Iowa State University

NSF Awards · FY 2025 · 2025-08

Filamentous fungi have a dramatic impact on the global economy (by one estimate, trillions of dollars annually) through both beneficial applications, such as pharmaceutical production and sustainable biomaterials, as well as harmful effects including crop destruction and human disease. In all these cases, fungi depend critically on their protective cell wall for success. Despite this importance, it is not fully understood how fungi respond to, and recover from, cell wall damage. This research investigates the fundamental biological question of how fungi detect wall stress, survive initial damage, and eventually restore normal growth. The research uses advanced microscopy, genetic tools, and computational modeling to uncover the molecular mechanisms that coordinate these responses in a model fungus. Understanding these processes will eventually enable "tuning" of fungal cell-wall properties for diverse applications, including: increasing productivity in bioprocess manufacturing, improving the physical properties of renewable mycelium-based materials that could replace petroleum-based products, and identifying new targets for antifungal drugs to protect crops and improve human health. The research also provides significant educational opportunities, training both undergraduate and graduate students in interdisciplinary approaches that combine biology, engineering, and computational sciences through collaborative teams across three universities. This project investigates how filamentous fungi respond to cell-wall stress, focusing on the model fungus Aspergillus nidulans. The molecular mechanisms involved in both immediate survival responses and subsequent recovery from wall damage are characterized using (i) advanced microscopy to visualize actin localization and dynamics during stress, (ii) genetic manipulation to identify key regulatory proteins, (iii) systems biology approaches to discover novel components, and (iv) mathematical modeling to integrate these findings into a cohesive network model. Specifically, the fungal response to inhibition of β-glucan biosynthesis is being characterized by testing the hypothesis that a two-phase response is involved. This includes an initial "survival phase," with rapid actin redistribution to form protective septa, which is followed by a "recovery phase" involving expression of specific proteins enabling growth resumption. In addition, a core set of stress regulators is being identified from proteomic analysis by comparing responses across multiple wall stressors, distinguishing universal responses from stressor-specific reactions. Finally, a hybrid modeling approach is being developed which integrates both mechanistic and machine-learning methods to infer the topology of regulatory pathways and their interconnections. 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

Photovoltaic (PV) technology is now the fastest growing energy technology in the world, with over 1600 GW being installed worldwide. Photovoltaic energy technology lasts 30+ years with little maintenance. This project is focused on improving its efficiency at converting solar energy into electricity. Two concerns with PV technology are that the conversion efficiency remains low in commercial panels, at about 20%, and that 90% of the panels are made from Si, where China has a stranglehold on raw Si, with 98% of raw Si coming from China. This is a national security concern. PV Technology based on CdTe is a promising alternative to Si, but it needs a higher efficiency to reduce costs. In this project, we propose investigating a new material system, an alloys of CdSe and CdS, and a novel device structure to make high efficiency tandem cells. Simulations show that the new device, in a tandem cell arrangement with the currently produced CdTe solar cells, is capable of making 35% efficiency cells without using Si, thereby increasing the efficiency of CdTe solar cells by about 40%. The new PV material system is an alloy of CdS and CdSe [Cd(S, Se)] whose bandgap can be varied by changing the S:Se ratio. We are aiming to develop a PV material with a bandgap of ~2-2.1 eV which will act as the appropriate material for the higher gap cell with the existing Cd(Se,Te) cell acting as the lower gap cell in a 4-terminal tandem cell arrangement. Simulations show that when combined with Cd(Se,Te) as the bottom cell, the new PV device, when optimized, can produce devices with 35+% conversion efficiency in production, a potential efficiency breakthrough for the Cd(Se,Te) technology which is currently the major thin film PV technology. The proposed technology does not use Si and is manufacturable using a technology (CSVT) similar to the currently used technology for Cd (Se,Te) cells. The research will focus on developing PV devices in the new material system [Cd(S,Se)]. The new material is a direct bandgap material with high absorption coefficient and thus, we can make thin film PV devices in a manner similar to devices in CdSe or CdTe. In the project, we will use appropriate p-type heterojunction materials such as p-a(Si,C):H and NiO to make p-n junction devices. Fundamental measurements of material and device properties will be an integral part of the project. The project will provide opportunities for education and research training of both graduate students and a post-doctoral researcher. The results will be disseminated in open literature and at scientific conferences. 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

With the availability of abundant data, deep learning models have advanced significantly and achieved unprecedented prediction capabilities. However, when these models are used in critical applications like medical diagnostics, it becomes crucial to measure how confident the models are in their predictions. Accurate uncertainty estimation can help decision makers understand the reliability of predictions and ensure transparency, safety, and trust in high-stakes scenarios. Recently, significant attention has been given to conformal inference, a statistical technique that offers rigorous measures to quantify the uncertainty of the predictions. This allows users to know how much potential uncertainty is the case for individual predictions. Its impact spans a broad spectrum of real-world application domains. However, existing conformal inference frameworks are mostly data-driven and face significant challenges when handling unreliable, real-world data (e.g., noisy and incomplete data), which are common in many applications. This project aims to tackle these challenges, improving conformal inference to better handle unreliable data and enhance its effectiveness in real-world uncertainty studies. This project develops a suite of novel conformal inference models and algorithms to enable reliable uncertainty-aware decision making with unreliable data. Specifically, it focuses on four research objectives. The first objective addresses challenges posed by data noise by relaxing overly optimistic data assumptions across the essential stages of the conformal inference framework. The second objective explores innovative solutions for imputing missing data within the framework, while addressing the optimistic assumptions that may be violated due to noise introduced during the imputation process. The third objective enhances the usability of the obtained uncertainty information to improve model performance. The fourth objective systematically validates the proposed research across various application domains and incorporates expert feedback to refine the approach. The outcomes of this project will empower researchers and practitioners to integrate predictive uncertainties in data mining and machine learning across diverse domains, enabling more informed and reliable decision making to advance scientific discovery. Results will be integrated into existing curricula and new developed courses. This project will also provide research opportunities for undergraduate and graduate students. Customized research and teaching initiatives will be developed to attract K-12 students to STEM fields and introduce them to conformal inference and data science research. 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 award supports research that looks to enable precision manufacturing of materials with desired properties as well as coordination and control of large collection of autonomous agents such as robotic or biological swarm, thereby promoting national prosperity and welfare. Direct and precise control of large population of individuals has emerged as a new frontier across engineering disciplines. However, existing solutions fall short in practice as they fail to account for realistic nonlinear agent models, inter-agent interactions, statistical errors, and constraints affecting the uncertain dynamics of the population. This project seeks to address this critical gap by designing theory and computational algorithms with performance guarantees. The research could transcend the discipline of control engineering and will be impactful in machine learning where there remains a critical need for precise control of data distributions. The project looks to train the next generation of students and engineers working in the broad areas of control, machine learning and their intersection, via several educational and outreach activities. This project explores a new vision advancing the theory and algorithms for the control of distributions. The distributions may correspond to the stochastic states of a single controlled dynamical system giving rise to time-varying state probability distributions. Alternatively, the distributions may correspond to population ensembles wherein the dynamics of an individual agent in the population can be nonlinear in state, non-affine in control, and the agents may interact in a nonlocal manner. The project focuses on three main challenges that remain in this area involving nonlinearity in dynamics, interactions among agents, and robustness of control policy. The project looks to deliver a suite of theory and algorithms for the control of distributions in either case, with optimality and robustness guarantees in the presence of hard deadline and controlled dynamical constraints, with or without process noise. The outcomes seek to enable scalable nonparametric gridless computation beyond second order statistics (i.e., covariance control), thereby unlocking the full potential of distribution control in practical applications. The broader impact of this project will result from fundamental advances in theory and computational methods to benefit the fields of stochastic optimal control, optimal mass transport, Schrödinger bridge -- all three are finding rapid adoption in generative diffusion models in 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 2025 · 2025-08

