Cornell University

NSF Awards · FY 2024 · 2024-08

This Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Hydrogen, a clean alternative to fossil fuels and one of the only zero-emission solutions to high-capacity and long-term energy storage, can provide a viable solution to the deep decarbonization of energy systems and thus bring profound impacts on climate change mitigation. Taking advantage of the most abundant and widely accessible renewable energy on Earth, splitting water using natural sunlight could not only meet potential hydrogen demand but also enable a fully sustainable approach without carbon emissions, i.e., green hydrogen production. Despite the great promise, existing solar-powered green hydrogen technologies suffer from (1) limited energy conversion efficiency (< 10% solar-to-hydrogen conversion efficiency) and (2) substantial clean water consumption (> 9 liters of water per kilogram of hydrogen), which has posed significant challenges to making real-world impacts. The overarching goal of this project is to design, develop, and optimize a sustainable electrolytic system with natural sunlight and seawater as the sole inputs, which is expected to achieve a promising solar-to-hydrogen efficiency with additional clean water as the byproduct. The research will be integrated into STEM education and workforce development by equipping graduate students with interdisciplinary expertise through hands-on research projects, incorporating cutting-edge topics of clean energy technologies into undergraduate and graduate courses, and hosting high-school and undergraduate students through workshops and summer research programs. Furthermore, the project aims to increase public awareness of clean energy technologies by displaying the prototype and performing field demonstrations in local communities. The research aims to develop a high-efficiency, low-cost, and off-grid solar-powered seawater electrolysis strategy for green hydrogen production combining interdisciplinary expertise in heat and mass transfer, electrochemistry, and material science. Realization of the design relies on the full-spectrum utilization of solar energy, which requires a mechanistic understanding of (1) the solar-electricity-water conversion to realize the optimal photovoltaic efficiency and solar evaporation rate, (2) the water-hydrogen conversion to approach the electrochemical kinetic limits of hydrogen evolution and the maximum clean water production, and (3) the coupling of key components toward the theoretical limits of solar-to-hydrogen conversion efficiency. The research will not only advance the fundamental understanding of energy conversion and transport associated with green hydrogen, but also demonstrate a fully sustainable pathway toward unprecedented solar-to-hydrogen efficiency and clean water production with natural sunlight and seawater as the sole inputs. 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 · 2024-08

Imagery around the world—from satellites to drones and social media photographs—provide vital information about our planet. There is a unique opportunity in the fields of artificial intelligence and computer vision to understand global and local phenomena from these images, providing insight about climate change, public health, and agriculture. However, the state-of-the-art methods in computer vision are not designed for these applications where decision-making is complex, and accuracy, robustness, and interpretability are required. Existing large-scale AI models, such as ChatGPT, only process individual images on the internet and cannot synthesize conclusions from planet-scale image collections. Even on single images, these models cannot reliably perform sophisticated logical reasoning, and building models to do such reasoning reliably requires unfeasibly large datasets. Creating such large models and datasets is a significant barrier for scientific and societal applications of computer vision, particularly for organizations that do not have the computational resources of large corporations. This project will create a new class of machine learning models, called programmatic foundation models, that have the capability and efficiency to scale to planetary-scale image and video datasets. These models can be queried by experts using natural language, thus empowering scientists and experts to benefit from AI related visual discovery from the vast amounts of visual information available in satellite imagery even if they lack expertise in machine learning. The proposed research has applications across public health, climate change, agriculture, security, and the economy. The research objective of this project is to tightly integrate visual representations and program synthesis together, thereby delivering an accurate, interpretable, and robust machine learning framework for answering questions about what is visible in image collections. Across two research thrusts, the project will drive the creation of these new programmatic foundation models. The first thrust proposes new techniques for building open-world recognition primitives across multiple sensing modalities based on vision-language models, but without any language annotations. It introduces new cross-modal contrastive learning techniques, as well as approaches for reasoning about temporal change. The second thrust proposes new techniques to learn to synthesize programs, incorporating uncertainty, learning from feedback and adaptive computation. Given a query, our proposed framework learns to synthesize a customized program that breaks the task down into constituent steps and control flow that can be directly executed for solving the vision task. To execute each step, the project proposes new methods for training open-world classification, detection and segmentation models for satellite, aerial, and ground imagery. Unlike prior foundation models, this integrated approach has many potential benefits in interpretability, logical soundness, modularity, compositionality, efficiency, and generality to different tasks. The two thrusts taken together combine program synthesis with open-world recognition models for analyzing satellite, drone, and ground imagery around the world. 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 · 2024-08

Agriculture in New York State faces increasing challenges from climate change, with rising temperatures and frequent flooding that threaten traditional farming practices. The Community-driven Innovations in Rice for Climate Adaptation in New York State (CIRCA-NYS) project addresses these challenges through the integration of rice farming into the local agricultural economy and landscape. This initiative aims to establish rice as a sustainable crop alternative, enhancing environmental sustainability and economic development in rural areas. By engaging local farmers, academic experts, and extension services, CIRCA-NYS promotes a community-centric, interdisciplinary approach to develop and implement environmentally sound and economically viable farming strategies. The project aims to adapt to immediate climatic challenges and foster long-term agricultural resilience and sustainability. CIRCA-NYS seeks to transform agricultural practices by integrating advanced spatial data analysis, field-based assessments, and active community involvement to develop a scalable model of sustainable rice cultivation adapted to changing biophysical and socioeconomic conditions in New York State. The project explores optimal conditions for rice farming by assessing soil types, water availability, and topographical features, alongside socio-economic factors like land use trends and changes in flood insurance risks. Methods include spatial analysis to map suitable farming areas, participatory research to identify barriers to adopting alternative practices, and pilot farms to test rice cultivation techniques. This integrative approach advances climate-smart agricultural practices and builds a robust community of practice among farmers, researchers, and policymakers. By focusing on co-developed solutions and practical experiments, CIRCA-NYS provides critical insights into sustainable farming adaptations necessary for combating the impacts of climate change on agriculture. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Essential societal decisions and allocations rely on imperfect crowdsourced data. For example, in resident crowdsourcing, the public reports problems (for example, fallen trees and power lines and flooding after storms) that the government needs to address. State and national public health decisions (for example, allocation of vaccines and resources) rely on individual testing and reporting. However, decisions made without incorporating what is known regarding the deficiencies of crowdsourced data can be wasteful. For example, past work has shown that data from some areas may be systematically missing, leading to under-allocation of resources there. This research project will improve public decision-making by developing statistical methods to understand differential reporting behavior and engineer more efficient, transparent systems that account for missing information and heterogeneous behavior. This knowledge will help government and public-interest organizations allocate resources where they are most needed. This project will also educate data scientists and researchers for the public interest, including by providing continuing education and publicly available resources for municipal technology workers and open data hobbyists. This research pursues three objectives: 1) measuring statistical errors in report data, with a focus on public crowdsourcing of incidents and community health monitoring, where needs are spatially correlated and varied; 2) auditing responses to reports when resources are capacity constrained and multi-stage; 3) in collaboration with government and non-profit decision-makers, improving every stage of the response pipeline in practice. The research will contribute methods for general Bayesian inference, optimization, machine learning, and data-driven decision-making , applied to auditing and engineering systems in complex environments, including education, health, and government broadly. 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 · 2024-08

