Grants

NSF

Oxygen is crucial for macroscopic life, yet the causes and repercussions of its accumulation in the atmosphere are poorly understood. A key question for resolving the trajectory of planetary habitability is if chemical shifts recorded in ~2.4-2.0 billion-year-old rocks reflect global-scale oxygen changes or regional-local conditions. To answer this question, rock cores from Gabon, which hosts the best-preserved sedimentary archive across this interval, will be analyzed for possible chemical imprints of oxygenation. This project serves the national interest by promoting the progress of fundamental science that identifies how Earth became habitable. Synergistic outreach objectives include initiatives such as community tables at farmers’ markets from Northeast-Midwest USA to enhance public scientific literacy and undergraduate curriculum development to support an American STEM workforce that is globally competitive through improved education. This interdisciplinary project applies stratigraphy, paleomagnetism, geochemistry, and geochronology to assess whether extreme geochemical shifts in the wake of the Great Oxidation Event (GOE) reflect global, regional, or local-diagenetic conditions. Laterally correlative drill cores across shallow-to-deep paleoenvironments in the Francevillian sub-basins of Gabon will be applied to test the hypothesis that, in the Paleoproterozoic, there was a prolonged overshoot in O₂ coeval with a widespread perturbation of the carbon cycle. The objectives are: 1) Create a detailed stratigraphic framework and isolate primary magnetizations to examine facies and latitudinal climate-belt controls; 2) Assess a variety of isotopic and geochemical criteria in carbonates to determine if a primary “oxygen overshoot” is preserved; and 3) Apply U-Pb zircon geochronology/geochemistry and carbonate Rb-Sr isotope compositions to evaluate if geochemical shifts are of global relevance. The results will facilitate detailed tracking of the dynamics of oxygenation and concurrent environmental changes in the wake of the GOE—including extraordinary disturbances to the carbon cycle—that are key for deciphering this tipping point in the trajectory of planetary habitability. 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

Oxygen is crucial for macroscopic life, yet the causes and repercussions of its accumulation in the atmosphere are poorly understood. A key question for resolving the trajectory of planetary habitability is if chemical shifts recorded in ~2.4-2.0 billion-year-old rocks reflect global-scale oxygen changes or regional-local conditions. To answer this question, rock cores from Gabon, which hosts the best-preserved sedimentary archive across this interval, will be analyzed for possible chemical imprints of oxygenation. This project serves the national interest by promoting the progress of fundamental science that identifies how Earth became habitable. Synergistic outreach objectives include initiatives such as community tables at farmers’ markets from Northeast-Midwest USA to enhance public scientific literacy and undergraduate curriculum development to support an American STEM workforce that is globally competitive through improved education. This interdisciplinary project applies stratigraphy, paleomagnetism, geochemistry, and geochronology to assess whether extreme geochemical shifts in the wake of the Great Oxidation Event (GOE) reflect global, regional, or local-diagenetic conditions. Laterally correlative drill cores across shallow-to-deep paleoenvironments in the Francevillian sub-basins of Gabon will be applied to test the hypothesis that, in the Paleoproterozoic, there was a prolonged overshoot in O₂ coeval with a widespread perturbation of the carbon cycle. The objectives are: 1) Create a detailed stratigraphic framework and isolate primary magnetizations to examine facies and latitudinal climate-belt controls; 2) Assess a variety of isotopic and geochemical criteria in carbonates to determine if a primary “oxygen overshoot” is preserved; and 3) Apply U-Pb zircon geochronology/geochemistry and carbonate Rb-Sr isotope compositions to evaluate if geochemical shifts are of global relevance. The results will facilitate detailed tracking of the dynamics of oxygenation and concurrent environmental changes in the wake of the GOE—including extraordinary disturbances to the carbon cycle—that are key for deciphering this tipping point in the trajectory of planetary habitability. 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

Nontechnical description: Chiral molecule sensing plays an important role in many areas such as pharmaceutical industry, biomedical diagnostics, and food analysis. It is challenging to accurately quantify the chirality of chiral medium due to the weak intrinsic circular dichroism signal of chiral molecules. Recently, chiral metasurfaces have been used to amplify the circular dichroism signal and improve the sensitivity in circular dichroism spectroscopy for the vibrational transitions of chiral molecules. However, the detection sensitivity is quite limited by the chiral metasurface design with certain structural geometries and optical properties. In this project, a new type of 2D chiral fingerprint metasensors with high sensitivity based on ultrathin 2D material chiral metasurfaces will be rapidly designed through a machine learning framework and demonstrated for the detection and identification of various kinds of chiral molecules with high sensing performance. This research will benefit many biomedical and photonic applications in point-of-care healthcare, food analysis, environment monitoring, quantum spectroscopy, and quantum communication. This project also includes educational activities for training graduate students, recruiting students and promoting broad participation, and mentoring high school students through outreach activities. Technical description: Chiral metasurfaces have been utilized to enhance the circular dichroism signal and improve the detection sensitivity for the vibrational transitions of chiral molecules. However, the detection sensitivity is quite limited by the chiral metasurface design with certain structural geometries and optical properties, which are insufficient to cover the whole design space to achieve the optimal sensitivity in chiral molecule sensing. The goal of this project is to study a new type of 2D chiral fingerprint metasensors based on ultrathin 2D material chiral metasurfaces which will be rapidly designed through a machine learning framework and demonstrated for the detection and identification of the mid-infrared vibrational fingerprints of chiral molecules with high sensitivity and selectivity. In this project, a new machine learning algorithmic methodology will be developed for rapid and precise inverse design of 2D chiral fingerprint metasensors with the maximized sensitivity. The underlying mechanism of high sensitivity in 2D chiral fingerprint metasensors will be revealed. Nanofabrication processes will be developed to fabricate the designed 2D material chiral metasurfaces. The 2D chiral fingerprint metasensors will be characterized for demonstrating the detection and identification of various kinds of chiral molecules with high sensitivity and selectivity. The project will advance the rapid design and integration of future 2D material-based photonic and optoelectronic metadevices. 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