Germanium and silicon have broad applications in electronic and optoelectronic devices, solar panels, and micro-/nano-electromechanical systems. Under high pressures, they possess multiple transformations into phases with unique electronic properties. However, required pressures are prohibitively high for engineering applications, and special technologies are needed for retaining useful high-pressure phases under normal conditions. This project support's research that will explore how large plastic deformations can drastically reduce the phase transformation pressures, manipulate transformation paths to desired phases, and retain them under normal conditions. It will include in situ experimental studies utilizing a new unique device, dynamic rotational diamond anvil cell, coupled to two-scale modeling and simulation efforts. Fundamental relationships and new rules for coupled large plastic straining, microstructure, and phase evolution look to be established. In addition to economic material synthesis, the results intend to be applicable to optimizing surface processing (polishing, turning, scratching, etc.) of strong brittle semiconductors and developing regimes of ductile machining, analyzing their friction and wear. These efforts have the potential to boost domestic research and manufacturing of semiconductors in the US. The project will provide opportunities to educate and train postdoc, graduate, and undergraduate students in the cross-disciplinary fields of multiscale mechanics, high-pressure and severe plastic deformation science, and nanostructured materials, through research in the PI's laboratory and at synchrotron radiation facilities at Argonne National Laboratory. The goal of the research is to find new fundamental rules for combined severe plastic deformations, multiple strain-induced phase transformations in germanium and silicon, and the evolution of the grain size and dislocation density under high pressure for broad ranges of strain rates and particle/grain sizes using dynamic rotational diamond anvil cell with rough diamonds. New methods of measurements and post-processing of in-situ, real-time synchrotron X-ray diffraction patterns look to be established. Phase-field approach to the interaction between discrete shear bands and multiphase transformations in a polycrystalline sample seek to be developed. Physically-based macroscale theory looks to be developed and utilized for finite-element simulation of sample behavior. Iterative coupling between experiments and simulations will allow refinement, calibration, and verification of all models and determination of the evolution of fields of stress, plastic strain, and strain rate tensors, the volume fraction of phases, grain size, and dislocation density. Various formulated hypotheses will be verified. New fundamental science intends to represent the foundation for producing the nanograined phases and multiphase structures by severe plastic deformation and for processes occurring during surface processing and friction. 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 2025 · 2025-07

Malaria remains a global health crisis, causing over 600,000 deaths annually, with most fatalities occurring in children under five. Plasmodium falciparum, the causative agent of malaria, relies on the apicoplast for key biosynthetic functions like isoprenoid production. The apicoplast is indispensable for parasite survival, making it a prime target for antimalarial therapies. Prex, a polyprotein containing DNA primase, helicase, and polymerase domains, is crucial for replicating the apicoplast genome. However, the proteases responsible for processing Prex into its functional subunits remain unidentified, representing a critical gap in our understanding of apicoplast DNA replication. Preliminary studies have identified three candidate proteases that potentially interact with Prex. This proposal will now focus on characterizing these proteases to better understand their role in apicoplast function and biogenesis. Aim 1 will confirm the localization of these proteases within the apicoplast, assess their importance for parasite growth and Prex maturation, and determine their complete set of interacting proteins. Aim 2 will characterize the biochemical properties of the proteases, using targeted and unbiased approaches to identify their substrates and map cleavage sites. This research will provide essential insights into proteolytic processes within the apicoplast, regardless of whether the candidate proteases directly process Prex. The findings will inform future high-throughput screens for small-molecule inhibitors targeting these proteases and support the identification of new therapeutic targets for novel antimalarial therapies.

NSF Awards · FY 2025 · 2025-07

This Mid-Career Advancement project develops affordable and accurate tests for detecting bacterial endotoxins—harmful substances released from the outer membrane of certain bacteria that can cause serious infections and inflammatory responses in humans. This advance will help the biomanufacturing industry reduce quality control costs and eliminate the need for horseshoe crab blood, which is currently used in testing but raises sustainability concerns. The project will benefit pharmaceutical companies, medical device manufacturers, healthcare facilities, environmental testing labs, and regulatory agencies by improving safety and efficiency in detecting endotoxins. The project will integrate commercialization, education, and outreach by mentoring undergraduates in transitioning research into commercial products, training a skilled biomanufacturing workforce, and developing a senior-level course on biomedical entrepreneurship. By enhancing scientific understanding and promoting cost-effective testing, this project supports industry growth while benefiting public health and sustainability. This project leverages biological sensing mechanisms by using natural transcription factors to develop artificial transcription factors (ATFs) for in vitro endotoxin detection. The research focuses on designing, modeling, and constructing ATFs through a modular approach to identify endotoxin-specific effector domains and linker peptides, optimizing them and conducting fluorescence polarization screening, assembling ATF-regulated gene circuits using a cell-free synthesis system, and ultimately implementing and commercializing the ATF-based sensor. Key innovations include on-chip ATF development using droplet microfluidics and a fluorescence polarization detector to identify high-affinity variants, cell-free expression of ATF-regulated gene circuits to enhance detection sensitivity, and integration with paper substrates and portable instruments for cost-effective, field-deployable testing. By providing a low-cost, alternative to traditional horseshoe crab blood-derived tests, this research has potential to transform endotoxin detection across multiple industries. 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 · 2025-07

