Colorado School Of Mines

NSF Awards · FY 2026 · 2026-08

Nontechnical description: Atomic-scale order and disorder play key roles in shaping materials behavior for modern technologies such as efficient computing and quantum computing. However, controlling and measuring the structure of atoms in materials with disorder is notoriously difficult. This project will develop new methods to control and observe the arrangement of atoms at the smallest scales, by coupling advanced synthesis and microscopy techniques. With these methods, the research team aims to create new materials for low-energy electronics for quantum computing. Furthermore, this CAREER project will strengthen the national high-tech workforce by training students in materials research. The principal investigator will host annual workshops for university students and lead outreach activities for elementary and middle school students. These efforts will train and inspire a new generation of American scientists and engineers in the U.S. technological sector. Technical description: The principal investigator will pioneer synthesis and characterization techniques to understand the effects of order and disorder on functional and quantum materials, focusing on hexagonal oxides. Depending on composition, their behavior ranges from ferroelectric to non-polar, or from magnetically ordered to hosting quantum spin liquids. Despite significant interest in this family of materials, control over polar order and disorder and its relationship to ferroelectric properties is not yet fully understood. This project will approach the issue along two avenues. First, researchers will control symmetry and symmetry-breaking in polar ordering of hexagonal ABO3 oxides using atomically precise molecular beam epitaxy growth, stabilizing novel functionalities and emergent phenomena. Secondly, researchers will advance scanning transmission electron microscopy methods to gain a full three-dimensional understanding of the local atomic structure using scanning electron diffraction techniques including tomography, cepstral analysis, and ptychography. This research will employ tight feedback between synthesis and characterization to accelerate novel materials for relaxor ferroelectrics for energy storage and low-power, next-generation quantum information technologies. 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 2026 · 2026-07

Artificial intelligence models typically reduce to optimization problems: find the best solution according to a problem-specific metric. Sometimes, the nature of the problems means that the standard calculus-based tools cannot be applied. This setting is known as gradient-free optimization, and is particularly relevant for small businesses, academic research groups, and public-sector organizations that lack large-scale computing infrastructure yet still need to fine-tune machine learning models or optimize complex simulations. This project develops new mathematical and computational tools that make gradient-free optimization dramatically more efficient by exploiting hidden low-dimensional structure in these problems. This will lower the computational barrier to entry for a broad range of users. The project will train PhD students in these interdisciplinary methods, produce openly available software, and develop instructional materials connecting linear algebra to modern deep learning. Gradient-free optimization (GFO) has deep theoretical foundations, yet remains poorly understood in high dimensions. This project will establish mathematical and algorithmic tools that break worst-case GFO barriers by exploiting structure in matrix-valued gradients. Algorithms for objective functions whose gradients exhibit various kinds of low intrinsic dimensionality, such as sparsity, low rank, or sparsity-plus-low-rank will be developed. These gradient estimation techniques will be wrapped into modern algorithms such as muon which involve matrix factorizations, projections, or solving linear systems in the Fisher information matrix. Drawing upon computational linear algebra, novel techniques for performing these matrix operations in the gradient-free setting will be provided. 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 2026 · 2026-06

Living systems build complex structures, such as cellular scaffolds or protein-based compartments, by assembling many small pieces in dynamic and often unpredictable ways. These processes do not always follow a single path. Instead, they can proceed through many possible routes depending on environmental conditions such as temperature and chemical signals. This project seeks to determine how and why certain assembly pathways are preferred over others, especially when systems are driven away from stable states by external influences. To address this challenge, the project will develop new computational tools that combine physics-based simulations with machine learning to track how structures form over time and to quantify the “irreversibility” of different pathways. By identifying which pathways are most likely to occur, this work will enable new strategies to design biomolecular materials that respond to their environment, with applications in biotechnology, medicine, and sustainable manufacturing. These advances will contribute to national priorities in health, energy, and advanced materials by enabling predictive design of complex molecular assemblies. In parallel, the project will create interactive learning tools, including hands-on simulations and visual modules, to introduce students to computational biology and data-driven science. These educational activities will help prepare the next generation of scientists to work at the interface of biology, physics, engineering, and artificial intelligence. This project develops a data-driven, multiscale computational framework to quantify pathway complexity during stochastic, out-of-equilibrium biomolecular assembly. The central question is whether path entropy production can serve as a unifying metric to distinguish thermodynamic versus kinetic control and to predict preferential assembly pathways under nonequilibrium conditions. To probe this question, the project integrates coarse-grained molecular simulations with deep learning-based probabilistic forecasting models to efficiently generate trajectory ensembles and estimate path probabilities in high-dimensional systems. Transformer-based architectures will be used to learn effective coarse-grained dynamics, including non-Markovian memory effects, enabling high-throughput simulation of biomolecular systems. Complementary entropy production estimator models will be developed to quantify irreversibility along trajectories under time-varying environmental conditions, such as changes in temperature and ion concentration. These methods will be applied to representative protein assembly systems, including bacterial microcompartments and coiled-coil assemblies, where morphology is highly sensitive to external stimuli. By connecting stochastic thermodynamics with machine learning-enabled coarse-grained modeling, this work establishes a generalizable framework for mapping high-dimensional assembly landscapes onto predictive, physically motivated metrics of pathway selection. 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 2026 · 2026-05

