convective forces drive flow in the oceanic asthenosphere? 2) Do mantle plumes drive a radial pattern of flow beneath tectonic plates, or are plumes captured by forces in the lithosphere-asthenosphere system? 3) Does the melt generated beneath mid-ocean ridges get segregated by shear into bands, or does the melt not interact with the background strain field? Addressing these questions at present is made difficult by the challenges involved in making SWS observations with ocean-bottom seismometers. A method is proposed to address two key challenges faced by traditional methods in oceanic environments – complex depth-varying anisotropy and low signal-to-noise ratios. Preliminary results at both the NoMelt site and Galapagos Archipelago show major improvements relative to more traditional approaches. The methodology will be converted to the Julia programming language for use by the community. The methodology implemented by this proposal increases the return from ocean-bottom seismometer experiments - both domestic and international - by increasing the number of datasets that return good SWS results. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This grant supports the next generation of researchers in mechatronics and robotics by providing travel funding for United States-based students to attend the inaugural Joint International Federation of Automatic Control (IFAC) Symposium on Mechatronics and Robotics in Paris, France, 15-18 July 2025. These fields are crucial for the future of advanced manufacturing, autonomous systems, human-robot interaction, semiconductor manufacturing, and many other industries vital to the United States' knowledge economy. This premier international event offers students unparalleled opportunities to present their research, gain exposure to global cutting-edge advancements, and network with leading academic and industry professionals. Given that most IFAC conferences are held overseas, US student participation is often limited. This support will broaden their exposure to international research, enrich their educational experience through specialized workshops, and provide valuable professional development. As the 1st Joint IFAC Symposium on Mechatronics and Robotics, the symposium merges the IFAC Symposium on Mechatronic Systems and the IFAC Symposium on Robotics. The symposium's technical program features a large number of presentations across seven categories, including contributed papers, extended abstracts, tutorial, invited, plenary, and keynote sessions. Topics span intelligent control, advanced manufacturing, biomedical robotics, autonomous systems, human-robot interaction, and many other overlapping areas. Three US-based researchers are among the four plenary speakers, and one of the six keynote speakers is also from the US. Speakers are drawn from academia as well as industry. The project's goal is to enhance student professional development and expose them to international research, thereby addressing current limitations in US student participation in key global forums in mechatronics and robotics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The project will support student travel to participate in the Project Connect program during the 2025 IEEE International Microwave Symposium, which will be held in San Francisco, CA on June 15-20, 2025. The conference is the largest flagship meeting of microwave engineering in IEEE, the world's largest professional society covering multiple fields in electrical and electronics engineering. The conference has a long history started in 1957 and now includes an industry exhibition of several hundred companies developing products or providing services in wireless and semiconductor industries. The week-long conference features plenary and invited talks, paper presentation sessions, interactive poster sessions, workshops, tutorials, panels, student design competitions, and many social networking events. The conference attracts several thousand attendees each year from industry, academia and government research labs to share research findings, foster collaborations, and identify new directions for research and development. The Project Connect program will include a variety of activities to help student participants build their network with other professionals in the technical community. The travel support of this project will provide students many exciting opportunities to learn the state-of-the-art technology advancements and interact with potential mentors in the microwave and wireless technological areas covering a wide range of spectrum from megahertz to terahertz. The project aims to develop the technical interests of the student participants, motivating them to be involved in undergraduate research and to pursue further studies in graduate programs. Through the Project Connect program, the student participants will develop a successful long-term professional career and contribute to the U.S. STEM workforce in the critical areas using advanced microwave and wireless technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

With the support of the Chemical Synthesis Program in the Division of Chemistry, Professor Filippo Romiti at the University of Texas at Dallas is studying the development of new chemical methods and synthesis strategies for the preparation of biologically active heterodimeric natural products. Pivotal to this work is the investigation of powerful and selective transformations that allow direct merger of monomeric natural products for the efficient generation of a wide array of complex heterodimeric bioactive natural products and new unnatural analogs. The targets identified for this program challenge the state-of-the-art in complex molecule synthesis and serve as springboards to design, discover, test, and improve novel chemical processes that can find applications in the preparation of pharmaceuticals, agrochemicals, and materials ultimately benefitting society. Undergraduates, graduate students and post-doctoral researchers