Iowa State University

NSF Awards · FY 2025 · 2025-03

A crucifer plant Arabidopsis thaliana, also known as mustard weed, is a reference plant organism studied by thousands of researchers worldwide. Knowledge obtained from Arabidopsis research usually can be applied to crop species, to rationally improve agricultural traits. The community of Arabidopsis researchers operates on the principles of sharing data, resources, tools, and techniques. The International Conferences on Arabidopsis Research (ICARs) are the main, highly effective venue for enhancing information exchange, creating new networks especially for young scientists, and establishing new collaborations. This award will support the travel of the US-based young scientists to the ICAR held in June 2025 in Ghent (Belgium). The majority of support will go to students, postdoctoral scholars, and beginning investigators. The program includes organized activities prior, during, and following the meeting to ensure that the supported scientists have ample opportunities to network with each other, and others at the conference, and to that their professional objectives are met. Altogether this travel award will support a sustainable pipeline of skilled scientists for the U.S. bioeconomy. A key factor in the synergistic interactions among Arabidopsis research labs has been the opportunity to meet and share research with colleagues from around the world. ICAR 2025 will cover a broad range of important and current topics in 21 platform talks by scientific leaders in sessions on genetics and genomics, genome editing, and quantitative biology. The program includes 26 community-proposed sessions on diverse topics such as predictive modeling to understand genotype-phenotype relationships, epigenetics, RNA modifications, quantitative imaging, omics approaches, single cell biology, gene editing, and biological discovery using artificial intelligence (AI) and predictive modeling. Early career researchers will be emphasized for oral presentations selections. 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.

NIH Research Projects · FY 2026 · 2025-02

PROJECT SUMMARY Malaria has had devastating impacts on human health and economic development throughout the world. Caused by Plasmodium parasites, the transmission of malaria requires the bite of an infected Anopheles mosquito. This essential requirement of the mosquito vector to spread disease also creates unique opportunities to interrupt malaria transmission, where the mosquito immune system is an important determinant of vector competence. For this reason, the mosquito immune system has garnered significant attention to better understand the immune mechanisms that limit malaria parasites, with the goal that this knowledge can be applied to strategies to interrupt the malaria life cycle in its intrinsic mosquito host. Towards this goal, considerable research efforts have increased our understanding of the mosquito immune system, yet despite these advances, the immune mechanisms that promote Plasmodium recognition and killing in the mosquito host remain poorly understood. Herein, we describe novel roles of mosquito extracellular vesicles (EVs) in mosquito immune function, providing evidence that mosquito EVs have essential roles in the complement-mediated recognition of Plasmodium ookinetes (Aim 1) and the activation/priming of mosquito immune cells (hemocytes) (Aim 2). Therefore, the overall objective of this application is to obtain a mechanistic understanding of EV function in the context of the mosquito immune system by defining the roles of EVs in malaria parasite killing and immune cell activation/priming. Our outlined experiments will examine the influence of distinct EV (exosome or microvesicle) biogenesis pathways, define the molecular contents of EVs, and perform single-cell experiments to respectively delineate the mechanisms by which EVs promote pathogen recognition and cellular immune function in mosquitoes. The expected outcomes of this proposal will provide fundamental new insights into the previously unexplored roles of EVs in pathogen recognition and immune cell activation in mosquitoes, such that our results will have broad impacts on the study of innate immunity and host-pathogen interactions across invertebrate systems that may inform novel strategies to manipulate mosquito vector competence.

NSF Awards · FY 2025 · 2025-02

Quantum computing, while incredibly powerful, still faces significant challenges with current technology, such as limited memory, short data lifespans, and errors. To tackle these issues, researchers use a method called circuit cutting, which breaks down large, complex tasks into smaller, more manageable pieces that can be solved independently. However, once these smaller tasks are solved, recombining their solutions into a final answer is not straightforward. It requires sophisticated methods to ensure the combined solution is accurate and efficient, especially for tasks in quantum-based machine learning. This project aims to explore and improve these recombination techniques, ultimately paving the way for more reliable and effective quantum computing. Such advancements could have a transformative impact on fields like artificial intelligence, drug discovery, and secure communication, while also preparing the next generation of researchers to tackle these cutting-edge challenges. Considering the Noisy Intermediate-Scale Quantum (NISQ) devices, the project will investigate the impact of circuit cutting techniques on variational quantum algorithms (VQAs) and quantum machine learning (QML). By investigating the role of information entropy and quantum entanglement, optimizing sub-circuit recombination, and developing advanced cutting techniques, the research aims to minimize sampling overhead while maintaining fidelity. These efforts seek to benchmark circuit cutting's impact on accuracy and efficiency, advancing quantum computational frameworks and enabling larger, more robust computations tailored to QML applications. 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

The broader impact of this I-Corps project is based on the development of innovative metal inks for high-resolution printing used in the additive manufacturing process that is used to produce electronic devices. Emerging printing methods like electrohydrodynamic (EHD) printing require a new generation of functional inks, and this technology fills that need with stable, high-resolution inks that can enhance the performance of electronic devices. These inks also make it possible to produce smaller, lighter parts, reducing the quantity of materials and energy needed. This advance could also benefit industries such as automotive, biomedical, smart packaging, and touch displays. 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. The technology is an innovative method to produce metal nano-inks for the manufacturing of electronic devices. These metal inks are designed for high-precision printing techniques, like electrohydrodynamic (EHD) and aerosol jet printing. The process involves an in-situ capping process to synthesize ultra-stable nanoparticle dispersions with a wide range of particle sizes, morphologies and materials. This highly tunable process meets the ink requirements of various printer systems, including EHD inkjet printing and aerosol jet printing. The nanoinks address the limitations of existing commercial products and have been successfully tested for EHD printing. These inks provide over 10 times the resolution of existing products, allowing for the creation of smaller, more compact electronic devices. 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.