This Pathways to Enable Open-Source Ecosystems (POSE) project create an open-source community that will support engineering design optimization software. Complex engineered systems such as hypersonic missile interception systems, next-generation aircraft and other vehicles, and energy grid infrastructure are essential for the security and prosperity of the United States. However, designing these and other systems is challenging. Complex engineered systems have many options to test and possible configurations to evaluate before finding the best solutions. Traditionally, this is done manually, and engineers often rely on their intuition in making designs, resulting in slower progress. Design automation tools address this problem by enabling engineers to quickly and automatically find optimum solutions. DAFoam is a powerful open-source design automation tool that has been applied to problems in aircraft, automobile, turbine, ship, and heat exchanger design. This project will enable DAFoam to expand its user base and allow more researchers, students, and engineers to benefit from design optimization technology. DAFoam cultivates a community-based governance structure for project decision making, supports and grows a vibrant and collaborative user community, and develops comprehensive training resources for new users. This POSE project supports workforce development in design science and engineering, promotes collaboration across academia and industry, and allows a better understanding of the complex design trade-offs in complex engineered systems. This POSE project aims to transition a multidisciplinary design optimization tool (DAFoam) into an ecosystem that can expand its user base across many engineering disciplines in a self-sustainable manner. First, this project will conduct comprehensive scoping activities to deepen the understanding of user needs. The activities include hosting DAFoam conferences and workshops to collect feedback, engaging with potential users at various technical conferences, launching a literature review campaign to identify unmet needs, and interviewing research and education users. This team will cultivate a vibrant user community by creating live training courses, fostering an interactive user forum, collaborating with external open-source communities, and creating comprehensive documentation and tutorials. Finally, this project establishes a self-sustainable governance structure by forming the DAFoam ecosystem committee and standardizing the workflow for new code contributions, robustness tests, and security checks. The DAFoam ecosystem will significantly facilitate design space exploration, reduce the design cycle time, and accelerate the development and deployment of innovative engineered systems. Furthermore, detailed analyses of DAFoam-optimized designs can reveal new physics and mechanisms in emerging systems, which would otherwise be hard to obtain solely by human intuition. 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 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. NSF EPSCoR Graduate Fellowships provide a total of three years of stipend and associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award at Iowa State University (ISU) will support ten EPSCoR Graduate Fellows with funding from the National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP). The Fellows' research will be aligned with goals and programs managed by NSF's Directorate for Biological Sciences (BIO) and the Directorate for Technology, Innovation and Partnerships (TIP). The goal of this project is to train the next generation of biologists who will contribute basic biological research and build the bioeconomy. Fellows will be trained by faculty spanning the breadth of biological sciences at ISU, and in many cases, will be co-mentored on projects that span subdisciplines. Professional development opportunities will prepare Fellows for careers in academia and, through internships with the bioscience industry, for careers that build the bioeconomy. Activities will help develop scientific infrastructure and drive innovation in the state of Iowa and across the nation. Ten graduate Fellows will be recruited from those who have received honorable mention designations from the NSF Graduate Research Fellowship Program (GRFP). These Fellows will be matched with faculty who align with their interests and trained in one of several ISU graduate programs in biology. A faculty team will train Fellows, oversee their progress toward their degrees, and facilitate their transition into the scientific workforce. Professional development for academia will include mentoring in teaching, grant writing, development of intellectual property, and University service. Fellows seeking careers in industry will be matched with internship opportunities in central Iowa, which is home to an extensive network of agriscience research and development. Overall, the award will develop a skilled workforce in Iowa that is prepared for STEM careers that meet needs of the US and contribute to the development of a strong bioeconomy. 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 · 2025-07

Mitigating corrosion is a grand challenge that costs the United States over half a trillion dollars annually. Current corrosion control measures, which mainly rely on chemical coatings, often come with high cost and risks to human and environmental health. Microorganisms play key roles in corrosion, in which they can either accelerate or inhibit corrosion of metal surfaces. In this project, the researchers will develop a biological coating system that can be applied for corrosion control of civil infrastructure. This bio-inspired coating design takes advantage of naturally occurring microorganisms that strongly inhibit corrosion. By harnessing the power of microorganisms and microbial biofilms, the biological coating developed through this project will provide long-lasting protection against corrosion to many types of metal-based infrastructure, improving their resilience. To develop this biological anti-corrosion coating, the researchers hypothesize that both the chemistry and microbiology of the coating material need to be deliberately designed and fabricated. This biotechnology development also requires co-production with a convergent team of interdisciplinary academic researchers and experts from industry and government. To accomplish these goals, this Phase 2 project will (1) develop a scalable prototype of the microbial coating system through accelerated technology development and maturation through partnerships, and (2) build a sustainability plan including trust building with potential end-users of the biotechnology. Both objectives will be carried out through an iterative and collaborative approach between the academic, industry, and government project partners, including experts in environmental and civil engineering, mechanical engineering, synthetic biology, polymer chemistry, mathematics, and advanced manufacturing. 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 · 2025-07

