Colorado School Of Mines

NSF Awards · FY 2025 · 2025-07

Around 2011, residents, tourists, and researchers began noticing unprecedented amounts of sargassum seaweed on and offshore of the southern U.S. coasts and Caribbean islands. Sargassum quickly began to disrupt fishing, tourism, nearshore ecosystems, and even caused health problems for populations exposed to rotting sargassum seaweed. Sargassum arrives on shore mixed with plastic trash and can be difficult and costly to clean up. An estimated 5-13 million tons of plastic enters the ocean each year, and sargassum blooms are estimated at over 20 million tons each year. While there have been increased efforts to track, collect, and create valuable products out of sargassum, research and development has vastly ignored the connection between sargassum and ocean plastics. This IRES project investigates plastic-sargassum interactions in the ocean and develops valuable products from sargassum-plastic pollution, such as concrete and composite lumber, for the building and construction industry. Simultaneously, this project trains U.S. students in innovation and international collaborations through mentored research experiences in the Dominican Republic. This research progresses through three of the most prominent challenges in creating valuable products from sargassum-plastic pollution: tracking, collection, and product development. One of the main challenges facing management of sargassum is tracking seasonal flows: when will the seaweed blooms reach shore, where will they arrive, and in what quantities? This project uses satellite and aerial images to map and measure sargassum flows and then correlates this data with plastic information taken from field measurements. Collection is usually slow and costly, as it is mostly performed manually and with small-scale equipment. This project designs and tests new methods for collecting and processing that are best suited to delivering sargassum-plastic material in the quantities and condition best suited for development of value-add construction and building products. Finally, product development ensures profitability and feasibility by designing technologies that do not require extensive preparation of sargassum-plastic waste. This project investigates the use of pyrolysis -a process that uses high temperatures to break down sargassum-plastic pollution- to upcycle sargassum-plastic wastes. Pyrolysis creates waxes, chemicals, and char that can then be used to make composite lumber and concrete. The development of pyrolysis as a feedstock agnostic recycling technique that results in high value products is considered of utmost importance for plastic recycling, since sorting, transportation and impurities are often barriers in the recycling industry. This research develops new methods and techniques to produce concrete and composite lumber products, which will help drive the economics for collection and management of harmful sargassum-plastic reaching US and Caribbean shores. Research is primarily conducted by U.S. collegiate student trainees; as such this project trains these students to be globally aware and internationally capable of collaborating and innovating to support the U.S. to remain at the forefront of science, technology, engineering, and mathematics. The student training program includes topics such as advanced research methods, science communication, and collaborating on international teams. 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 · 2025-04

PROJECT SUMMARY In this Trailblazer R21, our interdisciplinary team [polymers, machine learning (ML), and hematopoietic stem and progenitor cells (HSPCs)] will innovate ML-guided polymer discovery pipelines for intracellular delivery of ribonucleoproteins (RNPs). Chaperone-mimetic polymers developed in this project will augment the safety and site-specificity of genome editing by counteracting intracellular RNP misfolding and performing conforma- tional quality control. Transfer learning frameworks innovated in this project will streamline the discovery of pol- ymeric RNP carriers in hard-to-expand data-scarce cells such as HSPCs that are otherwise recalcitrant to ML- guided experimentation. RNPs are expensive to express and purify. Sadly, electroporation consumes substantial amounts of RNPs, exacerbating already high RNP costs. Polymers are not only affordable to manufacture but also load RNPs efficiently, thereby lowering the costs of RNP therapeutics. However, heuristic optimization of polymer lengths, architectures, or compositions is inefficient and experimentally onerous. We will pioneer ML frameworks that address 2 conceptual blind spots in RNP delivery: conformational quality control of intracellular RNPs (misfolded RNPs trigger deleterious edits) and tailoring polymers for data-scarce HSPCs. Protein payloads such as RNPs unfold during endosomal translocation but they must recover conforma- tional integrity in the cytosol. Otherwise, misfolded RNPs will fail to discriminate between target and off-target DNA sequences, jeopardizing the safety of genome editing. In Aim 1, we will engineer chaperone-mimetic polymers that refold RNPs back into catalytically competent conformations, augmenting the precision and effi- ciency of genome editing. During RNP refolding, polymers must promote peptide self-sorting while preventing aggregation. This requires careful modulation of polymer cationicity and hydrophobicity. Current tools fail to ex- plore vast design spaces along information-efficient trajectories. In