PROJECT SUMMARY When you reach for an apple and miss, you immediately make corrective submovements to make contact. And after contact, precise touches at your fingers reshape your grasp. Unknowingly, our tongues move with similar sophistication during speech and as we use the tongue’s sense of touch to handle, chew, and swallow food. Tongue discoordination in systemic neurological diseases results in aspiration pneumonia, a leading cause of death in Parkinson’s and ALS. But because tongue movements are extremely fast and hard to measure, neural mechanisms of tongue control have been understudied. Here we combine high speed videography and deep learning guided segmentation to quantify tongue kinematics with millisecond timescale precision. Next we develop new behavioral paradigms inspired by primate target-jump reach-and-grasp tasks (Aim 1). As with the primate limb, when the mouse tongue undershoots an unexpectedly withdrawn target (water spout), it immediately produces corrective submovements to make contact. And as with a grasping hand, when the mouse nicks a moved spout with the left or right side of its tongue, it immediately redirects the lick left or right for better contact. To identify signals and circuits underlying these corrections, we combine brainwide neural recording and manipulation (Aim 2). So far, neural recordings identify representations of misses, nicks, and aimed corrections in single neurons and neural population dynamics in multiple brain regions. Miss-guided corrections are impaired by both cortical and cerebellar inactivations. But mechanisms of touch-guided corrections are fundamentally different. The ability to re-steer a tongue after a nick is impaired by inactivation of superior colliculus, but not tongue-jaw sensory cortex (TJS1), tongue jaw motor cortex (TJM1), orofacial premotor cortex (ALM), or a lick-associated region of cerebellum (fastigial nucleus). This double dissociation suggests that touch-guided tongue steering may use midbrain pathways associated with visually-guided orienting. To test this idea (Aim 3), we combine neural tracing, tongue surface receptive field mapping, and optical microstimulation across precise locations of the colliculus. Our pilot data support the existence of a topographically ordered tongue touch-to-tongue steering map on the surface of the colliculus which essentially re-purposes both the logic and the layout of more commonly studied visually-guided saccades. In sum, our goals are twofold: First our proposed research will finally clarify which aspects of the extended motor system are important for which aspects of sensorimotor tongue control. Second, by comparing our findings to what’s known sensorimotor processes underlying reaching and orienting, we aim to distinguish idiosyncratic solutions to narrower sensorimotor control problems from general principles that hold across effectors and species.

NSF Awards · FY 2024 · 2024-08

Surveys provide fundamental data about the social and economic conditions of communities around the world. In the U.S., the National Science Foundation funds a number of important social science surveys that produce data relevant to nearly every aspect of American life. The goal of this conference is to bring together principal investigators from several of these surveys and other global experts in survey methodology for a first-of-its-kind meeting to discuss potential synergies across surveys, developments in survey methods including artificial intelligence applications, and best practices for enhancing the efficiency and impact of surveys on society. The conference will benefit the science of survey research by identifying state-of-the-art knowledge, including common challenges and opportunities, and by incorporating relevant advancements in artificial intelligence technology. The conference will benefit society by focusing on ways that surveys can better represent the views of diverse segments of the public, by inviting a diverse group of early career scientists to participate in the meeting, and by making key insights and conclusions publicly available through the conference website. Surveys have long been a prominent method for measuring social and economic conditions globally. In the U.S., the National Science Foundation funds a number of important social science surveys. Yet, changing social conditions and advancements in technologies present new opportunities and challenges for surveys and the methodologies they rely on. This conference will bring together principal investigators of several NSF-funded survey projects, early career scholars, and other global experts focused on survey research and methods, including large language models (LLMs) and AI, for a Thought Summit on the Future of Survey Science scheduled to take place in September 2024. Primary conference goals include developing synergies among NSF-funded surveys, increasing their societal impacts, and informing the future of survey research. Specific aims include: 1.) Strengthening collaboration across NSF projects 2.) Sharing best practices 3.) Identifying challenges, opportunities, and solutions in the survey research and AI space. With respect to intellectual merit, this conference will advance the state-of-the-art knowledge of survey methods and best practices, identify synergies between survey research and large language model (LLM) and AI research focused on modeling public beliefs, attitudes, and opinions, and generate new lines of inquiry at the intersection of survey methods and AI research, the outcome of which could alter the future of survey research. With respect to broader impacts, NSF-funded surveys enhance understanding about social, political, and economic conditions affecting diverse segments of the public, while attempting to represent the unique viewpoints of different groups as accurately as possible. This conference will support these broader impacts by helping to ensure that existing NSF-funded surveys are achieving their goals of capturing all voices and conducting representative surveys. In addition, the meeting will recruit a diverse group of junior scholars to ensure their perspectives are incorporated and to help create a more diverse and inclusive scientific community. Finally, to maximize the broader social impact of the meeting, key findings, conclusions, and recommendations from the meeting will be made public through the conference website. 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 · 2024-08