rigorously trained through this research in the theory, methods, and strategies of synthesizing complex organic molecules will become future scientists in the chemical, pharmaceutical, biotechnological, and materials fields. Furthermore, this grant will allow to enhance hands-on research experiences for local community college transfer students and high school students. In partnership with Ottem Labs, a non-profit organization, the Romiti group will establish an outreach program for local high school students in order to ignite their interest in chemistry, the art of organic synthesis and science in general. Complex dimeric bisindole alkaloids display superior bioactivity respect to their monomeric constituents; however, their total syntheses are rare. This award will support the synthesis of several natural heterodimeric bisindole alkaloids featuring the development of new chemical transformations. The first project involves the investigation of a novel catalytic chemoselective amide reduction/Friedel-Crafts process as a powerful and efficient method to directly couple monomeric alkaloid units to generate complex heterodimeric alkaloids. The succinct synthesis of kopsoffine and pleiomutine showcases the utility of the method. The second project entails the development of novel approaches for the synthesis of bisleuconothines and angustifonines, two families of complex heterodimeric bisindole alkaloids with promising anti-cancer activity that have never been synthesized to date. The third project tackles the first total synthesis of criophylline through the development of a novel amine to lactam electrochemical oxidation. These studies are expected to advance the state-of-the-art in complex molecule synthesis and enrich the toolbox available to organic chemists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project explores how to add a realistic sense of skin temperature in virtual reality (VR) so that virtual experiences can be more immersive and intuitive. Current VR systems often rely on sight and sound, leaving out how hot or cold a surface or object feels, limiting the ability to simulate real-world settings. This research takes a new approach called “thermal masking” to create a feeling of hot or cold at precise places on the body without having large, high-power devices. This research will make VR systems easier to use, lower power, and more practical for application areas such as education, training, and entertainment. This project will advance science and technology by making VR more realistic and user-friendly. It will enhance national prosperity and welfare by making new and better tools for learning, training, and interactive experiences. To achieve these goals, the project will address critical challenges in the design of VR systems by developing innovative thermal user interfaces that integrate thermal and tactile feedback. The research focuses on three objectives. The first objective is to understand the interplay of temperature and touch sensations to create realistic thermal illusions through thermal masking. The second objective is to advance rendering techniques to generate dynamic and continuous temperature changes in two-dimensional space. The third objective is to optimize actuator placement and system design to balance energy efficiency, performance, and usability. These activities will leverage thermal-tactile integration to achieve precise temperature feedback while minimizing bulk and power requirements. The project will employ experiments to map the spatial and temporal dynamics of thermal masking, develop algorithms for rendering thermal patterns, and validate the interfaces in VR applications. The outcomes are expected to improve a user’s sense of immersion, simplify the engineering of multisensory VR systems, and enable a broader range of applications and interactions in virtual environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project focuses on the development of a new class of chip-scale cooling micro-fans. Effective heat removal (cooling) continues to be a major bottleneck for achieving higher processing power in large scale integrated digital electronics. As electronics like smartphones, laptops, and medical devices get smaller and more powerful, they generate more heat in tighter spaces. This new technology provides powerful, silent, and compact cooling systems, helping devices run better and last longer without overheating. With the growing demand for artificial intelligence and consequently the need for ever-increasing higher processing powers and massive data centers, electronic cooling is also a growing challenge. This solution offers a chip-scale micro-fan that can be mounted directly on high performing electronic components, blowing a high velocity air stream onto the hot surface to provide a more efficient, effective, and compact solution. Successful commercialization of this technology may contribute to the growth of advanced chip manufacturing and data center capacity in the United States. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a tiny device, a chip-scale actuator, capable of producing up to ten-times more mechanical energy in the same amount of space compared to existing lead-based devices known as piezoelectric transducers. The technology works by using advanced three-dimensional silicon micro-manufacturing techniques to increase the surface area inside the silicon chip, which helps it create more energy. These devices can make tiny parts vibrate very fast: microscale cantilevers that vibrate at ultrasonic frequencies that humans can't hear, to power small air-blowers to become cooling micro-fans. This technology is important because it can cool small electronic devices more efficiently and in a much smaller space than current technology allows. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Being able to predict behaviors of other people is important to successfully navigate the social world. Prior research shows that one major way to achieve this is by learning about other people’s personality traits. This project explores how individuals learn not only about others’ personality traits but also about the situations that inform people’s actions, and in turn use situational learning in decision making. Impacts of this project include research training opportunities for graduate and undergraduate students, and dissemination of findings to the public. This project aims to characterize how individuals learn about situations and explore how different life experiences influence this process. The research team leverages behavioral methods, advanced neuroimaging techniques and computational modeling to (1) identify the neural and cognitive mechanisms involved in situational and trait learning and to (2) examine the role that situational and trait learning play in reasoning and decision-making across different contexts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The impact of this I-Corps project is based on the lab to market translation of a wireless neuromodulation system used in implantable medical devices. This novel system eliminates the need for a battery by using wireless power to activate the device only when needed, minimizing the need for post-implantation interventions. This solution addresses the current need for an on-board battery and/or frequent battery changes. This approach enables the development of smaller, more efficient devices focused on data acquisition and transmission, while also minimizing the size and complexity of the power-receiving components. The implantable medical device market is expected to grow significantly, with over 3 million potential patients in the U.S. by the end of the decade. This technology could benefit this patient population by improving the efficiency, safety, and longevity of implantable devices. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of phased-array-based simultaneous tracking, communication, and powering of an optogenetic neuromodulation implant. This implant can inhibit or excite specific genetically modified neurons using specific wavelengths of light emitted through micro light emitting diodes (LEDs). This new solution consists of a small, highly efficient, multiband antenna as a receiver (implant) antenna, an active phased-array transmitter, and a rectifier to convert the radiofrequency signal into a direct current (DC) signal. By enabling safer, battery-free medical implants, the technology reduces the need for invasive procedures, leading to better patient outcomes and lower healthcare costs. The miniaturization of the implantable devices paves the way for more accessible and portable medical solutions, fostering innovation in wearable health monitoring and personalized medicine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

The widespread proliferation of computing devices embedded into everyday products has ushered in an era of ubiquitous production and dissemination of malware, computer software that has the intent to cause damage. Traditional antivirus systems to protect against malware are ineffective due to their low accuracy in identifying modern, sophisticated malware. Additionally, antivirus software incurs high overhead on resource-constrained embedded platforms. This has propelled the development of hardware-assisted malware detectors, which use the trusted underlying hardware to help detect malware. However, detection based on hardware performance counters faces several inherent pitfalls, such as high false positives in malware detection. This research proposes an end-to-end framework for developing, analyzing, and securing fine-grained design-for-security primitives, which can be incorporated into the embedded hardware. This research enhances the effectiveness of hardware-assisted security solutions, leading to lightweight and robust design-for-security primitives for resource-constrained embedded devices utilized in applications such as automotive, medical, and military. The educational plan will enhance courses at both undergraduate and graduate levels by introducing hands-on experiences in hardware security. An educational game will be designed to improve K-12 students’ understanding of malware. The project develops security-aware design principles for next-generation embedded hardware, which includes meticulously crafted design-for-security primitives comprising instruction sequences and debug-level register information. The new algorithmic approaches for trace analysis utilize time-series and explainability-based classification to improve malware detection performance. This research also investigates methods to secures the fine-grained design-for-security primitives against adversarial and snooping attacks by developing novel defense strategies based on theoretical foundations. Research findings are being integrated into undergraduate and graduate course materials, embedded hardware design camps for high-school students, and an educational prototype game to ignite the passions of future investigative minds in embedded hardware security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