NIH Research Projects · FY 2026 · 2025-02

Project Summary/Abstract Natural products have a proven record of providing a significant fraction, either directly or as lead compounds, of human medicines. Among natural products, the terpenoids (isoprenoids) stand out as being the largest class (>80,000 already known), with the labdane-related diterpenoids (LRDs) that are the focus of our studies forming a significant fraction of these (>7,000 known). Notably LRD biosynthesis further models that of all terpenoids more generally, as the relevant diterpene cyclases and diterpene synthases are phylogenetically and mechanistically related to the oxido-squalene cyclases and terpene synthases from triterpenoid and smaller (mono-, sesqui- and other di-) terpenoid biosynthesis (respectively). More concretely, the extensive diversification of LRDs indicates that the manifold hydrocarbon skeletons that can be formed around the characteristic decalin core ring provides privileged scaffolds for derivation of biological activity. Indeed, a number of these LRDs are used as pharmaceuticals (e.g., the novel mutilin antibiotics derived from pleuromutilin) or are being investigated for such use (e.g., the tanshinones), while others serve important roles in widely grown crops (e.g., rice), such that engineering their biosynthesis may enable reduced agrochemicals application and, hence, improve food and environmental safety. Accordingly, we propose here to continue our productive studies of the enzymes required to produce LRDs. Specifically, we will build on our previous work in this area, which includes investigations of enzymatic structure-function that have provided novel biosynthetic access to an array of LRD backbones, such as those relevant to production of pleuromutilin, as well as further elucidation of the complex LRD metabolism in rice, which serves as a model system for the extensive diversification of these natural products throughout cereal crop plants more generally, and which we have shown play key roles as antibiotics against microbial pathogens as well as parasitic nematodes. This MIRA renewal proposal covers our systematic studies in this general area, advancing our long-term goal of engineering enzymes and metabolic pathways for production of targeted libraries and specific individual LRD ‘natural’ products, several of which are already in medical use and the utility of which in crop engineering for improved environmental and, hence, human health is already being realized.

NSF Awards · FY 2025 · 2025-02

The research experiences for undergraduates in the translational application of artificial intelligence to engineering (REU-TRACE) site will create novel research experiences for a talented and diverse group of undergraduate students from traditionally underrepresented populations with limited access to STEM research opportunities. Advances in Artificial Intelligence (AI) are helping engineers and scientists address fundamental questions of monitoring, understanding, and achieving the desired outcomes in human and animal health systems, agricultural productivity, and advanced manufacturing. To develop pathways for students with advanced technological skills, the ten-week research experience will focus on applying artificial intelligence principles, techniques, and technology to problems in 1) Design and Manufacturing, 2) Smart Infrastructure, 3) Biology, and 4) Sustainable Agriculture. The students will conduct research on AI applications to digital twins for advanced manufacturing, design of energy harvesting devices, prognostics of battery reliability, prediction of 3D protein structures, biomechanical characterization, manipulators for monitoring individual plants and others. The experience of working in an intensive research environment, where students will progress from dependent to independent contributors, will enable them to leave the program with life-long learning skills that will impact their contributions to science, engineering, and society. Over a three-year period, this REU Site program will engage ten undergraduate students per year in a 10-week intensive, summer research experience. The students will be active members of multidisciplinary groups and will interact with a diverse faculty team with broad research interests and an outstanding record of mentoring undergraduate students. The key components of this REU program include 1) well-defined research projects executed by students under the mentorship of faculty members and graduate students; 2) participation in cohort experiences such as programming bootcamp, joint seminars/meetings, workshops, and research facilities tours; 3) development of communication skills through technical reports and presentations; 4) professional development activities that include 'lunch and learns', short courses and communication workshops; and 5) an REU Research Symposium to provide feedback and recognize exceptional achievement. Students will also undergo training in the ethical implications of engineering conducted by Iowa State University's Bioethics program. A REU-TRACE alumni LinkedIn page will ensure that the participants have access to a built-in professional network that can help them to decide how to apply their AI research experience in their careers. This project is jointly funded by the Office of Advanced Cyberinfrastructure and the Established Program to Stimulate Competitive Research (EPSCoR). 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 award funds participation in two 2-day algebra conferences to take place on the campus of Iowa State University in Ames, Iowa in 2025 (March 1-2) and 2026 (dates to be determined). The CA+ conference series was held six times previously on the campuses of the host institutions: the University of Minnesota, the University of Wisconsin, and Iowa State University. The format of the conference series allows for 50 minute talks by 7 experts in commutative algebra and related fields, but also structured professional development sessions and short talks by graduate students. While many speakers and participants will come from the upper Midwest, the organizers will strive to gather a diverse audience, both mathematically and demographically. The CA+ Conference series was conceived to build a community of researchers in commutative algebra and to forge connections with mathematicians in related fields, such as combinatorics, algebraic geometry, and topology. This is partly achieved by inviting speakers from these fields and allowing time for them to interact with students and postdocs in the region. The next two meetings will continue the practice of matching speakers with students during lunch to discuss career opportunities and research ideas. A new aspect of these meetings will be a follow-up discussion in which the groups can share with all participants the best advice from the small group discussions. Information is available on our conference website: https://www-users.cse.umn.edu/~cberkesc/CA/CA.html 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.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Prostaglandins are important bioactive signaling lipids with key roles in the regulation of inflammation and innate immune function in both vertebrate and invertebrate systems. Derived from arachidonic acid, prostaglandins mediate conserved physiological responses in response to pathogen-associated signals that stimulate the activation, maturation, and migration of innate immune cells, conferring immune protection to infection. This includes mosquitoes, where prostaglandins have previously described roles in anti-pathogen immunity, immune cell function, and in mediating immune priming. Yet, despite the integral roles of prostaglandins on mosquito immunity, studies of prostaglandins have been limited, leaving significant questions as to how prostaglandins influence the mosquito innate immune system unanswered. Therefore, the overall objective of this application is to examine the contributions of prostaglandin E2 (PGE2) on cellular and humoral immunity in the mosquito, An. gambiae. Based on preliminary and previously published studies demonstrating that PGE signaling is an important mediator of cellular immune function, immune priming, and in limiting pathogen survival, here we outline experiments to address the cellular and transcriptional events following PGE2 activation using innovative single-cell and phenomics-based methodologies (Aim 1); the mechanisms of PGE2-mediated immune priming through recently developed genetic tools to promote tissue-specific silencing (Aim 2), and the potential of PGE2 dysregulation to influence pathogen infection outcomes and mosquito fitness by targeting enzymes involved in PGE2 degradation (Aim 3). Therefore, we expect that the outcomes of our proposal will provide an important fundamental insight into the mechanisms of PGE2 regulation, immune cell dynamics, and dysregulation of PGE2-mediated immunity in an emerging model system. We believe these data provide an essential foundation for future studies of mosquito PGE2 signaling and a valuable platform for comparative studies across invertebrate and vertebrate systems. Importantly, the expected outcomes of our proposed experiments also have tremendous potential for translation, where PGE2 dysregulation could serve as a novel target for mosquito control to reduce the heavy burdens of mosquito-borne disease.