The research project aims to advance organic solar cells (OSCs) to support the shift from non-sustainable to sustainable energy sources. While OSCs have achieved significant power conversion efficiency, their large-scale production faces challenges due to the use of toxic solvents that are harmful to both the environment and public health. This project will develop greener manufacturing processes using eco-friendly, biomass-derived solvents such as Cyrene and γ-valerolactone (GVL). These bio-based solvents offer reduced toxicity compared to the currently used solvents. One challenge is that the current components for OSCs are not compatible for processing with these two greener solvents. This project will use an innovative combination of experiments and molecular simulation to rationalize the design of new chemical functionalities with characteristics to improve their solubility. This collaborative project will provide outstanding opportunities for training graduate students in interdisciplinary approaches that integrate experiments and simulations. The project will also support educational opportunities and promote diversity in STEM by participating in local outreach activities such as the "Harvesting Sun Light" workshop for the Expanding Your Horizons conference, designed to inspire middle school girls to engage with STEM. The primary objective of this project is to design and synthesize zwitterlated conjugated polymers and organic molecules that can be processed using the biomass-derived solvents Cyrene and GVL. The researchers hypothesize that tuning the charged group pairs in zwitterionic sidechains will control the solubility and assembly of these materials in the selected solvents. The research approach combines computational and experimental research: molecular simulations will explore the thermodynamics of solvation and assembly of the polymers and molecules in Cyrene and GVL, while experimental efforts will focus on synthesizing these zwitterlated materials and characterizing their assembly and film morphology. The researchers aim to develop manufacturing processes that produce high-performance OSCs with power conversion efficiencies exceeding 15%. This research will advance understanding of the role of zwitterionic sidechains in solubility and assembly, illustrate their impact during the nonequilibrium solvent evaporation process, and result in the development of multiple high-performance zwitterlated polymer systems and manufacturing processes. The outcome of this collaborative project will be the molecular principles for designing zwitterlated polymers and molecules that can be used to produce high-performance OSCs using Cyrene and GVL-based processes and several such polymers and molecules. 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 · 2024-08

This award supports research that will enable engineered microfluidic devices to emulate the capabilities of biological surfaces that control and manipulate flows using the movements of tiny hairs, called cilia. The project integrates recently demonstrated micrometer-scale artificial cilia with optical, chemical, and thermal sensors, and closes the loop with programmable control circuits. The resulting modular robotic surfaces will be capable of sensing changes in their chemical and thermal environments and responding with the appropriate beating pattern to alter the fluid flows in desired ways. Combining these modules according to their complementary function will create metasurfaces capable of driving controlled and modifiable fluid flows for stand-alone centimeter-scale devices, with cilial robots lining microfluidic channels and controlling the flow chemistry. These devices would be potentially transformative for the field assays of blood, water, and chemical samples, and other testing and monitoring functions. Additionally, a number of education and outreach activities are planned to include science communication workshops, participation in research experience for undergraduate summer activities, and various outreach activities to the broader public. This project seeks to design and fabricate micrometer-scale cilia metasurface robot building blocks capable of sensing their environment, performing computation, and actuating a fluid flow in response to environmental changes. The project builds on the team’s newly developed artificial cilia platform where micron scale hairs can be electronically controlled to pump fluid. This project seeks to convert this platform into a robot by developing and integrating low power and robust optical, chemical, and thermal sensors as well as the ability to program the control circuits that decide what cilial beating pattern to implement based on the output of the sensing. Specifically, the project will entail: (1) designing, fabricating, and testing low power and robust optical, chemical, and thermal sensors that can output electronic signals; (2) designing, fabricating, and iterating (light) programmable low power CMOS control circuits that drive different cilial actuation patterns based on the sensor signals; and (3) redesigning the cilia to have internal hinges so that they enable more efficient pumping over a greater range of frequencies. Most importantly, these elements will be integrated into a robot that will be tested and validated. The robot building block design is modular and scalable, as each robot building block is powered by photovoltaic elements, contains sensors, and electronic circuits for controlling the resulting fluid flows. These robotic cilia building blocks could be used to make metasurfaces capable of driving controlled and modifiable fluid flows for stand-alone centimeter scale devices. Such devices could be used in field testing of blood, water, and oil samples, as well as applications in flow chemistry, where cilial robots could be used to line microfluidic channels to control chemical reactions taking place within the flows. In addition to these technological impacts, a number of education and outreach activities include science communication workshops, participation in research experience for undergraduate activities, and various outreach activities. 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 · 2024-08

Global food security and the demand for high-yielding grain crops are among the most urgent and ambitious drivers of modern plant sciences due to the current trend of population growth and decreasing arable land resources. The total grain yield is directly linked to crop and soil fertility. In this regard, the limited availability of the micronutrient copper in the soil causes copper deficiency in crop plants. This condition leads to plant infertility and, consequently, low yields. However, the physiological, molecular, and genetic mechanisms underlying this trait are unknown. Besides copper nutritional demands, plant reproduction is regulated by plant hormones. Among them, the hormone auxin has a recognized role in reproductive organ development and patterning. The existence of the relationship between auxin-mediated developmental programs and plant mineral demands has not yet been considered. This project will use multifaceted functional genetics and genomic tools to explore a newly discovered untraditional link between copper function in reproduction and auxin in a model plant, Arabidopsis thaliana. The discoveries made through this award will guide future studies on the interplay between micronutrients and hormone signaling for ensuring normal plant developmental programs. In a broader context, this project is relevant to the future of food security, as it has the potential to contribute to molecular breeding efforts directed at improving crop grain yield on marginal soils. This project will provide unique training opportunities. Through a partnership with the University of Texas at El Paso (UTEP), students at Cornell and UTEP will get hands-on experience in using cutting-edge synchrotron X-ray techniques to address complex biological questions. Copper is a redox-active micronutrient with a recognized role in plant reproduction and seed yield. Despite this knowledge, the specific function of copper in reproduction, the sites of its action, and the genes controlling its delivery to reproductive organs still needed to be fully understood. The phytohormone auxin regulates every aspect of plant development and is a recognized morphogen that controls reproductive organ development and patterning. Previous work has shown that copper localizes to anthers, pistils, and pollen grains in Arabidopsis thaliana; copper delivery to these reproductive structures requires two transcription factors, CITF1 and SPL7. Failure to deliver copper to these reproductive structures in wild-type grown under acute copper deficiency or in a double mutant lacking CITF1 and SPL7 leads to female and male infertility. Notably, some fertility and shoot architecture defects of copper-deficient and citf1 spl7 mutant plants resembled those observed in auxin synthesis, transport, or signaling mutants. This finding was intriguing, considering that the existence of the relationship between auxin-mediated developmental programs and plant mineral demands has yet to be considered. The proposed studies will uncover whether copper and the CITF1-SPL7-regulated copper homeostatic pathway influence reproduction via acting on auxin metabolism and/or signaling, thereby presenting a radically different perspective on the mechanism of copper action in plant development. 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 · 2024-08

