University of North Carolina at Chapel Hill

NSF Awards · FY 2025 · 2025-07

Plants create a variety of lateral organs, such as roots, leaves, and flowers, as they grow. Each of these new organs has specific functions that are important for plants, and they all impact agricultural yields in crop plants. Research suggests that there are both developmental similarities and differences between the different organ types. The basis of this is unclear. Many genes are known to contribute to the creation of lateral organs, with some genes impacting one organ type and others impact more than one. Understanding the common and different functions of plant genes in the development of different organ types is an important goal which can help crop improvement. However, there is a limitation in the technology available to study this at a scale that will be informative. The goal of this proposal is to create a semi-automated system to determine what genes are active in different lateral organ types. This system will be able to look at many genes in parallel, an advantage over older technologies. The project will also compare two divergent plant species and define the role of a cell signaling pathway on gene function in different organ types to help understand how evolution and cell signaling pathways shape lateral organ formation. Lastly, the project will create databases for other scientists to use in their research, and a workshop to train other scientists on the use of the semi-automated system, which will enable other researchers to use this approach in their studies. Lateral organ production, including flowers and roots, is a critical feature of many crop traits and as such the project will help inform future efforts to improve crop productivity and ensure food security. During plant development common signaling pathways and transcription factors (TFs) often operate in seemingly distinct processes. Understanding how individual plant signaling pathways and TFs are repurposed in different contexts remains a challenge. Traditional methods for defining individual TF functions are time consuming and labor intensive. As the number of TFs impacting plant processes increases, this creates a technical bottleneck. To address this, this proposal will develop a new high-throughput platform to identify TF targets in vivo and use this platform to identify context-specific TF binding sites during lateral organ development across tissue types and across species. The project team has identified a peptide signaling pathway as a novel driver of lateral organ formation in roots and shoots and identified a suite of TFs that impact peptide-mediated lateral organ formation. As such, the project will also use this platform to define how peptide signaling shapes TF targeting in different lateral organs and interrogate the mechanisms underlying context-specific TF binding sites using a combination of genetic, biochemical, and genomics approaches. The project will also generate web-based interfaces for other researchers to explore and utilize data from the project, and hands-on workshops to train researchers in the use of the platform, expanding the impact of the project. This award was funded as part of a lead agency opportunity between NSF and Swiss NSF where NSF funds the US investigator, Swiss NSF funds the Swiss partner. 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 support approximately 10 students based in the United States (US) to attend the 48th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025), scheduled to be in Padua, Italy, July 13-18, 2025. SIGIR represents the Association for Computing Machinery's Special Interest Group on Information Retrieval. The conference has long been a central platform for advancing research, development, and education in search technologies and other methods of information access. It serves as the leading global venue for showcasing, demonstrating, and sharing innovative research, systems, and techniques in the broad field of information retrieval. As a highly selective event, it features oral and poster presentations of peer-reviewed papers, panel discussions, and keynote speeches from distinguished experts across academia and industry. The conference covers a wide array of topics in information retrieval, including theoretical foundations, algorithms and applications, evaluation methods, and societal impacts. Ensuring a strong presence of students and researchers who are based in the US at SIGIR 2025 is vital for sustaining the nation's leadership in this critical field both now and in the future. The selection process for the student travel grant recipients is open to all students studying at US Institutions of Higher Education. The initiative aims to reduce financial barriers that may prevent U.S.-based students from attending the event. The award opportunity will be shared through multiple channels, including the SIGIR 2025 official website and social media platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The project will provide financial support for junior researchers to attend the 14th International Conference on Extreme Value Analysis (EVA 2025) to be organized and hosted at the University of North Carolina at Chapel Hill from June 23 to June 27, 2025. Extreme value analysis refers to the statistical modeling of extreme data observations, allowing to make probabilistic predictions beyond the range of observed data. With the mathematical foundation going back to the mid 20th century, the scope of EVA expanded considerably over the last decades, including applications in insurance, finance, engineering, atmospheric science, and other domains. The EVA conferences are bi-annual, and they attract some of the most prominent researchers in statistics and probability, as well as more applied domains working on extremes. The supported junior researchers will have opportunities to attend a satellite workshop with tutorials, to present their work in oral or poster sessions, and to participate in a data challenge and best student paper competition. EVA started with univariate models and frequentist methods for independent and identically distributed observations, but has since seen extensions to time series, spatial and spatial-temporal data, Bayesian methods, and so on. It has had a profound impact on a wide range of applications in atmospheric science (droughts, floods), finance (value-at-risk, market crashes), engineering (reliability, rare event prediction), social networks (viral tweets) and other domains. Over the recent past, the field's impact has grown by making and exploiting connections to high-dimensional statistics (graphical models, dimension reduction), machine learning (prediction of extremes) and AI (training of neural networks) methods. These various themes, both classical and modern, will be well represented at the conference EVA 2025. The conference website can be found at https://eva2025.unc.edu. 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 grant supports travel for 40 US participants to the Research Collaboration in the Science of Data and Mathematics, to be held at the University of North Carolina at Chapel Hill, NC, in the week of August 4-8, 2025. The proposed five-day workshop consists primarily of time spent in small, focused working groups led by prominent field leaders to address pre-defined open research problems. This format promotes intensive collaboration on key challenges, encourages the free exchange of scientific ideas, and strengthens the research skills of participants. The workshop is open to all researchers. In addition to research collaboration, the workshop will provide essential networking and mentorship opportunities that are particularly valuable for researchers at early career stages. The organizing committee has carefully selected six pairs of group leaders to guide research groups focused on challenging topics in mathematical data science. These topics include advanced modeling tools such as optimal transport, randomized algorithms, diffusion generative models, and manifold inference. The resulting research is expected to advance computational mathematics significantly, particularly in areas such as (randomized) linear algebra, differential geometry, and statistical learning. Some research groups will also address critical applications in areas like image processing, and single-cell biology. The results of the workshop will be published in a peer-reviewed volume by Springer. The project results will also be disseminated via a follow-up workshop in conjunction with major conferences including the SIAM Annual Meeting, Joint Mathematical Meeting, etc. For more information about the workshop, please visit https://datascience.unc.edu/wisdm-2025/ 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

