Arizona State University

NSF Awards · FY 2025 · 2025-10

Firefighters are faced with myriad stressors from hazardous work conditions and exposure to extreme heat that place their health at significant risks. The intense heat, smoke, shift work, long working hours, and stressful work put firefighters at substantial risk for heat-related injuries, long-term chronic complications, and mental health challenges. The health risks are further compounded in the Phoenix metropolitan area, which has one of the highest heat indexes in the nation. Therefore, there is a need to develop technologies to objectively assess the impacts of extreme heat and harsh working conditions on firefighters’ health and to provide actionable information to mitigate the health risks. This project develops, HeatMind, an AI-powered sensor-based platform that provides firefighters and community organizations with the tools to objectively monitor heat-related health and provide intervention strategies to minimize risks. The project brings together researchers with expertise in AI, pervasive computing, social and behavioral science, user-centered design, community engagement, heat resilience, and hydration science to collaborate with community partners including firefighters, fire and forestry departments, and nonprofit organizations. The project aims to improve physical and mental health of firefighters, reduce healthcare costs, improve performance, and enhance job satisfaction and efficiency. The developed technologies can be further refined for use in other communities, such as construction workers, miners, and agricultural workers, who experience prolonged heat exposure. This interdisciplinary project will design a scalable and adaptable infrastructure for continuous and objective heat-related health monitoring and proactive decision making by developing new methods for community engagement, passive monitoring of key aspects of heat-related health, real-time risk mitigation, and sustaining engagement in digital platforms. Specifically, the project will (1) establish a structured community engagement approach, called Design Studios for Health, where each design studio session will focus on collaborative discussions, prototype testing, and structured feedback collection from firefighters and community partners; (2) design deep learning algorithms that use multimodal wearable sensor data to continuously assess firefighters’ health; (3) develop AI methods that identify mitigation strategies to minimize firefighters’ health risks by generating counterfactual explanations that reason about the machine learning predictions and provide counterfactual feedback to avert impending high-risk events; (4) develop new techniques to ensure robust inference of the AI algorithms so that the HeatMind platform can be reliably deployed in uncontrolled settings and across different environments; and (5) implement a community-facing testbed that integrates sensors, data, and algorithms in a unified framework for data collection, visualization, inference, and intervention delivery. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Cloud computing has enabled individuals and organizations to delegate complex computations to external servers, which helps enhance scalability and reduce the need for local computational resources. Despite its significant advantages, cloud-based computation raises serious concerns about data privacy and security, especially when handling sensitive information. Homomorphic encryption (HE), which enables computations directly on encrypted data, provides a powerful approach to addressing these concerns. However, current HE schemes face major limitations in multi-user environments, such as limited support for dynamic participation, vulnerability to malicious users or servers, and high computational and communication overhead. This project will develop a novel threshold homomorphic encryption scheme that avoids the need for trusted setups, maintains constant cipher-text size regardless of the number of users, and supports asynchronous operations. The research will also include the development of protocols that ensure security in the presence of malicious clients and servers, support variable threshold access levels among users, and incorporate hardware-based security mechanisms to further enhance performance. These innovations will be applied to real-world scenarios such as federated learning and heavy hitter detection, with resulting tools implemented in a publicly available software library. The project will contribute to both the theory and practice of secure multiparty computation, offering broader impact across privacy-preserving data analysis, cybersecurity, distributed systems, finance, and healthcare. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Laser powder bed fusion (LPBF) is a metal additive manufacturing process that uses a focused high-energy laser to selectively melt metal powders layer by layer, enabling the production of geometrically complex parts for aerospace, biomedical, and other industries. However, localized laser heating creates a highly dynamic interaction region involving powder, molten metal, vapor jet, and gas flow. These elements fluctuate rapidly during laser scanning, causing process instabilities that result in defects and compromise part quality. One major contributor to these instabilities is the stochastic ejection of powder, known as spatter. To address spatter challenges, this project will conduct a fundamental in-situ investigation of powder dynamics during the pulsed LPBF process, thus uncovering the underlying mechanisms and driving forces. Based on these insights, an analytical model will be developed that links process conditions to powder dynamics, guiding the design of a spatter-free LPBF process. This project will deepen scientific understanding of powder dynamics in pulsed LPBF. Addressing spatter issues will enhance part quality and consistency, benefiting critical sectors such as aerospace, biomedical, and defense, thereby supporting national economy, health, and security. The project includes rich educational and outreach activities to introduce K-12, undergraduate, and graduate students to additive manufacturing and the use of in-situ monitoring tools to solve real-world challenges. During LPBF, laser-material interactions induce significant process instabilities that cause defect formation and reduce part quality. The stochastic formation of spatters is a major contributor to these instabilities. Eliminating spatter has long been considered unachievable due to the intrinsic ejection of powders by vapor jets. This research project aims to address this longstanding challenge by using a pulsed laser beam to control powder dynamics. To realize this, multi-agent multi-view in-situ characterization experiments will be conducted to capture powder dynamics during the pulsed LPBF process, thereby revealing the underlying mechanisms. An analytical model will be further developed that links pulsed laser parameters (frequency, duty cycle, laser power, scan speed) with dynamic powder behavior (movement, incorporation, ejection). The specific objectives of this project include: (1) uncover powder movement, incorporation and ejection dynamics and mechanisms in pulsed-mode LPBF; (2) develop an analytical model linking process conditions to powder dynamics; (3) predict and demonstrate spatter-free in real LPBF prints. The ultimate goal is to establish a process design framework that can reliably control powder dynamics and enable consistent spatter-free LPBF. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Algorithms permeate our modern world, driving everything from navigation, information storage, and data retrieval. In contrast, biological information is inherently physical, carried by molecules whose shapes determine their interactions with the environment. This NSF-funded program aims to explore and harness the interface between the deoxyribonucleic acid (DNA) “software” and the geometric “wetware” of molecules. The research will begin by developing mathematical tools to distinguish molecules based on their 3D shapes and structures. These tools will then be used to create a new programming framework: “algorithmic shape encoding.” Using small DNA tiles as modular pieces in a molecular-scale 3D jigsaw puzzle, the team will construct increasingly complex structures—drawing inspiration from nature’s ability to link form and function. The expected outcomes include breakthroughs in self-assembling materials, biocomputing, and optical communication systems. In addition to scientific discovery, this program will foster interdisciplinary training across mathematics, engineering, and chemistry from high school to the postdoctoral levels. DNA, with its predictable structure and ability to self-organize at nanometer precision, offers a powerful platform for designing next-generation materials. This project builds on the well-established tensegrity triangle motif to create a diverse set of 3D DNA motifs that self-organize into authentic 3D DNA building blocks. In Aim 1—Unit Design: Encoding Information in 3D DNA Motifs—researchers will identify key structural features of DNA motifs that can encode information through molecular shape. This will involve developing computational tools to predict and constrain topologies and verifying motif structures using X-ray and related techniques. In Aim 2—Algorithmic Shape Encoding for Large 3D Nanomaterials—the focus will shift from individual motifs to overall structural organization. The team will (1) design and characterize quaternary structures with defined chirality, and (2) develop periodic, hierarchical, and fractal-based arrays that require supramolecular-level algorithms rather than sequence-level design. Finally, the project will prototype optical materials capable of light-based computation and readout, paving the way for new advances in nanomaterials and biomimetic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Spheres, familiar shapes encountered in everyday life, from sports balls to celestial bodies, conceal deep and complex mathematical questions within their seemingly simple structures. This project explores challenging problems in discrete geometry specifically related to spheres. Understanding these problems not only advances mathematical knowledge but also has broad implications across science and technology, influencing fields such as data science, communications, and materials science. Central to this project is the integration of advanced mathematical research into education. By developing innovative curricula that blend theoretical exploration with practical problem-solving, this initiative will prepare undergraduate and high school students with critical thinking and analytical skills, fostering the next generation of mathematicians and scientists. The mathematical focus of this project addresses fundamental and intricate problems within spherical discrete geometry, including equiangular lines, spherical two-distance sets, plank coverings of spheres, the dual of the Komlós conjecture, and rainbow problems in combinatorics. These problems, while easily stated, require innovative approaches that go beyond traditional methods. The research draws on techniques from combinatorics, optimization, and harmonic analysis, and actively fosters collaboration across mathematics, physics, and computer science. Interactions with physics provide insight into geometric configurations that model physical systems, while connections with computer science inform algorithmic and computational aspects related to coding theory and data representation. Additionally, the project emphasizes the dissemination of mathematics through workshops and conferences, contributing valuable tools and perspectives to the broader mathematical community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

NON-TECHNICAL SUMMARY: The ability to precisely design and assemble three-dimensional biomaterials will open the door for significant impacts in many fields including biotechnology, bioremediation, drug delivery, and advanced medical technologies. To this end, this project will develop, validate, and apply novel approaches for the design and precise assembly of proteins, containing non-natural amino acids, into three-dimensional frameworks to create new classes of functional biomaterials. The proposed approach promises to be readily extensible to a variety of systems and applications, thereby making it an exciting new method for biomaterials design. In addition, this proposal includes research and teaching activities in biochemistry, structural biology, protein engineering, and chemical biology that provide unique opportunities for training highly interdisciplinary students and broadening participation in the future STEM workforce of the United States. TECHNICAL SUMMARY: The primary goal of the proposed research is to develop a general method for the assembly of large, ordered 3D protein biomaterials. Standard protein crystals are typified by properties including close packing between protein partners and a relative inability to design or control the lattices in which the assemblies form. The ability to control the geometric relationships and distances between assembled proteins within the lattice would allow for the development of a new class of ordered 3D protein materials; at present, this degree of control over assembly is not possible. This work proposes a novel approach for generating “Coordinated Protein Frameworks” (CPFs), which represent extended protein lattices with defined geometries and tunable distances between each of the proteins in the lattice. The approach leverages non-canonical amino acids (ncAAs), whose side chains include chemical functional groups not present in the twenty standard amino acids. ncAAs containing “bioorthogonal” chemical functional groups will be incorporated at specified positions in a target protein that will define the symmetric arrangement between proteins in the resulting lattice. CPF assembly will then be initiated using small molecule linkers that terminate in chemical functional groups that will specifically react with the ncAAs and will not cross react with any natural amino acids. The symmetries of the CPF assemblies and the “pore sizes” of the lattices can both be readily tuned by altering the position of ncAA incorporation and modulation of the linker length, respectively. This novel approach promises to create a new class of tunable protein biomaterials that are similar to well-characterized small molecule systems like metal organic frameworks. Furthermore, because of the generality of the approach, essentially any protein (or potentially multiple proteins) could be patterned in three-dimensional space in an unprecedented way. The efforts of this study will provide new insights into engineered protein self-assembly processes and guide further research efforts on biomolecular and biomaterial design. Finally, this proposal includes educational activities in biochemistry, structural biology, protein engineering, and chemical biology that provide unique opportunities for training highly interdisciplinary students and broadening participation in the future STEM workforce of the United States. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Heterogeneous catalysts are solid materials that facilitate the transformation of raw chemicals into useful products. Commonly, catalysts are metals with atoms arranged in ordered, repeating patterns—called crystalline structures. However, recent findings show that when these atoms are arranged randomly, in a so-called amorphous structure, the catalysts more effectively convert the raw chemicals into the desired product. This discovery opens the door to more energy-efficient and cost-effective chemical production, yet scientists still do not fully understand why the lack of atomic order improves the reaction rates. This project seeks to uncover how atomic disorder in amorphous catalysts increases reaction rates and selectivity, using the hydrogenation of carbon dioxide, i.e. the conversion of carbon dioxide and water into valuable fuels, over copper as a test case. By exploring how carbon dioxide and water interact with both ordered (crystalline) and disordered (amorphous) copper surfaces, the project aims to identify the atomic structures that make reactions faster and more efficient. Highly accurate computer simulations of atomic behavior and molecular transformation will map copper atom structure characteristics to chemical reactions and their barriers. Thus, it will provide the structure-activity relationships needed to develop new high performance amorphous catalysts. This work supports the NSF mission by deepening our understanding of fundamental chemical processes on complex materials and contributes to national interest by providing strategies to reduce the energy and cost of chemical manufacturing. It also advances education through the training of graduate students in catalysis and computational modeling and the organization of outreach activities that introduce students to engineering research. Despite their promise, amorphous catalysts are far less studied than their crystalline counterparts due to the difficulty of analyzing their complex disordered structures. This project aims to determine how disordered surfaces, water arrangement on said surfaces, and metal composition affect the activity and selectivity of amorphous copper catalysts in the electrocatalytic reductive conversion of carbon dioxide into valuable, higher-order (C2+) hydrocarbons. Using a machine-learning-accelerated computational approach, the researchers will develop highly accurate models based on quantum mechanical calculations. These models will calculate the minimum energy pathways of elementary reaction steps of electrochemical CO2 hydrogenation. The resulting minimum energy path reaction energetics and product distributions will reveal the effects of amorphous copper surface structures and the associated local water arrangements on catalytic performance. This data will be used to build structure–activity relationships, guiding the design of next-generation amorphous catalysts. This project will advance the broader field of catalysis by clarifying how catalytic site distribution of amorphous materials, and water structure influence reaction pathways. These findings have significant implications for the many reactions where water is present, and structures are disordered. Even more broadly, the computational approach developed here provides a generalizable method for studying amorphous materials, supporting their study and use in a wide range of applications such as energy storage, low-energy separations, and corrosion-resistant systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The goal of the project is to develop novel advanced materials integrated with real-time process feedback, assisted by a machine learning algorithm, to enable scalable, autonomous in-situ manufacturing of electronics. The technology will provide capabilities for on-demand fabrication, adaptive repair, and dynamic reconfiguration of circuits, functions that are particularly critical for long-duration space missions where resupply is difficult. These enhanced materials and manufacturing processes will support future space exploration initiatives. Beyond space applications, the methods developed here may also transform multiple technology sectors including flexible hybrid electronics for wearable devices, neuromorphic computing systems that mimic brain functions, and distributed manufacturing solutions for remote or resource-limited environments. The research incorporates workforce development initiatives to train students in cutting-edge techniques spanning materials science, artificial intelligence, and advanced manufacturing. Participants will gain hands-on experience in functional materials synthesis, intelligent process control systems, and semiconductor device fabrication, skills directly aligned with emerging needs in the advanced manufacturing sector. The project specifically addresses national workforce development priorities in critical technology areas including additive manufacturing, semiconductor processing, and autonomous production systems. This project develops a new method to manufacture electronics in space using 2D materials like molybdenum disulfide (MoS₂). These ultra-thin materials are ideal for space applications because they are lightweight, radiation-resistant, and energy-efficient. The key innovation combines three critical components: (1) specially designed chemical inks that transform into functional electronics at relatively low temperatures, (2) an artificial intelligence (AI)-controlled printing system that adjusts in real-time to produce perfectly aligned layers, and (3) precision laser processing that fine tune the material's properties after printing. First, new ink materials and formulations will be created, where the molecular structure determines how well the material performs in final functional semiconductor devices. Then AI systems will be implemented to monitor and optimize the printing process, catching and correcting any defects in real-time. Finally, laser sintering will be utilized to control and enhance the material's electrical properties, enabling complete electronic device processing onsite. This integrated approach solves a major challenge in space manufacturing by eliminating the need for complex equipment or high temperature processing. The methods could enable in space manufacturing of electronics during long missions without relying on Earth-based supplies. The same technology may also improve manufacturing of flexible electronics and advanced computing systems on Earth. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Ricci flow is the prototypical example of a geometric heat flow -- a natural process which evolves the geometric structure of a space by a heat-type differential equation, smoothing out bumps and other irregularities much in the same way that the laws governing the diffusion of heat drive the temperatures of all objects in a room, hot or cold, toward the same value over time. Geometric flows arise as models for physical phenomena as diverse as the evolution of grain boundaries in annealing metal and the weathering of stones at the ocean's edge, and through their tendency to "improve" a given space into something more symmetric and homogeneous, they have proven to be remarkably effective tools in efforts to resolve fundamental mathematical questions at the intersection of geometry and topology. This project belongs to these efforts, seeking to better understand the extreme situations where the analogy between the (linear) heat equation and the (nonlinear) Ricci flow begins to break down. The main aims are to study the nature of solutions in singular regions (where the space is becoming irrecoverably curved or pinched), and to extend the analytic theory of the equation to solutions which may become arbitrarily highly curved near spatial infinity. The project also includes an educational component, naturally incorporating the mentorship and research training of graduate students. This project has two components. In one direction, the PI will build on his past collaborative work, using methods from the theory of unique continuation for elliptic and parabolic equations to study the classification problem for noncompact shrinking solitons and