Terrestrial ecologists have faced a long-standing challenge - how to separate the uptake of atmospheric CO2 by terrestrial ecosystems (net ecosystem exchange, NEE) into its two offsetting components: gross primary production (GPP) and ecosystem respiration (Reco). A similar challenge has been plaguing hydrologists - how to quantify the two components of water vapor flux from land: transpiration (T) through plant stomata (pores) and evaporation (E) from non-stomatal surfaces. Despite numerous efforts in the past decades to partition the “direct observables” of NEE and ET, considerable biases remain in our estimates of the components. Without credible in-situ observation of GPP, Reco, T, and E, it will be impossible to understand or predict the complex dynamics of coupled carbon and water cycles under changing climate with the needed precision. Biases in these component flux estimates can further distort our understanding of their relationships, e.g., T:ET ratio and water use efficiency (WUE = GPP/T), driving uncertainty in how they are affected by environmental change. Therefore, it is urgent to develop innovative approaches that accurately partition NEE and ET into their component fluxes. This will reduce uncertainties in current terrestrial biosphere models (TBMs), improve water resource management, and inform nature-based climate solutions. This project aims to harness new theoretical and data advances in the remote sensing of Solar-Induced chlorophyll Fluorescence (SIF) to jointly partition ecosystem ET and NEE fluxes. It has three technical aims: 1) developing a mechanistic approach to jointly partitioning ET and NEE into their component fluxes using concurrent canopy-scale SIF and flux measurements across diverse NEON ecoregions and hydroclimatic regimes; 2) disentangling interacting mechanisms that control the temporal and spatial dynamics of individual component fluxes, T:ET ratio, and WUE across biomes and hydroclimatic regimes, and 3) improving the NCAR Community Land Model (CLM5) by better representing the interacting mechanisms among component fluxes through a hybrid modeling approach that embeds mechanisms into a deep learning framework, i.e., Biology-Informed Neural Networks (BINN). The proposed SIF-based joint-partitioning framework, guided by new ecophysiological theories, will provide valuable datasets of individual component fluxes, T:ET ratio, and WUE across diverse biomes and hydroclimatic regimes. These datasets will enhance the fidelity and realism of TBMs in predicting ecological and hydrological dynamics at multiple scales under climate change. This project will support a diverse array of students via the annual training course “New Advances in Land Carbon Cycle Modeling,” engaging high-school students via the Project SEED program, and contributing to undergraduate/graduate education at Cornell University and Indiana University Indianapolis. 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 · 2024-08

Ultracold gasses form our most versatile platform for studying quantum mechanics: By combining basic building blocks scientists are able to produce complicated quantum systems that are used to test hypotheses about the emergent behavior of interacting quantum systems. This research program is aimed at expanding the range of physics which can be studied through these cold gas experiments. The goal is then to apply this knowledge to the development/understanding of new quantum materials and new quantum technology. Graduate student training is integral to the program and the participants will become experts in this quantum technology. The research program also contains an education component aimed at improving physics education for students in life sciences and those who are pursuing health careers. The research program is organized around three questions: (1) How does one controllably create highly entangled quantum states of matter? (2) What are the emergent properties of strongly interacting quantum systems? (3) What aspects of fundamental physics can be explored through cold gas experiments? Sophisticated numerical techniques, such as the density matrix renormalization group, will be used to model various scenarios. The researchers will develop new approaches to state preparation, explore the properties of quantum simulators of strongly correlated physics, and build tools for experimentally realizing lattice gauge theories. 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 · 2024-08

NONTECHNICAL SUMMARY This award supports research and education in the fields of computational modeling and materials engineering. Materials that exhibit properties intermediate between typical liquids and typical solids are important for numerous applications. Examples of such multipurpose materials include rubbers, liquid-crystals, and glasses. Having a dual character with some specific combination of solid-like and liquid-like features is instrumental to their enhance functionality in such applications as shock absorbers, electronic displays, specialty glues, packaging, etc. The challenge tackled in this project is the design of materials that possess a solid-like high mechanical strength, but also a liquid-like plasticity to allow for significant extent of self-healing after large deformations. This type of material fills a different region of the intermediate solid-like/liquid-like property space which more conventional materials already occupy. The proposed materials will be designed to have high toughness and self-healing abilities by virtue of using small-molecule building blocks, the presence of solid-like and liquid-like inter-penetrating regions, and an imprintable ‘shape’ memory arising from tunable bonds. Such materials can find potential applications as shock absorbers, mechanical cloaking devices, flexible semiconductors, and biomimetic materials like artificial cartilage and bone. Resilience and self-healing properties allow materials to have longer service lives and lower user demands, thus potentially reducing their associated manufacture costs and carbon footprint. This project will use molecular modeling methods to establish correlations between the structure and mechanical properties of this type of material and is complementary to experimental efforts by Cornell research groups involved with designing new organic materials. The project will enable the training of a doctoral student and an undergraduate student in computational materials research. Relevant results will be disseminated through multiple forums including Cornell’s Soft Matter seminar series, professional meetings, and will contribute to a hands-on lab (about ‘Engineering liquid-like solids and solid-like liquids’) for the Women’s group outreach initiative in the Chemical Engineering Department that targets rural High School girls. TECHNICAL SUMMARY This award supports research and education in the fields of computational materials science and soft matter engineering. The overarching goal of the project is to predict how polyphilic molecules assemble into percolating mesophases having solid-like and liquid-like domains so that upon crosslinking they can encode a structural memory that endows them with the ability to both absorb the stress of large strain deformations and to self-heal. Such materials lie in the design space between small-molecule frameworks and macromolecular networks, and are envisioned to have solid-like mechanical strength and liquid-like reconfigurability. The building blocks of interest are polyphiles having different blocks whose lack of mutual affinity leads to ordered nano-segregated percolating structures with solid-like frameworks filled by amorphous channels. Rigid aromatic blocks are suitable to form the framework struts, polar hydrogen-bonding moieties are suitable to glue the struts into percolating patterns, and flexible side chains of hydrophobic segments can be added as structure-directing agents to modulate the framework geometry. While the self-assembled ordering (and associated hydrogen bonding) encodes a soft physical ‘memory’ for the material to recover its structure upon deformation, the project explores the use of crosslinking of the pre-assembled structures as a strategy to imprint a tunable, more permanent chemical memory. Molecular dynamics simulations using both coarse-grained and atomistic models will be used to investigate: (1) The tensile deformation of linear crosslinked triblock oligomeric systems preassembled into lamella mesophases as the baseline for understanding the correlation between stress response and mechanisms of molecular deformation, nano-phase transitions, and hysteresis, (2) how physical and chemical modifications of the triblock can tune the tensile response of lamellar-preassembled crosslinked networks, and (3) the tensile deformation behavior of crosslinked, branched polyphiles preassembled into phases other than the lamellar phase, particularly, of network phases like the diamond and gyroid. The project will provide a framework to train a doctoral student and an undergraduate student in the areas of computational multiscale modeling, statistical mechanics, and soft matter science. This project will also strengthen collaborations with Cornell research groups involved with designing new organic materials. Results will be disseminated primarily through professional meetings and will contribute to the creation of new teaching material and outreach activities in the Department. STATEMENT OF MERIT REVIEW 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 · 2024-08