Understanding how airway cells function is essential for studying lung diseases and developing improved treatments. The tiny hair-like structures on airway cells, known as cilia, play an important role clearing mucus and harmful particles from the lungs. When cilia do not function properly, conditions such as cystic fibrosis and chronic respiratory diseases can develop. Current imaging methods for studying cilia have limitations. These methods require artificial dyes that may interfere with the cells or lack the ability to capture the complex motion of cilia in three dimensions. This project aims to develop a new imaging technology which will allow scientists to image cilia movement in 3D without the need for labeling dyes. This advancement will provide a clearer picture of how airway cells function in health and disease. The proposed capabilities could aid in the development of better treatments for respiratory disorders. Through partnerships with the Colorado Photonics Industry Association (CPIA) and the Washington University Cardiovascular Research Summer (CardS) Program, undergraduate and graduate students will gain valuable experience in advanced imaging technologies. The project will also help prepare a skilled workforce for future biophotonics innovations, addressing industry needs and supporting economic growth in science and technology fields. This project will develop the first high-speed 3D Dynamic Contrast Microscopic Optical Coherence Tomography (3D DyC-μOCT) system, a label-free optical imaging technology designed to study human airway organoids with high temporal and spatial resolution. Traditional imaging methods either lack sufficient depth and speed for real-time volumetric studies or require fluorescent labels that introduce experimental complexity and phototoxicity. 3D DyC-μOCT overcomes these challenges by integrating a novel swept-source laser architecture with parallel imaging using space-division multiplexing and lithium niobate on insulator (LNOI) photonic integrated circuit technology. The project is structured around four key objectives: (1) engineering a broadband, high-coherence LNOI-based swept laser source to achieve superior axial resolution and imaging depth, (2) designing a scalable optical imaging platform to overcome current voxel rate limitations, (3) integrating 3D DyC-μOCT with widefield fluorescence microscopy to allow cross-validation of imaging data, and (4) applying 3D DyC-μOCT to study ciliary beating in airway organoids, demonstrating its potential for pulmonary research. The system will provide a new tool for studying airway physiology in disease models with applications in respiratory health research, drug screening, and personalized medicine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Understanding how airway cells function is essential for studying lung diseases and developing improved treatments. The tiny hair-like structures on airway cells, known as cilia, play an important role clearing mucus and harmful particles from the lungs. When cilia do not function properly, conditions such as cystic fibrosis and chronic respiratory diseases can develop. Current imaging methods for studying cilia have limitations. These methods require artificial dyes that may interfere with the cells or lack the ability to capture the complex motion of cilia in three dimensions. This project aims to develop a new imaging technology which will allow scientists to image cilia movement in 3D without the need for labeling dyes. This advancement will provide a clearer picture of how airway cells function in health and disease. The proposed capabilities could aid in the development of better treatments for respiratory disorders. Through partnerships with the Colorado Photonics Industry Association (CPIA) and the Washington University Cardiovascular Research Summer (CardS) Program, undergraduate and graduate students will gain valuable experience in advanced imaging technologies. The project will also help prepare a skilled workforce for future biophotonics innovations, addressing industry needs and supporting economic growth in science and technology fields. This project will develop the first high-speed 3D Dynamic Contrast Microscopic Optical Coherence Tomography (3D DyC-μOCT) system, a label-free optical imaging technology designed to study human airway organoids with high temporal and spatial resolution. Traditional imaging methods either lack sufficient depth and speed for real-time volumetric studies or require fluorescent labels that introduce experimental complexity and phototoxicity. 3D DyC-μOCT overcomes these challenges by integrating a novel swept-source laser architecture with parallel imaging using space-division multiplexing and lithium niobate on insulator (LNOI) photonic integrated circuit technology. The project is structured around four key objectives: (1) engineering a broadband, high-coherence LNOI-based swept laser source to achieve superior axial resolution and imaging depth, (2) designing a scalable optical imaging platform to overcome current voxel rate limitations, (3) integrating 3D DyC-μOCT with widefield fluorescence microscopy to allow cross-validation of imaging data, and (4) applying 3D DyC-μOCT to study ciliary beating in airway organoids, demonstrating its potential for pulmonary research. The system will provide a new tool for studying airway physiology in disease models with applications in respiratory health research, drug screening, and personalized medicine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Understanding how the human brain enables us to sense, move, learn, and perceive is a fundamental goal of neuroscience. The brain is made of billions of neurons that communicate with one another through electrical and chemical signals. Neuroscientists rely on fluorescence detecting probes to monitor the activities of neurons using light. However, these probes can be bulky and rigid, causing bleeding and inflammation. Some probe light sources, such as micro-light emitting diodes (LEDs), produce light with a broad spectrum which diminishes the quality of measurements. This project will overcome these issues by developing probes that are much smaller in size, have a softness similar to the brain, and produce light with narrow spectra. The results of this research could enhance our understanding of brain functions and causes for neurological diseases such as Alzheimer’s and Parkinson’s diseases. Furthermore, the research team plans to develop educational materials, including a new course in bioelectronics with hands-on laboratory experience for students. This project proposes to elucidate the effects of beam collimation, compact chip size, and mechanical flexibility of implantable optoelectronic probes on the quality of in vivo deep-brain fluorescence recordings. This will be achieved by vertically integrating inorganic single-crystalline LED and photodiode films obtained via layer transfer technology, distributed Bragg reflectors, and absorption filters to obtain 3D-integrated micro-photometer chips that are less than 1000 times smaller in volume compared to conventional probes, enabling minimally invasive, long-term stable, and high-quality fluorescence recording in deep brain regions. This will improve the signal quality and stability and allow recording in delicate brain regions such as the hindbrain, that preclude the use of traditional bulky/rigid implants. The outcomes of this project will facilitate a fundamental understanding of the neural circuits in these brain regions and advance the knowledge of sensory information processing, complex behavior generation, and neurological disease progression mechanisms in the brain. 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