Non-Technical Summary The Colorado School of Mines will host a Research Experiences for Undergraduates (REU) site focused on training the next generation of scientists and engineers with expertise at the intersection of materials science and data analytics. With the goal of providing cutting-edge research to students with limited research opportunities at their home institutions, students are actively recruited from community colleges and primarily undergraduate institutions. Training will occur through formal instruction, hands-on research, engagement with data-intensive local laboratories and businesses, and preparing and delivering public presentations of research results. The research projects focus on developing transformative materials that enable new technologies, spanning next-generation computing, separations, and electrochemical systems. Technical Summary The REU site addresses the Materials Genome Initiative within the context of energy materials. Students are gaining experience in the generation, curation, and analysis of large materials science datasets. Research projects span experiment and computation and are quite diverse, ranging from porous materials for selective separations, to battery materials discovery, to multiferroic and quantum materials for next-generation computing. The research projects are inherently interdisciplinary, involving faculty members from different departments and their affiliated graduate students. Many of these projects involve strong collaborations with national laboratory and associated user facilities or industry partners. Beyond technical skills development, significant effort is devoted to enhancing students’ professional skills and self-identity as a scientist. Together, these training objectives culminate in the final week with preparing a poster and associated presentation for a national conference. 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 2026 · 2026-05

Population growth and urban development are increasing the global demand for raw materials. This rising demand creates supply risks that can affect the economy and national security. Critical metals are especially important because they are needed for clean energy, transportation, defense systems, and modern electronics. This CAREER project studies an electrochemical method to recover critical metals from waste materials. The method uses controlled reactions to dissolve and separate metals from complex waste, such as used lithium-ion batteries. Compared with current recovery methods, this approach can improve metal separation while reducing chemical use and environmental impact. The project examines how applied voltage, solution chemistry, and reactions at the metal surface affect how metals dissolve and separate. By improving this understanding, the project will support stronger and more reliable U.S. supplies of critical metals while also training students through research and outreach activities. This CAREER project establishes an integrated research, education, and outreach program that advances selective electrochemical leaching for critical metal recovery from complex feedstocks, using lithium-ion batteries as a model system. The research investigates how applied potential, electrolyte speciation, interfacial coordination, and redox kinetics govern metal dissolution pathways and selectivity, addressing fundamental gaps in electrochemical separation science and environmental engineering. The project tests hypotheses that metal selectivity is controlled not only by thermodynamic redox potentials but also by electrolyte chemistry and interfacial processes, and that temporal modulation of electrochemical potential can exploit kinetic differences among metals with overlapping standard potentials. To validate these hypotheses, ligand-assisted dissolution experiments, electroanalytical methods, and speciation-informed thermodynamic modeling are integrated to quantify redox behavior at reactive interfaces, while programmable potential waveforms and real cathode materials are used to evaluate mechanism-driven leaching strategies under realistic conditions. The resulting mechanistic framework guides the design of modular leaching systems that connect molecular-scale processes to macroscale separation performance. Educational activities integrate research findings into undergraduate laboratory modules and training experiences that strengthen understanding of electrochemical processing and critical metals extraction, while outreach efforts engage community college students and broader audiences through hands-on demonstrations and accessible learning tools. These vertically integrated efforts advance fundamental knowledge, develop the future workforce in critical materials, and support resilient and resource-efficient critical metal supply chains. 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 2026 · 2026-03

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Alfred University, Colorado School of Mines, and Missouri University of Science and Technology. An estimated 45 scholars pursuing undergraduate degrees in ceramic engineering and glass engineering will receive scholarships of up to $15,000 for up to five years. Scholars will receive faculty, peer, and industry mentoring and the project will build strong scholar cohorts through an intensive paid summer program, visits to industry partners, and participation in student professional society chapters. Additional activities for scholars include opportunities for undergraduate research, internships, and travel to conferences. The overall goal of this Track 3 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. There are a small number of accredited ceramic and glass engineering programs across the United States. However, professionals in these disciplines are important throughout the STEM workforce, including in the semiconductor, energy, and space sectors, as well as other key areas of national need. The project will be assessed by an experienced evaluator that will examine the project’s progress through surveys, interviews, focus groups, and a review of student artifacts. The data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Environmental change, resource scarcity, and rapidly advancing technologies like Artificial Intelligence (AI) present increasingly complex global challenges that transcend traditional disciplinary boundaries. Effectively addressing these demands a new generation of engineers who can seamlessly integrate technical knowledge with critical societal and environmental considerations into their solutions. However, current engineering education often inadvertently separates these dimensions, leading students to view societal and environmental impacts as secondary or outside their core professional responsibilities. This project will transform how engineers are educated, cultivating a holistic "Enviro-Socio-Technical" mindset essential for designing truly sustainable and sound solutions for the future. For example, creating more efficient electricity grids isn't just about advanced technology; it’s equally about understanding the environmental variability of renewable energy sources, the patterns of electricity consumption across different communities, and the broader implications of energy storage options. Similarly, developing AI applications demands careful consideration of their significant energy and water footprints and robust data security protocols. By bridging this critical gap in engineering education, this NSF-funded research will empower future engineers to become leaders in tackling grand societal challenges, directly contributing to national well-being, economic strength, and a sustainable future for all. This project aligns directly with NSF's commitment to advancing foundational research that yields broad societal benefits, strengthens STEM education, and builds a workforce prepared for the challenges and opportunities of the 21st century. This project will systematically advance the field of engineering education by investigating how engineering students conceptualize the interconnections among technical, societal, and environmental aspects of engineering problems. It will rigorously address ways of thinking that hinder the development of durable sustainability solutions including those which (1) prevent students from seeing how technical and societal systems are intertwined; (2) separate engineering from societal values; and (3) obscure the environment's connection to sociotechnical systems. The research will then develop and test effective teaching methods aimed at cultivating an integrated, systems-thinking approach. This will involve designing and implementing "micro-interventions"—short, adaptable teaching modules that incorporate real-world case studies, such as the Salton Sea, which illustrate the intertwined technical, social, and environmental dimensions. These interventions will be integrated across diverse engineering and humanities curricula at the Colorado School of Mines and the University of Colorado Boulder. The effectiveness of these teaching strategies will be rigorously assessed through a multi-stage survey methodology (pre-test, post-test, and retrospective pre-test) administered to approximately 500 students annually, complemented by semi-structured interviews to provide rich, in-depth qualitative insights into their learning experiences and evolving perceptions. The project will also establish and nurture a multi-disciplinary collaborative group among faculty from both institutions, encouraging shared learning and the broad distribution of effective practices for integrating the "Engineering for One Planet" (EOP) framework. This research provides a theory-informed and empirically grounded foundation for curricular transformation, addressing a key gap in understanding how students internalize EOP competencies across various disciplinary and university contexts. The intellectual contribution lies in its methodological rigor, offering a nuanced understanding of student development by combining measured and perceived learning. Expected outcomes include a deeper empirical understanding of student conceptualizations, the creation of proven teaching tools for promoting holistic thinking, and an expanded network of educators prepared to shape engineers who are not only technically proficient but also socially and environmentally aware problem-solvers. Over 1,000 engineering students will be directly impacted by these pedagogical innovations, with findings and publicly available educational resources widely shared through journal publications, national conferences (e.g., American Society for Engineering Education), and workshops, thereby expanding the EOP collaborative and influencing engineering education nationwide. This project is funded by the Division of Engineering Education & Centers with additional support provided by The Lemelson Foundation. 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