This grant provides funding to support a platform for sharing progress, exchanging ideas, and facilitating further collaboration between interdisciplinary researchers, stakeholders, and public, private and government representatives. This grant will design, organize and execute the 2025 Dynamics, Control and Systems Diagnostics (DCSD) PI Meeting at National Science Foundation (NSF) in Alexandria, Virginia on September 4 and 5, 2025. The two-day program will provide a centralized and lasting repository of information illustrating research ideas explored and milestones achieved by DCSD funded projects. The PI Meeting will advance knowledge by: exposing the rich progress of current DCSD funded research; reviewing new developments in DCSD foundations and the efficacy of new, emerging applications; exploring technology transfer and partnerships; discussing future directions of the DCSD program by identifying long-term challenges and opportunities; allowing PIs to showcase their research results; providing a networking environment; and promoting general outreach. The 2025 NSF DCSD PI Meeting will be the first PI Meeting since the establishment of the DCSD program. Hence it will provide an opportunity for this community to have fruitful discussions and collaborations between established and junior DCSD researchers to discuss innovative ideas and ultimately to expand the number of new DCSD PIs. The program will specifically provide support for DCSD PIs from R2 institutions and non-PIs who either have not been successful in pursuing an NSF DCSD award or who have never applied for a DCSD grant but would like to. The meeting will provide a pathway for aspiring PIs to directly interact with current DCSD awardees and program directors to understand what NSF will expect to see in a successful DCSD research. The mini-workshops and discussion panels with NSF program directors will provide opportunities for PIs of interest to consult or discuss their projects, thereby providing an interactive pathway for direct engagement and for quickly seeking advice or feedback on the scope and significance of the future proposals. If successful, this type of session could be a standard program component of future DCSD PI Meetings. 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 · 2025-07

Machine learning systems have made revolutionary advances in several areas, including (but not limited to) automated speech and image recognition, scientific discovery, human health and national security. These advances have been made possible in large part by the training of high-capacity models that are able to capture and infer complex relationships between exhorbitant amounts of data, such as images, video, and speech. Such training is quite resource-intensive and failure-prone and typically requires the deployment of large groups of computers that operate collaboratively to achieve the overall objectives. For instance, by conservative estimates, the training of current state-of-the-art models for language understanding consume enough energy to power over one thousand average US households for a year. Moreover, a rule-of-thumb within distributed computing states: "failures are the norm, rather than the exception". This project will investigate resource-efficient and fault-tolerant schemes for distributed model training within machine learning. Specifically, the training time depends on the reliability and speed of the computers and the speed of communication between them. This project will examine techniques for simultaneously increasing both the reliability and speed of the process. If successful, this will result in significant energy and monetary savings across the board in scenarios where machine learning is routinely deployed. The ability to work with large-scale computing clusters is an essential skill for the workforce, and this project will help train undergraduate and graduate students in such techniques. The team of researchers will volunteer for mathematics tutoring activities as part of the CyMath initiative at Iowa State; CyMath offers free and open-to-all, after-school math tutoring for elementary and middle school students in Ames area schools. Overall, the goals of this project will lead to the acceleration of corresponding machine learning driven advances in a variety of fields, e.g., science and human health, and contribute towards the US economy and society. Distributed machine learning model training typically involves minimizing a loss function that depends on the training dataset with respect to a parameter vector. The number of parameters in many problems of real-life interest can range from hundreds of millions to billions. Such training is the driver of key technologies such as deep neural networks and large language models. In these systems, the workers are required to compute gradients on the data points assigned to them. Depending on the underlying architecture, the workers either communicate to-and-from a central server or exchange messages amongst themselves to perform gradient descent over several iterations. In addition to worker-node computation, communication is well recognized as being a significant part of the overall training time. It is well recognized that worker nodes (especially within cloud platforms) are prone to straggling (slow-downs and/or failures). The foundational goal of this project is to investigate failure/slowdown-resilient and communication-efficient schemes for distributed model-training for different classes of learning architectures. This will be achieved by introducing redundancy in the assignment of data points to the worker nodes and using coding-theoretic ideas for the recovery of the gradient, either exactly or approximately. The research team will examine the design of numerically stable and communication-efficient schemes that leverage the work performed by slow (as against failed) workers. They will also research classes of schemes that vary depending on the amount of system knowledge across the workers and the assumed communication between them. A key goal will be to provide rigorous guarantees of the quality of the gradient that is recovered by the system. The successful completion of the goals of this project will result in significant reductions in training times of distributed models and corresponding resource savings. 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 · 2025-07