This Partnerships for Innovation - Technology Translation (PFI-TT) project focuses on dental restoration technologies through the advancement of 3D printing techniques specifically tailored for zirconia ceramics. Zirconia is a material that is well-regarded for its durability and biocompatibility. By enhancing the adhesion properties of zirconia used in dental crowns, this project addresses a critical challenge in dental restorations: the longevity and reliability of crowns within the oral environment. This innovation has the potential to improve patient outcomes by reducing the frequency of crown replacements due to better initial fit and stronger bonding with the natural tooth structure. Additionally, the project supports the dental industry's shift towards more personalized and rapidly produced dental solutions, thereby reducing overall healthcare costs and improving patient care. The commercial impact of this project includes the creation of efficient production capabilities in dental laboratories, improved quality and accessibility of dental care, and the adoption of advanced manufacturing technologies within the healthcare sector. This project will advance the manufacturing of 3D printed dental crowns made from yttria-stabilized zirconia (YSZ). The primary research objectives include developing innovative methods to enhance the adhesion of 3D printed zirconia to dental cements, a longstanding issue due to zirconia’s smooth and non-etchable surface, a surface that traditionally resists effective bonding techniques. To address this, the team will integrate customized micro-patterns and micro-porosity on the inner surfaces of the crowns using sophisticated 3D printing technologies. The solution is aimed at improving mechanical interlocking and thus, the overall retention and durability of dental crowns. Additionally, the project may reduce production time by integrating a newly developed ultra-fast de-binding and sintering technology that utilizes advanced heating mechanisms to accelerate time-consuming stages. This approach will dramatically enhance the efficiency of manufacturing processes, reducing the overall turnaround time for creating dental restorations. The research will leverage advanced digital design tools and a stereolithographic 3D printing technology to create precise and reproducible textural enhancements on zirconia surfaces. Anticipated technical results include validated improvements in crown adhesion strength and a reduction of de-binding and sintering times compared to traditional methods, without compromising the quality of the final product. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

One of the interesting features of the Earth’s ionospheric F-region is the Equatorial Ionization Anomaly (EIA), which is characterized by low electron densities over the geomagnetic equator, while enhancements on either side of the equator. The presence and variability of the EIA significantly influences radio communication and navigation/positioning at low and middle latitudes, which are crucial to public safety and national security. Importantly, the EIA peaks often exhibit pronounced interhemispheric asymmetry (IHA), which can vary significantly over solar cycles or on a day-to-day basis. However, understanding the characteristics, formation mechanisms, and potential space weather effects of the variations in the IHA of the EIA across different timescales remains a poorly understood area. Addressing this knowledge gap is the primary goal of this project, aiming to enhance the predictions of the EIA variation and mitigations of the impacts on global communication and navigation systems. The project provides vital support and training for an early career researcher. Additionally, the research project will also serve as a cornerstone for the thesis work of a physics graduate student. This project aims to (a) to explore the variability of the IHA of the EIA in the American Sector across different solar cycles and elucidate its physical mechanisms, and (b) unravel the mechanisms driving the significant IHA of the post-sunset EIA in the American Sector and its correlation with scintillation activity during SSW events The methodology involves analyzing total electron content (TEC) data obtained from ground-based GNSS receivers distributed across the American Sector to examine the IHA's variability over multiple solar cycles. Additionally, the team will investigate the influence of lower atmospheric forcing on the IHA variations using NCAR TIEGCM simulations. The latter will be employed to investigate the formation of the significant IHA in the post- sunset EIA, while the GNSS measurements will be utilized to study the relationship between post-sunset IHA and scintillation occurrences. This project will significantly advance our understanding of the variability of the IHA of the EIA in the American Sector across different solar cycles. Moreover, it will shed light on the impact of the lower atmospheric forcings on the IHA of the EIA and its variability over solar cycles. Additionally, the study will provide valuable insights into the formation of the significant IHA of the post-sunset EIA and its linkage to the L-band scintillations during SSW events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Global Navigation Satellite Systems (GNSS) such as the US Global Positioning System are satellite constellations that provide positioning, navigation, and timing services on a global or regional basis. They play an even-increasing role in the modern economy but at the same time are commonly used for remote sensing of the near-Earth plasma environment. This is because disturbances in the total electron content (TEC) can be detected by ground-based GNSS receivers and imaged by dense GNSS receiver networks. GNSS signals are also used to study ionospheric effects of small-scale structures in the upper atmosphere since they cause rapid variations in the signal phase and/or amplitude called scintillation. This project will create an advanced, yet low-cost ionospheric scintillation and TEC monitor with several capabilities of relevance to geospace studies that require observations by a distributed array of small instruments. The measurements will have resolutions that will be comparable to those provided by state-of-the-art commercial scintillation and TEC monitors but be obtained with a monitor that will cost only a fraction of the commercial price. New monitors will improve imaging capabilities when arrayed in dense networks, which will help to advance understanding of variabilities associated with events occurring on the Earth’s surface and at lower atmospheric altitudes, including those related to volcanic eruptions, thunderstorms, tornadoes, and tsunamis. The effort will be also centered around education and training of the workforce in the fields of science, technology, engineering, and