contrast, Bayesian optimization (BO) will rapidly identify polymeric chaperones-cum-nanocarriers of optimized hydrophobicity and cationicity. RNP delivery to HSPCs has transformed the therapeutic landscape for hemoglobinopathies but electro- poration-based therapeutics such as exa-cel are prohibitively expensive ($2M per patient). Polymers load RNPs efficiently, consuming fewer RNPs per dose than electroporation and dramatically improving affordability. How- ever, polymers have met with limited success in delivering RNPs to HSPCs. Importantly, HSPCs are challenging to expand into large cell populations, which makes them ill-suited for testing large polymer libraries via data- intensive ML approaches. If we start with 1 million HSPCs, only 3 polymer–RNP complexes can be tested even in miniaturized experimental set-ups; a 150-strong polymer library will require 50–100 million HSPCs, which is experimentally onerous. Instead, in Aim 2, we will transfer information from data-rich cellular domains to tailor polymers for data-scarce HSPCs via transfer learning. This TrailBlazer R21 will alleviate the financial burdens of RNPs by developing an innovative ML-driven blueprint to unlock the potential of polymeric nanocarriers.

NSF Awards · FY 2025 · 2025-04

Fresh groundwater within New England Atlantic continental shelf sediments extends 100 km offshore. However, this onshore-offshore freshwater system has not been studied to assess freshwater emplacement mechanisms, freshwater residence time, and how long-term climate conditions and meteoric recharge have influenced these systems. This project will drill submarine wells in the offshore portion of the onshore-offshore freshwater system on the Atlantic continental shelf south of Martha’s Vineyard, MA, USA to study sediment cores. Understanding emplacement mechanisms of freshwater lenses offshore New England will also lead to a better fundamental understanding of this worldwide hydrogeological phenomenon and its impact on biogeochemical cycling. The project will maintain dedicated outreach activities to inform the local population about the research outcomes and will include an outreach officer, speaking at community events, visiting classrooms, and speaking with the media. Groundwater within New England Atlantic continental shelf sediments in Plio-Pleistocene aquifers is fresh (salinity <1000 mg/l) and water with salinity <3000 mg/l extends 100 km offshore. The impact of these dynamic freshwater systems on microbial processes and rates, and fluxes of carbon, nitrogen, other nutrients, and rare-earth elements to the ocean are also unknown. This project will study the dynamics of the offshore portion of the onshore-offshore freshwater system on the Atlantic continental shelf south of Martha’s Vineyard, MA, USA by characterizing the sediments, fluids, and microbes at three drill sites on NSF-IODP3 Expedition 501. The campaign will help to constrain and quantify the (1) spatial distribution of subseafloor freshwater and overall porewater geochemistry, (2) emplacement mechanisms of this freshened groundwater, (3) microbe diversity and activity, and (4) anomalous pressure distribution. Understanding emplacement mechanisms of freshwater lenses offshore New England will also lead to a better fundamental understanding of this worldwide hydrogeological phenomenon and its impact on biogeochemical cycling. All data will be provided through an open-access repository for the broader science 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.

NIH Research Projects · FY 2026 · 2025-04

Project Summary: Lung cancer is the leading cause of cancer death among men and women, making up almost 25% of all cancer deaths. For cancers beyond stage 1 some form of chemotherapy is usually given often causing side effects including cytotoxicity, cardiotoxicity, and nephrotoxicity. Avoiding systemic methods by directly delivering chemotherapeutics to the lungs can avoid some of these; however, toxicities remain because chemotherapeutic drugs are not selectively delivered to cancerous cells. We propose here the use of aerosolized and then in situ assembled microbots capable of carrying drug for transport and delivery of chemotherapeutics to specific locations within the lung. Our scientific premise is that individual µbot components of optimal aerodynamic size range can be inhaled into the lungs and assembled in place for subsequent travel down airways to target lung cancer for delivery of chemotherapeutic agents. As both µwheel assembly and driving forces are provided by an external magnetic field, once the procedure is finished, microbots disassemble into small beads removable by the body's natural mechanism for removal of dust and other foreign particles in the mucus lining. Our aims include: Aim 1. Identify magnetic field conditions for aerosolized µbot swarm targeted delivery within in vitro models. Focused on targeted transport within increasingly complex 2D and 3D in vitro lung models, here magnetic-field based targeting approaches will be tested, first within 2D bifurcating microfluidic models and then 3D printed models based on available lung mapping. Aim 2. Determine aerosolized µbot design for drug delivery within in vitro models. Focused on drug transport, we will investigate µbot design and manipulation for the delivery of chemotherapeutic drugs. Aim 3. Quantify the functional benefit of targeted µbot delivery in vivo. Focused on in vivo transport and efficacy, we will use a mouse model for testing of both µbot targeting and drug delivery.