Non-Technical Summary The North American Solid State Chemistry Conference (NASSCC) is a biennial meeting to discuss emergent topics in solid-state chemistry broadly defined, including synthesis, characterization, crystal structure, chemical bonding, and properties of inorganic and hybrid extended solids. The 2025 NASSCC is held on the Iowa State University campus in Ames, IA, on July 28-31, 2025. The conference showcases the cutting-edge research from scientists across all career levels, including distinguished faculty, researchers from government laboratories, postdoctoral fellows, and graduate and undergraduate students. The focus topics of this year’s NASSCC are aimed at stimulating interdisciplinary discussions in the area of advancing fundamental materials research as well as education in the field of materials science. By gaining a deeper understanding of how materials properties are related to their makeup and structure, researchers are driving innovations to advance national health, economic competitiveness, and energy security via the development of materials that reduce reliance on critical elements and minerals. NASSCC promotes the creative exchange of research findings and ideas by engaging scientists from academia and government laboratories. NSF's Solid State and Materials Chemistry Program in the Division of Materials Research support provides registration and travel support for junior investigators at various career stages, including those from the underrepresented groups in STEM, ensuring knowledge dissemination to the next generation of solid state and materials chemists and contributing to the mission of strengthening and expanding the STEM workforce. Technical Summary Solid state chemistry comprises the synthesis, structure, properties, and applications of solid, oftentimes crystalline, materials. At this year’s NASSCC, the scientific discussion centers on four major areas, including: (i) Novel Synthetic Methods, (ii) Theoretical Studies and Computational Modeling, (iii) Energy Conversion and Storage, and (iv) Advanced Characterization. Novel Synthetic Methods - Discussion of the exciting advances in synthesis that have enabled the development of new materials for exploration of their structure and properties, along with the prediction of new materials, their properties, and the synthetic pathways towards them. Theoretical Studies and Computational Modeling - This section highlights theoretical and computational studies that shed light on the synthesis, structure, and/or properties of complex inorganic solids. Energy Conversion and Storage - Discussion of advanced materials for energy storage and conversion, including ionic and mixed conductors for batteries and fuel cells, advancements in anode/cathode materials for rechargeable batteries, thermoelectric, photocatalytic, and photovoltaic materials. Advanced Characterization – Focus on advances in characterization methods, including various in situ and operando techniques, that are critical for understanding structure/property relationships in solid-state materials. Oral presentations set a strong tone for the sessions that extend into a contributed poster session from a broad group of faculty, graduate and undergraduate students, and postdoctoral researchers. To encourage deeper scientific inquiry, the format of the conference provides multiple discussion opportunities during the oral and poster sessions. 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 will test how savanna ecosystems respond to the removal of invasive species. Invasive species are introduced from outside their natural range that rapidly expand across areas where they were introduced. Invasive species negatively affect the economy and can change ecosystems in undesirable ways. Because of these negative effects, many restoration efforts involve removing invasive species. The project will test if ecosystems are resilient and can return to their original state after invasive species are removed. This project will also examine how long it takes for ecosystems to return to their original state once invasive species are removed. Both questions will be answered by experiments done in central Kenya, where an invasive ant has displaced native ants that defend Acacia trees against elephants and other browsing mammals. In invaded areas, browsing on trees is common, transforming savanna woodlands into open landscapes with few trees. Through targeted removal of the invasive ant, the project will discover whether invaded areas can be returned to their original state. Removal of invasive ants may restore the partnership between trees and native ants and reduce browsing by elephants. Across much of East Africa, Acacia trees are critical to the bioeconomy because they provide food for black rhinoceros, giraffe, and other animals. These trees are also used for fuel by humans. This project provides an opportunity to answer important questions about ecosystem resilience. Research in this system will address conservation issues that are relevant for land managers and restoration planning. This project will test the hypothesis that a foundational ant-Acacia mutualism responsible for giving rise to near monocultures of the whistling-thorn tree is resilient following the removal of the invasive big-headed ant. Invasion fronts occur along a rainfall gradient, providing an opportunity to quantify whether and under what contexts the ant-acacia mutualism is resilient to removal of big-headed ants. Specifically, this project will answer two questions: (1) following big-headed at removal, how faithfully do stability-promoting feedbacks return to approximate those from uninvaded areas (i.e., is the foundational ant-acacia mutualism resilient)? (2) does resilience hinge on time since invasion, rainfall, or both? To answer these questions, the project will employ large-scale removal of big-headed ants, assays of photosynthesis, and demographic modeling to quantify the restoration of feedback loops involving symbiotic ant activity, elephant browsing, and whole tree photosynthesis. 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

Biomanufacturing is a transformative branch of manufacturing that leverages biological systems, such as living cells or enzymes, to produce a wide array of products, from pharmaceuticals to biofuels. The commercialization of biomanufacturing faces challenges, including slow strain development, scale-up production difficulties, and high costs of downstream processing. Addressing these issues, this project introduces the in vitro Biomanufacturing on a Chip (iBOC), which enhances in vitro prototyping by providing rapid sensing feedback to optimize enzyme combinations in metabolic pathways. This approach accelerates strain development timelines, reduces reliance on empirical methods, and improves the scalability and cost-effectiveness of biomanufacturing. The project will significantly benefit the US bioeconomy by enabling the efficient production of economically viable bioproducts, strengthening domestic biomanufacturing capabilities, and reducing reliance on foreign imports. Moreover, transitioning to bioproduction has the potential to cultivate a more resilient economy. The iBOC technology will integrate a photonic crystal biosensor and a surface-enhanced Raman scattering sensor to monitor cell-free synthesized enzymes and metabolites, respectively. The microfluidic component will enable the assembly and testing of target metabolic pathways by providing precise control over the mixing of gene cassettes. The iBOC's capabilities include evaluating metabolic pathways, identifying rate-limiting steps, and optimizing metabolic flux to achieve a balanced distribution of metabolites. If successful, metabolic engineers can utilize the iBOC system to prototype their biosynthesis pathways, expedite the cycle of design-built-test-learn, and guide the development of cell-based bioproduction. The project will focus on optimizing the dopamine pathway as a representative example. To maximize the project's impact, the proposed research activities will be seamlessly integrated with educational and outreach initiatives. Collaboration with the recently funded EPSCoR Center (Chemurgy 2.0) at Iowa State University will facilitate the training of students in bioproduction expertise, thereby expanding the nation's workforce in biomanufacturing. The team will also work with the Chemurgy 2.0 Center to enhance the existing biology curriculum for K-12 students and stimulate their interest and involvement in STEM. 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 · 2025-06

7. Project Summary/Abstract Research documents that youth in foster care exhibit elevated risk for substance use problems compared to the general adolescent population. Yet substance use prevention programs often fail to reach youth in foster care due to a number of factors, including high rates of placement instability, a critical lack of substance use programming tailored for the unique needs of youth in foster care, and implementation challenges. Increasing referrals is not an effective strategy alone, and substance use prevention is not part of services as usual for youth in foster care. The objective of this R01 is to leverage a robust existing service delivery program for youth in foster care, Court Appointed Special Advocates (CASAs), to deliver an evidence-based intervention (EBI) specifically focused on trauma-informed substance use prevention, called CASA Brief Intervention (CBI). We will first adapt core components of existing EBIs so that they are tailored for the needs of youth in foster care and can be delivered by CASAs (Aim 1). We will then conduct a large-scale hybrid type 1 effectiveness- implementation randomized trial to test whether the adapted intervention improves substance use outcomes for foster youth compared to CASA services-as-usual (Aim 2 and Aim 3). CASAs are ideal interventionists for youth in foster care because the structure for hiring, training, and monitoring CASAs to support youth in foster care already exists nationwide. In this proposal, we build on an existing partnership between the University of Oregon, CASA of Lane County, and the joint CASA program of Josephine and Jackson Counties. The overall goal of this project is to expand our current partnership with CASA to co-adapt and deliver effective substance use programming to youth in foster care. Findings will clarify the implications of using a lay health worker model to improve the implementation, sustainability, and scalability of substance use prevention programming for youth in foster care. Given that CASA is currently in 49 U.S. states, serving approximately 242,000 (60%) youth in foster care, the potential for scalability and reach is exceptional if the current project demonstrates that the EBI delivered by CASAs effectively prevents substance use and improves outcomes for youth foster youth.