The theory of dynamical systems describes how mathematical structures change over time according to prescribed constraints and laws. Dynamical systems model a host of complex phenomena, ranging from celestial mechanics to financial systems to human social behavior. An important but difficult question is to understand when small perturbations of the initial state of a system will qualitatively change the long-term behavior. The stability problem is frequently investigated through a rigorous study of the classifying space of all relevant dynamical systems, known as moduli space. This project focuses on a broad class of one-dimensional dynamical systems satisfying a geometric constraint known as conformality, along with the associated moduli spaces. Tools from complex analysis, hyperbolic geometry, and arithmetic geometry will be combined to address longstanding conjectures and to open new directions for investigation. The project will generate research opportunities for undergraduate and graduate students and will facilitate collaboration among early-career researchers via the organization of seminars and workshops. The research will also result in visually compelling representations, including intricate fractal images and videos, which will be shared with the broader public. Three distinct but interrelated directions lie at the core of this research project. First, the investigator will use recently developed techniques for the study of degenerations of rational maps and a priori renormalization bounds to study boundedness questions in conformal dynamics. These methods suggest promising approaches to tackle longstanding conjectures about the boundaries of hyperbolic components for rational maps. Next, the investigator will extend the correspondence between rational maps and Kleinian groups. This extension yields novel hybrid dynamical systems combining rational maps and Kleinian groups, where renormalization and rescaling methods can be used to understand rigidity and the deformation spaces. Finally, the investigator will pursue a recently developed renormalization theory for infinite circle packings. These new techniques hold promise in solving various conjectures regarding the quasiconformal geometry of circle packings, thereby addressing some open questions about uniformization and offering insights into conjectures from geometric group theory. 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 · 2024-08

NON-TECHNICAL SUMMARY: As the mRNA delivery technology using lipid nanoparticles (LNPs) has recently achieved remarkable success in COVID-19 vaccines, mRNA/LNPs have been now extensively explored for many applications beyond COVID-19 vaccines, particularly cancer, infectious diseases, or auto-immune disease vaccines. However, these new applications pose much greater challenges to the current mRNA/LNP technology than COVID-19 vaccines. This work proposeds to develop a new mRNA delivery system based on functional phosphoserine (PS) lipids to resolve today's issues for mRNA delivery for applications beyond COVID-19 vaccines. This proposal aims at understanding various properties using unique functional PS materials. This work underscores the pivotal role of biomaterials in unlocking the full potential of mRNA/LNP technology across diverse applications. This technology, along with a fundamental understanding, has the potential to revolutionize a wide range of applications such as gene therapy, protein-replacement therapy, and cancer immunotherapy in the post-COVID era. Support of this project will provide multiple-disciplinary research opportunities to students. Knowledge will be disseminated through organizing international conferences, participating in Expanding Your Horizons (EYH) on the Cornell campus, and involving projects at the Sciencenter in Ithaca. TECHNICAL SUMMARY: The mRNA delivery technology using lipid nanoparticles (LNPs) has been now extensively explored for many applications beyond COVID-19 vaccines. However, the current mRNA/LNP systems lack targeting. It is also desirable that LNPs for vaccination should be able to modulate the immune response. This proposal aims at understanding these properties using unique phosphoserine (PS) functional materials. Previously we have shown that while a natural PS lipid was added to LNPs, most PS-modified LNP formulations efficiently deliver mRNA to the secondary lymphoid organs (SLOs), including the spleen and lymph nodes. We have also shown the immunosuppressive properties of PS moieties. It is proposed here to develop functional PS lipids. It is hypothesized that LNPs with functional PS-lipids are expected to exhibit unique characteristics, precisely target therapeutically relevant organs and cells, and achieve longer circulation and actively mitigate the immune response, all in one carrier. Thus, these functional PS lipids have several advantages, including higher lipid solubility, enhanced SLO targeting, greater cell transfection rate, longer blood circulation, reduced immunogenicity, and stronger immunomodulatory effects. All these features will distinguish the proposed LNPs from conventional ones. The proposed work presents a biomimetic approach that effectively tackles the challenges of targeting, low-immunogenicity, and immunomodulation simultaneously, all of which are currently encountered in mRNA/LNP technologies. Support of this project will provide multiple-disciplinary research opportunities to students. The PI will develop a workshop, allowing girls and their parents to have hands-on experiences with biomaterials and cancer vaccines and a demo, allowing K-12 students to learn how biomaterials impact precision medicine in collaboration with local organizations. The PI will continue to organize International Conference on Bioinspired and Zwitterionic Materials to disseminate knowledge. 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 · 2024-07

Polynomial equations are ubiquitous in science, describing important physical principles and serving as mathematical models for complex natural phenomena. Algebraic geometry studies geometric structures arising from solutions to systems of polynomial equations. To gain a better understanding of these structures, it is useful to study how they change when the corresponding equations are slightly perturbed. This is achieved by studying a “parameter space” for these structures. The overarching goal of this project is to use techniques from commutative algebra to tackle longstanding questions related to the Hilbert scheme, a parameter space for polynomials with fixed properties. The project’s broader impacts include developing new packages for the open-source computer algebra system Macaulay2, organizing local seminars, and organizing mathematical conferences. The investigator will focus on three areas of commutative algebra and algebraic geometry: 1) Singularities of the Hilbert scheme of points on a threefold: The main goal is to understand the singularities of the Hilbert scheme of points on a smooth threefold. In particular, the investigator will focus on determining the smooth points and explaining some of the patterns appearing in the structure of the singularities. 2) Exploring multigraded Hilbert schemes and other moduli spaces: The investigator will study the space of branch varieties, a close analogue of the Hilbert scheme, and focus on studying the projectivity of this moduli space. 3) Varieties in weighted projective spaces: The investigator will focus on developing a set of tools to extend classical theorems in projective space, such as Macaulay’s theorem on the existence of Hilbert functions and the del Pezzo-Bertini classification of varieties of minimal degree, to weighted projective 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 2024 · 2024-07