NSF Awards · FY 2025 · 2025-01

The extraordinary advances of Artificial Intelligence (AI) have been demonstrated in various aspects of our lives, from healthcare to transportation. However, the ever-growing capability of AI is accompanied by an exponential increase in computational complexity, leading to unprecedented energy consumption and ever-growing demand for computing/memory resources. As a result, developing efficient and scalable intelligent hardware systems is critical for (1) addressing the environmental and sustainability implications of AI computation and (2) enabling broad adoption of AI to improve the productivity and economic competitiveness of our society. One promising direction for developing efficient AI computing systems is to exploit stochasticity (randomness) to process the various statistical learning models. However, the hardware implementation of truly-random functionality on standard complementary metal-oxide-semiconductor (CMOS) components lacks efficiency due to significant area and power overheads. Moreover, the conventional approach of incorporating stochasticity for brain-inspired computing focuses on cell-level emulations of neurons and synapses, facing significant challenges in scalability. In this project, a novel system architecture with emerging devices/circuits will be developed to create intelligent computing systems based on ensembles of stochastic processing elements. The knowledge on designing scalable and robust AI computing systems created from this project will be disseminated to a broader community of our society through a multi-level portfolio of educational efforts. Such educational efforts are designed to advance AI literacy among a population with diverse backgrounds and cultivate a strong future workforce with cross-disciplinary expertise in AI and microelectronics. To accelerate various critical AI operations from matrix-vector multiplications to softmax in the attention layer, device-architecture-system cross-layer co-optimization will be conducted to integrate spintronics with intrinsic stochasticity and deterministic silicon components into energy-efficient non-von Neumann computing systems. Next, ensemble-based hardware-aware computational models will be developed to enable such novel hardware fabrics for large-scale learning tasks. Furthermore, this project will design a unified and versatile hardware platform that supports a broad range of AI applications, including deep neural networks and emerging neuro-symbolic models. Such development will tackle the long-standing challenges of scalability and flexibility associated with stochastic computation and create a new venue for developing next-generation versatile intelligent computing systems. This project is jointly funded by the Software and Hardware Foundations (SHF) program in the Computing and Communication Foundations (CCF) division and the Established Program to Stimulate Competitive Research (EPSCoR). 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

The research objective of this CiviL Infrastructure research for climate change Mitigation and Adaptation (CLIMA) project is to integrate low-energy mechanisms into the designs of structural components, enabling them to adapt their shapes and configurations with minimal energy input. This project aligns with NSF’s mission of promoting the progress of science and advancing the national health, prosperity and welfare by creating new knowledge in energy-efficient and adaptable structures and design methodologies, and by enabling more resilient infrastructure against environmental challenges. The research will establish a firm analytical foundation that enables: (a) the integration of multi-stable systems in engineering designs; (b) the establishment of integrative design methodologies based on additive manufacturing and generative design; and (c) the combination of architectural and performance-based design that will empower system-level design and deployment of adaptive systems. The theoretical innovations will be realized by creating adaptive building enclosures that can change their shapes as necessary to improve building energy performance. Potential applications include structural systems that can adapt to changes in the environment, mechanical systems that can be rapidly deployed, and medical devices that can be modulated to accelerate healing. This project also includes the development of educational methods to train the next generation of engineers and architects interested in the broad concept of adaptive structural systems. The overarching research goal of this CLIMA project is to establish a strong foundation for the design and implementation of multi-stable components in structural and mechanical systems in order to achieve low-energy geometric modulation. The research draws upon architectural and structural design, additive manufacturing, and generative design theory to produce a new realm of adaptive capabilities through multi-stable systems. The research is innovative in its study of (a) snap-through mechanisms that enable adaptive structural functions attuned to various environmental configurations; (b) the integration of additive manufacturing and generative design to empower the efficient fabrication of components capable of high geometric and structural performance; and (c) system-level implementation of adaptive capabilities through design simulations of optimized building enclosure geometry with multi-stable connections and laboratory demonstrations. Results from this research will empower new engineering systems that can be modulated by altering stable states, thus requiring no energy to maintain the system in a given position. The research has broad societal impacts by paving a path to new structural system concepts in both existing and new structures to produce new geometric adaptation capabilities, and by focusing on the climate change mitigation and adaptation theme of reducing raw materials, maximizing utilization of materials, efficient manufacturability, and improving sustainability and resilience of buildings. The comprehensive education plan involves the integration of undergraduate students in research, development of curriculum, and creation of pre-kindergarten through high school educational materials and resources within the area of adaptive structures. 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