NSF Awards · FY 2025 · 2025-06

Biological cells utilize ions to signal and communicate with neighboring cells and their environment. Despite the critical role of ionic signaling in biological systems, our understanding of such phenomena remains limited. Ionic kinetics, crucial for cellular functions and signaling, are only understood qualitatively due to the lack of near-real-time monitoring technologies. Studying and manipulating these ionic signals not only helps in understanding how microorganisms communicate, evolve, and develop, but also enables the probing and controlling of cellular activities. This proposal aims to develop innovative soft ionic transistors to measure and analyze the ionic characteristics of biological cells and their environments. This project seeks to bridge the gap between rigid electronic devices and soft ionic biological systems by designing soft ionic transistors that use cellular ions to trigger their gating mechanism. This advancement would enable the study and could significantly enhance the understanding of cellular functions, stress responses, and the role of ions and ionic signals in biological systems, thereby providing deeper insights into biological mechanisms. The outcomes of this project are expected to have broad societal impacts, including significant effects on drug discovery and testing, personalized medicine, aging studies, and novel therapeutic strategies such as pain management and rehabilitation. Ionic kinetics and the resultant intracellular-extracellular ion concentration gradients control cellular functions and intercellular signaling and communications. These ion concentration gradients indicate the health and functionality of cells and organs. Despite its critical role, the current understanding of cellular ionic kinetics remains largely qualitative, and the capacity to evaluate cellular functions based on their ionic activities is very limited. These limitations stem from the absence of technologies that can monitor ionic activities at the cellular level in near-real-time and continuously. This proposal aims to advance the understanding of cellular ionic kinetics by developing soft ionic transistors capable of continuously monitoring ionic activities at the cellular level. These transistors feature a novel gating mechanism triggered by ions secreted during cellular activities, allowing for near-real-time, quantitative evaluation of cellular ionic functions and addressing the current technological gap in monitoring ionic activities in biological systems. The central hypothesis of this research is that ion concentration gradients can polarize the ionic environment inside a transistor, forming electron-conductive ionic double layers at the bioenvironment-transistor interface. This hypothesis will be tested through in-vitro cell studies facilitated by soft ionic transistors. The project will involve designing, fabricating, and characterizing soft ionic transistors, followed by in-vitro cell studies to test the central hypothesis. Machine learning algorithms will be employed to develop a comprehensive model of the ionic attributes of biological systems, establishing relationships involving multiple interrelated physical, chemical, and biological variables. The anticipated outcome is a significant enhancement in the quantitative evaluation of cellular ionic activities and understanding of the correlations between ionic kinetics and cellular functions, thus bridging a critical gap in current knowledge of cellular ionics and their role in biological systems. The project outcomes are expected to have broad scientific and societal impacts. The ability to chronically study developing and growing biological systems will significantly impact various scientific fields, including drug discovery, aging research, personalized medicine, and novel therapeutic strategies such as pain management, all of which have broad societal impacts. 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 · 2025-05

This grant provides funds to support students for travel to the Association of Computing Machinery (ACM) International Conference on the Foundations of Software Engineering (FSE 2025) and the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2025), which are collocated and will take place in Trondheim, Norway in June 2025. FSE is one of the two flagship conferences in the field of Software Engineering, while ISSTA is the premier conference in software analysis and testing, one of the most popular research areas within software engineering. The grant will provide travel and registration support for US-based students. The FSE conference this year will have a doctoral symposium and will also feature the ACM Student Research Competition. Conference attendance is important for the technical exchange of information and research conversations/collaborations made possible by the conference, as well as advances in the field made possible by these interactions. The conference provides opportunities for education, training and mentoring to build the next generation of researchers and practitioners in the field of software engineering. The international nature of this conference helps develop a globally-aware workforce of research and educators within the US and helps build the community of researchers in the field of Software Engineering. 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 · 2025-05

This I-Corps project is based on the translation from lab to market of a technology for printing electronic materials onto three dimensional surfaces using aerosol jets. This technology uses a computer-controlled printing system to create electronic circuits for three-dimensional objects/surfaces, allowing for the integration of digital features into everyday objects. This solution addresses the complexity of designing and then manufacturing electronics for a variety of surfaces and shapes in a reliable manner. Aerosol jet printing is an emerging field with broad relevance for energy, healthcare, aerospace, automotive, and consumer electronics sectors. The commercialization of this technology has the potential to benefit the U.S. economy and society by integrating electronic functionality on complex surfaces to support miniaturization, improved performance, and reduced cost for sensing, communicating, and performing other digital tasks across a broad spectrum of markets. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of conformal printed electronics. Aerosol jet printing is a digital, non-contact patterning method well-suited to the precision fabrication of electronics on nonplanar surfaces. This new solution integrates two technologies into a coherent workflow. First, real-time process monitoring capabilities improve manufacturing reliability, standardization, and automation. Second, the technology is integrated with conformal motion planning tools and multi-axis printing hardware for nonplanar patterning. The benefits of this approach include accelerating design iterations for functional prototyping, streamlining the transition from prototype to manufacturing, and allowing a broader base of users to engage in new forms of digitally-enabled design and manufacturing. 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 · 2025-05