Non-technical Description: Ferroelectric wurtzites show great promise for enabling advanced communications technologies and for reducing computational energy consumption, both of which are key goals of the nation and the National Science Foundation. Their commercial deployment is hindered by limited understanding of the impacts of defect populations on properties, but current state-of-the-art computational techniques rely on unrealistic dilute-limit assumptions that ignore defect interactions with one another and/or with interfaces. This research aims to rigorously capture the interactions and effects of point defects such as heterovalent substitutions (e.g., oxygen replacing nitrogen) and extended defects (e.g., structural damage from bombardment during sputter growth) on properties in wurtzite nitrides. The team includes world experts in simulation, synthesis, characterization, and testing from the U.S. and Germany, and it includes partners from the Army Research Laboratory (ARL) and an industrial advisory board (IAB) who will build on relevant findings to accelerate scale-up and deployment as appropriate. The goal is to bridge the gap between calculations requiring simplifying assumptions and real films grown using commercial techniques to accelerate deployment of these and future DMREF-developed materials. Technical Description: To-date, when charged defects are simulated computationally (particularly within the electronic nitride space), they are assumed to be dilute and non-interacting, which is invalid for substitution levels in the several- to tens of atomic percent, such as those common in ferroelectric wurtzite alloys. This research will treat defects as components of complex alloys to capture disordered configurations as well as interactions of defects with one another and, eventually, with interfaces. Such calculations will be informed and validated by high resolution electron microscopy capable of measuring not only structural and chemical but also—via electron energy loss spectroscopy (EELS)—local bonding characteristics. The rigorous mechanistic understanding will enable predictive capabilities around interacting (non-dilute) point defects including heterovalent substitutions and will advance towards quantitative predictions of coercive fields, multiscale switching dynamics, and potential degradation processes important to the very devices that the ARL and industry partners on our team will be simultaneously advancing towards deployment. 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

Converting hydrogen and carbon-containing feedstocks, such as CO and CO2, to synthetic fuels would augment US energy security and independence. Specifically, CO and CO2 hydrogenation are critical components in the industrial production of methanol, one of the most important platform chemicals in the chemical industry. Improving our understanding of these reactions will improve the competitiveness of the US chemical industry while potentially lowering energy and capital costs. These reactions rely on catalytic conversion processes that occur at low temperatures and high pressures. The high pressure drives the CO and CO2 molecules onto the metal catalyst surface, resulting in crowded surfaces, where molecular interactions among bound intermediates play a key role in changing the reaction dynamics and activating strong chemical bonds. This project will examine the mechanistic role of densely covered surfaces in mediating the various CO and CO2 hydrogenation reactions. The effort will focus on two benchmark systems: methanol synthesis on Cu-based catalysts and methanation on Ni-based catalysts. These systems will be probed with complementary kinetic, spectroscopic, isotopic, and computational studies examining the role of these extended catalytic microenvironments. Educational videos discussing scientific principles and methods in catalysis research broadcast over social media channels accessible to researchers of all backgrounds will provide training and broaden awareness of science and engineering principles involved in the production of fuels. This research is based on the premise that higher entropic demands of bimolecular reactions among bound surface intermediates on densely crowded surfaces are compensated by lower energy barriers for catalytic reactions on such surfaces. The research efforts are motivated by previous work, which showed the high pressure and low temperature conditions of methanol synthesis over Cu-based catalysts result in Cu surfaces highly covered with H-adatoms. Such high H-coverages provide novel routes for CO2 activation via molecular intermediates that are only stable on surfaces with high H-adatom coverage. The project will involve the systematic, intentional generation of high H-atom coverage microenvironments. Microenvironments will be controlled by changing pressures, temperatures, gas composition, titrants, and surface composition. Reversibility formalisms, spectroscopic methods, and density functional theory (DFT) calculations will be used to develop a kinetic framework to understand the reaction environments. The collaborative computational and experimental nature of this project will provide unique opportunities for interdisciplinary education and training of undergraduate and graduate students.. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Reactive oxygen species and reactive nitrogen species are oxidative metabolites that play important roles in human health and disease. While these oxidative metabolites are required for a diverse array of cellular processes, including cell death, differentiation, and signaling, uncontrolled production of reactive oxygen and nitrogen species can contribute to the underlying inflammation of cancer, heart disease, respiratory diseases, autoimmune diseases, and stroke. We are developing new imaging probes that span the visible spectrum to map reactive oxygen and nitrogen species with improved specificity and versatility to help understand the roles these oxidative metabolites play in inflammation. The scientific premise is that building an imaging toolkit for oxidative metabolites will provide new tools to further our understanding of the dynamics of reactive oxygen and nitrogen species in redox biology. This R15 application uses principles from physical organic chemistry, synthetic chemistry, and molecular probe design to develop new peroxynitrite-specific imaging probes with optical characteristics enable multi-analyte imaging. These tools will be applied in cell culture models of macrophages. Specifically, we will understand the structure-function relationship of diazaborines, a new motif for selective metabolite detection, and pursue diazaborine-based probes with excitation/emission profiles spanning from blue to green to red. Embedded in this work is a research-centric pedagogy to train undergraduates at the Colorado School of Mines to join an increasingly collaborative and diverse biomedical workforce.