Mechanical metamaterial, i.e., engineering materials whose properties arise from geometry rather than chemical composition, hold great promise for applications in aerospace, biomedical engineering, robotics, and beyond. By integrating active elements, these materials can respond dynamically to environmental stimuli such as heat or light, enabling adaptive and programmable behavior. Despite their potential, the practical design of such materials remains constrained by computationally expensive and highly specialized modeling tools. This award supports fundamental research to establish a general, experimentally validated continuum modeling framework for active mechanical metamaterials. This framework will enable efficient prediction of material behavior and systematic design across a wide range of geometries and actuation mechanisms. The project advances fundamental understanding while supporting technological innovation in advanced manufacturing, adaptive devices, and reconfigurable structures. It also leverages interdisciplinary collaboration and integrated fabrication-modeling workflows to train students across educational levels and to engage the public through outreach initiatives. The research integrates theoretical, experimental, and computational approaches. A continuum modeling framework will be constructed to connect unit-cell geometry with macroscopic deformation, including both planar and three-dimensional behaviors. The models will incorporate internal variables and compatibility conditions to capture soft deformation modes and the effects of actuation. Experimental work will validate the framework through fabrication, including direct ink writing, molding, and conventional 3D printing, and mechanical testing of passive and active metamaterials, making use of digital image correlation to quantify deformation. A systematic scaling study will define the limits of continuum applicability. The final phase will implement the models into finite element simulations to enable inverse design, allowing metamaterials to be tailored for specific responses. Collectively, these efforts will yield general design principles and computational tools to accelerate the adoption of mechanical metamaterials in advanced engineering 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

Mechanical metamaterials, i.e., engineering materials whose properties arise from geometry rather than chemical composition, hold great promise for applications in aerospace, biomedical engineering, robotics, and beyond. By integrating active elements, these materials can respond dynamically to environmental stimuli such as heat or light, enabling adaptive and programmable behavior. Despite their potential, the practical design of such materials remains constrained by computationally expensive and highly specialized modeling tools. This award supports fundamental research that seeks to establish a general, experimentally validated continuum modeling framework for active mechanical metamaterials. This framework looks to enable efficient prediction of material behavior and systematic design across a wide range of geometries and actuation mechanisms. The project seeks to advances fundamental understanding while supporting technological innovation in advanced manufacturing, adaptive devices, and reconfigurable structures. It also leverages interdisciplinary collaboration and integrated fabrication-modeling workflows to train students across educational levels and to engage the public through outreach initiatives. The research integrates theoretical, experimental, and computational approaches. A continuum modeling framework will be constructed to connect unit-cell geometry with macroscopic deformation, including both planar and three-dimensional behaviors. The models will incorporate internal variables and compatibility conditions to capture soft deformation modes and the effects of actuation. Experimental work looks to validate the framework through fabrication, including direct ink writing, molding, and conventional 3D printing, and mechanical testing of passive and active metamaterials, making use of digital image correlation to quantify deformation. A systematic scaling study intends to define the limits of continuum applicability. The final phase will implement the models into finite element simulations to enable inverse design, seeking to allow metamaterials to be tailored for specific responses. Collectively, these efforts seek to yield general design principles and computational tools to accelerate the adoption of mechanical metamaterials in advanced engineering 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

This collaborative research project investigates the factors that impact variability in STEM training programs across the nation. The investigators specifically measure the institutional factors that contribute to development of career-ready science curricula and how successful best practices can be leveraged for translational impact on workforce development for industry. The investigators also identify strategies for addressing curricular challenges with a goal of strengthening the STEM workforce. The broader impacts identify strategies for curricular and institutional change in STEM training programs that would support the workforce and career development of students and translate to leveraging partnerships between higher education and industry. The project has a significant training component in social science research methods and best practices in STEM translational research for students at the PI and Co-PI institutions. The study sample will represent a range of educational institutions across the nation. Interview, focus group, and behavioral data will be collected with stakeholders in STEM training and education, and with undergraduate and graduate students and alumni at each institution to understand the factors that impact the development of a career-ready STEM curriculum. The research is focused on measuring institutional change in STEM training and education, and the training and reskilling/upskilling of the STEM workforce for education and industry. Broader impacts will leverage partnerships with various sectors to enable the translation of STEM education to other sectors of priority in the economy. 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