Nitrate pollution from agricultural runoff and industrial wastewater has been linked to significant human health concerns, such as birth defects and cancer, and environmental impacts, such as algae blooms. This project will focus on developing catalysts to convert nitrate to inert nitrogen gas or ammonia for reuse in fertilizers. In particular, this project will seek to understand how the atomic-scale details of catalyst structure and composition can control the selectivity between these possible reaction products in electrocatalytic transformations. The outcomes of this project will provide critical strategies and technological advancement for nitrogen cycle remediation in wastewater streams. The knowledge gained will also influence broader understanding of the role of catalyst structural details in governing the formation of reaction products. The research objectives of this project are integrated with an education plan that will drive excitement for computational materials design in rural communities as well as in undergraduate chemical engineering coursework. This project seeks to understand the detailed effects of local catalyst structural features in controlling the reactivity and selectivity of transformations on nanoparticle catalysts. Computational modeling on realistic nanoparticles is limited by the high computational cost of such models along with the myriad possible active sites that can be generated by an evolving nanoparticle. This research is founded upon a structure-sensitive modeling approach that utilizes local structural descriptors to predict the energetics of elementary reaction steps based on the stability of the local binding site. This framework simultaneously enables an investigation of active site and catalyst restructuring, identifying features likely to form under reaction conditions. This project will apply density functional theory calculations to establish correlations between catalyst structure and the activation energies of elementary reaction steps in electrocatalytic nitrate reduction on disordered surface geometries; it is hypothesized that manipulating the local coordination environment of a bifunctional silver-based catalyst can tune reaction selectivity between dinitrogen and ammonia. This project will seek a self-consistent description of reaction energetics by incorporating data science toolkits to describe interactions between adsorbed reactive species, enabling spatially-resolved kinetic Monte Carlo simulations of reactivity on representative nanoparticle models. This framework will be applied to understand the effects of catalyst support and nitrate reduction intermediates on the reconstruction of silver-based bimetallic catalysts under reaction conditions, enabling a holistic simulation of catalyst activity, selectivity, and stability. The fundamental knowledge obtained in this study will provide key mechanistic insights into nitrogen cycle chemistry and inform the design of selective electrocatalytic transformations. The educational objectives of this research will train and inspire the next generation of innovators, focusing on deployment of STEM outreach efforts to rural communities. A new outreach activity will allow students to participate in the hands-on design of catalysts using toy construction kits to build models that are scanned and analyzed using the research-based model to predict the reactivity and selectivity of the exposed geometric features. This activity will augment the science toolkits of both rural middle school students and their educators, with further extension into core undergraduate numerical methods coursework. Together, these initiatives will foster excitement in the next generation of scientists for the role of computations in driving innovation for sustainable chemical transformations. 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 · 2025-05

A fundamental concept in biology is that life on Earth changes over time. The Iowa State University Research Experience for Teachers (ISU BIORETS) program aims to help teachers and their students better understand how organisms adapt or fail to adapt to a changing environment by promoting scientific literacy and curiosity, which impacts attitudes towards public policy and its implementation. Teachers play a crucial role in fostering scientific literacy, yet studies show that many lack confidence when teaching about how organisms adapt and the research processes related to this field. The ISU BIORETS program addresses these issues by providing teachers hands-on summer training in university research laboratories. Helping the educators develop their content knowledge and curriculum development skills will build their teaching proficiencies. In turn, their students will wrangle over real scientists' questions in the classroom to convey the cumulative nature of science - that new data adds to a more complete understanding. In addition to summer training for teachers, the program will build innovative, on-going relationships among university researchers, teachers, and their students throughout the school year, offering teachers and students opportunities to make valuable scientific contributions towards solving real-world problems. The project addresses an important issue in biology: how do organisms respond to a changing environment? This will be addressed through research that integrates multiple levels of biological organization from genotype to phenotype, and their interactions with the environment. Teachers will collaborate with faculty to address questions about adaptability in field and lab based projects, focusing on the theme of stability and change, a cross-cutting concept in the Next Generation Science Standards. Weekly meetings will develop and reinforce conceptual integration across different biological systems. Relationships among teachers, research mentors, and program leaders will be year-long through classroom visits, opportunities for student visits to campus, and biweekly coaching for teachers to translate research experiences into authentic and relevant learning so that students inquire more deeply and work like scientists. Where possible, deliverables will include both in-person and virtual activities, providing the flexibility needed for modern classrooms. The broad goals of this continuing BIORETS program at Iowa State University include: 1) help teachers and their students understand the process of scientific research; 2) learn how organisms adapt or fail to adapt to environmental change; 3) promote scientific literacy of both teacher and student; and 4) build opportunities for promoting student scientific curiosity. 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 · 2025-05

Due to the vast amounts of data and the growing adoption of Artificial Intelligence (AI) applications, there is an increasing demand for programs that can fully leverage the power of High-Performance Computing (HPC) systems. This project aims to develop a framework capable of automatically rewriting programs to run more efficiently on modern multicore and manycore architectures. The project’s novelties are its ability to gain a deeper understanding of complex programs by employing advanced machine learning techniques and ensuring the optimized program aligns with its intended functionality. By analyzing the syntax and structural design of programs, the proposed framework identifies tasks that can be executed in parallel, significantly improving performance and speed. This enables developers to identify optimization opportunities. As programs are transformed to become faster and more efficient, maintaining reliability is paramount. The framework validates that optimized versions produce results consistent with the original, ensuring trustworthiness, particularly in critical domains like scientific computing. The project's impacts are streamlining the scientific software development process, creating faster, more reliable, and purpose-driven programs, and supporting the growing demands of modern computing platforms and technologies. This project introduces a comprehensive framework for improving program performance and reliability by integrating advanced methods in program analysis, parallelization, and validation. The presented framework aims to both identify and generate parallel versions of sequential code. By employing a graph-based multimodal contrastive learning approach, it combines two views of a program: its graph representation using low level virtual machine (LLVM) intermediate representation based graphs and its textual representation from the original source code. Using Graph Neural Networks (GNNs) and Large Language Models (LLMs), the framework uniquely learns intrinsic parallel characteristics. It directly generates parallelized code, eliminating the need for additional data structures or manual annotations. To support parallelization and ensure robust code transformations, this project also focuses on understanding the underlying computations of a program. By leveraging neural architectures, it analyzes and classifies data flow graphs to comprehend program characteristics and generates extensive program variants to train the model. This method enhances the precision of detecting program clones and obfuscated transformations, which is crucial for identifying computational equivalence and guiding optimization decisions. Finally, the project also addresses the challenge of verifying the correctness of complex program transformations, particularly those involving reduction parallelization patterns. The reduction pattern, which leverages algebraic properties like associativity and commutativity, often results in transformations that cannot be directly validated using existing equivalence-checking methods. This module develops new polyhedral validation techniques to handle combinations of reduction parallelization and classical loop transformations (e.g., tiling, skewing, unrolling), ensuring the correctness and reliability of transformed programs. The project aims to advance the state of the art in automatic parallelization, program understanding, and transformation validation, providing a robust framework for optimizing and verifying modern software 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 · 2025-05