NSF Awards · FY 2025 · 2025-04

The significance of this I-Corps project is based on the translation from lab to market of high-voltage, silicon-carbide (SiC)-based, multilevel power electronics building blocks (PEBBs) — a technology with the potential to modernize critical energy and propulsion systems. Power electronics refers to the branch of electrical engineering that deals with the conversion, control, and management of electrical power using semiconductor devices such as diodes, transistors, and silicon-carbide (SiC) components. These systems enable efficient power conversion across different voltage and current levels, making them essential for modern energy applications. The U.S. faces urgent challenges in upgrading its aging power grid, integrating new energy sources, and accelerating electrification in aerospace and space power systems. The impact of this technology extends beyond engineering innovation; it supports a more efficient and resilient energy infrastructure, fostering economic growth and technological leadership in the U.S. power and electrification industry. This project investigates the commercialization of a class of high-voltage, silicon-carbide (SiC)-based, multilevel power electronics building blocks (PEBBs). The PEBB is a generic block that can be connected to form a class of modular and scalable high-voltage and high-power converters capable of performing any conversion type, alternating current (AC) - direct current (DC), DC-AC, DC-DC, and AC-. The PEBB uses a neutral-point clamped (NPC) topology, compared to existing full-bridge topologies, unlocking the ability of achieving higher-voltage levels, increased power-density, reliability, efficiency, and improved control, while enabling higher switching frequency compared to state-of-the-art commercial products. Additionally, the PEBB incorporates built-in overvoltage and overcurrent protection and an integrated power management system, enhancing operational reliability. As such, this technology’s modular, scalable, and cost-effective PEBBs have the potential to enhance system reliability, increase energy efficiency, and improve energy accessibility — all while reducing maintenance costs, minimizing downtime, and extending the operational lifespan of power electronics systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Sediments beneath the ocean, within 62 miles (100 km) of the U.S. shoreline, contain large volumes of low-salinity groundwater similar to freshwater. This project aims to study its origin, salinity changes, and biological responses by monitoring water properties at two locations south of Martha’s Vineyard, MA. Sensors for pressure, temperature, and electrical resistivity will be installed in boreholes drilled during an international ocean drilling expedition. Data will be used to refine groundwater models. Public engagement includes a podcast series and educational activities, while the project also trains a graduate student in groundwater science and engineering design. Scientific ocean drilling and hydrological modeling suggest that 1 million cubic kilometers of offshore freshened groundwater (OFG) may exist in continental shelf aquifers worldwide. However, key uncertainties remain about its emplacement, recharge, discharge, and role in nutrient cycling. This project aims to constrain the hydrogeologic flow system by installing Simple Cabled Instrument for Measuring Parameters In-situ (SCIMPI) systems at NSF/IODP3 Expedition 501 Sites MV-08A and MV-03C offshore Martha’s Vineyard, MA. These sensors will provide continuous data on fluid pressure, temperature, and resistivity to refine groundwater models and improve understanding of OFG dynamics. Additionally, a podcast docuseries will engage the public in marine and freshwater science, incorporating educational activities for K-12 and undergraduate students. A graduate student will gain hands-on experience in engineering, groundwater science, and science communication. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

NON-TECHNICAL SUMMARY: Genome editor proteins will change how doctors treat both rare genetic disorders (such as sickle cell disease) and more common diseases like cancer or diabetes. Researchers struggle to deliver these expensive proteins into diseased cells where they are needed. Right now, tiny particles made from polymers, lipids, or gold are used to carry genome editor proteins into cells. Polymers, which are long chained molecules, consume fewer proteins per treatment, which makes it easier to produce these treatments in large amounts at lower costs, benefiting more people. However, polymer chemists do not fully understand how the length of the polymer chain, the chemical makeup, and other properties of the polymer match different genome editor proteins and different cell types. This proposed research will innovate methods to efficiently test different combinations of polymer designs and quickly find the best designs for each cell type and each genome editor protein. The goal is to apply machine learning and advanced polymer science methods to understand why some polymers work well in delivering genome editor proteins to cells while others do not. Polymers discovered in this CAREER project using these cutting-edge methods will advance basic biological research and improve public health. In addition to the research, the principal investigator plans three educational activities that will support this research