This collaborative research project investigates the factors that impact variability in STEM training programs across the nation. The investigators specifically measure the institutional factors that contribute to development of career-ready science curricula and how successful best practices can be leveraged for translational impact on workforce development for industry. The investigators also identify strategies for addressing curricular challenges with a goal of strengthening the STEM workforce. The broader impacts identify strategies for curricular and institutional change in STEM training programs that would support the workforce and career development of students and translate to leveraging partnerships between higher education and industry. The project has a significant training component in research methods and best practices in STEM translational research for students at the PI and Co-PI institutions. The study sample represent a range of educational institutions across the nation. Interview, focus group, and behavioral data will be collected with stakeholders in STEM training and education, and with undergraduate and graduate students and alumni at each institution to understand the factors that impact the development of a career-ready STEM curriculum. The research is focused on measuring institutional change in STEM training and education, and the training and reskilling/upskilling of the STEM workforce for education and industry. Broader impacts leverage partnerships with various sectors to enable the translation of STEM education to other sectors of priority in the economy. 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

The tree of life is a powerful tool for understanding evolution that can also be used for research on medicine, wildlife management, and agriculture. However, recent studies indicate that genes transferred between species can make it difficult for scientists to build an accurate evolutionary tree or understand the relationships between species. This project will use parrots, one of the most endangered and illegally trafficked groups of animals, to better understand how often species transfer genes and how that impacts scientists’ ability to accurately build an evolutionary tree. The researchers will collect genetic data, including sequencing genomes, for all parrots. These data will be used to build an evolutionary tree of parrots and answer questions about gene transfer between parrot species. This data will also be used to aid conservation efforts by developing forensic DNA barcodes that will help law enforcement correctly identify illegal products made from endangered parrot species. The project will build collaborations between forensic scientists and natural history museums, provide training and mentoring for students through early-career researchers, and share findings with the public and scientific communities. To estimate accurate phylogenetic trees in the genomic era, the effect of genomic architecture (the structure, organization, and content of a genome) on phylogenetic signal must be understood. Large-scale phylogenetic methods often do not account for gene flow, nor do they address the interaction between genomic architecture and phylogenetic signal, which can lead to well-supported but inaccurate evolutionary relationships. Using the global radiation of parrots as a focal system, this project will reconstruct a nearly complete time-calibrated phylogeny of the clade to assess gene flow across an entire radiation and predict genomic regions prone to biasing phylogenetic estimates. The researchers will generate new genomic resources, including five chromosome-level reference genomes and genomic-scale markers for over 400 previously unsampled taxa. The project aims to test the following hypotheses to make general predictions about the factors causing gene tree discordance and to inform a species-level taxonomic revision: 1) non-monophyletic species are a common feature across the parrot evolutionary tree; 2) non-monophyletic species and weakly supported relationships are associated with a higher prevalence of gene flow that spans millions of years; and 3) genomic architecture is relatively conserved across the parrot radiation and can be used to mitigate the effects of gene flow in phylogenetic inference across temporal scales. This work will offer a high-resolution view of the interaction between phylogenetic signal, gene flow, and genomic architecture in parrots, and demonstrate how this framework can be used for improved taxonomic classification and wildlife management. 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

The tree of life is a powerful tool for understanding evolution that can also be used for research on medicine, wildlife management, and agriculture. However, recent studies indicate that genes transferred between species can make it difficult for scientists to build an accurate evolutionary tree or understand the relationships between species. This project will use parrots, one of the most endangered and illegally trafficked groups of animals, to better understand how often species transfer genes and how that impacts scientists’ ability to accurately build an evolutionary tree. The researchers will collect genetic data, including sequencing genomes, for all parrots. These data will be used to build an evolutionary tree of parrots and answer questions about gene transfer between parrot species. This data will also be used to aid conservation efforts by developing forensic DNA barcodes that will help law enforcement correctly identify illegal products made from endangered parrot species. The project will build collaborations between forensic scientists and natural history museums, provide training and mentoring for students through early-career researchers, and share findings with the public and scientific communities. To estimate accurate phylogenetic trees in the genomic era, the effect of genomic architecture (the structure, organization, and content of a genome) on phylogenetic signal must be understood. Large-scale phylogenetic methods often do not account for gene flow, nor do they address the interaction between genomic architecture and phylogenetic signal, which can lead to well-supported but inaccurate evolutionary relationships. This project will reconstruct a nearly complete time-calibrated phylogeny of the clade to assess gene flow across an entire radiation and predict genomic regions prone to biasing phylogenetic estimates. The researchers will generate new genomic resources, including five chromosome-level reference genomes and genomic-scale markers for over 400 previously unsampled taxa. The project aims to test the following hypotheses to make general predictions about the factors causing gene tree discordance and to inform a species-level taxonomic revision: 1) non-monophyletic species are a common feature across the parrot evolutionary tree; 2) non-monophyletic species and weakly supported relationships are associated with a higher prevalence of gene flow that spans millions of years; and 3) genomic architecture is relatively conserved across the parrot radiation and can be used to mitigate the effects of gene flow in phylogenetic inference across temporal scales. This work will offer a high-resolution view of the interaction between phylogenetic signal, gene flow, and genomic architecture in parrots, and demonstrate how this framework can be used for improved taxonomic classification and wildlife management. 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