NSF Awards · FY 2025 · 2025-09

Wildfires are among the most common and widespread natural hazards that impact landscapes and communities throughout the Western United States. Beyond their immediate effects, wildfires cause vegetation loss that can make watersheds susceptible to postfire debris flows and flash floods for several years, posing risks to downstream communities, properties, and vital infrastructure. Existing early-warning tools for postfire debris flows address specific contexts, such as inland and dry coastal regions, but they are not yet well-adapted to cooler, wetter areas like the coastal Pacific Northwest. This project integrates Artificial Intelligence (AI) and process-based earth surface response models into a Framework of AI-enhanced Modeling of Wildfire Geohazards (FAIM-WG). This framework will enable the identification of rainfall thresholds for triggering debris flows, explore new and missing processes to improve predictions, and develop transferable models for postfire debris flows to aid early-warning and risk mitigation efforts. This project will first create a comprehensive AI-ready, multi-modal dataset that includes topographic, meteorological, and environmental variables for all major wildfires across the Western U.S. This dataset will serve as the foundation for developing probabilistic models to predict postfire debris flow initiation using machine learning methods such as gradient-boosted decision trees, taking into account the unique characteristics of different regions in the Western U.S. These data-driven models, along with process-based initiation mechanisms, will be implemented into the Landlab earth surface modeling toolkit. At several study sites where extensive postfire geomorphic responses have been observed, Landlab models will be established to develop baseline model simulations of eco-hydro-geomorphic dynamics driven by stochastic weather and wildfires. These baseline Landlab models will be used in a discrepancy modeling framework where a new model identification method that involves Sparse Identification of Nonlinear Dynamics with SHallow REcurrent Decoder networks (SINDy-SHRED) will be coupled with Landlab to discover new formulations of postfire watershed response and geomorphic transport laws. The AI-ready curated dataset will be distributed through the AI platform HuggingFace, and models will be shared on Landlab’s GitHub codebase. This project will train two graduate students and a postdoctoral researcher. The research will be disseminated through in-class experiential learning and via asynchronous and open-access teaching materials in the form of YouTube videos and Jupyter notebooks executable on the project's JupyterHub. 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 advances quantum information science and technology by quantifying a key mathematical challenge to the promise of quantum computing. Understanding and advancing quantum computing is important because current classical computers are rapidly coming to their limit in energy and efficiency, and quantum computing offers a powerful alternative in certain cases. By understanding this particular challenge, called near-integrability, and so far unsolved in the quantum context, the proposal will develop new workforce talent via student training and result in better design of quantum algorithms and hardware. Quantum computing is important to US national security on many fronts including encryption, optimization, and materials science. The best-known classical algorithms currently challenging quantum computing are tensor network / neural network based. This project will pursue a complementary alternate route to both quantum advantage and classical challenges to quantum computation: near quantum many-body integrability. A key new toolset newly ported from machine learning (ML) into the quantum computing context will be introduced in the process, emergent topological complexity. Both these developments will be highly useful to academic-industry collaborations pursuing scientific and commercial quantum advantage. 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