area. First, a mentoring program will prepare high school students and undergraduate students from under-represented backgrounds to prepare for careers in science, technology, engineering, mathematics, and medicine. Second, the PI and her lab members will continue creating and delivering lesson plans that spark interest in polymers research in high school chemistry classrooms across Colorado. Finally, the PI will design and teach an undergraduate course to impart data science and lab skills that students need to succeed in the gene therapy industry. TECHNICAL SUMMARY: Genome editing tools such as CRISPR/Cas9 and base editors have revolutionized both basic biological research and the therapeutic landscape for several diseases (e.g., sickle cell anemia). Ribonucleoproteins (RNPs) are biomolecular complexes of genome editor proteins and single guide RNA that perform site-specific genome editing. RNPs are far more precise and efficient than plasmid- and messenger RNA-based genome editors but are also more challenging to deliver. This CAREER project will realize RNPs’ potential in research and in medicine by overcoming intracellular delivery barriers unique to RNPs. The first goal is to reveal how the chemical identity and spatial distribution of hydrophilic, hydrophobic, or charged monomers modulate polymer–RNP binding affinity, RNP loading per polyplex, and polyplex size distribution. Then, the principal investigator and her team will ask whether polymer design criteria overlap or diverge across surface-chemically diverse RNPs: spCas9 nucleases and the more cationic ABE8e base editors. Segmental similarity analysis will tailor polymer composition to accommodate each RNP species’ unique distribution of amino acid residues and surface chemical features. Bayesian polymer design will simultaneously explore untested chemical domains and exploit high-performing regions in the vast design space. The final goal is to elucidate design principles underlying cell-type-preferential RNP delivery. Brute-force testing of all polymers in all cell types is infeasible, especially if we consider the difficulty of expanding and maintaining some primary cells. Instead, recommender systems (e.g., algorithms that match Netflix users with movies given incomplete information on user preferences) will map polymers’ cell type preferences. This project will innovate biomaterial design frameworks that are generalizable to molecules beyond RNPs (antibodies, probiotics) and materials beyond polymers (lipids, polypeptides). The project will grant new mechanistic insights into polymer-mediated RNP delivery and lower the cost of deploying RNPs in basic biological research and genetic medicine. This project will also diversify the future workforce through a unique educational plan that will engage students from high school all the way to graduate school through near-peer mentoring, high school outreach, and an undergraduate class on data-driven biomaterial design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

This project aims to serve the national interest by developing an empirically validated software tool that will provide scalable, personalized support to students in large classrooms. The goal of this IUSE:EDU Level 2 Engaged Student Learning project is to develop an AI-driven software system that integrates educators' materials and rules that guide large language models to provide students with Virtual Teaching Assistants (VTAs) that produce tailored responses that directly reference specific course content. The VTAs will be designed to match the instructor's teaching style and goals, offer responsive support for students, and will provide a model for responsible AI integration in education. The overarching goal of the project is to provide students with personalized educational support while equipping educators with the tools they need to scale their teaching capabilities and deepen their insights into student learning. This project seeks to develop an AI-driven platform creating VTAs that integrates educators' materials and rules, which will guide large language models in producing tailored responses that directly reference specific course content. The system will consist of several key components: a subsystem for sorting and processing student inquiries, a subsystem to include prompt constraints and keep course adherence, a safeguard module to ensure the ethical use of AI in education, and an analytical module for detailed evaluation of student interactions. The system's retrieval-augmented generation framework will aim to match student queries with relevant content, ensuring accuracy and context. With features for privacy, secure data management, and real-time analytics, the system is intended to enable educators to scale support effectively and provide personalized learning experiences aligned with course goals. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Global sea level rise and associated socioeconomic impacts will be one of the most significant challenges facing society this century. However, ice sheet models, used to predict sea level outcomes for policy making, remain under-constrained. Improvements in ice sheet modeling occur via testing simulations of ice change against known histories of ice sheets. These histories are derived from records of ice-sheet response to episodes of planetary warming in recent Earth history, which hinge on geological dating methods. The most widely applied method to accomplish this task is radiocarbon dating, where thousands of analyses (past and ongoing) are ever more precisely pinning down the timing and rates of past ice sheet changes. Radiocarbon dating, however, is an analytical method that is constantly undergoing improvements. Hence, legacy data require constant updating, and the discipline is challenged in making best use of this valuable and growing resource. To date, there is no centralized, live, maintained community resource available to maximize use of radiocarbon information. To make a leap in ice-sheet model improvement, the community must first be enabled with an easily accessible and dynamically updated source of radiocarbon data. This project will develop a transparent-middle-layer data management and analysis tool to enable synoptic applications of radiocarbon datasets for the development of accurate ice-sheet histories. The project team will work directly with the ice sheet science community to ensure community buy-in and utilization of the radiocarbon data management and analysis tools via in person community workshops and virtual tutorials, both associated with existing annual conferences, and those targeted at specific user bases. The proposed research tool seeks to sustain scientific innovation at Earth's poles and reaches across disciplinary boundaries of polar, oceanographic, and Earth science research. As such, the developed computational infrastructure is comprehensive and interoperable, and has potential to make significant impact in a broad array of disciplines. This work is guided by Findable, Accessible, Interoperable, and Reusable (FAIR) principles, with a key emphasis placed on working toward improved data accessibility, rescue, and re-use. This project will develop a transparent-middle-layer data management and analysis tool to enable synoptic applications of radiocarbon geochemistry, geochronology, paleoclimatology, and carbon-cycle research around Earth's remaining ice sheets. At present, geologic constraints on past ice sheet change derived from marine archives are scattered across decades of publications and static data repositories. The lack of cyberinfrastructure to simultaneously analyze and utilize past constraints from all environments, thus, leaves researchers to the laborious tasks of data rescue, compilation, and standardization at an individual level, ultimately limiting the research community's ability to carry out transformative research. The development of Radiocarbon Cyberinfrastructure (RAD-CI) seeks to improve scientists' ability to evaluate the changing role of the polar cryosphere in Earth's climate system, as it will offer a means by which geological constraints on past ice sheet change can be dynamically compiled, calculated, and utilized in data-model comparison efforts. RAD-CI answers the calls of the National Academies report on Future Directions for Southern Ocean and Antarctic Nearshore and Coastal Research (NASEM, 2023) and the Intergovernmental Panel on Climate Change Special Report on Ocean and Cryosphere in a Changing Climate (Meredith et al., 2019), which both urge the polar research community to employ geologic constraints on past ice sheet change to validate models that project future sea level rise. This project seeks to sustain scientific innovation at Earth's poles by catalyzing fundamental discovery of the role of existing ice sheets in Earth's changing climate system, which will help to develop tools and numerical modeling techniques to prepare, mitigate, and adapt to risks associated with climate change. This award by the Office of Advanced Cyberinfrastructure is supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information 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 2024 · 2024-12

Wildfires are becoming more prevalent and are expanding out of wilderness areas and into the wildland-urban interface, including densely packed suburbs. The most devastating effects of these fires are apparent - the loss of homes, communities, and sometimes lives. Beyond the physical destruction, one unanswered question about the environmental impact of structure burning is whether fire liberates metal particles from household and structural components. Metals are found in everything from pipes to electronics to paints and pigments. Metals such as lead, copper, zinc, cadmium, and others can be toxic to the environment and humans. Once released, small metal particles can be transported by wind with subsequent deposition by rain and snow throughout different environments. The December 31, 2021 Marshall fire in Boulder County, Colorado burned more than 1000 structures, causing unknown effects on metal release to the surrounding environment. The research team at Colorado School of Mines will employ state of the art particle analysis techniques to determine if metal particles are present in ash samples from burned structures. If found, further analysis will determine whether the concentrations are significant and humans are at risk from toxicological effects. The potential for metals to be absorbed by human lungs will be assessed by leaching metals from the ash into simulated lung fluids. The project will provide essential information to increase understanding of fire-derived metals while furthering analytical capabilities. The research team will hold virtual and classroom forums involving the Boulder community aimed at educating the community on the findings of the