This project will establish an SFS program at Iowa State University (ISU) to address the national need for a well-trained cybersecurity workforce, particularly in critical infrastructure protection. This initiative will prepare students to secure essential systems, such as energy grids and telecommunications networks, that underpin the nation's safety and economy. Graduates will enhance national security, address emerging threats, advance the cybersecurity field, and bolster the federal cybersecurity workforce. The project will recruit CyberCorps scholars, strengthen community engagement through cybersecurity education and training, and build partnerships across academic, public, and private sectors. The program's comprehensive approach ensures graduates are technically adept, leadership-ready, and capable of addressing the sophisticated challenges of securing critical infrastructure. This project builds on ISU’s longstanding cybersecurity education and research expertise, focusing on critical infrastructure security. The project will produce a cohort of skilled cybersecurity professionals in B.S. and M.S. programs through comprehensive education, hands-on experiential learning, and rigorous mentorship. Students will engage in research and real-world applications using state-of-the-art cybersecurity testbeds, gaining expertise in artificial intelligence, network security, and cyber-physical systems. The project team closely works with state and federal government agencies as well as national labs. Students will undertake thesis projects in critical and emerging areas such as AI applications in cybersecurity and critical infrastructure protection, and contribute to cybersecurity research. This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal Government organization for the same duration as their scholarship support. 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

Liquid-phase printing technologies can create complex parts using computer-controlled deposition of materials in the form of inks or pastes. By creating such inks or pastes with electronic materials, these technologies can fabricate functional devices, such as sensors, antennas, and circuits. Silver is a common material for conductive inks, because it is resistant to oxidation. The ability to print other materials, including air-sensitive metals, is desired to broaden the range of functionality for printed circuits. This award supports fundamental research to understand ink development and processing for air-sensitive materials, with a specific focus on materials for electronics in harsh environments, such as high and low temperatures. This collaboration with NASA researchers will study fundamental challenges in the development of inks based on air-sensitive materials, how particles can be fused together following printing to create cohesive wires, and the reliability of the resulting circuits under adverse conditions. Electronic systems that can function in extreme environments are highly relevant for energy, infrastructure, aerospace, and space applications, and this research thus contributes to advancing national prosperity and security. Related activities to mentor undergraduate researchers and support outreach to K-12 students will also support workforce development and diversification within engineering. The objective of this project is to develop material-process-structure-property relationships for additively manufactured electronics in extreme environments, emphasizing fabrication and reliability physics of low redox-potential conductors. The low-volume, high-mix nature of printed electronics aligns with diverse needs for electronics compatible with harsh environments, as exemplified by space electronics. Iowa State and NASA Marshall Space Flight Center have complementary capabilities for printing of air-sensitive metals and alloys for electronic devices and circuits, and this project will leverage NASA expertise in space electronics and reliability testing. The research activities comprising this project are structured into three objectives focused on printing process fundamentals, material sintering, and characterization for reliability, respectively. These efforts will use nickel alloy nanoparticles as a case study, particularly constantan (CuNi) and invar (FeNi) for their unique thermal properties – reduced sensitivity of electrical properties to temperature changes and low temperature thermocouple utility for constantan, and a small coefficient of thermal expansion for invar. Printing capabilities under inert atmosphere will support processing of these materials in nanoparticle form via aerosol jet and extrusion printing. Sintering will be a critical element of the research, with efforts to compare thermal and photonic sintering to better understand effects of sintering strategies and parameters on material microstructure, and ultimately on the resulting functional properties. Direct research efforts will be guided by adverse environments of interest to NASA, particularly low-temperature operation and stability to thermal cycling, but the broader knowledge and strategies will also inform high temperature printed electronics. 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 · 2024-12