In this CAREER project, funded by the Chemical Mechanism, Function, and Properties Program and the Chemical Catalysis Program of the Chemistry Division, Professor Megan Jackson of the Department of Chemistry at the University of North Carolina at Chapel Hill is bringing new molecular-level understanding to electrochemical energy conversion reactions. Moving towards greater reliance on sustainable energy sources requires electrocatalysts that can efficiently convert between electricity and fuel. This work will develop fundamental mechanistic insights into the factors that make a material an efficient catalyst. The results of this work will be leveraged in the development of new electrocatalysts that can be used in devices like fuel cells and electrolyzers. This project will also support a Cyclic Voltammetry Boot Camp for researchers desiring to incorporate electrochemistry into their research as well as the development of accessible electrochemistry activities for children and adults with intellectual and developmental disabilities. This project will use multimodal spatially resolved techniques to identify the kinetic and thermodynamic factors governing interfacial inner-sphere proton-coupled electron transfer (PCET) reactions at edge sites and basal plane sites in two phases of the transition metal dichalcogenide, MoTe2, as a model system. Specifically, Professor Jackson and the rest of the research team are: (1) Identifying property–activity relationships for interfacial, inner-sphere PCET reactions at edge and basal plane sites of 2H and 1T’ MoTe2; (2) Determining the mechanism of interfacial inner-sphere PCET steps at step edge and basal plane sites of 2H and 1T’ MoTe2; and (3) Systematically tuning MoTe2 electrodes, proton donors, and electrolyte properties to identify opportunities for synthetic control over the local thermodynamic and kinetic parameters that govern interfacial inner-sphere PCET reactions. Long-term, they envision leveraging these insights to facilitate fast, selective reactions in a wide range of electrochemical energy storage and conversion reactions. 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

Today's artificial intelligence (AI) systems are powerful but often operate as opaque "black boxes," making decisions without clear explanations. This lack of transparency limits trust in AI, particularly in critical domains such as healthcare, finance, and autonomous systems, where understanding the reasoning behind decisions is essential. At the same time, decades of research have produced mature, well-established, and theoretically proven algorithms. This project introduces Algorithm-Informed Neural Networks (AINNs), a new approach that integrates these proven algorithmic principles into the design of neural networks. By embedding logical steps into AI architectures, AINNs enhance explainability, reliability, and efficiency, making AI systems more interpretable and reducing their dependence on large datasets. This advance is particularly beneficial in fields where data is scarce or sensitive, such as medical diagnostics or regulatory decision-making. By addressing these challenges, the project contributes to the development of trustworthy, transparent, and efficient AI technologies that can drive scientific progress and benefit society. To achieve these goals, the project is structured around two key research tasks. First, it focuses on algorithm-mapped neural models, which construct neural networks by systematically integrating well-established algorithmic logic. Instead of relying solely on training data, these models leverage predefined logical rules — ranging from pseudocode to flowcharts — to ensure reliability and trustworthiness in AI decision-making. This approach reduces training data requirements while improving generalization and interpretability. Second, the research develops latent behavior analysis of neural blocks, a novel debugging tool that enables AI systems to be systematically inspected for correctness. By analyzing the execution patterns of neural subnetworks, this method detects input-specific anomalies and traces them back to logical inconsistencies, facilitating targeted debugging and improving model robustness. The project will evaluate AINNs across diverse tasks, from algorithmic reasoning to perception-based applications, using key metrics such as data efficiency, error localization accuracy, and generalization performance. Expected outcomes include AI systems with greater transparency, lower data dependency, and enhanced reliability, making them more effective in real-world applications. The project will publicly release datasets, models, and tools to promote broader adoption of algorithm-informed AI across multiple domains. 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

Scientists are working to better understand how brain cells are organized and function, but traditional studies using animal models don’t fully capture the complexity of the human brain. To address this, researchers are turning to brain organoids—tiny, lab-grown models that mimic parts of the human brain. These organoids offer a new way to explore how human brain cells interact and function. However, current tools for studying organoids mainly focus on their structure and gene activity, rather than their electrical activity, which is key to understanding how brain circuits work. What is missing is a way to reliably connect with these organoids, sending and receiving signals without harming them, over long periods of time. The project aims to create a new technology called "HotPocket" — a soft, flexible electronic system that gently wraps around the organoid. This device will allow researchers to study how brain circuits respond to different signals and stimuli, and it could help uncover new insights into brain disorders and potential treatments. Beyond neuroscience, this tool could also be used by the pharmaceutical industry to test new drugs safely and effectively. Elucidating the principles of neuronal circuit organization has traditionally relied on animal models, which fail to fully recapitulate the cellular diversity and genetic specificity of the human brain. Brain organoids represent an emerging platform for studying human-specific neuronal architecture and activity. However, current approaches are predominantly focused on transcriptional profiling and cytoarchitectural analyses, with limited exploration of electrophysiological dynamics. This gap is largely due to the lack of advanced technologies for effective bidirectional interfacing with organoids at high spatial and temporal resolution and over a long period. There is a critical need for engineered systems capable of simultaneously sensing and stimulating neuronal activity at near-cellular resolution over extended durations without inducing cellular damage. Such systems are pivotal for fundamental neuroscience, the investigation of psychiatric and neurological disorders, and the development of therapeutic interventions. Given the spatial heterogeneity of neuronal composition across the organoid surface, an interface that enables global communication across the entire periphery is essential. Moreover, since organoids require prolonged culture periods to achieve electrophysiological maturity, it is imperative to avoid irreversible attachment or damage to preserve their reusability. To address these challenges, this project proposes the development of "HotPocket," a soft, biocompatible, conformal 3D microelectronic network. This system is designed to longitudinally monitor and modulate organoid activity through both electrical and biochemical modalities. By enabling precise, noninvasive, and bidirectional interactions with brain organoids, HotPocket offers transformative potential for basic neuroscience research and high-throughput drug screening 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 · 2025-04