Material fractures present a major obstacle to safety and economic efficiency across a wide range of industries, including construction, manufacturing, transportation, and aerospace. This highlights the urgent need for new material systems that offer fracture resistance far beyond the capabilities of traditional materials. This award supports fundamental research that seeks to enable the design and creation of a new class of fracture-resistant mechanical metamaterials (MMs). These materials exhibit extraordinary mechanical properties due to their structural geometry rather than chemical composition. Despite their promise, how these MMs break and how to design them to resist fracture remain underexplored. This project looks to address this gap by developing a new, hierarchical approach to analyze and optimize fracture resistance in MMs across global and local scales. The research is expected to generate new insights and design principles for fracture-resistant MMs, reduce economic losses resulting from material failure, and enable the development of safer and more durable technologies. In addition, the project seeks to contribute to national educational goals by developing interactive educational tools and training students in a multidisciplinary environment that spans mechanics, materials science, and computational design through university programs and courses. This project aims to develop a hierarchical framework for understanding and designing multi-scale MMs with enhanced fracture resistance. The key hypothesis is that local constitutive behavior can serve as an intermediary, linking local structural deformation to global fracture behavior. This conjecture decouples the complex problem into three manageable tasks: fracture of general networks, fracture of local structures, and their integration. At the global network level, the project investigates fracture behavior in homogeneous, defective, and heterogeneous networks under diverse loading conditions using theoretical and computational tools. At the local level, it explores and seeks to reveal geometric characteristics that achieve specific nonlinear constitutive behaviors for superior fracture properties using topology optimization and data-driven generative models. These components look to be integrated into a design pipeline for creating multi-scale MMs with unprecedented fracture properties. The resulting framework will be validated through simulation and experiment. The outcomes seek to contribute to the fundamental understanding of fracture mechanics in architected materials and enable the design and fabrication of MMs with unprecedented toughness for real-world 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

Material fractures present a major obstacle to safety and economic efficiency across a wide range of industries, including construction, manufacturing, transportation, and aerospace. This highlights the urgent need for new material systems that offer fracture resistance far beyond the capabilities of traditional materials. This award supports fundamental research to enable the design and creation of a new class of fracture-resistant mechanical metamaterials (MMs). These materials exhibit extraordinary mechanical properties due to their structural geometry rather than chemical composition. Despite their promise, how these MMs break and how to design them to resist fracture remain underexplored. This project will address this gap by developing a new, hierarchical approach to analyze and optimize fracture resistance in MMs across global and local scales. The research is expected to generate new insights and design principles for fracture-resistant MMs, reduce economic losses resulting from material failure, and enable the development of safer and more durable technologies. In addition, the project will contribute to national educational goals by developing interactive educational tools and training students in a multidisciplinary environment that spans mechanics, materials science, and computational design through university programs and courses. This project aims to develop a hierarchical framework for understanding and designing multi-scale MMs with enhanced fracture resistance. The key hypothesis is that local constitutive behavior can serve as an intermediary, linking local structural deformation to global fracture behavior. This conjecture decouples the complex problem into three manageable tasks: fracture of general networks, fracture of local structures, and their integration. At the global network level, the project investigates fracture behavior in homogeneous, defected, and heterogeneous networks under diverse loading conditions using theoretical and computational tools. At the local level, it explores and reveals the geometric characteristics that achieve specific nonlinear constitutive behaviors for superior fracture properties using topology optimization and data-driven generative models. These components are integrated into a design pipeline for creating multi-scale MMs with unprecedented fracture properties. The resulting framework will be validated through simulation and experiment. The outcomes will contribute to the fundamental understanding of fracture mechanics in architected materials and enable the design and fabrication of MMs with unprecedented toughness for real-world 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

An offspring’s traits, such as appearance or behavior, are shaped by both its genes and its environment. Scientists now know that parents' experiences, well before offspring exist, also influence offspring traits. This transgenerational parental impact allows parents to help offspring prepare for survival in risky or stressful environments, even if the parents and offspring never physically meet, by altering how genes are expressed (turned on or off) in their offspring. Most research has focused on how mothers pass along information or cues about their environment to their young, but in reality, both mothers’ and fathers’ experiences are important. For example, if both parents experienced similar environments, their combined cues to offspring might make transgenerational plasticity more beneficial compared to the mother’s cue alone. On the other hand, if mothers and fathers experience different environments, the information they provide their offspring might be contradictory and not beneficial. This research uses both theoretical models and laboratory experiments to ask how offspring respond to maternal and paternal information that differs and whether parents develop behaviors to avoid or manage these mismatches, including choosing mates with similar experiences or changing how they care for their young. By exploring how both parents’ experiences collectively affect their offspring, this work will help us predict when offspring respond to parental cues and when they ignore them. The project will also train undergraduate and graduate students by offering long-term internships that provide students with independent research opportunities, coding workshops to train students in mathematical modeling, and seminars to prepare students for careers in science after they graduate. Biologists are particularly interested in understanding why plasticity occurs, when it is adaptive, and how it influences biological patterns. While transgenerational plasticity (TGP) can benefit offspring beyond what is possible for within-generational plasticity alone, mismatches in parental experiences (e.g., maternal cues of low predation and paternal cues of high predation) can result in traits that are maladaptive for the offspring. We hypothesize that parents can gain and respond to environmental information from mates in ways that may rescue offspring from the detrimental effects of mismatching. We will use mathematical models to understand whether TGP is more likely to arise when maternal and paternal cues match and when differential allocation of care in response to mismatching cues is possible. We will then use experiments with threespined sticklebacks (Gasterosteus aculeatus) to evaluate whether females use mate choice to reduce the frequency of mismatching maternal and paternal cues. Finally, we will empirically test whether males differentially allocate paternal care in response to maternal experience, if this differential allocation reduces the fitness costs of mismatching maternal and paternal experiences, and if it alters the ways in which TGP persists across generations. We predict that mate choice and differential allocation in response to parental cues may allow for the evolution of TGP in environments that would otherwise not select for TGP. This could explain the ubiquity of transgenerational plasticity across taxonomic groups, despite existing theory predicting it should evolve only under limited 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