further questions pertaining to finite-time singularity formation in dimensions four and higher. In another direction, the PI will seek to localize methods developed in his prior work on problems of uniqueness and unique continuation for geometric flows in order to extend them to Ricci flows with potentially unbounded curvature and other singular features. The main questions concern uniqueness and issues of preservation of structure (symmetry, restricted holonomy, positivity of curvature) for general complete flows, the long-time existence and analyticity of solutions within special subclasses, and the asymptotics of solutions near fixed points of the equation. A theory flexible enough to accommodate such nontraditional solutions could potentially find important geometric applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The composition of rocky planets orbiting other stars (exoplanets) can differ according to silicon, magnesium, calcium, and aluminum content. These differences relative to Earth may impact their planetary evolution and habitability. In previous lab experiments on materials like those expected for rocky exoplanets, the project team found small changes in composition could suppress the cycling of elements necessary for life and cause a long-lived lava ocean. In this project, high pressure and temperature experiments are carried out on materials with a wider variety of element ratios to cover the potential spread of exoplanet properties. As a result, astronomers will be able to target stars whose planets are most likely to have Earth-like geochemical conditions. A graduate student and several undergraduate students will be trained in laboratory methods. Training in science communication for the project’s students will culminate in disseminating the project via public outreach events and an episode of “Strange New Worlds: A Science and Star Trek Podcast.” Petrologic experiments on hypothetical bulk silicate exoplanet compositions different from Earth or other solar system bodies will be performed to acquire fundamental data about how planet compositions influence long-term habitability, as well as planetary cooling and structure. This project consists of five tasks: (1) conduct initial set of experiments on four hypothetical exoplanet compositions to determine shallow mantle melting curves, melt compositions, and melting reactions; (2) use results from Task 1 to guide selection of compositions for a second set of experiments; (3) conduct a second set of experiments on four hypothetical exoplanet compositions selected in Task 2; (4) package data for incorporation into the geochemical modeling code exoMELTS; and (5) prepare a prescriptive filter of FGK-type stars that are likely to host geochemically habitable exoplanets on the Hypatia Catalog 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 2025 · 2025-09

Hydrogels have great potential for applications in healthcare, robotics, and more. Additive manufacturing of hydrogel enables building 3D objects with sophisticated structures. However, a key challenge is the lack of cost-efficient methodologies for designing the hydrogel synthesis and printing processes together to achieve desired product performance. This award enables research in creating hydrogels with customized features tailored for distinct applications with lower cost, reducing the cost of additive-manufactured hydrogel products. If successful, the outcomes of this research are expected to shorten the design cycle of new materials and processes and facilitate wide utilization and rapid scale-up to industry. This research aims to develop a unique analytical design framework, consisting of (a) interpretable and efficient uncertainty quantification models that adaptively accommodate the model complexity and (b) a unique decision algorithm for the multistage experiments that simultaneously decides the next experimental operation and the volume of material, in order to make diverse products achieving multiple targeted functionalities. Additionally, the experimental platform, dynamic-fluid-assisted micro-continuous liquid interface printing (DF-μCLIP), offers a dedicated “hardware in the loop” system that synergizes in-situ hydrogel synthesis and printing for implementing the design optimization procedure. The resulting integrated material discovery and manufacturing platform has broad potential impact across material and manufacturing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project explores the physics of extreme environments where strong magnetic fields interact with relativistic matter at very high temperatures and densities. Such conditions occur in the early Universe, within compact stellar objects like magnetars, and in quark-gluon plasma produced during high-energy nuclear collisions at relativistic colliders. Such systems exhibit unusual quantum behavior and offer critical insights into the fundamental forces of nature. The project seeks to deepen our understanding of how strong magnetic fields influence the behavior of matter under extreme conditions. This research aims at advancing scientific discovery in theoretical nuclear and astrophysics. Broader impacts include the training of undergraduate students and postdoctoral researchers. Outreach efforts will support project-based physics education at the university level and help develop a new generation of scientists equipped with advanced theoretical and computational skills. This work promotes the integration of research and education and contributes to building a strong scientific workforce. This research addresses fundamental questions in the theory of strongly magnetized relativistic systems. Using advanced quantum field theory techniques, the project investigates the influence of strong magnetic fields on the self-energy of fermions, the photon and gluon polarization tensors, and the vertex function in relativistic plasmas. While prior work has elucidated the absorptive parts of the self-energy and polarization tensors, this project aims to advance the understanding of their real parts and apply this knowledge to predict a range of observables in relativistic systems. The study focuses on systems where Landau quantization significantly alters the dynamics, such as in the magnetospheres of pulsars, the interiors of neutron stars, heavy-ion collision environments, and possibly the early Universe. Particular emphasis is placed on the interplay between quantum anomalies, such as the chiral anomaly, and strong magnetic fields. By bridging quantum theory with phenomenological applications, the research aims to generate novel predictions and insights that can guide both theoretical developments and experimental investigations in nuclear physics, astrophysics, and cosmology. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project will harness the methods of artificial intelligence (AI) to design structures and tools for bionanotechnology. Bionanostructures are small, artificial self-assembled devices that can perform tasks at nanoscale. Like a robot in a factory combining materials to build a car, these nanostructures can put together materials such as proteins, gold nanoparticles, and DNA molecules with promising applications in diagnostics, therapeutics and material science. Currently, the design of bionanostructures is a tedious iterative process, often based on costly and time-consuming trial and error approaches. This project will develop novel techniques based on generative AI algorithms to automate design and characterization of these complex nanodevices built out of DNA. It will thus allow construction of more complex devices as well as scaling up and simplifying the process, thus enabling large-scale manufacturing of new types of bionanotechnology for a variety of applications. The overarching goal of this project is to harness generative AI methods for automated design of nucleic acid nanostructures and experimentally verify them by realizing nanoscale devices. To speed up the design process, this project will introduce the following applications of AI into the bionanotechnology field: 1) Develop new methods to speed-up computational characterization of nucleic acid nanostructures based on generative deep neural network architectures trained on data from coarse-grained model of nucleic acids simulations; 2) develop reinforcement learning-based algorithms for automated design of nanostructures with feedback from simulated or experimental environment; and 3) enable human-language prompting and interactive nanostructure design using large language modeling tools trained on datasets collected through our design software. The project will create training opportunities for undergraduate and graduate students and develop AI-based tools for a biochemistry education program. It will also create easy-to-use interactive generative design tools for outreach events for the general public. This is a project jointly funded by the National Science Foundation and the Italian Ministry of Universities and Research (MUR) via the NSF-MUR Lead Agency Opportunity on Artificial Intelligence, where NSF funds the U.S. investigator and MUR funds the partner(s) in Italy. The U.S. investigator is supported by the Foundations of Emerging Technologies program and the Office of International Science and Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Several types of soil bacteria can actually glide across surfaces using specialized molecular machinery. This ability has evolved in multiple groups of microbes, and understanding how it works holds potential for advancing the bioeconomy, especially in agriculture. The research integrates work of specialists in genetics, biophysics, and in cryo-electron tomography (cryo-ET) to explore how multiple rotating motors on the bacterial surface coordinate to drive a protein-based conveyor belt on the bacterial cell surface, enabling cell movement analogous to a molecular snowmobile. By identifying the location , shape, and reactivity of the proteins involved, the project will uncover the fundamental structure and mechanical principles underlying bacterial gliding, providing insights for bio-inspired technological innovations and advances in soft material robotics. The investigators will also collaborate with the Arizona State 'Ask A Biologist' program to develop interactive online educational tools to enhance public understanding and student engagement in microbiology. This research specifically examines the molecular and mechanical intricacies of the bacterial gliding machinery, emphasizing its macromolecular assembly and torque-generation mechanism. Primary objectives include determining how multiple rotary motors cooperate to propel the conveyor belt and elucidating the distribution of tension across this belt. The project also aims to identify the polymerization mechanism of the conveyor belt and the molecular basis underlying its directional control. Employing a multidisciplinary approach, the research combines genetic manipulation to elucidate protein function, biophysical assays to characterize motor dynamics and conveyor belt properties, cryo-ET for high-resolution structural visualization in intact cells, and computational simulations to model molecular interactions and dynamics. Collectively, these methods will yield comprehensive insights into gliding motility at molecular and cellular scales, substantially advancing the understanding of biological nanomotors. This project is funded by the NSF/BIO/MCB Cell Dynamics & Function Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Non-Technical Abstract: Understanding quantum materials where electronic, magnetic, and structural properties are strongly intertwined has the potential to transform technologies in computing, energy, and sensing. This project supports a two-day educational workshop focused on using state-of-the-art electron microscopy techniques to study quantum materials. It brings together students, academic experts, and industry professionals to discuss challenges in using electron microscopes for quantum materials, including how to prepare samples and analyze data. It provides hands-on training, expert-led tutorials, and panel discussions, promoting cross-disciplinary learning and mentorship and helping prepare young students for careers in academia and industry. The workshop also encourages accessibility by offering travel scholarships to at least ten young students. By supporting the development of future scientific leaders and fostering knowledge exchange, this project addresses national priorities in Quantum Information Science and Technology. Technical Abstract: The two-day workshop addresses the need for atomically resolved, spectroscopically capable metrology in the study of quantum materials. While significant advancements have been made in optical, scanning probe, and X-ray-based techniques, there remains a metrological gap in simultaneously achieving high spatial and spectral resolution to capture spin, lattice, charge and orbital couplings at the angstrom scale. This research activity focuses on advancing multimodal electron microscopy methods for resolving atomistic correlations that govern emergent behaviors such as superconductivity, quantum coherence, and topological transport. The workshop highlights critical methodological challenges, including sample preparation for cryogenic and operando experiments, hardware limitations in data acquisition, and software bottlenecks in data analysis. Technical sessions address developments in electron energy loss spectroscopy, ultrafast electron diffraction, correlated light-transmission electron microscopy and electron ptychography. The project brings together leading scientists and instrument developers to present current solutions and emerging strategies, with a strong focus on training graduate students in practical data acquisition, interpretation, and machine learning-assisted analysis. This integrated effort supports the broader goal of building a technically proficient and collaborative research community and workforce around quantum materials characterization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Measurements of the spatial clustering of matter in the universe can reveal clues about the nature of the mysterious “dark energy” that is causing the universe’s expansion to accelerate. A new powerful way to measure this clustering at early epochs in the universe’s history is to use radio telescopes to detect the hydrogen gas that is ubiquitous in all galaxies. A team of scientists from Arizona State University, Massachusetts Institute of Technology, Yale University, and West Virginia University, is using a custom-built radio telescope, the Canadian Hydrogen Intensity Mapping Experiment (CHIME), to make one of the first measurements of dark energy from observations of radio waves emitted by hydrogen in the universe. This project will develop several new techniques to leverage the latest developments in signal processing, detector technology, and theoretical modeling. In parallel, this project will expand several outreach programs that teach high school and college-aged students about astronomy and scientific thinking. The goal of this project is to solve critical calibration and analysis challenges for CHIME that will reduce residual foreground contamination by an order of magnitude and enable the detection of the large-scale structure of the universe with the 21cm line, independent of other probes. CHIME’s recent measurements of cross-correlations between 21cm intensity maps and eBOSS galaxies up to redshift 1.4, and the Lyman-alpha forest up to redshift 2.3, have demonstrated CHIME’s potential for high-precision large-scale structure measurements. In this project, the team will develop new modeling frameworks and analysis pipelines for 21cm cross-correlations; generate improved beam models and integrate them into the analysis; implement new radio-frequency interference excision algorithms based on cyclostationary signal processing; and deploy innovative foreground filtering techniques that are robust to the dominant systematic errors in the data. These advances should lead to a CHIME-only auto-correlation detection of large-scale structure and ultimately the baryon acoustic oscillation signal. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This Pathways to Enable Open-Source Ecosystems (POSE) project creating an open-source ecosystem (OSE) around software for cyber-physical systems (CPS), which integrate software with physical-world processes. CPS applications have great societal and national importance; They include manufacturing, robotics, transportation, energy systems, and healthcare. Achieving the required levels of safety and reliability in these systems is increasingly challenging. Since the intelligence and network connectivity required for the software adds to system complexity, system designers must become experts in concurrent software, distributed computing, real-time software, and virtualization technologies, on top of their domain expertise. To address these challenges, this project focuses on developing an open-source software framework that provides developers with an intuitive programming model for managing complex system demands that is tightly matched to the requirements of CPS applications. This project provides the framework to strengthen the OSE, expand its community of users, and apply the results in real-world industrial applications. This POSE project replaces traditional embedded software running on bare metal or a real-time operating system (RTOS) with containerized applications mixing edge computing, specialized hardware for machine learning (ML)-based artificial intelligence (AI), and cloud computing. The project has four primary objectives: 1) strengthening the OSE infrastructure; 2) maturing existing experimental capabilities; 3) enhancing OSE community culture; and 4) creating the OSE governance structure. The project strengthend the continuous-integration/continuous-deployment (CI/CD) infrastructure around the OSE to include continuous testing on embedded platforms, distributed platforms, real-time systems, and virtualized platforms. This is necessary to build trust in the framework for use in critical applications. The project matures existing experimental capabilities, such as fault detection and management, distributed real-time scheduling, self-adaptive programs, integration of ML-based AI, and libraries of reusable components. The project team focuses on improving the culture of code quality and security, testing and verification, quality documentation, community interactions, and maintenance. Key stakeholders in industry and academia, working with the project team, contribute to creating a governance structure with steering and governance committees, core teams, and key processes such as those for reviewing and accepting code changes. Dissemination and outreach of OSE resources will be done through workshops, bootcamps, tutorials, and community-focused instructional materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Pipelines play a vital role in transporting energy resources that are essential for America’s economy and the well-being of the American people. A significant challenge facing the domestic pipeline infrastructure is the gradual deterioration of materials and structural components, which can result in serious safety risks. While regular in-line inspection (ILI) of pipelines is crucial for risk management, existing tools are often labor-intensive, expensive, and hard to adapt to different pipeline geometries. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) award advances fundamental and interdisciplinary research to enable safe, efficient, and automated inspection of gas transport pipelines for accurate risk assessment. The research integrates new knowledge in robotics, non-destructive evaluation (NDE), risk engineering, artificial intelligence (AI), and policy analysis to iteratively develop Learning-based Autonomous Risk Assessment Systems (LARAS). The project will include lab and field testing at different scales, create partnerships with industry and collect stakeholder feedback throughout the planned activities. The broader impacts of the project include the development of new curriculum and undergraduate research opportunities, outreach to local communities and professional societies, and online modules for workforce development. This interdisciplinary project will advance the science and engineering of automated pipeline inspection and risk assessment through the development of novel robotics, sensors, physics models, AI algorithms, and decision-support policies. The project has four core objectives, specifically to: (1) design low-cost and flexible in-pipe inspection robots to autonomously navigate in pipelines with complex configurations; (2) exploit and integrate novel NDE sensing solutions with the pipe inspection robots to collect multimodal data for detecting cracks and leaks; (3) predict failure risks with uncertainty quantification by combining physics-based simulations and machine learning using sensor data and historical accident reports, and; (4) engage stakeholders from industry, government agencies, researchers, and public interest groups to understand the emerging landscape of safety regulations and guide the design and deployment of the risk assessment systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Earth formed by a series of collisions between smaller rocky bodies. At some point during this process, the Earth also acquired the elements, known as volatiles, that make up its atmosphere and oceans. But how and when the Earth formed, and how and when it acquired its volatiles, are still very uncertain. This matters, because the volatile elements are essential for life as we know it; if we can understand how the Earth acquired its volatiles, that will help us understand how other planets did so, in this solar system and elsewhere. To solve this problem the investigators use two main tools. One is a series of natural “clocks”, derived from radioactive elements that decay; these tell us how fast things happened. The second is experiments to determine whether the volatile elements were sequestered into the Earth’s iron core, or whether they were left behind in the rocks and atmosphere. As part of this investigation the team will train graduate and undergraduate students in experimental and analytical techniques, adding to the technically-trained workforce. In this proposal the investigators explore the combined effects of volatile loss and core sequestration on a range of moderately volatile and refractory elements. They will use four isotopic chronometers (Hf-W, Pd-Ag, U-Pb and I-Pu-Xe), with different half-lives and chemical characteristics, to disentangle these two effects. The modeling efforts use N-body accretion models, allowing provenance to be tracked and isotopic evolution to be tracked; they also propose to carry out necessary experimental measurements on partitioning behavior and mantle Xe isotopic compositions. The team will use the four isotopic systems to answer three major questions: 1) are the Grand Tack or conventional accretion scenarios more consistent with the observations? 