Gas seeps on the sea floor inject huge amounts of methane into the ocean along the coasts. Most of this is eaten by tiny organisms who rely on oxygen to consume the methane, a process called 'methane oxidation'. Oxygen levels vary naturally throughout the world’s oceans, so it is not known whether the ability of these organisms to consume methane is disrupted in some places. Custom one-of-a-kind instruments to measure methane consumption rates and monitor what organisms are present have been built by the investigators. The instruments will be sent to the bottom of the ocean for weeks and in different locations. The goal is to see how methane oxidation and the types of organisms present adjust to different oxygen concentrations and other environmental conditions. Natural gas seeps along the continental margins inject huge amounts of dissolved methane into overlying waters. This methane is largely oxidized by microbes. Although microbial methanotrophs are largely microaerophilic, it is not known how differences in oxygen concentrations enhance or hinder their ability to respond nor whether methane consumption rates are controlled by deep-sea oxygen concentrations. Recent work has shown that microbial aerobic methane oxidation (MOx) in the deep sea occurs with widely varying rate constants. The project is to explore factors that drive this variation by making in situ measurements of MOx and microbial community dynamics at known seep sites offshore Louisiana, an area with frequent episodic methane releases, and on the Cascadia Margin offshore Oregon where highly variable bottom water dissolved oxygen (DO) will provide vital evidence for how oxygen limitation affects MOx in situ. The in situ measurements are possible due to newly-developed benthic landers that can make MOx rate measurements on timescales of hours to weeks using advanced laser methane and optode DO sensors. An in situ collection and incubation scheme allows collection of microbial time-series, tracking the relative abundance of methanotrophs (through 16S rRNA surveys and metagenomes) and their activity (through metatranscriptomes) to study methanotrophic responses to varying ambient conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This award supports the development of new mathematical tools and biological experiments that are essential to understanding the mechanisms of virus spread and extinction. A new framework, to enable an integrated experimental-mathematical study, will be developed to control the spatial distribution of the host cell population and to quantify how such spatial structure affects viral evolution and decay. The project has basic research, medical, and public health impact, since the analytical and experimental methods can be extended to elucidate mechanisms of infection spread by viruses of public health importance, including influenza A virus, Zika virus, and coronaviruses. As an interdisciplinary study, the research will cross-train mathematicians, biologists, and engineers, contributing significantly to workforce development. Broader objectives include increased participation and diversity in STEM fields while promoting a broader understanding of science and technology by the public through wide dissemination. The project goal is to determine both the probability of virus extinction during infection spread and the spreading speed in terms of the spatial structure of host cell populations. A new mathematical framework, stochastic reaction-diffusion equations on metric graphs, will be developed to study the dynamics of virus infections over any network structure. The biological experiments are cutting edge: virus infections will be performed on micro-patterned host cells that enable quantification of population level features of infection spread in any network structure, a key advantage over traditional Petri-dish studies. Analysis of the experimentally informed stochastic equations has the potential to push the frontier of current knowledge about the role of space and stochasticity in population dynamics. This new framework motivates problems that cut across several mathematical disciplines (probability, partial differential equations and mathematical biology) and that are of interest to a large group of applied mathematicians and applied scientists. These problems include (i) What is the probability of extinction of virus and the propagation speed in terms of geometric properties of the metric graph, such as the branching structure and the edge lengths of the graph? (ii) What is the probability of coexistence of virus and defective interfering particles during co-infection spread, and the effect of the underlying spatial structure on this probability? The project brings new probabilistic tools and perspectives to solve these problems and to generate mechanistic insights about virus infection spread. This award is being co-funded by the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program and by the Division of Molecular and Cellular Biosciences (MCB) through the Systems and Synthetic Biology and the Cellular Dynamics and Function Cluster. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