mathematics (STEM) with emphasis on development and deployment of instrumentation. Additionally, the effort will create engaging and interactive exhibits that will increase literacy about scientific and technological aspects of the geospace environment. This project is funded by the Geospace Facilities program with co-funding from the Aeronomy program in the Division of Atmospheric and Geospace Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project involves the study of approximation theory in the setting of complex functions, with applications to complex dynamics. Approximation theory seeks to understand the extent to which the behavior of a general function can be effectively modeled by that of functions drawn from a more restricted class. Efficient approximation of functions is of relevance for numerical calculation. Since the only calculations that can be carried out numerically are the elementary operations of addition, subtraction, multiplication, and division, in practical terms it is of importance to understand when the values of general functions are well approximated by the values of either polynomial or rational functions. In many situations, the values of the approximant resemble those of the general function only for a sampling of input values. What can be said about values of the approximant for other choices of input? This is the main question studied in this project, with the following application in mind: when a general function is iterated to produce a dynamical system, to what extent does the dynamical behavior of an approximant resemble the dynamical behavior of the original function? The project will also contribute to the development of human resources through educational outreach at the high school level as well as mentoring and training at the undergraduate and graduate levels, and will facilitate the interaction of different fields of mathematics through the organization of conferences and seminars. The Principal Investigator will study the approximation of analytic functions in one complex variable by polynomials, rational functions, and transcendental entire functions. Quasiconformal mappings will be a major tool in this study. The quasiconformal approach to this particular subject is largely unexplored, and affords control over geometric properties of the approximants such as location of critical points and critical values. Such control can be used to understand the intricate dynamics recently proven to exist for transcendental entire functions. Another anticipated application lies in an improved understanding of the geometries of lemniscates (level sets of polynomials or rational functions) which relate to the emerging field of pattern recognition as a tool to distinguish between different planar shapes. The PI also will investigate whether a better understanding of the geometry of polynomial or rational approximants yields new insight on the numerical implementation of root-finding algorithms which rely essentially on such approximants. Interactions between the different fields alluded to above (approximation theory, geometric function theory, complex dynamics, and numerical analysis) will be fostered via the organization of conferences, meetings, and seminars. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

With the support of the Future of Semiconductors (FuSe) Program, Professors Julia Hsu, Cormac Toher, and Kevin Brenner of The University of Texas at Dallas, Professor Howard Katz of Johns Hopkins University, and Professor Chih-Hao Chang of The University of Texas at Austin will establish a groundbreaking framework to develop new materials for advanced computer chips. This project will use indium-based materials to co-design two key aspects of chip manufacturing: materials used to create tiny chip features, and the transistors (miniature switches) on these chips that enable them to do computing. The new materials will be designed for extreme-ultraviolet (EUV) patterning, to produce smaller, more precise features on chips, leading to better performance and energy efficiency. Additionally, a novel low-temperature method will be used to convert these features into high-performance transistors, potentially reducing production costs and environmental impact. The project will also include a workforce development program to train community college students for careers in the semiconductor industry, addressing the growing need for skilled technicians in North Texas. Collaborations with industry partners will provide students with hands-on experience and career opportunities in this crucial field. The research objectives will include: (1) Designing novel indium-based EUV resists using computational and machine learning tools to optimize both material and device properties, (2) synthesizing and characterizing precursors for indium oxide transistors, and assessing their performance in film patterning and transistor fabrication, (3) evaluating the resolution, line edge roughness, and sensitivity of the resist films, and (4) creating high-performance indium oxide transistors using photonically cured EUV-patterned resists and measuring their electrical performance. The co-designed materials will leverage the high EUV absorption cross-section of indium, and the potential high performance of indium oxide transistors, increasing material economy and device performance and reducing processing steps and waste. The project will investigate hypotheses such as whether pre-complexed fuels can increase indium density for more sensitive resists, if lower-nitrate indium precursors can reduce feature roughness and environmental impact, and whether incorporating visible-light-absorbing chromophores can enhance photonic annealing for better transistor performance. Additionally, machine learning will be employed to drive the co-design of EUV resists and back-end-of-the-line devices, testing the fundamental limitations of sol-gel processing in material scalability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.