This award investigates expanded and improved use of stochastic simulation models for optimal decision making under uncertainty. Simulation optimization (SO) can guide decisions that effectively hedge against risk, thus greater adoption will have many practical benefits across problems of importance to society in, for example, healthcare, transportation, and finance. The award addresses the lack of well-developed cyberinfrastructure for SO, which has hindered progress in the design and testing of efficient and reliable software for solving SO problems known as solvers. Significant steps will be taken to enhance the "SimOpt" testbed of SO problems and solvers to make it more powerful, widely applicable, aligned with emerging data-driven applications, and integral to the research community. Wider use of SimOpt through online content and tutorial workshops will foster more rigorous and reproducible experimentation in SO for researchers and practitioners in different fields and yield high-performing solvers for practical use. The improved library will also provide carefully curated resources for simulation educators to incorporate into their teaching efforts at all levels. Research completed for this project will help SimOpt achieve its full potential by improving the existing code base and increasing interoperability, expanding the kinds of experiments and analyses that can be carried out, and extending the role data plays in driving the library's models and problems to open up new frontiers in methodology and algorithm design. The next generation of SimOpt will accelerate advances in SO, including solver development and testing, more extensive experiments comparing new solvers to the state of the art, and hyper-parameter tuning to improve solver performance. The work will create a new data-centered capability in SimOpt that enables more comprehensive study of trace-driven simulation and an empirical risk minimization capability that bridges to closely related areas in machine learning. These data-centered initiatives will enable researchers from diverse fields to better identify and tackle critical open problems in calibration, empirical risk minimization, and distributionally robust optimization. The resulting cyberinfrastructure will enable significant developments in SO solver capabilities, leading to enhanced use of these powerful engines in applications and intellectual bridges to adjacent research communities. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Operations Engineering program in the Division of Civil, Mechanical and Manufacturing Innovation within the NSF Directorate for 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.

NIH Research Projects · FY 2026 · 2024-07

Project Summary This study seeks to advance heterobifunctional degraders that induce targeted protein degradation through the ubiquitin proteasome pathway. Typically, molecular degraders involve a ligand that recruits an E3 ubiquitin ligase and another that targets a protein of interest (POI), forming an E3:Degrader:POI ternary complex, leading to POI ubiquitination and subsequent degradation by the 26S proteasome. Peptide-based proteolysis targeting chimeras (PepTACs) offer distinct advantages over small molecule degraders for targeting protein-protein interactions due to their specificity, manufacturability, ease of design, and expansive binding surface area. However, due to challenges related to their limited cellular permeability and stability, which is evident in the modest potencies (micromolar range) of recently reported PepTACs, structure-function studies to improve their catalytic activity have not been investigated. We highlight a recent breakthrough where we facilitate PepTAC transport at nanomolar concentrations into cells via lipid nanoparticles (LNPs), establishing a robust platform for our proposed studies. We aim to explore the structural attributes of PepTACs to improve their catalytic activity and enhance target protein degradation. Our hypothesis revolves around modifying PepTAC structure and amphipathicity to improve degradation efficiency, leveraging prior literature showing that small molecule degraders with enhanced ternary complex stability drive greater target protein degradation rates. Our study comprises three key aims: the first focuses on identifying the optimal location of the E3 ligand to create a PepTAC that promotes enhanced positive cooperativity and rapid ubiquitination. The second aim investigates how PepTAC structure impacts LNP loading, stability, and intracellular transport. Finally, our third aim proposes a universal strategy for LNP loading based on tuning PepTAC lipophilicity to enhance LNP encapsulation and systemic stability. Achieving these aims would unlock the potential of PepTACs as valuable tools for cell-specific targeted protein degradation and broadening access to a valuable class of peptide-based protein degraders.

NSF Awards · FY 2024 · 2024-07

The Canadian Arctic Archipelago hosts a cluster of ice caps and ice fields that amount to 14% of Earth’s total glaciated area. However, a warming ocean and atmosphere is melting the ice and causing it to flow into the ocean at an increasing rate. This is reducing the volume of Earth’s freshwater reservoir, increasing global sea level, and changing the Arctic marine ecosystem. These changes have global societal and environmental implications. This project will study Milne Fiord, which is the final remaining fjord along the northern coast of Ellesmere Island, Canada, with perennial ice cover – a system that is now breaking up. Milne Fiord hosts a patchwork configuration of sea ice, a marine ice shelf, and a floating glacial ice tongue that covers its sea surface year round. This configuration dams freshwater in the upper water column between floating ice bodies, above their keels, to create a unique epishelf lake ecosystem, where specific organisms reside in a freshwater layer underlain by seawater and separated by a sharp halocline. A calving event in 2020 has triggered rapid changes to this system, which is now breaking up and the epishelf lake is draining. The lake drainage and ice shelf and tongue weakening appears to center around a predominant basal channel in the ice shelf and a set of full-thickness rifts in the ice tongue near the grounding line. A total collapse of this system will result in drainage of the last epishelf lake in Arctic Canada, increased ice-ocean-atmosphere interactions in Milne Fiord, and likely acceleration of the upstream glaciers. This work proposes to test that the long-term increase in atmospheric and oceanic temperatures is increasing basal melting of the Milne ice tongue through increased turbulent ocean heat flux. The project will collect in situ hydrographic profiles and water samples across the fjord. These measurements will be compared with melt rates from phase-sensitive radar to better understand the spatial distribution in ocean forcing and glacial melting in Milne Fiord. These measurements will then be placed into the longer-term context by comparing them to previous hydrographic profiles and mooring timeseries data collected by an ongoing international project between Canadian and US researchers. 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 · 2024-07

Project Summary/Abstract The ability for transcription factors to achieve specificity in the nucleus is critical for proper gene regulation. Mis-targeting of protein binding can disrupt an organism's ability to maintain homeostasis and result in disease states. While sequence-specific transcription factors ostensibly derive their specificity for binding based on preference to specific DNA patterns (motifs), multiple confounding variables such as epigenetic state, co-factor binding partners, and chromatin accessibility make the reality far more complicated. This research program applies a high-throughput (robotic) biochemical genomic approach with machine learning algorithms to identify the rules and mechanisms that govern the binding of proteins to the genome. We have previously developed multiple high-resolution genomic assays (e.g., ChIP-exo, PB-exo, WhIP-exo) that quantify genome-wide binding of proteins to DNA with varying levels of regulatory features present. We demonstrated the utility of these assays to understand the native binding preferences of general regulatory factors in the yeast model organism. The next stage of this research will be to apply these assays on human transcription factors in ultra-high throughput using a liquid handling robotic system to identify the mechanisms underlying transcription factor sequence specificity. The first major direction will be to determine the ability of purified transcription factors to bind naked DNA genome-wide (PB-exo) and to examine how the epigenetic status of the DNA can change the detected binding of a protein. By using genomic DNA sourced from different cell states, we will be able to precisely characterize the effect of cell state-specific DNA methylation on protein binding at base-pair resolution. We will also apply AI/ML algorithms that we have developed to cross-validate biological discoveries and make new testable hypotheses. An orthogonal and complementary approach will apply the WhIP-exo assay to examine transcription factor binding specificity again on naked DNA, but this time in the context of various cellular extracts. In addition to uncovering the effects of cell-state specific co-factors on protein binding specificity, we will also incorporate ChIP-mass spectrometry to identify the co-factors complexing with transcription factors of interest when they are bound to DNA. These assays will provide downstream testable hypotheses with regards to which protein co- factors are responsible for modulating binding specificity. The goals of this research program will result in a detailed understanding of the features responsible for regulating binding in hundreds of transcription factors along with identities of the protein co-factors that modulate binding.