“Photoelectrochemistry” describes the process of taking sunlight and converting it, first, into electrical energy and, second, into chemicals. The chemicals produced could be a fuel like hydrogen, or they could be other desirable products with other commercial uses. Usually, the energy from sunlight is collected using photovoltaic solar cells made from silicon that generate electrical energy. These solar cells are typically seen on rooftops. However, these systems are not currently used to produce chemicals because it is not cost-effective. This project uses the same semiconductor material silicon to make millions of microscopic particles that each behave as a miniature solar cell. The project will design the particles to produce high voltages that make them capable of doing chemistry. Unlike a panel on a rooftop, these particles can simply be suspended in water to do the chemistry, providing a new method to perform photoelectrochemistry. The project will develop the fundamental principles to make the particles, stabilize them in water, and make them perform chemistry efficiently. It will involve undergraduate and graduate students who will gain experience in nanomaterials synthesis, microfabrication, electrochemistry, solid-state catalysis, optical measurements, and optoelectronic modeling. A broad set of outreach efforts will be pursued, including programs and demonstrations in elementary schools and local libraries and an annual public science exposition. Multijunction Si nanowire photocatalysts blur the distinction between, on the one hand, a photovoltaic structure connected to an electrolyzer, and on the other, a semiconductor photoelectrode performing catalysis. The structures are created by a bottom-up vapor-liquid-solid growth process that permits the synthesis of a precise multijunction structure containing both p-i-n solar cells and n-p tunnel junctions in a high aspect ratio particulate form factor hundreds of nanometers in diameters and microns in length. They offer unique advantages for particle suspension reactors, including (1) usage of earth-abundant and non-toxic Si, (2) broadband light absorption into the near infrared, (3) photovoltage tunable for specific reaction chemistry by number of p-i-n junctions, and (4) spatial separation of anodic and cathodic reactions due to the high aspect ratio and axial orientation of the photovoltaic structure. Because the nanostructures uniquely contain both photovoltaic and photoelectrode elements, numerous fundamental scientific questions arise about the single-particle properties, ensemble interactions, and co-catalyst functionality. Thus, the overarching goals of this project are to understand (1) the chemical effects of liquid solution on individual nanowire photovoltaic and photocatalyst properties, (2) the electrochemical and photonic interactions of nanowires in suspension, and (3) co-catalyst design principles that enable well-controlled and efficient photoelectrochemical reactions. The project combines measurements on single nanowires and ensembles of nanowires in suspension with simulation and modeling efforts to predict and interpret their properties. The project will provide fundamental insights into the operation of particle suspension reactors that are applicable not just to Si nanowires but also to a range of photocatalyst material 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 · 2025-03

This I-Corps project is focused on the development of algorithms that are used for solving a problem or performing a computation. The goal is to minimize model assumptions in data analysis making the procedure widely applicable. Large scale, high-dimensional data sets are becoming ubiquitous in modern society, particularly in physical, biomedical, and commercial applications. While there has been a significant increase in the amount of data generated by these applications, existing scientific statistical models are often lacking in accurately and quickly analyzing these kinds of data. This solution may provide more robust data interpretations with large amounts of data. The technology uses efficient software packages that are about 100 times faster than existing methods. This speed may help to enhance the replicability and reliability of studies in various areas, such as economics, engineering, genetics, and neuroscience. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of distribution-free modeling and inference for complex dependency in data science. The algorithms use atomic information in data bits applied to fundamental problems in modeling, testing, and computing by connecting model-free inference to important concepts in statistics and computer science. This method of bitwise dependence detection utilizes hierarchical decompositions and compressive networks tailored to the compressible features of high-dimensional data. Bitwise technology offers significant improvements in computational efficiency and interpretability over existing methods, making it a powerful tool for various applications. In addition, this technology may enable a deeper understanding of nonparametric inference for dependency and for enhancing powerful, robust, interpretable, and computationally efficient nonparametric procedures. The technology also may help build a stronger connection between statistics and computer science, as the algorithms work directly with binary digits, which are fundamental to computing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Humans are remarkable in their ability to flexibly learn from experience, and studies have shown that this applies to language too. The uniquely human ability to understand language is shaped by the words and structures that are most frequently encountered, in a process known as adaptation. However, most work on adaptation has focused on “small units” like speech sounds, words, and sentences. Much less is known about how people use common patterns in conversation and discourse to develop robust mechanisms for understanding language in context. This project tests the ability to track patterns in reference – that is, who or what is referred to – including how human learners extract reference patterns and how doing so changes interpretation of a speaker’s meaning. Understanding this kind of adaptation in language could explain how people adjust to differences in communication styles and why language use changes across generations. This project provides scientific training for the next generation of STEM scholars by supporting the development of skills in experiment implementation, statistical analysis, and written and spoken science communication. This project focuses on words that depend on context to clarify the speaker’s intended meaning. For example, in “the professor told the student to edit their paper,” “their paper” could refer to the student’s paper, the professor’s paper, or a joint paper by the professor and student. The context is useful, in part, because people learn which types of references are most frequent. However, it is not yet known how many different frequency patterns people can track. Using an experimental approach, this project tests how adaptation to specific linguistic patterns influences reference interpretation. Specifically, this study examines whether people track multiple reference patterns simultaneously and how such adaptation is used to develop effective processing strategies. By testing the generalization of adaptation, this project discovers whether different types of referring contexts are considered the same for the purpose of adaptation. Moreover, this work examines adaptation as a mechanism for explaining why language changes over time and how speech patterns lead to shifts in how language is interpreted. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Explosive objects in the distant universe can now be studied by simultaneously combining information from multiple messengers - gravitational waves, particles, and light. The investigators will develop software to deliver new discoveries and physical constraints concerning the nature of explosive objects. The investigators will provide students opportunities for cross-institutional internships and collaborations with amateur astronomers and citizen scientists. The research, methods, and visualizations will be directly included in developing courses at multiple institutions. The work will provide training for students in critical areas for astrophysics and beyond, including robust application of machine learning. The team will partner with the LIGO Science Education Center and The Baton Rouge: Bringing Youth Technology, Education and Success programs to utilize multimessenger astronomy to inspire K-12 students in the state of Louisiana. A 4-year research program led by investigators at the Louisiana State University, Harvard University, University of Minnesota-Twin Cities, and University of Maryland, College Park will improve our understanding of explosive transients. The exotic zoo of explosive transients is still being explored, and the overlap of signals seen at different wavelengths is key to their taxonomy. Explosive transients occur at the extremes of physics, beyond the reach of terrestrial laboratories. Multiwavelength and multimessenger observations of these transients enable advances in areas including gravity, fundamental physics, dense matter, cosmology, and the origin of the elements. The proposed work will enable new discoveries through the power of the Vera Rubin Telescope with concurrent observations provided by high energy and gravitational-wave observatories. The research team will combine observations of compact objects with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time with space-based gamma-ray burst monitors and ground-based gravitational-wave interferometers. Focusing on gamma-ray bursts and supernovae, the team will construct new optical transient classifiers, develop the formalism to associate distinct signals across wavelengths and messengers from the same event, characterize these events through dedicated follow-up, and enable global discovery via public alerts. The result will be an end-to-end multiwavelength and multimessenger discovery machine. 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