In this project, funded by the Environmental Chemical Sciences Program of the Chemistry Division, Professors Timothy Strathmann and Shubham Vyas are leading a team at the Colorado School of Mines that is advancing a promising photochemical destruction technology for per- and polyfluoroalkyl substances (PFAS) that have been identified as ubiquitous and highly persistent contaminants of the nation’s drinking water resources. Recent research has identified photochemically generated hydrated electrons as effective chemical reagents for attacking and destroying PFAS “Forever Chemical” pollutants. The goal of this research is to identify important steps in the underlying mechanisms responsible for PFAS decomposition and defluorination during reactions with hydrated electrons. The project lies at the interface of environmental chemistry and water quality engineering, and research is being integrated with education activities providing interdisciplinary training for graduate, undergraduate and K-12 students. Hydrated electrons are powerful reducing agents that can be generated by UV photoexcitation of appropriate sensitizers (e.g., sulfite and iodide ions). Although hydrated electrons reactions with a wide diversity of PFAS structures have been documented, critical discrepancies in our understanding of the underlying reaction mechanisms and pathways remain. The proposed research will probe the importance of fluoroalkyl acid radical anion species, which form upon the initial reaction between hydrated electrons and PFAS, in controlling the ultimate fate of the contaminants. Specific objectives of the project will include (1) establishing the relationship between rates for hydrated electrons decay and rates for transformation of different PFAS structures, (2) identifying molecular and water quality characteristics responsible for the shifting predominance of different PFAS transformation pathways, and (3) establishing a multi-step reaction network for predicting the fate of diverse PFAS structures during photochemical reactions with hydrated electrons. These objectives will be accomplished by combining constant irradiation experiments with high resolution mass spectrometry analyses along with laser spectroscopy and density functional theory calculations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY I have presented four research projects to illustrate my vision—harnessing functional polymer synthesis to present and amplify biochemical cues catalyzing cell biological phenomena such as macrophage (Mp) polarization. Dysregulated Mp polarization profoundly alters the trajectories of wound healing, chronic inflam- mation, and tumor progression. Signaling cascades underlying Mp polarization are activated by soluble and substrate-bound ligands that bind and cluster Mp receptors. How do we model the chemical diversity and spati- otemporal complexity of ligand–receptor interactions—individually fragile yet collectively powerful—to advance our fundamental understanding of Mp polarization? Our overarching hypothesis is that polymer brushes will exploit spatially complex patterns of ligand presentation to extract general principles underlying Mp polarization. Polymer brushes—ultra-thin coatings that are tens to hundreds of nm thick—are formed by grafting polymer chains at sufficiently high densities from cell culture substrates. We will interrogate Mps on ligand-functionalized polymer brushes and disclose how ligand identity, density, and spatial distribution sculpt Mp polarization. We will synthesize polymer brushes functionalized with ligands governing Mp phenotypic changes (e.g., glycosaminoglycan(GAG)-mimetic functional groups, phosphatidyl serine (PS), or mannose) and pursue four objectives: (1) GAG-mimetic polymer brushes will clarify how Mps resort to “bet hedging” to diversify polarization responses and cope with environmental uncertainty presented by infections or wounds, (2) PS-functionalized polymer brushes will reveal how Mps decode PS spatial presentation patterns, discriminate between apoptotic and non-apoptotic cells, and improvise phagocytic responses, (3) mannose-functional polymer brushes will reveal how Mps resolve “mixed messages” in tumors, wounds, and other settings where inflammatory and anti- inflammatory signals co-exist, (4) by grafting polymer brushes from multi-compartmental electrospun scaffolds, we will independently control scaffold stiffness, ligand identity, and spatial patterns of ligand presentation. Three- dimensional cell culture platforms with programmable mechanical, topographical, and surface chemical features will elucidate how Mps integrate physical, chemical, and biological stimuli to devise polarization responses. Although we focus on Mp polarization in this initial application to demonstrate proof-of-concept, the toolset we develop here will find broad application. Our long-term research goal is to interrogate fundamen- tal cell biological phenomena beyond Mp polarization via creative surface engineering of cell culture substrates We will establish cell- and disease-agnostic experimental platforms that recapitulate the chemical diver- sity and spatiotemporal complexity of ligand–receptor interactions orchestrating fundamental cell bio- logical phenomena. Building on the platforms established in the first 5 years, and in partnership with collabo- rators, we will interrogate stem cell differentiation, T-cell activation, exosome biogenesis, and other cell biological phenomena in our renewal application.

NSF Awards · FY 2025 · 2025-09

Fractured rocks beneath Earth’s surface play a vital role in supplying clean water, extracting energy resources, and storing carbon dioxide and hydrogen. Water and other fluids move through these fractures where they react with the surrounding rock resulting in changes to the fluid chemistry in ways that influence engineered and natural Earth systems. This postdoctoral fellowship project will help advance the ability to predict the chemical reactions between water and rocks by analyzing data from a deep underground laboratory in South Dakota in conjunction with a new computer model approach. In addition to the scientific research, the project supports hands-on educational outreach to K–12 students, open-source educational tools, and professional development for the postdoctoral fellow, including fieldwork and mentoring experience. The project supports NSF’s mission by advancing geoscience, helping society address challenges like the energy transition, water sustainability, and environmental protection, and by facilitating student participation in geoscience and hydroscience. Fractured subsurface systems play a critical role in transporting reactive fluids and solutes but are notoriously complex to model accurately at the field scale. The postdoctoral fellowship work supported by this project will develop ways to move beyond that complexity with a reduced-order, bipartite graph-based framework that captures essential flow, transport, and geochemical interactions in fractured rock by leveraging extensive experimental data from previously funded projects. The approach integrates core characterization, geochemical sampling, tracer tests, and electrical resistivity tomography from the Sanford Underground Research Facility to investigate geochemical processes in fractured rock through laboratory experiments. The field and laboratory results will be used to refine and validate both high-fidelity discrete fracture network models and reduced-order bipartite graph models. By overcoming the computational cost of full-physics fracture modeling, this project aims to enable faster, more robust uncertainty quantification and site-specific predictions of reactive transport. Ultimately, this work should advance mechanistic understanding of reactive fracture-rock matrix exchange and improve the ability to predict and manage subsurface systems and related applications such as carbon dioxide sequestration, formation of and exploration for critical minerals, geothermal reservoirs, and managed aquifer water recharge. 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