NIH Research Projects · FY 2026 · 2024-07

Project Summary Sex chromosomes are key drivers of intersexual diversity by harboring sex-limited genes and mediating sex- specific expression from the autosomes. In recent years, we have come to understand sex as a complex phenotype, with morphology and behavior that can be tremendously diverse not just between the sexes (intersexual diversity) but also within the sexes (intrasexual diversity). Intrasexual diversity has long been appreciated in evolutionary biology as the substrate for sexual selection, but the mechanisms by which sex chromosomes generate such intrasexual diversity are less known. The non-recombining nature of Y chromosomes subjects them to unique evolutionary processes, which have made them powerful regulators that drive differences in autosomal gene expression between the sexes. To determine the processes and mechanisms by which Y chromosomes also generate diversity within the sexes, we have developed the powerful Poecilia parae study system. Males of this fish are always one of five discrete morphs that differ substantially in size, color, and behavior yet live in the same population. We found these morphs are entirely inherited through highly diverged Y chromosomes. Our system uniquely provides the characteristics necessary for such research: (1) clear, diverse phenotypes that correspond to discrete Y-haplotypes in a homogenous autosomal background, (2) breeding lab populations for controlled studies of development, and (3) high-quality genomic resources. We will draw on this powerful new system to first identify how Y diversity alters gene regulatory networks across the autosomal genome (Aim1). While the non-recombining nature of Y chromosomes is generally thought to decrease the power of selection to generate adaptive diversity; the converse can also be true. Thus, we will identify how the unique processes of Y chromosome evolution can actually generate diverse supergenes (Aim2). Recent work in Drosophila has shown that Y chromosomes can alter heterochromatin structure across the rest of the genome by acting as “heterochromatin sinks”; thus altering autosomal gene expression. We will determine if heterochromatin sinks alter autosomal heterchromatin stochastically or in a targeted manner, generating intrasexual diversity (Aim3). The importance of understanding the mechanisms generating and maintaining diversity within the sexes has direct implications for our understanding of gene regulation and the developmental processes generating that diversity within the same population.

NSF Awards · FY 2024 · 2024-07

Comparing probability models is a fundamental task in almost every data-enabled problem, and Optimal Transport (OT) offers a powerful and versatile framework to do so. Recent years have witnessed a rapid development of computational OT, which has expanded applications of OT to statistics, including clustering, generative modeling, domain adaptation, distribution-to-distribution regression, dimension reduction, and sampling. Still, understanding the fundamental strengths and limitations of OT as a statistical tool is much to be desired. This research project aims to fill this important gap by advancing statistical analysis (estimation and inference) and practical approximation of two fundamental notions (average and quantiles) in statistics and machine learning, demonstrated through modern applications for measure-valued data. The project also provides research training opportunities for graduate students. The award contains three main research projects. The first project will develop a new regularized formulation of the Wasserstein barycenter based on the multi-marginal OT and conduct an in-depth statistical analysis, encompassing sample complexity, limiting distributions, and bootstrap consistency. The second project will establish asymptotic distribution and bootstrap consistency results for linear functionals of OT maps and will study sharp asymptotics for entropically regularized OT maps when regularization parameters tend to zero. Building on the first two projects, the third project explores applications of the OT methodology to two important statistical tasks: dimension reduction and vector quantile regression. The research agenda will develop a novel and computationally efficient principal component method for measure-valued data and a statistically valid duality-based estimator for quantile regression with multivariate responses. The three projects will produce novel technical tools integrated from OT theory, empirical process theory, and partial differential equations, which are essential for OT-based inferential methods and will inspire new applications of OT to measure-valued and multivariate data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Chlorine disinfection of drinking water has played a critical role in preventing outbreaks of waterborne diseases. Unfortunately, chlorine also reacts with dissolved organic matter in water supplies to form chlorinated byproducts. Consumer exposure to these byproducts is associated with slightly increased health risks. Drinking water regulations thus balance the need to reduce consumer exposure while maintaining effective disinfection. In the past, these regulations have focused on controlling the accumulation of small molecule end products that form as chlorine progressively breaks down dissolved organic matter. However, recent research has indicated that the larger byproducts formed during the process may be more important drivers of toxicity. The goal of this collaborative study by a chemist and a toxicologist is to compare the concentrations and contributions to toxicity of the large intermediate byproducts compared to smaller end products. The study will target water samples collected from drinking water utilities from different sources that use different disinfection techniques. The results of this project will be used to develop guidance to utilities on which disinfection conditions and techniques result in the formation of byproducts contributing to toxicity. The results also will benefit society by helping regulators identify byproducts that can serve as improved metrics of consumer exposure, thus contributing broadly to public health. Drinking water regulations have focused on trihalomethanes (THMs) and haloacetic acids (HAAs) as metrics of disinfection byproduct (DBP) exposure since the 1970s. Research over the past two decades has shifted to other unregulated 1-2 carbon DBPs such as haloacetonitriles that may contribute more to the toxicity of disinfected waters due to their greater cytotoxic and genotoxic potencies. While 1-2 carbon DBPs accumulate as terminal products, the higher molecular weight intermediate DBPs formed from the initial chlorine reactions with organic matter have received less attention. Recent research has indicated that these intermediate DBPs contribute more to the toxicity of disinfected drinking water. This project will compare the concentrations and contributions to toxicity of several novel classes of intermediate DBPs (halogenated phenols, proteins, and lipids) versus 1-2 carbon DBPs under different disinfection scenarios. The first objective is to compare DBP classes during chlorine/chloramine disinfection versus granular activated carbon (GAC) treatment followed by chlorine disinfection. The second objective is to assess how the formation of these DBP classes is affected by water age and water source (e.g., pristine source water, algal-impacted water, and wastewater-impacted water). The third objective is to assess whether nitrifying biofilms in chloraminated distribution systems increase the formation of these DBP classes by emitting DBP precursors. A fundamental assumption behind the current use of THMs and HAAs as metrics of DBP exposure is that their formation correlates with the toxicity drivers in disinfected waters. This study will test this assumption. The results will benefit society by providing information to regulators to assess whether alternative metrics of DBP exposure are needed to more accurately assess toxicity and whether efforts to reduce THM/HAA concentrations effectively reduce health impacts associated with DBP exposure. 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 · 2024-07