Subduction zones occur at convergent plate boundaries where one tectonic plate slides underneath another, which is one of the most essential expressions of Earth dynamics. Subduction zones host some of the world's most hazardous and intense seismic and volcanic activity. When an oceanic plate sinks under another plate in the subduction zone, large amounts of fluids are released. At depth, the fluids interact with the surrounding rocks, which also affects the generation of earthquakes and volcanoes. Subduction zone fluids also influence how critical elements cycle between the Earth’s crust and mantle. This project investigates the sources of subduction zone fluids, the scale of fluid migration, and fluid interactions with surrounding rocks in the deeper sections of subduction zones. This work also aims to understand how deep fluid-rock interactions are correlated to their chemical signatures and element exchanges. Educational programs of this proposal include activities for North Carolina youth and K-12 Earth/Environmental Science teachers. STEM Career & Summer Enrichment Academies will engage approximately 40 K-12 youth. Professional Development Workshops will include 40 teacher participants during four summers. The proposed study will lead to several educational and societal outcomes in conjunction with anticipated scientific advancements. These outcomes include training undergraduate and graduate students and enhancing museum rock collections. The goal of this proposal is to assess whether pervasive fluid circulations in serpentine-bearing subduction complexes are restricted to limited focus areas or migrate up-dip over kilometer scales, triggering chemical alteration in the subduction interface. It also aims to test fluid sources and mixing and assess element partitioning during fluid-rock interaction. To resolve this, samples of high-pressure – low-temperature (HP-LT) vein-related rocks and metasomatized eclogites from six localities representing warm and cold subduction gradients were selected. This project involves four sequential tasks: (1) conduct a detailed petrological assessment to characterize fluid-rock interactions at depths of ~25-60 km; (2) constrain the pressure-temperature-time of key samples to reconstruct the depths and timing of fluid-rock interaction; and combine (3) whole-rock geochemistry and Sr-Nd-Pb isotopic analysis, with (4) in-situ trace elements in hydrous minerals, to constrain the signatures, sources of the fluids, and element partitioning during fluid-rock interaction. This proposal includes two Educational activities: (a) broadening the participation of youth in geoscience by conducting STEM Career & Summer Enrichment Academies over two summers ( ~40 participants) for North Carolina K-12 Youth and (b) increasing NC K-12 Earth/Environmental Science content knowledge and pedagogy through a two summer professional teacher development workshop focused on curriculum materials aligned with newly adopted NC Essential Standards for Science and Next Generation Science Standards in partnership with the UNC Center for Public Engagement with Science (~40 participants). Other broader impacts include teaching, mentoring, and training undergraduate and graduate students at UNC Chapel Hill, enhancing community petrological collections, and disseminating findings in local and international geoscience communities. 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 doctoral dissertation research project investigates the impacts of transnational scientific exchanges on innovations in agricultural technology. The investigators specifically study the interactions and relationships between public and private agricultural researchers and how these collaborations strengthen the global science of food production and agriculture. A systematic, social scientific study of these relationships furthers our understanding of global scientific exchanges in the agricultural technology industry. In addition to training a doctoral student in anthropological science, broader impacts of the research include dissemination of findings to global agricultural technology business partners. Findings will also inform professional development for agricultural scientists and strengthen collaborations across global scientific communities in the agricultural technology sector. In order to understand the impacts of global, technology-driven agriculture on food production, investigators employ a mixed methods approach including quantitative survey data collection, ethnographic interviews with scientists, observations at food technology labs, and participant observations at food production factories. The research contributes to environmental anthropology, agricultural science, the science of global food production, and to the social scientific study of the strengthening of global science infrastructures. 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