As the polar seas melt, a new maritime frontier is emerging from the ice. The imminent opening of the northern oceans to navigation, science, and industry is an event of historical significance. Governments, corporations and communities are already planning for these changes. Of particular interest are undersea deposits of critical minerals essential to the supply chains of semiconductors and other technologies used in artificial intelligence, cybersecurity and energy transitions. While it is certain that a new rush to the North is well underway, we know little about: 1) the wider dimensions of seafloor critical mineral mining in the Arctic; 2) how its hazards, risks, and benefits are being understood and distributed; and 3) how planning for this maritime industry is related to the national security concerns, infrastructure development, and economic changes that are shaping Arctic futures. More social scientific research is needed into these topics of broad importance for the United States. This planning project supports the activities needed to organize a future study about the initiation of undersea critical mineral mining in the new Arctic maritime frontier. It answers the questions: Why is the rise of this maritime industry significant and deserving of social scientific research? And what will it take to launch the first longitudinal and interdisciplinary study about the ways that undersea critical mineral mining and its impacts are coming into focus along the Western Alaskan coast? To assess the feasibility for later research, this planning project identifies high priority issues for investigation and tests how to build a team of experts and stakeholders best suited to explore these concerns. This project will thus advance understandings of all four priority areas of the U.S. Arctic Research Plan 2022-2026, as it lays the foundation for successful research into issues related to complex systemic interactions, community resilience and health, risk and hazard mitigation, and economies and livelihoods in the Arctic. 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

With the support of the Chemistry of Life Processes program in the Division of Chemistry, Professor Holz from the Colorado School of Mines will investigate the enzymatic biodegradation of the fungicide chlorothalonil and the herbicide atrazine, two halogenated aromatic hydrocarbons, which are highly toxic and carcinogenic. Their removal is challenging because they are chemically stable, resistant to degradation, and lipophilic. Nevertheless, some enzymes can catalyze their degradation to less-toxic analogs. The project focuses on understanding the mechanism of enzymatic biodegradation. The project will also provide exceptional training for undergraduate and graduate-level scientists and serve as the basis for community engagement activities. This research project seeks to gain molecular-level insights into two Zn(II)-dependent hydrolytic dehalogenases, namely a chlorothalonil dehalogenase (Chd) and a triazine hydrolase (TrzN). These enzymes degrade chlorothalonil and atrazine, respectively, to their corresponding less-toxic alcohol derivatives. The catalytic mechanisms of both Chd and TrzN will be examined using an interdisciplinary approach that includes kinetics, spectroscopic, biochemical, computational, X-ray methods, and biomaterial synthesis. Three important unanswered questions will be addressed: (i) What is the allosteric and structural influence on Chd activity, including its impact on active site residues that are catalytically important?, (ii) Can biomaterials developed using Chd and TrzN be used to degrade chlorothalonil and atrazine, respectively, at room temperature and physiological pH? and (iii) Can Chd catalytically dehalogenate fluoro- and bromo-substrate analogs of chlorothalonil and inform the discovery of new dehalogenases? 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 early Paleogene (~66-48 million years ago) was an important time in Earth’s history: It immediately followed the mass extinction of all dinosaurs (except birds), many modern groups of mammals first appeared, and the Paleocene-Eocene Thermal Maximum (a significant climate event) occurred. Knowledge of these events is mostly based on a well-dated and characterized North American stratigraphic record; a global perspective on these events is missing. This project will apply modern, high-precision, age-dating techniques to the sedimentological and mammal fossil records of Mongolia. These methods will allow the building of a critical framework for comparing the North American and Asian fossil records across this important time interval. New physical and digital collections of fossils and a pop-up traveling exhibition on Paleogene mammal evolution and climate will be created. Developing a modern chronostratigraphy and paleoenvironment reconstruction for the highly fossiliferous Naran Bulak and Gashato Formations in Mongolia is the goal of this project. Four geochronological methods will be used, including magneto- and chemo-stratigraphy and Ar/Ar and U-Pb geochronology. Age and correlation data will be combined with careful sedimentological and paleoenvironmental analysis. These methods will be used to precisely constrain this important fauna and permit precise correlations with other parts of Asia and with the North America record. This geochronologic focus will be coupled with detailed sedimentologic analysis and stable isotope analysis of ancient soil and lake deposits to identify the PETM boundary by its signature global negative carbon isotope excursion. 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 award supports convening the 2025 West Antarctic Ice Sheet (WAIS) Workshop in Coupeville, WA, enabling multidisciplinary scientific exchange on marine ice-sheet science at a time of increased focus on the impacts of Antarctic change and Antarctic international governance. At the 2025 Workshop, organizers will: (1) convene sessions on innovative WAIS scientific research with extended time for discussion and audience interaction to identify future research targets and logistical needs; (2) hold an open discussion with all participants on strategies for achieving strong US scientific leadership and presence in Antarctica; (3) livestream and archive recordings of the scientific program so that it is available to all stakeholders and trainees; and (4) continue to provide workforce development opportunities through post-agenda half-day mini-workshops. The fate of marine-based ice sheet sectors in Antarctica, in particular WAIS, and their interaction with other Earth systems has had a central role in nearly every major report about the impacts of global Earth-system change for the past decade. An ice sheet of such fundamental global importance and active research requires a dedicated, annual workshop that focuses and organizes the scientific community studying WAIS, its role in the Earth system, and its impact on the US population. The WAIS Workshop has been held annually to provide the key venue to foster scientific exchange across the wide, multidisciplinary intellectual range of marine ice-sheet science in both formal and informal side meetings. The workshop also incorporates modeling of the past, present, and future of WAIS and other marine-based ice sectors to quantify their contribution to global environmental change, short- and long-term paleoclimate studies that contextualize the ongoing evolution of WAIS, and relevant research from other ice masses. For 2025, organizers will continue the successful blind-abstract review and streaming and archiving of WAIS Workshop presentations. The team will also convene an all-hands discussion covering strategies for achieving strong US scientific leadership and presence in Antarctica, critical for meeting the goals of US investment in Antarctic infrastructure and science. 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