The Q-BLUE project will support a three-pronged effort to significantly advance quantum computing: (1) analog quantum hardware development, (2) an optimized compilation process, and (3) advances in three applications – quantum chemistry (QC), condensed matter physics (CMP) and nuclear physics (NP). Each of these applications will reap significant benefits from quantum computing. In quantum chermistry time-dependent quantum simulations will enable the exploration of complex molecular dynamics, ushering in a revolution in drug discovery and material design. In nuclear physics, a wealth of unprecedented insights into the behavior of exotic nuclei emerges, with direct implications for nuclear energy and astrophysics, will be obtained. In condensed matter physics, the inner workings of quantum many-body systems will be revealed, potentially paving the way for the creation of groundbreaking materials. The Q-BLUE project will bolster partnerships and cultivate a skilled workforce, particularly excelling in education for both graduates and undergraduates. Nurturing a proficient software engineering workforce plays a pivotal role in advancing the field of quantum computation. The project will actively involve students in research initiatives alongside engineers specializing in the development of trapped-ion quantum computers. This comprehensive educational strategy ensures that our graduates are exceptionally well-prepared to contribute to academic research and industrial applications within the field of quantum computing. Time-dependent simulations on analog Noisy Intermediate-Scale Quantum (NISQ) quantum computers hold considerable importance in chemistry, nuclear physics, and condensed matter physics. Such simulations do not merely provide glimpses into the dynamic behavior of quantum systems but also have the potential to showcase “quantum advantage”; i.e. the ability to solve complex problems exponentially faster or to address computational challenges that are infeasible for classical computers to tackle within a reasonable time. The potential of quantum computing to redefine such applications arises from its inherent capacity to manage the exponential complexities inherent in quantum systems, thereby forging new pathways for scientific discovery and technological advancement, pathways that were once considered unattainable. While the digital approach has proven its universality, the quantum analog mode offers a key advantage by being less susceptible to errors. In the 9th month of our project, the Q-BLUE team will organize a Town Hall, conducted virtually to maximize participation, including by under-represented groups. This project advances the objectives of Quantum Information Science and Engineering at NSF in response to the National Quantum Initiative Act for the continued leadership of the United States in QIS and its technology applications. This project is jointly funded by the NSF National Quantum Virtual Laboratory program and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2024-12

Wind energy is the largest renewable and carbon-free energy source. While over one-third of wind turbines are installed in cold climate regions, wind turbine icing is found to cause significant power loss and additional maintenance and operational costs, valued up to billions of dollars in the fast-growing wind energy market. The overarching goal of this project is to advance understanding of the complex multiphase flow dynamics pertinent to wind turbine icing phenomena under real-world conditions. The new knowledge will facilitate the development of effective and robust de-/anti-icing systems to ensure safer and more efficient wind turbine operations in cold climates. In the long term, this project is expected to benefit the nation’s economy and promote a zero-emission and environment-friendly society. In addition, this proposed program will create new course modules, organize summer workshops, develop outreach programs for kindergarten through 12th-grade students and teachers, and broaden participation in engineering research. The research objective of this project is to better understand wind-driven water film flow dynamics, which is responsible for the dangerous glaze ice accretion process over wind turbine blades. To this end, this project will create a tightly integrated numerical and experiment framework to accurately analyze the underlying driving mechanism of turbine icing phenomena under various conditions. In this integrated framework, the experiment corrects defects in the numerical model, and the corrected model complements lab-scale flow analyses and extends knowledge for real-world conditions. A solver-in-loop, multiphase field inversion machine learning framework will be created to identify and correct defects in existing flow models. Then, the corrected model will be used to analyze the impact of the main driving force (local wind shear at the air-water interface) on the wind-driven water film flow dynamics, such as the water film thickness, waterfront contact line movement, film/rivulet morphologies, and interfacial waves. Finally, the trained model will be further extended to analyze wind-driven water film flow dynamics for a utility-scale wind turbine, which facilitates the development of active and passive anti-/de-icing systems. The training and validation datasets and the machine learning framework will be open to the public to promote further developments and collaborations. 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

Biological phenomena are often driven by complex dynamic regulatory networks. In natural or engineered systems, complicated structures can be generated from simpler building blocks, or modules. This notion of complex systems built from modules is also prevalent in modern systems biology. However, a clear theoretical foundation of modularity, including useful definitions of basic concepts and mechanisms, is still missing. This research project will fill this gap by defining modular structures in biological systems in a mathematically rigorous way. The research will determine why modularity can be advantageous to an organism and elucidate how modularity can be leveraged to advance our understanding of molecular systems. Studying the modularity of specific gene regulatory networks underlying salamander limb regeneration as well as hormone regulation in plants harbors the potential to reveal novel biological insights. Through involvement of students in all aspects of the research, this project contributes to the interdisciplinary training of STEM workforce. The dissemination of results through a dedicated project website and webinars enables anyone to analyze biological network models. The foundation of this project is a rigorous, structure-based definition of modularity in the context of Boolean networks, a common modeling framework in systems biology. Through computational, experimental, and theoretical studies, it will be shown that this definition of modularity (i) is biologically meaningful, (ii) implies a decomposition of the dynamics of Boolean networks, which can be employed to efficiently compute their dynamics, and (iii) that modular networks can be controlled effectively. The theoretical results, including theorems and implemented algorithms for practical computation, will advance the body of knowledge in the fields of network analysis, systems biology, and developmental biology. The validity of the project will be demonstrated through (1) in vivo analyses in the model plant Arabidopsis, (2) in silico analyses in an emerging animal model, axolotl. This will yield novel biological insights regarding (1) the interplay between phytohormones during Arabidopsis organogenesis, and (2) gene regulatory networks directing fibroblast reprogramming in axolotls. 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