Infants improve their motor skills, such as sitting, crawling, and standing, through practicing these skills in everyday life. However, limited tools for measuring infants’ movements in the home are a barrier to understanding the factors that give rise to individual differences in opportunities for movement as well as individual differences in caregiving practices that can predict motor learning outcomes in infancy. This project uses new wearable sensor technologies combined with Artificial Intelligence to record infants’ behavior across a week-long period to understand how the patterns of infants’ movements unfold over time. Results of this research advance understanding of motor development and provide useful information for clinicians to help promote healthy motor development in infancy. This project aims to collect data from families across the United States by mailing wearable sensors for infants to wear over the course of a week. In contrast to current methods that rely on labor-intensive manual coding of infant movement data, this project leverages Artificial Intelligence to (1) measure the amount and types of movement that 7-month old infants engage in at home during a typical week, (2) examine caregiving practices that give rise to individual differences in opportunities for movement, and (3) predict individual differences in motor development outcomes, including sitting and standing proficiency, at 11 months of age. Collecting and sharing a large longitudinal dataset of wearable sensor data enables advances in understanding motor development trajectories as well as advances in Artificial Intelligence methods for robustly identifying human movement. 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

Infants improve their motor skills, such as sitting, crawling, and standing, through practicing these skills in everyday life. However, limited tools for measuring infants’ movements in the home are a barrier to understanding the factors that give rise to individual differences in opportunities for movement as well as individual differences in caregiving practices that can predict motor learning outcomes in infancy. This project uses new wearable sensor technologies combined with Artificial Intelligence to record infants’ behavior across a week-long period to understand how the patterns of infants’ movements unfold over time. Results of this research advance understanding of motor development and provide useful information for clinicians to help promote healthy motor development in infancy. This project aims to collect data from families across the United States by mailing wearable sensors for infants to wear over the course of a week. In contrast to current methods that rely on labor-intensive manual coding of infant movement data, this project leverages Artificial Intelligence to (1) measure the amount and types of movement that 7-month old infants engage in at home during a typical week, (2) examine caregiving practices that give rise to individual differences in opportunities for movement, and (3) predict individual differences in motor development outcomes, including sitting and standing proficiency, at 11 months of age. Collecting and sharing a large longitudinal dataset of wearable sensor data enables advances in understanding motor development trajectories as well as advances in Artificial Intelligence methods for robustly identifying human movement. 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

Infants improve their motor skills, such as sitting, crawling, and standing, through practicing these skills in everyday life. However, limited tools for measuring infants’ movements in the home are a barrier to understanding the factors that give rise to individual differences in opportunities for movement as well as individual differences in caregiving practices that can predict motor learning outcomes in infancy. This project uses new wearable sensor technologies combined with Artificial Intelligence to record infants’ behavior across a week-long period to understand how the patterns of infants’ movements unfold over time. Results of this research advance understanding of motor development and provide useful information for clinicians to help promote healthy motor development in infancy. This project aims to collect data from families across the United States by mailing wearable sensors for infants to wear over the course of a week. In contrast to current methods that rely on labor-intensive manual coding of infant movement data, this project leverages Artificial Intelligence to (1) measure the amount and types of movement that 7-month old infants engage in at home during a typical week, (2) examine caregiving practices that give rise to individual differences in opportunities for movement, and (3) predict individual differences in motor development outcomes, including sitting and standing proficiency, at 11 months of age. Collecting and sharing a large longitudinal dataset of wearable sensor data enables advances in understanding motor development trajectories as well as advances in Artificial Intelligence methods for robustly identifying human movement. 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