Hyperspectral remote sensing is a data gathering technique that uses advanced sensors - attached to satellites, drones or other devices - to measure the reflection of light off of the Earth's surface. These data can be used to analyze the shape and makeup of a landscape, and can be used to make inferences about the underlying mineral patterns, tectonics, and magmatic processes of a scanned region. This project will develop a new artificial intelligence (AI) framework to analyze hyperspectral data to test critical hypotheses about the formation of ore deposits. By improving the effectiveness of hyperspectral mineral mapping, this project will accelerate the identification of critical mineral resources to improve the nation's economic competitiveness and security. The project will also help develop a modern workforce by training graduate students at the intersection of geosciences and AI. Outreach through workshops, mentorship opportunities, undergraduate internships, and participation of community college students will further broaden the impact. By demonstrating the power of integrating AI with domain expertise in geosciences, this work will serve as a model for interdisciplinary collaboration that can be applied to other disciplines facing similar data-intensive challenges. The proposed research introduces significant innovations at the intersection of AI and geosciences. First, a novel encoder-decoder architecture will be developed for decomposing hyperspectral data into physically meaningful latent structures, enabling efficient compression while preserving the nonlinear spectral relationships essential for accurate mineral identification. Second, a new hierarchical spectral alignment approach will coherently integrate multi-resolution hyperspectral data while systematically quantifying uncertainties inherent in real-world data. Third, the AI models will be aligned with geological principles to support geoscience. Together, these innovations will yield a unified framework that improves the accuracy, efficiency, and scientific rigor of hyperspectral data analysis. This research will simultaneously test geoscience hypotheses about the spatial relationships between surface mineral assemblages and the underlying tectonic and magmatic processes, enabling quantitative analysis of the geological parameters which coincide with ore deposit formation, regardless of their age or location. 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 investigators aim to advance the statistical methods used to analyze spatial data, a critical component in fields such as geosciences, environmental science, and remote sensing. As data in these areas continue to grow in both size and complexity, new techniques are needed to extract meaningful insights and improve decision-making processes. This project addresses the challenge of analyzing spatial data that is irregularly spaced, which complicates traditional methods that rely on regular intervals, such as time series data. By developing novel statistical tools, the project offers solutions to improve the understanding of complex spatial relationships and uncertainty present in data. The developed methods have broad implications for a wide range of scientific fields and have the potential to improve decision-making in areas such as economic modeling, computer experiments, environmental science, and more traditional geostatistical applications, such as pollution modeling and monitoring. This work will also support education by advancing the field of spatial statistics and offering new methods that can be integrated into teaching and research initiatives. Ultimately, the project will contribute to the national interest by providing more accurate models and robust inference tools for spatial data analysis, with applications that can support informed policy decisions and scientific advancements. The goal of this project is to tackle two significant challenges in resampling methods for spatial data using advanced spectral methods. First, it introduces new spatial frequency domain resampling methods. These methods are designed to work in a general setting with minimal assumptions, allowing for more flexible and effective uncertainty quantification. By addressing the problem of irregularly spaced spatial data, these techniques maintain the advantages of spectral analysis while improving the practical applicability of spatial data analysis. Second, the project focuses on a key issue in robust spatial inference - specifically, the need to estimate parameters in the presence of outliers. The investigators will study an empirical likelihood-based approach that uses a novel variant of the Wasserstein metric to concentrate near a specified parametric family of spectral densities, providing a powerful tool for robust inference. The integration of this framework with a prior distribution on model parameters leads to the development of a robust posterior, offering improved estimation techniques in the presence of noisy or anomalous data. The methods are widely applicable to a range of inference problems, improving both the reliability and interpretability of spatial models. 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

As our planet continues to warm, we are facing a future climate unlike anything humanity has ever experienced. To better understand and prepare for what might happen, this project examines periods in Earth’s distant past when global temperatures were much higher than today. While previous studies have focused on single warming events, this research will analyze more than ten consecutive events, providing more accurate and dependable insights into how the Earth responds to extreme warming. Using new, cutting-edge methods, the team will measure changes in rainfall intensity, flooding, and other climate shifts in ways that were not possible before. The knowledge gained will improve our ability to anticipate and manage risks such as flooding and extreme rainfall, strengthening our nation’s resilience and protecting public welfare. Beyond advancing the frontiers of climate science, the project will help to build a strong, competitive STEM workforce by integrating research findings into K–12 outreach and university teaching materials, thereby directly contributing to national priorities. This project aims to advance our fundamental understanding of how rainfall patterns and river systems respond to a changing climate by collecting new and unique data on extremely warm climates of the past. The team expects to generate novel insights, as this will be the first terrestrial dataset to (1) quantify both climate changes and Earth system responses across more than ten consecutive global warming events, and (2) apply innovative reconstruction and analytical methods to measure rainfall intensity and intermittency—key information that is difficult to obtain using traditional approaches. Combining these new methods with the traditional state-of-the-art stable isotopic and geochemical methods will allow the team to collect new and original data on precipitation changes and extremes, and determine whether these changes occurred gradually or involved sudden "tipping points" that lead to dramatic shifts. To reduce uncertainty typically associated with sedimentary record-based climate reconstructions, the study will focus on a single paleo-river system. Together, these efforts promise to deliver transformative insights into Earth’s past, providing critical context for understanding our planet’s future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-09