Agricultural productivity is threatened by climate change and shrinking growing lands. Maize is the second largest cereal crop in the world, grown mostly for grain production. Climate change introduces more drought events, which delays maize silk growth. Maize silk is the stigmatic tissue in the female flower (ear) of maize that receives pollen leading to fertilization and seed setting. Maize carries separate male and female flowers in the same plant and so the timing of silk emergence and pollen shedding must be synchronized for high grain yield. Despite the importance, silk biology is a poorly explored area of scientific research. The research team has previously identified a new gene in silk biology opening an avenue to understand the molecular underpinnings of silk emergence. This project aims to uncover the molecular network underlying silk growth with an expectation that the knowledge gained will offer opportunities to manipulate silk growth to sustain yield under a changing climate, If successful, this study will generate groundbreaking knowledge and insights into silk growth that can be potentially used to remove manual detasseling and the associated labor and costs in ‘baby corn’ (a specialty corn delicacy) cultivation. With respect to outreach and training, the project will provide research training opportunities to undergraduate students and postdoctoral researchers. In addition, educational materials for teaching genetics will be developed for use in both traditional and online platforms. Maize silk growth biology remains a poorly explored area of scientific research, despite its huge importance. This could be attributed to the rarity of genetic mutants in maize silk growth and development. For example, only a handful of genes have been described for silk initiation or senescence; by contrast, no genes have been described thus far on silk growth. Recent studies have identified a mutant in which silk growth is severely compromised but not fertility, thus representing a unique genetic pollination control system that is of high interest to baby corn breeders and unlike the already existing male sterility and gametophytic incompatible systems. This preliminary finding suggests that a single gene plays a major role in controlling silk growth and, as such, provides an avenue to unravel the molecular regulatory circuitry controlling silk growth in maize. The goal of this project is to provide a framework for the molecular pathway(s) underlying silk growth and to leverage this new information to optimize silk growth for a sustained high yield under a changing climate for baby corn production. Specific objectives include: (1) using interactomics and transcriptomics to uncover the genes and gene regulatory network underlying silk growth; and 2) evaluating the applicability of the newly identified mutation in baby corn breeding using haploid inducer mediated genome editing technology in Indian baby corn germplasm. All data and resources generated will be made available to public after publication and through public repositories. 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 Civic Innovation Challenge Planning Grant (CIVIC-PG) supports research on design and implementation of an online game that focuses on disasters in the Midwest. Communities face major barriers during pre-disaster planning as emergency preparedness and response involve multiple organizations and decision makers, each with different roles and responsibilities and limited resources. The type and severity of disaster, its timing, and its location are uncertain. To overcome these barriers, Iowa State University and Polk County (Iowa) Emergency Management will collaboratively develop the Disaster Multiplayer Online Game (DMOG). DMOG brings decision makers together in an engaging, online interactive activity in which participants (e.g., law enforcement, fire, medical services, city and state officials) explore disaster scenario (i.e., a derecho during the Iowa State Fair), grapple with the uncertainty and trade-offs, and learn about the roles and responsibilities of different organizations. Such an immersive experience enables stakeholders to gain more intuitive judgment about disaster planning and make better emergency planning decisions. DMOG introduces innovation to training and education for emergency management profession. The learning objectives of DMOG are to: (i) increase knowledge sharing about roles in emergency management and enhance emergency preparedness decision making, and (ii) foster continuous collaboration and communication among emergency management decision makers. To what extent an online synchronous disaster game enhances knowledge gain and preparedness capabilities is systematically assessed. The Stage 1 planning grant focuses on identifying, interviewing, and recruiting project participants, especially in organizations involved in emergency management and community leaders in Polk County. DMOG adopts an iterative participatory design, resulting in a Game Design Document (GDD) at the end of Stage 1. The GDD serves as the blueprint for game play, players and their roles, and specific characteristics of the disaster scenario. The project team then conducts experiments to assess the effectiveness of DMOG during Stage 2 against existing training and tabletop exercises. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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

According to the Centers for Disease Control and Prevention, foodborne pathogenic bacteria cause 1.35 million illnesses, 26,500 hospitalizations, and 420 deaths in the United States each year. To address this challenge, there is a strong demand for novel biosensing technologies that can accurately count specific bacteria from food products. Bacteriophages, the widely existing viruses that naturally infect and kill bacteria, have evolved to target specific types of bacteria. This project aims to re-engineer bacteriophages to serve as biosensors for the precise enumeration of pathogenic bacteria. Leveraging advancements in synthetic biology and paper-based sensors, the proposed research will develop Synthetic Phages for Identifying and Enumerating Strains (SPIES) of pathogenic bacteria. SPIES incorporates synthetic gene circuits into the phage genome, allowing precise control over cell breakdown and reporter gene expression levels. These elements are essential for achieving high sensitivity and specificity in targeting pathogenic strains. To expand the impact of this research, the project will integrate teaching and outreach activities focused on promoting diversity and inclusion, improving retention rates, and providing hands-on experiences for K-12 students. This project not only aims to advance scientific knowledge but also contributes to the national interest by promoting the technology advancement in a real-world context, enhancing public health and safety, and supporting educational and societal welfare. Current methods for detecting and counting pathogenic bacteria, such as culture-based methods, genotyping tests, and existing phage-based sensors, encounter accuracy challenges and necessitate trained personnel, specialized laboratory equipment, and time-consuming processes. The proposed SPIES technology aims to overcome these obstacles by developing synthetic phages that can express reporter genes in direct response to specific bacteria. This will be achieved by integrating toehold riboswitches into the phage genome, enabling translational-level regulation of the reporter gene, which will be activated upon detection of mRNA molecules specific to the target bacteria. Additionally, the synthetic phage will utilize a transcriptional repressor to suppress the expression of phage genes associated with host cell lysis, thereby facilitating the quantification process. By incorporating these genetic-level regulations, the engineered phage can accurately identify pathogenic Shiga toxin-producing Escherichia coli and distinguish highly virulent serotypes, such as E. coli O157. A paper-based sensing platform will be employed to store the synthetic phages, carry out phage infection, and count the infected bacterial cells. Through the integration of these innovative strategies, the SPIES technology introduces a novel bacterium sensing paradigm. It offers rapid assay time, cost-effective sensing, high specificity, and the remarkable capacity to directly count single cells with minimal user interventions. 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