This project performs research that looks to leverage recent advances in behavioral medicine, computational modeling, and physical medicine and rehabilitation to develop a computable theory of human motivation and behavioral change. It explores its physical realization through an intelligent wearable exercise "reminder" system called "Souvenir" and an adaptive machine intelligence that optimizes personalized interventions. The system looks to deliver physical and textual smartphone "nudges" to motivate therapeutic exercise after a stroke. This work looks to address a significant problem: many stroke survivors habitually refrain from using their hemiparetic limbs despite retaining sufficient ability to move them, a phenomenon known as "learned non-use". Learned non-use limits a person's ability to perform activities essential for independent living. The project will conduct fundamental research needed to develop an explainable machine intelligence capable of assessing patient motivation in real time, adapting the challenge level of therapeutic activities based on those assessments and the patient's current ability, and cueing activities in an unobtrusive manner. By leveraging the behavioral science of motivation to increase compliance with prescribed exercise, the project looks to advance the mission of the National Science Foundation by promoting scientific progress and enhancing national health, prosperity, and welfare. The project also includes public outreach and educational activities designed to promote science literacy among students and the general public. The current state-of-the-art treatment for addressing learned non-use after stroke is a two-week intensive intervention known as constraint-induced movement therapy, designed to engage a virtuous cycle of functional recovery. While this approach can lead to clinically meaningful improvements in paretic arm function that may persist for up to two years, most clinicians do not adopt it due to its high demands on time and lack of specialized training. A low-cost, low-burden alternative is urgently needed, one that both clinicians and patients will actually use. This project looks to advance the goals of the M3X program by conducting fundamental research in embodied reasoning (patient modeling and state estimation) as mediated by bidirectional sensorimotor interactions. This involves motion monitoring and the exchange of physical and textual cues between a human patient and a synthetic actor (Souvenir). The project will follow a user-centric design approach, iterating through computational modeling, software engineering, user focus groups, pilot testing, and assessment to develop and refine an explainable machine intelligence. This system seeks to assess patient motivation in real time and adapt the challenge level of therapeutic activities based on those assessments and the patient's current ability. The project concludes with a feasibility demonstration through human subject experiments, testing how an intelligent machine can influence individual human behaviors to optimize sensorimotor performance and recovery after stroke. 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

Suspended particulate material is a complex mixture of living and detrital organic and inorganic material. The composition and concentration of suspended particulate material vary greatly throughout the ocean and can be considerably influenced by local processes. The microbial and biogeochemical processes occurring within this material greatly influence the flow of carbon and other nutrients through the marine environment. The scale of the ocean makes these particulate processes globally relevant and, at the same time, challenging to fully characterize. Equipping robotic vehicles with a sensor system capable of rapidly distinguishing environmental variations in marine particle size, shape, and composition will enable navigation based on the properties of the ambient particle field. More specifically, it will enable the targeted collection of samples by autonomous robotic vehicles to those zones of the ocean where particle processes are most relevant and dynamic. This research will develop an optical sensing instrumentation suite and integrate it with existing robotic sampling tools for both remotely operated vehicle Jason and the autonomous underwater vehicle Clio and optimize their use to characterize hydrothermal plume particle processes in an engineering sea trial in the vent fields of the Juan de Fuca Ridge. This research will develop an optical sensing system to directly characterize marine particles and enable adaptive robotic collection of biogeochemical and biological samples from autonomous vehicles and remotely operated platforms. The optical sensing components of this system will characterize marine particles based on multiple parameters indicative of size, shape, and composition. Optical sensing will be based on a tightly integrated sensor suite consisting of camera-based particle imaging, using both wide-field stereoscopic and microscopic cameras, fluorometry, and optical transmission sensors. These sensors will be controlled by a single board computer capable of running real-time classification algorithms that can be used to control adaptive particle and fluid sampling systems. The close integration of the sensing elements is intended to both achieve a smaller overall payload size and allow for maximum control of sensing parameters including timing, sequencing, and frequency. The intent is to maximize the use of open-source software and hardware so that the resulting design can be shared within the broader community to allow for modification, adaptation, and experimentation. The goal is to improve the oceanographic community's ability to target novel biogeochemical environments using robotic oceanographic vehicles so that we can more efficiently study geochemically important environments in an otherwise very large ocean. This optical sensing system will be designed to enhance the observational capabilities of both large and small underwater vehicles and platforms. The system will consist of a stereo camera pair, a flow-cell/microscope camera, a commercial chlorophyll-a fluorometer, a commercial backscatter sensor, an electronic stack in a custom pressure housing, and an adaptive sampling subsystem. The operation of the system will be tested both in the laboratory and in field on remotely operated vehicle Jason and autonomous underwater vehicle Clio in the hydrothermal plumes of the Juan de Fuca Ridge during an engineering sea trial cruise in 2026. 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