PROJECT SUMMARY Small GTPases of the Arf and Rab families are master regulators of intracellular trafficking in eukaryotic cells that direct the key trafficking events of vesicle formation, cargo recognition, and transport. Cells exert tight spatiotemporal control of these GTPases through a network of GTPase effector proteins (GEFs) and GTPase activating proteins (GAPs). Perturbations to these key regulators give rise to numerous human diseases. Despite their importance, many GEFs and GAPs have yet to be identified — meaning we lack a comprehensive understanding of how cells manage the flow of materials between different membrane- bound compartments. There is a critical need, then, to study the modulators of GTPase activity and determine their roles in membrane homeostasis. The overarching goal of this proposal is to identify and characterize novel regulators of GTPase activity. I have recently identified a novel family of GAPs using structural prediction approaches. The specific aims of this proposal are to characterize the physiological role of these new GAPs. Using a combination of genetic and live-cell imagining techniques, Specific Aim 1 will focus on identifying the target substrate for each of the GAPs. I will knock out and overexpress the novel GAPs and subsequently monitor fluorescently-tagged GTPases. The rationale being that improper regulation of GTPases will cause their mislocalization. Aim 1 will also determine how these proteins localize to distinct subcellular locations and how this ultimately drives their regulation of GTPases. Together, this specific aim will elucidate the biological niches in which each of these proteins function. Specific Aim 2 will investigate the structure and function of these novel GAPs to better understand how they act to regulate GTPase activity. Using in vitro GAP assays, the enzymatic activity of purified GAPs will be reconstituted on synthetic membranes — quantifying their activity and substrate specificity in vitro. In order to validate my structural predictions, the structure of GAPs in complex with their substrate GTPase will be determined using cryogenic electron microscopy. Crucially, these structural studies will involve proteins bound to synthetic membranes, providing unique insights into the molecular architecture of membrane-bound GTPase inactivation. Completion of the research in this fellowship proposal will provide valuable insight into the regulation of the secretory pathway and, by extension, will have broad impact in cell and membrane biology.

NSF Awards · FY 2024 · 2024-07

Heuristics are simple rules that allow for approximately solving complex problems in a timely manner. Heuristics commonly used in chemistry embody fundamental lessons or correlations between properties of interest. Leveraging these heuristics can promote rational design of useful self-assembled materials, i.e., materials possessing desirable properties and intricate structures which are encoded by the features of designable building blocks. For many decades, such heuristics have guided the design of materials based on elements that form salts and on metals that form alloys. However, no comprehensive heuristics exist to guide the engineering of materials made of larger building blocks like nanoparticles, which can contain hundreds or thousands of atoms each and can now be fabricated in a great variety of sizes, shapes, and patterns. This work is aimed at filling this gap in our scientific knowledge. This investigation is complementary to experimental efforts by collaborators and in the future could have broader impacts by guiding researchers in formulating nanoparticle-based crystals for such materials as catalyst supports, photonic materials, and smart inks. The project will enable the training of a doctoral student and an undergraduate student in computational materials research. Relevant results will be disseminated through multiple forums including Cornell’s Soft Matter seminar series and Wikipedia, and will contribute to a hands-on lab for the Women’s group outreach initiative in the Chemical Engineering Department that targets rural High School girls. This proposal aims to formulate and test molecular-simulation based heuristic rules for predicting the correlation between nanoparticle-nanoparticle interactions and their self-assembly into target superlattice structures of either pure-component or multicomponent crystals. In the case of binary mixtures, the work focuses on nanoparticle components with tunable geometric features that can generate stoichiometric compounds or interstitial solids. In terms of thermodynamic behavior, it is hypothesized that a non-additive metric for volume mixing, a readily computable property obtained from the densest mixed-crystal and pre-mixed structures, is a good predictor for the formation of substitutionally ordered compounds, whether stoichiometric compounds or interstitial solids. In terms of kinetic behavior, it is hypothesized that accessibility to the ordered structure is enhanced by designs that: (i) Engender a stable or metastable mesophase occurring in between the isotropic and crystal states, (ii) generate a more positive mixing additivity metric, (iii) possess a lower isotropic-crystal coexistence free-energy, and (iv) exhibit ordered states having larger entropy. For this purpose, advanced methods will be deployed, including techniques to map free-energies over multiple relevant order parameters, nucleus-size pinning methods to efficiently estimate free-energy transition barriers, and forward flux sampling to measure transition rates for selected cases. Given the emphasis on entropic effects, the components to be investigated include particle shapes possessing non-trivial packing such as polyhedra and patchy particles. 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 · 2024-07

This award funds theoretical research on several different topics in relativistic astrophysics and general relativity. One focus is the exploration of newly discovered conservation laws involving black holes, and their implications for deep and poorly understood issues in quantum gravity such as the information loss paradox discovered by Stephen Hawking. A second focus is on tests of general relativity and on sources of gravitational radiation that have been detected by NSF's Laser Interferometer Gravitational-wave Observatory (LIGO) and that might be detected by future space-based detectors. Theoretical studies of sources of gravitational waves funded by this award will be useful to aid detection of signals from black holes and other sources, and also to aid in their interpretation. Gravitational wave studies can tell us about the nature of gravity, properties of black holes, and properties of the early Universe. The award supports the training of graduate students and postdocs in STEM research. In more detail, the principal research topics of this award are (i) the development of methods to compute the gravitational waveforms from point particles inspiralling into spinning black holes, using a combination of analytical and numerical approaches; (ii) demonstrating that the two body dynamics is Hamiltonian when the dissipation is removed; (iii) developing a comprehensive description and understanding of conservation laws involving soft hair on black holes, and (iv) investigating the consequences of these laws for the quantum evaporation of black holes and the ultimate fate of information that falls into black holes. 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.