Recent climate patterns have caused more frequent and more extreme weather events, which incur more damage to electric power utilities than ever before. These changes have led to prolonged outages with severe social and economic consequences, especially for rural utilities and communities. This project will introduce a new digital infrastructure to help utilities address the challenges associated with maintaining the operation and service of electric power utilities, especially in the face of natural hazards. The high-fidelity simulation platforms and infrastructure developed through this project will enable rural utilities to take informed preventive and corrective actions that assess the power system performance and possible risks during natural hazards. Specifically, by using the principles of responsible design, development, and deployment, the rural utilities and their customers are engaged through direct collaborations to ensure benefit to the communities they serve. This project’s primary objective is to introduce a transformative platform based on responsible design, development, and deployment strategies to engage rural utilities and their customers in enhancing the resilience of power utilities under climate change and extreme weather events. For this purpose, the scope of activities includes three interrelated thrusts: (1) systematic and foundational interactions with various stakeholders, including public utilities and the communities they serve, to understand technical and societal impacts associated with increased damage to electric power utilities; 2) development of a platform that links physical assets, power network models, weather data, geographic features, climate projections, outage information, and customer preferences; and 3) collection and integration of relevant data from multiple sources for effective decision-making processes, taking into account current and future climate patterns. The project team will directly work with a wide spectrum of stakeholders through workshops, focus group meetings, and surveys. In addition, the educational modules and dissemination activities will assist in enhancing public knowledge and preparing a diverse group of students for future careers that involve responsible design, development, and deployment of 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 2024 · 2024-10

Mathematics and computer science are inextricably linked. However, it is well-known among educators and education researchers that undergraduate computer scientists often do not appreciate the relevance of mathematics to their discipline. This disconnect adversely affects students, especially as they progress from concrete, practical introductory courses centered on programming to theoretical upper-level courses rooted in abstract mathematics. Researchers have observed that student performance in the classroom and retention within the major falter when these connections are not established. This project's impact is to address these concerns by developing pedagogy that (a) unites the mathematical foundations and practice of computer science together in a way that all undergraduates can appreciate and directly apply in their future endeavors and (b) is adoptable by as many institutions as possible, especially those with limited room to expand their curriculum. To accomplish these goals, the investigators develop, deploy, and evaluate new pedagogy that integrates formal methods techniques within the existing undergraduate computer science curriculum. Specifically, this pedagogy introduces program reasoning, an activity all computer scientists perform, as the primary vehicle for studying the mathematical foundations of computing in the contexts of introductory programming, discrete mathematics, and algorithms courses. Such pedagogy bridges the gap between mathematics and computer science for all undergraduate computer scientists and makes relevant formal methods for a new generation of programmers. Additionally, the project promotes the relevance of formal methods to undergraduate computer science educators, as exemplified by this pedagogy, through a series of workshops at the regional and national levels. 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

Cardiovascular aging, which involves various structural and functional changes in the vascular, valvular, and ventricular systems, is a significant risk factor for heart diseases and associated morbidities. These conditions typically progress silently and become noticeable only in advanced stages, making early detection and intervention crucial. Traditional diagnostic methods, which rely on symptoms to guide further testing, are increasingly challenged by a growing patient population and a short-staffed clinical workforce. This project aims to transform aging care by developing personalized digital twin technology that will integrate data from wearable devices, echocardiographic measurements, and advanced cardiovascular modeling. This initiative will enhance the monitoring and understanding of cardiovascular aging through noninvasive methods, allowing for better therapeutic interventions. The digital twin technology will have broad applications in healthy aging care and disease monitoring. Additionally, the project will provide educational opportunities in mathematics, scientific computing, and biomedical science, promoting diversity and inclusion in STEM fields. Outreach efforts will emphasize the importance of healthy lifestyles and scientific literacy to the broader community The central theme of this project is to utilize computational and animal models to develop a physics-based personalized digital twin for monitoring and understanding cardiovascular aging. By integrating subject-specific simulations, artificial intelligence, and multiscale noninvasive data, the digital twin will enhance insights into cardiovascular health. The project will focus on developing a data-driven, physics-informed digital twin for real-time monitoring and prediction of aging-related cardiovascular diseases, using mechanics-based markers. Leveraging advanced modeling techniques, scientific machine learning, and noninvasive measurements, this project aims to fill significant knowledge gaps and create a transformative tool for personalized healthcare. The development of novel algorithms for handling multiscale, multimodal data is anticipated to enhance the understanding of mechanical changes associated with cardiovascular aging. Expected outcomes include a physics-based digital tool with predictive capabilities, validated against animal models, offering the potential for early detection and intervention in aging-related cardiovascular diseases. 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