2) how did the composition of material added to the Earth change as accretion proceeded?; 3) how much volatile loss happened during and after accretion itself? In answering these questions the investigators will provide a more focused picture of the formation and earliest evolution of the Earth. The proposed research involves an inter-disciplinary collaboration between a modeler, an isotope geochemist and a high-pressure mineralogist. As such, it cuts across traditional disciplinary boundaries and will provide an opportunity for the three groups to educate each other and integrate experiments, measurements and modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Studies of noble gases, such as helium (He), suggest that parts of the mantle have largely escaped melting at, or near, the Earth’s surface. These mantle domains are characterized by high ratios of the isotopes 3He to 4He (3He/4He). Much work has been done to understand how these mantle domains within Earth’s interior have escaped melting in the face of billions of years of mantle convection and plate tectonics. New observations of these high 3He/4He domains suggest that, rather than reflecting previously unmelted portions of the mantle, these domains may be the result of Earth’s core leaking into the mantle. Through this leak, pristine noble gases from the core imprint their helium ratios on mantle rocks that sit above the core-mantle boundary. Subsequently, the mantle rocks are transported to the surface and sampled by intraplate volcanism. This project will involve experiments using metal and magma at temperatures and pressures similar to the Earth’s core to explore the question of whether the core is the source of the high 3He/4He found in parts of the mantle. If true, and the core is the source, this would provide new information about the chemistry of the core and would mean that high 3He/4He mantle domains may not, in fact, be unmelted and pristine. This helps us understand Earth’s early evolution. This proposal seeks to test the hypothesis that the core can be the ultimate high 3He/4He source. Experiments will be conducted that react metal and magma under controlled conditions that range up to the pressures and temperatures directly applicable to core formation within Earth. The noble gas distribution in the experiments will be analyzed using laser-ablation mass spectrometry. These analyses will yield constraints regarding the ability of the core to incorporate noble gases during its creation. These constraints will be applied towards developing a core formation model that predicts the isotopic composition of the core for both He and Xe for various plausible formation conditions. Important model parameters include core formation timing, nature of Earth’s primordial atmosphere, the prevailing redox state associated with core formation, and core formation pressure-temperature conditions. Model predictions will be compared to existing geochemical analyses to identify if the core is a plausible source of high 3He/4He noble gases. This proposal will support the training of two PhD students in cutting-edge experimental and analytical methods, the production and characterization of noble gas analytical standards that will be made available to the community, and summer internships for college students in and around New Orleans. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Galaxies continuously form stars over billions of years. To keep forming stars during these exceedingly long timescales, galaxies need a fresh supply of gas flowing right into their centers. Gas is the primordial building material of stars. This program will study how gas flows from outside galaxies into their inner regions, sustaining the process of star formation for billions of years. To achieve the main objectives of this program, the investigators will analyze data using several astronomical telescopes, among them the NSF-funded Very Large Array and Atacama Large Millimeter Array. This program will support the research work of a graduate student. The PI will continue her work with the Prison Education Program (PEP) at Arizona State University and support Maricopa County prison educators in developing a curriculum for juvenile and adult education. Graduate student volunteers of the PEP Astro program will travel to the prisons to deliver lectures and conduct hands-on activities. The primary objective of this program is to characterize gas flows responsible for sustaining star-forming disks in the present epoch. The investigators of this program will identify gas flows into, through, and out of galaxy disks directly and infer gas accretion in the recent past via metallicity offsets of HII regions within the disk. This program will obtain high-resolution mapping of the cold neutral gas in the HI 21cm transition to trace the content and kinematics of the interstellar medium in 14 nearby (<100 Mpc) Milky Way mass spiral galaxies. These data will be combined with optical spectroscopic measurements of HII regions probing metallicity and dust content. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This program develops technology to advance the field of optical intensity interferometry using single photon detectors coupled to small telescopes. Measurement of photon correlation signals between multiple telescopes leverages advances in time-to-digital converters, ultra-fast imaging and optical communications, and novel approaches to high resolution spectroscopy. Near term science opportunities include characterizing the diameters of stars and material around stars, as well as characterizing the properties of binary star systems. The program sets the stage for intensity interferometers with 10s of kilometers of baseline separation reaching micro-arcsecond angular resolution, thereby opening a new discovery space for extragalactic and galactic astronomy. This program supports the education of undergraduate and PhD students and training of a STEM workforce through both laboratory research and contribution to courses on digital signal processing and astronomical instrumentation. Community outreach presentations take advantage of institutional and local museum venues, as well as local secondary schools and Astronomy clubs. This program pursues advances in optical intensity interferometry in three areas: (i) Development of a time calibration system to enable automated measurements of photon correlations on small telescopes using commercial room temperature single photon counting detectors and time to digital converters; (ii) multi-band measurements of photon correlations on small telescopes using a high resolution Virtual Image Phased Array (VIPA) spectrometer feeding a linear array of single photon counting detectors; and (iii) coupling small telescopes to single and few mode fiber using low cost adaptive optics to enable coherent detection and correlation measurements with entangled photons. The program builds on a previous successful effort to develop an in-house time to digital converter and use it for characterization of photon statistics from bright incoherent sources. Technology developed for the project will also have applications in deep space optical communications, exoplanet transit high resolution spectroscopy and quantum communications and quantum sensing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This CAREER project will develop a framework to guide researchers in analyzing, developing, and refining psychological theories to effectively incorporate engineering ways of knowing. The framework will help develop new knowledge based on a more robust understanding of engineering students in higher education. Theories are a crucial part of research projects; they help drive the questions we ask, the methods we use, and the conclusions we draw. They provide a roadmap to guide the data analysis and interpretation and position researchers within a body of existing knowledge. Traditional psychological theories often take a generalized approach to human motivation, overlooking the specificities of engineering contexts. The research outcomes