Project Summary Protein complexes are organized structures composed of hundreds to thousands of copies of one to a few types of proteins. Nature has evolved these protein complexes as a means to create biological machines with diverse cellular functions, including those critical to human health. To date, the organizing principles for protein complexes have focused on hierarchically-evolving spatial organization and the challenge is to extend this framework towards inherently dynamic complexes with structures and functions that vary in time, often in response to environmental signals. The goal of my research program and this MIRA proposal is to investigate the structure-dynamics-function relationships that regulate the biogenesis of protein complexes with a current emphasis on proteinaceous organelles and envelopes. Here, we specifically target two families of bacterial proteins – S-layers and microcompartments – that will serve as excellent model systems to further our fundamental understanding of the spatiotemporal organization of protein complexes. Our work will address two challenging paradigms: (i) complexity due to large combinatorial space (from many types of interacting proteins) and (ii) complexity due to large conformational space (from many protein domains bound by flexible linkers). Our approach is to develop and to apply new computational models, simulation strategies, and analysis frameworks that will make the study of these systems tractable. Our findings will have substantive impact on the broader understanding of bacterial biogenesis, and in the long term, can be applied to develop new therapeutics that target pathogenic bacteria, adapted to study dynamic protein complexes relevant to disease progression, and repurposed to aid synthetic biology efforts for protein-based biotechnology.

NSF Awards · FY 2025 · 2025-08

Separating a mixture of many chemical species into its constituents is a common operation in the chemical industry. One separation method is to adsorb molecules of the desired species onto the surface of an adsorbent. Later, the same molecules can be desorbed and recovered. The efficiency of the separation could be improved by modifying the adsorbent for each desired separation. Promising adsorbents can be identified by screening vast “libraries” of possible candidates using a computer. However there is a major hurdle: the attractive forces between the desired molecule and each candidate adsorbent in the library are unknown. This project will develop machine learning tools to rapidly predict the forces. These predictions will be used to identify optimal adsorbents for some industrially important separation processes. The results of this project will improve the efficiency of adsorptive separation processes. Additional benefits will come from outreach to high school students through summer research programs, to professionals via online courses, and to broad audiences through an educational video. Chemisorption is the adsorption of a molecule on a surface accompanied by the formation of chemical bonds. Chemisorption is relevant to many separation processes that are based on adsorption/desorption using an adsorbent surface. A major ongoing research effort is the computational design of adsorbents that can selectively adsorb desirable species out of a mixture. A crucial step in such computations is to develop interaction potentials to describe the chemisorption between the molecule and the surface. This project seeks to reduce the computational effort to develop such interaction potentials. Simulations will be conducted to obtain interaction potentials between specific molecules and adsorbent surfaces. These data will be fed to a machine learning model to predict the interaction potential between the same molecules and other adsorbent surfaces. The central hypothesis to be tested is that adsorbent surfaces can be treated as “building blocks” so that the interaction potentials are transferrable from one adsorbent to another with similar building blocks. This project will dramatically boost data generation capabilities to train artificial intelligence frameworks for adsorbent discovery. The project will rapidly screen hundreds of thousands of metal organic frameworks to identify adsorbents that can separate mixtures of small molecules such as ethane/ethylene, ammonia/nitrogen/hydrogen, etc. Separation of such mixtures is immensely important in the chemical industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

While laser based additive manufacturing (AM) offers significant advantages for fabricating intricately shaped metallic components, high incidence of undesired microstructural features and defects remain barriers to a wider industrial adoption of this process. A few recent studies have investigated an approach, referred to as ultrasonically assisted (UA)-AM, that is based on superimposing ultrasonic vibration during laser AM for suppressing defects and refining microstructure. However, the fundamental mechanisms of the UA-AM process are not completely understood, and the current models ignore the transient nature and far-from-equilibrium conditions of the process. This project will utilize synchrotron imaging and diffraction with high space and time resolutions to observe the core mechanisms of the UA-AM process. The knowledge derived from this research can benefit AM industries and facilitate effective and innovative incorporation of UA into various manufacturing processes that involve rapid melting and solidification. The project will contribute to multidisciplinary workforce training and promote STEM education with an e-Learning module and hands-on activities for K-12 students. To observe the underlying physics of UA including the melt dynamics and microstructure evolution, this project will develop an innovative UA laser melting system that fits into the advanced synchrotron radiation facilities. UA effects on the AM process will be analyzed through in situ synchrotron imaging and diffraction analysis, followed by postmortem characterization and integrated multi-scale, multi-physics modeling. In situ imaging will target: (1) melt geometry overview (2) melt flow dynamics (3) dendritic microstructure growth and interactions. Diffraction analysis will focus on melt interior and heat affected zone to determine the phase transformation, thermal history, and stress development. Also, residual stress distribution will be measured with ex situ diffraction analysis. A computational fluid dynamics model coupling the acoustic field will be developed to determine the spatial distribution and temporal evolution of temperature, velocity and pressure fields. These physical fields will serve as input for the phase field modeling of alloy solidification, fully revealing UA effects on the thermo-mechanical-chemical fields and microstructure evolution during laser-based AM processes. 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.