A Reconfigurable Intelligent Surface (RIS) is a programmable structure that can control the reflection of electromagnetic (EM) waves such as 5G and 6G cellular transmissions. Simultaneous Transmission and Reflection Reconfigurable Intelligent Surfaces (STAR-RIS) can simultaneously program the transmitted and reflected EM waves. Most existing RIS panels are passive or nearly passive, limited to reflection only, and are constrained by their low power for short-range communication. Current STAR-RIS designs struggle to minimize the power required to direct incoming waves in any direction while maintaining or increasing reflection gain, due to a lack of individually controllable elements that can switch flexibly between passive and active modes. This project aims to develop a metasurface-based hybrid STAR-RIS panel capable of digitally adjusting both the amplitude and phase of the reflection and transmission coefficients of EM waves. The elements on this new panel will be programmed to function as passive or active, enabling simultaneous reflection and transmission across frequencies ranging from the sub-6 GHz band to the millimeter-wave regime. The outcome of this project will enhance the functionality of RIS, supporting the resilient operation of the 5G and future wireless networks, which are crucial for the widespread deployment of Internet of Things (IoT) devices. The objective of this project is to design STAR-RIS to support the resilient operation of massive IoT networks that are integral to 5G/6G wireless communication systems and beyond. The STAR-RIS panel elements can be passive, active, and dynamically programmable, allowing them to switch between passive and active modes as needed. A nearly passive RIS panel will first be developed on a standard printed circuit board (PCB). An active RIS panel will then be developed using patch antenna arrays with tunnel diode amplifiers and varactor diodes. Additionally, both the nearly passive RIS and active RIS will be optimized to have a hybrid fully dynamic STAR-RIS panel by incorporating an array of piezoelectric plates on a separated PCB. The RIS panels developed at each step will be characterized for reflection coefficients. The advancement of the proposed STAR-RIS will enhance the adaptability of RIS panels through dynamic programming, reducing the power necessary for modulating both amplitude and phase in wireless communication. In addition to supporting extensions to 5G and 6G services, the broader impacts of this project encompass hosting the annual “ISU-Wireless Communication Devices and Systems Day”, incorporating new findings into both graduate and undergraduate curricula, engaging undergraduate students through collaboration with existing programs, and offering opportunities for senior design projects focused on wireless communication 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 2024 · 2024-10

Wireless applications have become pervasive in numerous fields, and relays play an essential role in extending wireless network coverage. They also, however, can introduce significant security risks, particularly regarding data integrity. Adversaries can potentially compromise relays, leading to unauthorized data modifications or injections that disrupt system operations. Current cryptographic methods, which rely on computational hardness, are at risk of being compromised by adversaries equipped with high-performance computing resources such as quantum computers. As these technologies advance, the threat to traditional cryptographic methods becomes increasingly pressing. This project aims to develop a secure method for verifying message integrity in wireless relay networks, addressing the urgent need for enhanced data security against emerging quantum threats. The project team will disseminate findings through conference presentations and journal publications, develop course modules, and implement outreach programs to attract students from diverse backgrounds to wireless security research. The project will develop a novel approach to message authentication using non-orthogonal multiple access techniques. This method involves transmitting the message through a relay while sending the authentication tag directly to the destination, enabling detection of message modifications even if adversaries gain access to the key. This approach leverages the inherent properties of wireless channels to enhance security, providing a layer of protection beyond traditional cryptographic methods. Additionally, the project will develop a physical-layer authentication mechanism to verify message integrity when wireless channel impairments prevent tag decoding. The research will explore synergies between cryptographic and physical-layer authentication, leveraging the strengths of both approaches to create a comprehensive security solution. Furthermore, the project will investigate leveraging multiple network nodes to enhance authentication precision. The project also aims to extend physical-layer authentication to federated learning frameworks, addressing the unique security challenges in this emerging field of distributed machine learning. 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

In conventional models of computation, algorithms have access to the complete data set throughout the computation. However, in many modern real-world scenarios, data arrives as a continuous high-volume stream and the processing algorithms do not have enough memory to store the entire data set. The data stream model is a well-studied abstract computational model for handling computations over continuous, high-volume data. This model has become pivotal in algorithm development for large datasets and has significant applications in fields such as data mining, network monitoring, and security. The goal of this project is to study several new and underexplored directions in data stream computing. By involving graduate and undergraduate students in research and mentoring them, the project will contribute to training the next generation of scientists and engineers. This project concentrates on three major research themes: (1) Initiate a study of a new data stream model known as the `right to forget' model. This study is motivated by modern considerations arising due to the explosive growth of data generation as well as privacy concerns. (2) Explore a new and emerging notion of randomized computations known as pseudodeterministic computations in the context of streaming algorithms. (3) Investigate the Delphic set streaming model where each item in the stream is succinctly represented as a set. This investigation is motivated by the recent discovery that connects data streaming algorithms to model counting algorithms--two seemingly disparate research topics. Each of these directions represents a strategic step towards advancing the field of data stream computations, addressing contemporary challenges, and unlocking new possibilities. 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 is a collaborative project between universities in the United States and India to enable sustainable next generation cellular wireless (6G) services. Traditional approaches to resource allocation in wireless communications networks are based on mathematical models with known parameters. However, such models, along with the complete knowledge of the parameters, are unlikely to be available in 6G systems. An online learning paradigm capable of adapting to evolving and uncertain situations will prove invaluable in this scenario. The project develops learning-based control strategies for sustainable network operations with enhanced energy efficiency and improved resource usage in future mobile networks. The project also includes an innovative education plan contributing to workforce development from K-12 students to STEM and an innovative workforce development and training plan through short-term training programs for students and industry/working professionals. The proposed research comprises three comprehensive thrusts and an evaluation plan. Thrust 1 focuses on creating a learning-based framework for resource allocation in the core network. Thrust 2 focuses on developing real-time resource allocation strategies for improving energy efficiency and sustainability in Radio Access Networks (RAN), with support for massive connectivity. Thrust 3 includes the development of an adaptive security mechanism for the 6G network. The algorithms developed in the project are implemented and evaluated on ns3-ai software integrated with learning capabilities, and a simulator and a testbed available at IIT Bombay. 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.