About one third of our national energy consumption and one quarter of carbon dioxide emissions come from the manufacturing sector, 30 percent of which is chemical manufacturing. Ethylene and ammonia are among the top five most produced commodity chemicals, which largely tend to rely on energy intensive processes. This interdisciplinary Future Manufacturing Research Grant (FMRG) project aims to advance fundamental knowledge for the electrification and decarbonization of current processes that rely on heat and catalysts for production. A combination of green ammonia production (from waste nitrogen), and green ethylene production (from waste carbon dioxide) will be targeted. Using electro-manufacturing with a simultaneous utilization of renewable electricity, this project will explore sustainable ways to upgrade the production of ethylene and ammonia with positive impacts on the economy and domestic manufacturing. The overarching objective of this FMRG project is to explore an electro-manufacturing platform for ethylene production by combining the efficient capture of carbon dioxide with green ammonia synthesized from waste nitrogen, and the selective conversion of bicarbonate. An interdisciplinary research team will focus on the following research tasks: 1) Bicarbonate concentration via anion-specific membranes and ammonium bicarbonate separation by electrodialysis; 2) Green ammonia synthesis through high-throughput alkaline electrolysis; 3) carbon dioxide capture by ammonia in advanced absorption; 4) Green ethylene synthesis via bicarbonate electroreduction; 5) Supply chain development and integration with the renewable energy grid. Technoeconomic analysis of electrified ethylene manufacturing and life-cycle assessment and survey studies on environmental impacts will be conducted. The research will gain insights into the fundamental sciences of bicarbonate separation, green ammonia synthesis, carbon dioxide management, selective ethylene production, functional electrodes, supply chain development, techno-economic analysis, life-cycle assessment, and policy framing. The acquired knowledge can be broadly applied to various areas, such as electrochemistry, catalysis, functional materials, polymer science, chemical separation, wastewater treatment, system integration, economic analysis, and data science. Education and workforce training will be provided through an interdisciplinary environment to strengthen STEM fields, and by promoting convergent training of a manufacturing workforce. This Future Manufacturing award is jointly funded by the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences (MPS), the Divisions of Chemical, Bioengineering, Environmental and Transport Systems (CBET) and Civil, Mechanical and Manufacturing Innovation (CMMI) in the Engineering Directorate (ENG), 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 · 2025-04

Interstitial fluid in the spaces between cells in the brain cortex can be driven by ionic waves that induce cell swelling, although the precise flow physics is not understood. Modeling interstitial fluid dynamics is crucial because of its essential role in brain health and neurological disorders. The brain tissue can be considered a porous medium consisting of a liquid phase and an absorbent solid phase (cells) that can swell. The overall goal of the project is to develop a swelling-porous media model to understand how traveling waves in the brain drive interstitial fluid flow during phases of sleep, during working memory, and during acute conditions like migraine, brain injury, and seizures. The research will advance the fields of porous media flows, biofluid dynamics, and neuroscience. It could drive the development of therapeutic strategies against neurological disorders that are the leading causes of death and disability worldwide. The project’s educational activities include mentoring PhD and undergraduate students, creating a course on “Neuro-fluid Dynamics,” and organizing a workshop for high school students. The project will develop a swelling-porous media model to understand how traveling waves in the brain during acute and physiological conditions drive interstitial fluid flow. The research proposes a rigorous system of volume-averaged conservation equations to model interstitial fluid dynamics in the porous brain cortex. The project will have the following objectives: 1. Model interstitial fluid dynamics by using a volume-averaging technique and deriving a modified Darcy’s law for flows through a porous media with spatiotemporally varying permeability profiles; 2. Quantifying interstitial fluid flow driven by traveling waves by implementing physiological reaction-diffusion equations, traveling wave models, and osmosis-induced porosity fluctuations that will be coupled with the interstitial fluid flow model; and 3. Quantifying the influence of wave properties on interstitial fluid flow by systematically varying the wave properties. Parameter regimes will be identified where interstitial fluid flow is optimized in healthy and diseased brains. The research will constitute the first model of traveling wave-induced interstitial fluid dynamics in the brain cortex and will side-step the experimental bottleneck of visualizing interstitial flow in the cortex. The broader impacts include providing insights into therapeutic strategies against neurological disorders, mentoring graduate and undergraduate students, creating a course on neuro-fluid dynamics, and organizing a workshop for high school students. 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 · 2025-04

Mimics of Enzymes and Antibodies for Glycans and Other Biological Targets Project Summary/Abstract Molecular recognition and catalysis are the foundations for nearly all biological processes. Scientists have developed strong abilities to inhibit biomolecules at their ligand- binding or catalytic sites. However, when it comes to complex glycans, long strands of peptides, or proteins on nonligand-binding sites, it remains difficult to have supramolecular hosts/materials with biologically competitive binding affinity and specificity. Scientists have long argued that enzymes are “not different, just better” than synthetic catalysts. Inability to construct substrate-tailored active sites with precisely installed functional groups, however, has prevented researchers from accomplishing the catalytic feats of even relatively simple enzymes. The PI’s group has developed water-soluble, protein-sized, molecularly imprinted nanoparticles (MINPs) to bind carbohydrates with micromolar affinities and peptides with tens of nanomolar affinities. In the most recent R01 grant, MINPs have been converted into synthetic mimics of glycosidase and esterase with enzyme-like specificity and proficiency ((kcat/Km)/kuncat = 109–15 M-1) that operate under physiological conditions. In the next five years, the PI seeks to develop (a) efficient MINP-based artificial enzymes for selective hydrolysis of glycans, esters, and amides and (b) MINP–MINP conjugates for complex biomolecules such as glycoproteins and post-translationally modified proteins. The MINP glycosidase mimics are designed to cleave sialic acid-containing glycans and the glycosidic bonds in the core 2, 4, 6 of mucin-type O- glycans that currently lack suitable enzymes for cleavage. The MINP–MINP conjugates will be used to bind two separate epitopes on a biomolecular target, including glycoproteins and proteins with posttranslational modifications (PTMs). MINPs have already enabled applications/collaborations for controlling peptide/protein PTMs, elucidation of T cell signaling, and targeted degradation of amyloid proteins in neuron cells. All these applications depend critically on the strong and selective binding of MINPs for their biological targets in water, as well as their abilities to penetrate cell membranes. As their binding and catalytic properties are further enhanced, new applications are expect to emerge and new collaborations be made possible, to help solve important biomedical problems that could benefit from robust, designable, water-soluble synthetic mimics of antibodies and enzymes.