Stochastic models of reaction-diffusion type are crucial for modeling spatial interactions and randomness in dynamical systems across numerous scientific disciplines. Despite their utility, these models are mathematically challenging, due to issues including high dimensionality and nonlinear interactions. This project will address these challenges by focusing on the critical role of space in influencing population dynamics, which is pivotal for questions in ecology, evolutionary biology, and virology. The outcomes of this project may provide insights that improve management of ecosystems and treatments for viral infections. The research will also contribute to the development of novel mathematical methods and promote the participation of a diverse group of student researchers. Our specific focus is on a class of stochastic partial differential equations (SPDEs) where space is modeled as a general metric graph, allowing for a detailed examination of spatial effects on population dynamics. This approach not only addresses the theoretical challenges but also bridges the gap with microscopic particle models. PI will explore several key phenomena, including traveling wavefronts, the asymptotic speed of stochastic waves, and genealogies in expanding populations. By integrating innovative techniques from various branches of mathematics including probability and spectral graph theory, this project aims to significantly advance the understanding of SPDEs on metric spaces. 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 planning project aims to develop CLAIM: The Center for Climate Leadership and AI-driven Integrity and Mitigation, which seeks to investigate the societal, regulatory and governance implications of private sector climate commitments in the realm of generative Artificial Intelligence (genAI). The PI and team will lead efforts to demystify genAI’s role in climate information to enhance the credibility and integrity of climate actions by researching how it affects trust, policy support and individual behavior. The center will generate education and training opportunities through pilot projects, represent underserved communities, and equip future leaders to navigate AI’s societal implications. More broadly, this project will support climate regulation by providing credible information, distinguishing reliable sources, unlocking new data to assess various actors’ climate impacts, and creating open-source datasets to address data gaps and benefit broader research. Through an interdisciplinary collaboration that includes social scientists, computer scientists, law and policy experts, and practitioners from various sectors, the project activities will create safeguards, metrics, and institutions to effectively leverage AI for transparency and tracking of climate actions. The planning project focuses on three primary aims: (1) rigorously interrogating genAI models to address information; (2) designing new metrics and benchmarks to evaluate the accuracy and credibility of genAI information regarding climate commitments; and (3) convening specific projects to examine the societal impacts of genAI on corporate climate behavior and governance. During the planning phase, this project will identify and convene stakeholders to conduct a comprehensive gap analysis, address key challenges, assess vulnerabilities, gather insights on real-world impacts, develop robust metrics for evaluating AI-generated content, review policy frameworks, and establish the center’s aims and flagship pilot projects. 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

Bacterial symbionts are widespread among animals and plants and often provide beneficial functions that profoundly influence their hosts' biology. Insects, in particular, have repeatedly formed symbioses with microbes for metabolism, nutrition, and protection. Insect immune systems are tasked with the challenge of controlling and regulating beneficial microbes while combating often closely-related pathogenic microbes. A critical unanswered question is whether the insect immune system is a key mediator of both symbionts and pathogens across different host species. This project will combine cutting edge genomics with experimental manipulations that will link immune responses with symbiont associations across diverse host species. Gaining a mechanistic understanding of how immune systems interact with symbionts is not only critical for understanding the evolution of host-associated microbiomes, it also has relevance to the invertebrate pests that vector devastating diseases of crops, livestock, and humans. In addition, there are research opportunities available for undergraduate students to train the next generation of STEM researchers. This project will use aphids as a model system and involves research at the University of Tennessee Knoxville and the Queen Mary University of London. Aphids are uniquely suited for this work because they have strong non-random associations with microbial symbionts: a symbiont species may be common in one aphid species, yet rarely found in a close relative. This suggests a dynamic interplay between host and microbe that can drive symbiont spread or loss over relatively short evolutionary timescales. Preliminary data has shown that hosting certain symbionts leads to a sharp decrease in the expression of key immune genes in aphids. This suggests host immunity is of central importance to the evolution of symbiotic relationships. The primary objective of this project is to use transcriptome sequencing and immune assays to test the hypothesis that host immune suppression is a key mechanism explaining the distribution of symbiotic microbes across aphid species. A secondary objective is to determine whether there is a trade-off in the ability to harbor symbionts and resist pathogens across species. Genomes of virulent and non-virulent symbiont strains will also be compared using a novel symbiont culturing technology to identify genetic features that underlie pathogenicity in symbiotic microbes. Together, this cross-species approach will transform our understanding of how host immunity moderates relationships with symbiotic microbes. This project will thus provide key insight into how invertebrates form and maintain relationships with microbes that can drive rapid adaptive evolution in species of central importance to food security and health. This collaborative US/UK project is supported by the US National Science Foundation and the UK Biotechnology and Biological Sciences Research Council. 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

Therapeutic monoclonal antibodies (mAbs) are emerging as an encouraging therapy for many chronic conditions such as cancer, autoimmune conditions, and infectious diseases. While these are an incredibly promising treatment, there exists a need to measure the bodily concentration of these mAbs in real-time, as this would enable clinicians to appropriately dose patients according to their needs. A specific example of this is the use of bevacizumab, an FDA approved mAb used to treat several types of cancers, each requiring specific active amounts of bevacizumab. This research aims to develop foundational technology for the continuous monitoring for mAbs using bevacizumab as an example. This interdisciplinary research will train students in biomolecular engineering, biochemistry, biosensors, and electrochemistry - all key fields in the future of biosensing technology development. The findings of this work will be intergraded into both graduate and undergraduate course work, thereby disseminating the fundamental findings to a larger audience. Therapeutic humanized monoclonal antibodies (mAbs) are used to treat chronic conditions such as cancer, autoimmune and infectious diseases. Effective titration of these therapies relies on providing clinicians with feedback regarding patient-specific pharmacokinetics. This project aims to develop a continuous electrochemical sensor for an FDA-approved mAb, bevacizumab. Bevacizumab is a humanized IgG1 mAb that binds to vascular endothelial growth factor-A (VEGF) for use in the treatment of several types of cancers. However, a challenge with monitoring therapeutic humanized mAbs is their discrimination between human IgG in biological fluid. This initial challenge was overcome through the development of an anti-idiotype bevacizumab aptamer. The second challenge in realizing in vivo and continuous monitoring systems is the in situ regeneration of the aptamer binding site. Due to the low dissociation constant of this aptamer, binding site regeneration is only possible by denaturing both the target protein and/or aptamer structures using chaotropic reagents, limiting the in vivo application of these systems. To accomplish this, anti-idiotype bevacizumab aptamers will be designed with azobenzene, a molecule that undergoes reversible structural changes through cis/trans photoisomerization. Upon UV light exposure, conformational shifts from trans to cis in the aptamers will consequently lead to target dissociation. The central hypothesis of this research is that azobenzene engineered bevacizumab aptamers will exhibit light-sensitive, reversible conformational changes, allowing continuous monitoring of mAbs. In this study, anti-idiotype bevacizumab aptamers incorporating azobenzene will be designed and evaluated electrochemically, validating their ability to regenerate a binding signal in a controlled manner. 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