Approximately 3000 km beneath Earth’s surface lies the boundary between the liquid iron core and the rocky mantle above. The nature of this region, the deep mantle and the shape of the boundary, remains mysterious, but it is important in that the heat emanating from this region powers mantle convection, the process that drives plate tectonics. If the properties of this region can be constrained, much progress in understanding the evolution of our mantle and the flow of the liquid outer core can be made. This project involves combining observations of global scale Earth processes—including large scale vibrations of the Earth triggered after an earthquake, the warping of Earth crust due to the unloading of ice sheets and its associated changes in earth’s gravitational field— to constrain the density, viscosity and shape of this boundary. With this knowledge, the team will make interpretations that will have implications for Earth’s mantle thermal and chemical evolution. The project will contribute to the education and professional development of high school students, undergraduate students, and graduate students. New tools developed through this project will be shared with the research community through workshops. The geodynamic landscape of the deep mantle holds implications for the evolution of chemical and thermal heterogeneity in Earth’s mantle, the heat escaping the outer core and its flow driving the geodynamo, and the large-scale nature of plate tectonics. However, its key characteristics – namely core-mantle boundary topography, its density and viscosity distribution – remain unconstrained and are often considered in isolation. These properties dictate the driving (buoyancy) and resistive (viscous) forces of convection. The difficulty in constraining these properties stems from several issues: for buoyancy variations, seismic waves are largely insensitive to density and therefore few density tomography models exist. Those models that use seismic normal modes apply consequential approximations that are avoided in this project. Because viscosity variations are expected to be several orders of magnitude, constraining 3D variations is computationally challenging. This project takes a holistic view in considering 3D fields self-consistently, using observations that span the seismic (∼seconds) to convection bands (more than 100 million years). The project begins with a 3D density and core-mantle-boundary field produced by a state-of-the-art inversion methodology for seismic normal mode, which enables exploration of the most dynamically consistent 3D viscosity fields using convection data. Finally, results may be confirmed by comparing consistency with variations of Earth’s rotation (∼1-year timescale) and gravitational changes due to adjustment from the last glacial maximum (∼1000-year timescale) – both of which are sensitive to deep mantle viscosity structure. 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

Approximately 3000 km beneath Earth’s surface lies the boundary between the liquid iron core and the rocky mantle above. The nature of this region, the deep mantle and the shape of the boundary, remains mysterious, but it is important in that the heat emanating from this region powers mantle convection, the process that drives plate tectonics. If the properties of this region can be constrained, much progress in understanding the evolution of our mantle and the flow of the liquid outer core can be made. This project involves combining observations of global scale Earth processes—including large scale vibrations of the Earth triggered after an earthquake, the warping of Earth crust due to the unloading of ice sheets and its associated changes in earth’s gravitational field— to constrain the density, viscosity and shape of this boundary. With this knowledge, the team will make interpretations that will have implications for Earth’s mantle thermal and chemical evolution. The project will contribute to the education and professional development of high school students, undergraduate students, and graduate students. New tools developed through this project will be shared with the research community through workshops. The geodynamic landscape of the deep mantle holds implications for the evolution of chemical and thermal heterogeneity in Earth’s mantle, the heat escaping the outer core and its flow driving the geodynamo, and the large-scale nature of plate tectonics. However, its key characteristics – namely core-mantle boundary topography, its density and viscosity distribution – remain unconstrained and are often considered in isolation. These properties dictate the driving (buoyancy) and resistive (viscous) forces of convection. The difficulty in constraining these properties stems from several issues: for buoyancy variations, seismic waves are largely insensitive to density and therefore few density tomography models exist. Those models that use seismic normal modes apply consequential approximations that are avoided in this project. Because viscosity variations are expected to be several orders of magnitude, constraining 3D variations is computationally challenging. This project takes a holistic view in considering 3D fields self-consistently, using observations that span the seismic (∼seconds) to convection bands (more than 100 million years). The project begins with a 3D density and core-mantle-boundary field produced by a state-of-the-art inversion methodology for seismic normal mode, which enables exploration of the most dynamically consistent 3D viscosity fields using convection data. Finally, results may be confirmed by comparing consistency with variations of Earth’s rotation (∼1-year timescale) and gravitational changes due to adjustment from the last glacial maximum (∼1000-year timescale) – both of which are sensitive to deep mantle viscosity structure. 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

Black holes swallowing gas produce the brightest, most powerful sources of radiation in the Universe. That energy can dramatically change everything around the black hole, fundamentally reshaping how stars and galaxies form. This project will develop a new generation of models for gas inflow into black holes, with strong magnetic fields that can completely change how these systems behave. Black holes are one of the topics in modern science that most excites the public and stimulates a younger generation to explore scientific careers. This project will engage directly with that effort by training graduate students in science and by producing scientific results that can be communicated to the broader public through talks and movies available on YouTube and used in planetaria and other venues. Our understanding of supermassive black hole growth and accretion, as well as feedback on galaxy formation, is at a transformative juncture. Theoretical models are finally able to connect realistic models of the interstellar medium on galactic scales to the inner black hole accretion disk. This project will support study of a new class of strongly magnetized black hole accretion disks, including their theoretical properties and their connection to observations of the massive black hole ecosystem. Collectively the proposed work will dramatically advance our understanding of black hole growth and the properties of active galactic nuclei. 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

Black holes swallowing gas produce the brightest, most powerful sources of radiation in the Universe. That energy can dramatically change everything around the black hole, fundamentally reshaping how stars and galaxies form. This project will develop a new generation of models for gas inflow into black holes, with strong magnetic fields that can completely change how these systems behave. Black holes are one of the topics in modern science that most excites the public and stimulates a younger generation to explore scientific careers. This project will engage directly with that effort by training graduate students in science and by producing scientific results that can be communicated to the broader public through talks and movies available on YouTube and used in planetaria and other venues. Our understanding of supermassive black hole growth and accretion, as well as feedback on galaxy formation, is at a transformative juncture. Theoretical models are finally able to connect realistic models of the interstellar medium on galactic scales to the inner black hole accretion disk. This project will support study of a new class of strongly magnetized black hole accretion disks, including their theoretical properties and their connection to observations of the massive black hole ecosystem. Collectively the proposed work will dramatically advance our understanding of black hole growth and the properties of active galactic nuclei. 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.