include (1) a responsive understanding of engineering students’ achievement goal pursuits and their impact on academic outcomes and (2) a new evidence-based responsive survey instrument manual. Achievement goal theory (AGT) will be used as a case study to help develop and refine the processes needed to create the reimagining guiding framework. This project will employ qualitative research methods which use collected data to develop new theories (known as a "grounded theory approach") to uncover relevant achievement goals specific to the lived experiences of engineering students. This project will answer the following research questions: When using a grounded theory approach to reimagine AGT, what context-specific achievement goals emerge among engineering students? To what extent do the prominent themes identified in this research effectively capture the varied achievement goals among engineering students? Given the achievement goals, what are the distinct goal profiles of engineering students, and what are the implications of these profiles for their academic outcomes, engineering identity, and engagement? Through qualitative inquiry, this phase will elucidate the nuances and unique perspectives that shape the motivational pursuits of students within the engineering context. Twenty to forty engineering students will be recruited to participate in the interviews. Building on the insights gained from the initial research question, a contextually relevant survey instrument will be developed. This instrument will be designed through a focused approach, which is based on the salient achievement goals identified in Phase 1. Their shared experiences will inform the development of a responsive survey scale, which will be validated with 1,600 engineering undergraduate students via factor analysis. Validity evidence will be gathered to examine how these achievement goals influence academic outcomes through latent profile analysis. Collectively, these research questions will support the development of a reimagined guiding framework. The education outcomes are (1) a cadre of engineering education researchers who are equipped to reimagine psychological theories and (2) a graduate-level course teaching emerging scholars how to examine extant theories. This project aligns with NSF’s commitment to enhancing engineering education by addressing gaps in our theoretical understanding of all engineering students and providing actionable processes that can transform engineering education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will advance education in STEM fields among rural students. A large and skilled STEM workforce is essential for driving national innovation and economic prosperity, as well as supporting community well-being. Yet, rural communities face unique challenges in accessing high-quality STEM education that both reflect local culture and is supportive of local needs, in contrast with traditional curricula often overlook the cultural knowledge and priorities of rural communities. By engaging teachers and students from the Navajo Nation region in hands-on, culturally responsive engineering curricula, the project aims to inspire future generations to integrate their individual cultural practices and engineering knowledge to address community and industry challenges. The project’s collaborative approach enriches STEM education while empowering educators to help inspire the next generation of engineers to both serve their communities and join the engineering workforce. Technically, the project will employ a participatory, design-based research model to foster collaboration between university researchers, and teachers and community advisors in the Navajo Nation. Building on prior successful partnerships, the project will expand engineering curricula to elementary and high school levels, engaging over 20 teachers and 1,500 students across the Navajo Nation region. Teachers will participate in intensive professional development, co-design and adapt curriculum modules, and lead classroom-based research to assess and refine educational practices. Data will be collected through qualitative and quantitative methods, including classroom observations, teacher and student interviews, and analysis of student work. The project will also develop a digital curriculum hub to support dissemination and sustainability. Research findings and curricular resources will be shared through workshops, publications, and online platforms, providing a scalable model for community-driven STEM education. The project will contribute new knowledge on stakeholder participation in culturally responsive engineering education, with potential applications for rural and other communities nationwide. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Advances in laser and x-ray technology have enabled scientists to study quantum processes in atoms, molecules, and materials with unprecedented time resolution. This project will apply x-ray pulses that are shorter in duration than a millionth of a billionth of a second to investigate a fundamental question in quantum physics: how fast is the electron ejected from a molecule hit by a light pulse? The awardee will study the timescale of the electron ejection, and its dependence on the molecular environment. The research team funded by this award will use ultrafast measurements to understand how electrons interact with each other, and how the light-molecule interaction is impacted by the positions of atoms within the molecule. The knowledge gained from this research will facilitate strategies for controlling the flow of charge and energy inside molecules for practical applications in the fields of light harvesting and energy storage. Additionally, it will enhance our understanding of the electronic processes behind DNA damage and vitamin D synthesis, and contribute to the development of ultrafast quantum devices for national security applications. The students engaged in this project will gain cutting-edge technical expertise in ultrafast science and develop scientific proficiency to initiate their own research investigations. This will foster scientific innovation and enhance the competitiveness of our nation in critical research areas. To conduct this research, the research team will employ attosecond extreme-ultraviolet and soft-x-ray pulse techniques to investigate the fundamental topic of photoionization and its novel applications in probing correlated electron processes. In this endeavor, the PI and students will measure the photoionization delays with attosecond interferometry to probe the role of electronic interactions and molecular environment. Additionally, elementally specific transient absorption will be employed to study the charge migration induced by the correlations during photoionization. The team will employ advanced diagnostic techniques, including velocity map imaging and attosecond transient absorption, to conduct these studies. In molecular systems, the photoionization delay measurements will illuminate the effect of shape resonances, electron-nuclear couplings, and non-adiabatic dynamics. Such studies will establish photoionization delay as a highly sensitive probe of many-body dynamics and provide a testing ground for the theoretical models. Correlations in the photoionization process can also mix orbital configurations and create superpositions of ionic states, driving hole migration in large molecules. The localization of the hole charge can subsequently guide the chemical reactivity of the molecule, paving the way for attosecond chemistry. Therefore, the application of attosecond soft-x-ray pulses for spatiotemporal mapping of the charge dynamics in complex systems will serve to establish attosecond science as a versatile spectroscopy technique. 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.