Feathered wings are a fascinating part of bird flight, yet they are not well understood. These wings have unique properties, from tiny barbicels that maintain structural integrity to individual feathers that change position, shape, and alignment. Flight feathers are both flexible and strong, and act individually or together depending on the flight situation. The close arrangement of feathers enables complex airflows between them, affecting aerodynamic force generation and feather deformation. However, our knowledge of these dynamics is limited. This project aims to investigate the unique properties of feathers and feathered wings that enhance flight capabilities, including their porosity and deformability, and their ability to change shape during flapping. The research combines experiments on isolated feathers, groups of feathers, computational models, and live bird observations to study this complex problem. The fascination and intrigue evoked by bird flight and the multi-modal research approach adopted here will be leveraged for outreach to undergraduate and K-12 students. The students and trainees involved in this project will become part of a new generation of scientists and engineers capable of applying computational and experimental methods across disciplines to tackle complex problems. The goals of this project are to: (1) investigate the aerodynamics and aero-structural dynamics of individual feathers; (2) study the interactional flow effects in multi-feather configurations; and (3) explore the aerodynamics of flapping flight with feathered wing-inspired models. First-of-their-kind computational models will be developed to incorporate not only the complex vortex dominated flows generated by feathers but also the aero-structural deformations and feather permeability. These computational models will be parameterized by structural testing and wind-tunnel studies of feathers as well as flying birds. The simulations will use innovative modeling approaches and efficient computational algorithms to bridge the very large range of scales that are encountered in this multi-physics problem. Micro-computed tomography imaging, micro-tensile testing, and wind-tunnel recordings of the aeroelasticity of feathers will provide key data for input and comparison with the simulations. The computational models of multi-feathered flapping wings parameterized from feather kinematics extracted from birds in flapping flight will significantly advance understanding of the function of this unique and intriguing flight “device.” The findings could improve designs for drones, making them lighter, quieter, and more efficient. The research could also lead to better understanding of flow over porous surfaces, benefiting various fields like aeronautics, biomedicine, and engineering. Finally, this research will enable exceptional educational and training opportunities for students at the intersection of biology and 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 2024 · 2024-11

Scientific data analysis is a large-scale process that involves instruments generating data at one site and networks moving the data to a high-performance computing facility where the analysis happens. Programmable networks are capable of reading the data inside data packets, opening the possibility of performing computations while data is still in transit. However, computing within the network is challenging given the limited compute and memory resources of programmable network devices. The SciWiT project plans to implement a prototype system for performing scientific data analysis using in-network and near-network resources in an optimal way. Many scientific workflows continuously monitor a phenomenon in search for rare events. This process generates enormous amount of data; thus, researchers rely on change detection algorithms to locate the rare event information. SciWiT is a computing model where programmable network resources operate on the raw data streamed through it. While data is still in transit, network identifies the regions of interest from the data stream and to provide feedback to the instrument. SciWiT plans to investigate how scientific workflows can leverage network-based in-transit computing and to develop novel in-network and near-network computing mechanisms to operate on scientific data streams. SciWiT will benefit scientific applications that rely on change detection. Moreover, this project will enhance the viability of making programmable networks an inherent computing element in the scientific data processing pipeline, effectively making the technology widely available to the scientific community. Similar to cloud environments, in-transit computing environments distributed across campuses will onboard scientific computing community to leverage the benefits of high speed programmable networks. Wide-spread adoption of the developed solutions and the downstream research enabled by the findings of this project could result in acceleration of the scientific discovery process through a fractional increase in the resources, thus benefiting the wider public. More details on SciWiT can be found at [https://gitlab.com/sciwit/public/] This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project plans to investigate fundamental questions in STEM learning and learning environments, specifically how to design and integrate artificial intelligence (AI) to support equitable K-12 science education. The project will explore a promising solution involving multimodal AI to design automated feedback for formative science assessments. Insights from this research will illustrate how AI feedback can be applicable to a variety of assessment modalities and science content, promote students’ science identities, and inform instructional goals and practices. Multimodal formative assessments—incorporating textual, audio, and visual representations—create expansive spaces for students to demonstrate their scientific understanding. Following the assessments, in-time feedback that highlights and further elicits students’ ideas can deepen their science practices and participation. This project will generate design principles to develop in-time AI feedback for multimodal formative assessment, and understanding of how such feedback supports science learning and teaching. There will be three project phases mapping onto the three project years. Year 1 will include three iterative design cycles to develop, evaluate, and refine the AI feedback using large multimodal models. These cycles will be guided by ambitious and equitable science learning frameworks. They will be conducted in collaboration with a Design Team of middle school teachers and the Advisory Board, along with iterative testing with teachers and students in different assessment contexts. This approach ensures that the designed feedback is ecologically valid and aligned with a range of instructional settings. Year 2 will involve a year-long classroom pilot of different AI feedback conditions (static versus interactive feedback) with 60 sixth graders, to examine how the feedback supports science practices, science identities, and instructional practices. Year 3 will focus on analysis and dissemination. 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

An estimated 350,000 individuals experience out-of-hospital cardiac arrest (OHCA) in the United States (US) annually; only ~10% survive. For every minute that passes without cardiopulmonary resuscitation (CPR) and defibrillation, survival likelihood decreases by 10%. Survival is most likely when CPR and defibrillation occur within 5 minutes, but the median emergency medical services (EMS) arrival time in the US is 8 minutes and is far longer in rural areas. Drones have the potential to decrease AED delivery time, especially in the ~80% of OHCAs that occur in the home, and especially in rural areas. Our prior work has shown that a statewide drone network could decrease median AED arrival time from 7.7 to 2.7 minutes in North Carolina (NC) and double survival rates (24.5% v. 12.3%). However, EMS-integrated AED-drones have not been evaluated in the US. Our study will evaluate an innovative application of drone technology using a multidisciplinary approach in a real-world environment in rural, residential, and urban areas of NC. Using a multidisciplinary approach in a real-world environment in rural, residential, and urban areas of NC, we propose to evaluate the feasibility and time savings of integrating an AED-drone delivery system into an existing EMS dispatch/9-1-1 telecommunication system. This fully integrated drone-AED-EMS system will be ready to respond to suspected OHCA cases in the field and augment traditional ground transport. We will also evaluate EMS readiness to implement a statewide AED-drone network across NC. Finally, we aim—for the first time in NC and possibly the US—to dispatch an AED-drone to a live OHCA via an EMS-integrated system. This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs 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

This incubation project aims to examine technical, political, and cultural challenges that non-federally recognized Indigenous Tribes confront. The project seeks to address these challenges by focusing on the experiences and knowledge of non-federally recognized Tribes in the state of North Carolina. The project offers a unique opportunity to envision ethical research partnerships between U.S. Indigenous Peoples and other groups. The project will facilitate the ability of such partnerships to contribute to solutions for climate change and other pressing environmental crises. The project team will determine how ethical and responsible research partnerships can lead to understanding and promoting respectful scholarship to environmental solutions based on Indigenous knowledge systems. The project is motivated by ethical questions around policies and practices concerning research engagement with Indigenous Peoples whose tribes are not officially recognized by the federal government. The project includes activities aimed at (1) elucidating Indigenous perspectives on environmental research ethics, (2) evaluating the breadth and depth of current environmental research engagements within their communities, and (3) assessing their perspectives and gathering feedback on emerging research policies. This work is a first step in research that has the potential to bring these specific issues to light. The project will contribute to a more nuanced understanding of ethical considerations around research engagement with Indigenous Peoples. This project is jointly funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences and the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

There are more than 50,000 islands in the world, accounting for 17% of the total land area and inhabited by 10% of the global population. The US accounts for 18,617 islands, where the cost of electricity such as in Alaskan and Pacific islands can be 4-8 times higher than the average in the US. The same is true for remote coastal communities, such as 200 miles of Outer Banks of North Carolina, 120 miles of Florida Keys, and many islands in the Great Lakes. For power utilities, these communities rely on imported fossil fuels or miles of umbilical cables, which are vulnerable to earthquakes, wildfires, hurricanes and storms. While the electricity supply is one of the challenges limiting socio-economic development of remote island and coastal communities, vast energy resources are available from ocean waves along the 95,471 miles of US coastline. The power density of ocean wave energy is over 10 times that of solar power and 5 times as much as wind power. Attempts to harvest this resource date back to 1799, when the first patent was issued. To date, about 250 concepts of wave energy converters (WECs) have been proposed, but none of these have achieved commercial success. There is not even a widely-accepted criterion by which to judge which WEC concept is most favorable. The objective of this project is to drive and achieve research convergence of ocean wave energy conversion for empowering remote coastal communities through transdisciplinary research across engineering, economics, environmental, and sociological dimensions. The team expects to achieve convergence for powering remote communities within 4-5 years. In the longer term, wave energy can directly benefit a large proportion of the U.S. population without long-distance transmission, since over 53% of the U.S. population is concentrated within 50 miles of the shoreline. The project will provide significant potential to improve the economic development of under-served coastal communities by identifying a practical route to renewable electricity, thereby increasing their resilience to natural disasters, and empowering the local economy. It will also substantially benefit education from K-12 to graduate students in four universities with an emphasis on professional skills development. This project will drive convergence of ocean wave energy research through community-engaged decision making, 3D techno-economic socio-environmental assessment, and transdisciplinary co-design methodology. The goal will be achieved in two phases. Phase I will develop the WEC convergence roadmap, screen and down-select 2-3 lead WEC design concepts. This will be achieved by creating 3D assessment metrics to systematically evaluate technological feasibility, economic viability, and socioenvironmental acceptability in the early foundational concept and design stage. Phase II will investigate the leading design concepts through transdisciplinary co-design and optimization, and validate the convergence through community engagement and ocean tests. Inspired by the drug discovery process, the project will use a market-pull convergence procedure based on the needs of remote coastal communities to screen various WEC concepts from the beginning. This is in contrast to the prevailing approaches in wave energy research and development. The project includes a multidisciplinary team consisting of experts in engineering, environment, sustainability, social science and an external advisory board with community end users and OEM developers to implement a transdisciplinary, community-engaged approach to this research challenge. 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.