research, sharing the health impacts of nanoscale particles, and providing hands-on experiments for students. The ash and smoke generated from the Marshall fire has the potential to be more harmful to both the environment and human health than materials generated from wildland fires. Specifically, the anthropogenic materials burned may lead to the presence of metal-containing nanoparticles (diameter < 100 nm) in the resulting ash and soot. The project goal over the next 12 months is to determine the amount of metal (Cu, Cd, Hg, Pb, Zn among others) present in nanoparticle form in ash and soot. The research team will collect samples from the area affected by the Marshall fire, focusing on ash from within burned homesites, nearby soils, and surface waters located downwind of the burned area. Samples collected in non-urban, upwind sites will serve as controls. This RAPID project will utilize the novel technique of single particle Inductively coupled plasma mass spectrometry (ICP-MS) to detect and quantify the nanoparticles present in deionized water suspensions. The research team will investigate effects of particle size on metal bioaccessibility by performing single particle ICP-MS analysis as part of dissolution experiments using simulated lung fluids. Timely analysis of the burned materials will advance knowledge of metal speciation in post-fire debris and may inform a responsible remediation effort with respect to human health and environmental impact. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Plastic waste is contaminating oceans, waters, and soils at an alarming rate. Microplastics are plastics so small that they may be barely seen by the human eye. Sometimes microplastics are engineered to be tiny, such as when they are used in cosmetic products, and sometimes microplastics are formed from the breakdown of larger, macro plastics. Microplastics pose a growing but uncertain threat to human health and the environment. They have been found everywhere, in food, in humans, and in drinking water. While it is known that microplastics are being found everywhere, currently it is not well tracked where they’ve come from. It is not known who or what are the biggest sources of microplastics for humans and the environment. Without an accurate picture of where plastics of all sizes come from and how they move through systems and the environment, it is not possible to design effective solutions. This research will collect new data on the amount and type of plastics found in different geographic environments. These data will be used to create a model of plastic flows through engineered systems, products, and the environment and will experiment with potential solutions to minimize plastic flows to humans and the environment. The project will engage and educate US and Caribbean students in learning how to collect and measure microplastics in the environment. This project will build a Material Flow Analysis (MFA) for plastics in the US and Caribbean. The research aims to evaluate whether secondary micro and nano plastic concentrations can be correlated to macro-plastic concentrations. If correlations can be made, then the research will explore whether satellite and aerial data can be used to estimate the presence of plastics in aquatic environments. This research will identify where plastics move through and accumulate in the environment via MFA, as well as identify opportunities for resource recovery or mitigation of their release to the environment. To answer these research questions, this project will build a probabilistic MFA that includes macro, micro, and nano plastics. Subsequently, solution spaces will be identified via scenario analysis conducted with expert stakeholder input. First, the MFA will enable understanding of where the largest flows, losses, and accumulation of plastics occur. Second, and perhaps most important, is that this information will then enable researchers to identify and experiment with potential solutions that support sustainable management of plastics. Data mining and data estimation techniques will be used to populate data for the MFA. Field measurements of micro and nano plastics will be taken from 6 different geographic locations (CA, CO, MI, NY, Dominican Republic, Belize) to supplement mined data. Field measurements will follow analytical procedures outlined in the literature and by ASTM for sampling and analysis of microplastics. Once the MFA is constructed, model refinement, validation, and scenario analysis will be conducted to evaluate potential innovative solution scenarios with expert stakeholders. This project will curate a plastics database and create an MFA that investigates different geographic locations, the intersection of macro plastics with primary and secondary micro and nano plastics, flows into unique compartments such as food and humans, and an assessment of possible solutions and waste management practices leveraging a stakeholder network of over 200 people. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The West Antarctic Ice Sheet (WAIS) Workshop has been held annually for the past three decades to provide a key venue for transdisciplinary scientific exchange on the state and behavior of WAIS and other marine-based ice-sheet basins, processes that influence their changing behavior, and projections of their future mass balance and sea-level contribution. The community-based structure of the workshops brings together students, early-career, and senior scientists who are investigating marine ice sheets using a wide variety of tools. Researchers present findings obtained from ground-based and shipborne methods, airborne geophysics, satellite remote-sensing, and numerical modeling. Group interactions facilitate collaborations across institutions and disciplines and help researchers answer difficult, but essential, questions regarding the fate of the Earth’s marine ice-sheet basins. This award provides partial support for the 2024 WAIS Workshop in Gainesville, Florida, At this workshop, the organizers will: (1) convene a workshop on innovative WAIS science, with extended time for discussion and audience interaction that is accessible to scientists and educators at all career stages in an effort to define the NSF community needs for the upcoming International Polar Year; (2) ensure a transdisciplinary perspective on WAIS evolution by welcoming new scientific ideas and inputs from other disciplines underrepresented in past WAIS Workshops (e.g., biology, oceanography, (geo)engineering, data science); (3) redesign the web-presence of the workshop to ensure the 30-year history of WAIS Workshops is maintained for understanding the context and evolution of marine ice-sheet science for future generations of NSF-funded scientists; and (4) engage scientists of all levels by implementing half-day post-agenda mini-workshops that support training in NSF-funded data repositories and open-science practices. They will continue successful programs to enhance equity and accessibility, such as blind-abstract review, synchronous streaming of the WAIS Workshop science agenda, and archiving videos of WAIS Workshop presentations for free public access to the latest marine ice-sheet 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 2024 · 2024-10

This project aims to serve the national interest by waging a campaign to increase the number and diversity of STEM teachers in K-12 classrooms. Get the Facts Out (GFO) is a national multi-disciplinary effort to supporting STEM faculty and others in recruiting STEM teachers. It is grounded in evidence that enhancing prospective teachers' knowledge of the profession boosts exploration and enrollment in teacher certification programs. GFO resources and strategies draw from research on student and faculty perceptions; extensive national data on grade 7-12 math, science, and computer science teaching; and comparative data for STEM career paths. The project approach is a radical change in practice, celebrating the positives of the profession, providing much needed balance to the conversation. It emphasizes sharing detailed transparent local data on teacher salaries, benefits, retirement plans, day-to-day responsibilities, retention, and job satisfaction. GFO, an Improving Undergraduate STEM Education (IUSE) Community and Institutional Transformation Level II project, partners with five national societies and more than 20 universities as study sites. With the potential to significantly reduce the STEM teacher shortage, the project aims to provide high quality, diverse STEM teachers, contributing to the competitiveness of the United States in STEM fields. The project team includes the Colorado School of Mines, the American Chemical Society, the American Physical Society, the Association of Mathematics Teacher Educators, the American Association for Employment in Education, the National Council for Women & Information Technology, teacher recruitment and retention specialists, educational researchers, current and former teachers, and more than 20 universities as study sites. Building on previous NSF-funded work, GFO aims to implement and study the effectiveness of nationwide recruitment efforts which should: 1. Improve perceptions of the teaching profession among STEM faculty, teachers, students and their parents by, a) building towards a cultural change, and b) becoming a part of the STEM careers conversation; 2. Support the continued development of the GFO Teacher Recruitment Community (current members represent over 200 institutions) as it matures and becomes self-sustaining; and 3. Increase numbers of STEM majors who enroll in a licensure program. To achieve these goals, the project plans to develop additional resources and strategies aimed to support faculty actively involved in initiatives to shift campus culture, fostering a more positive and accurate perception of the teaching profession; collaborate with national societies to expand upon and improve the representation of the teaching career within their discipline specific careers resources; and provide ongoing support to individuals and organizations involved in recruiting and retaining STEM teachers. The ambitious research arm includes development and testing of all resources; site visits to five institutions per year who are focusing on either cultural change, enrollment, or career education; and the development and validation of two new instruments - the Perceptions of Departmental and Institutional Culture Towards Teaching (PrDICTT) and the Student PrDICTT. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by developing an innovative and much-needed Quantum Sensing (QS) curriculum and effective roadmap for quantum engineering education. Although engineering is essential to the success of QS applications, few academic programs in the US teach QS to engineering students outside of physics classes. In support of the National Quantum Initiative and the quantum industry, it is important to teach topics in quantum information science and technology to engineering students. This project will test out a QS module in two different research university environments – one a research-intensive polytechnical university, the other a large-scale public research-intensive university. Specifically, the project involves collaboration between Colorado School of Mines, University of California Davis, and MITRE (a non-profit research organization). A portable QS curricular module will be created for incorporation into quantum courses for engineering students across a broad variety of programs nationally. The QS module will significantly assist instructors involved in quantum engineering education with development of programmatic materials. The module will utilize a magnetic sensing platform based on nitrogen vacancy (NV) centers in diamond. The widely used Canvas learning management system will be employed for pre-class reading, lecture notes, active learning and homework assignments. For hardware, a relatively inexpensive mobile diamond NV center-based quantum magnetometer OSCARCUBE will be adopted for hands on lab education. Important educational research questions will be investigated, for example, what teaching methods in quantum sensing create more engineering student engagement and inclusion leaving students with a positive attitude toward engineering as a whole and quantum information in particular? What are the minimal set of key physics concepts necessary to understand quantum sensing? Also, what kind of affordable and effective quantum sensing training hardware platform can be created for deployment in teaching-focused schools? QS itself is of high importance in many fields providing effective alternatives to MRI technologies for medicine and neuroscience as well as options for navigation and timing to support the nation's armed forces and space vehicles. The project will contribute significantly to training of engineers to work in quantum-related fields. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to develop innovative technology for environmental monitoring. The researchers are inspired by the natural design of dandelions to create a swarm of tiny, lightweight sensors that can be deployed in hard-to-reach areas by unmanned aerial vehicles (UAVs). These sensors will harness wind currents to spread across vast and difficult terrains, collecting crucial data on environmental conditions. This technology promises to improve our understanding of ecosystems and aid in disaster management by providing real-time, detailed information in places that are otherwise inaccessible. The project brings together expertise from three institutions and will enhance research capacity, support education, and promote diversity in STEM fields. The project focuses on developing a biomimetic swarm sensing system that emulates the dispersal method of dandelion seeds. The system includes sensors with pappus-like structures for flight and energy harvesting, and achene-like components for sensing and communication. Key research goals include designing and optimizing the sensor structures for aerodynamic efficiency, developing robust energy harvesting and communication circuits, and creating a transformer-based deep reinforcement learning architecture for autonomous UAV-assisted sensor deployment. The performance of the system will be validated through simulation and experimental testing. This interdisciplinary research integrates aerodynamic analysis, solid mechanics, microelectronics, signal processing, communication theory, and deep reinforcement learning, aiming to advance both basic science and the practical application of biomimetic swarm-based remote sensing technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Despite advancements in wireless technologies like cellular and WiFi, issues such as spectrum scarcity and inefficiency persist and are limiting connectivity for millions. Current systems often restrict access and hinder collaboration among stakeholders, especially in marginalized communities. This project aims to develop technical solutions for a more open wireless access ecosystem. By leveraging open-source technologies and decentralized models, it seeks to promote competition and collaboration among service providers. This approach aims to enhance spectrum efficiency, expand Internet availability, and improve reliability. Key goals include fostering trust through transparent contracts, ensuring verifiable service and spectrum sharing, and encouraging active participation from diverse stakeholders. Beyond technical advancements, the project aims to revolutionize wireless access for sectors like national security and smart communities, and support emerging applications such as autonomous agriculture and connected healthcare. It emphasizes openness and collaboration through publications, presentations, and partnerships with industry leaders like IBM, Google, and Verizon. All developments are open-sourced, ensuring accessibility and fostering innovation. This project also enriches curricula at Colorado School of Mines and NC State University, focusing on networks, Internet protocols, and incentive mechanisms, and fosters diversity in STEM through mentoring and engagement with underrepresented minorities. This transformative research designs Opennect, a framework for democratized wireless access. It yields several advances: 1) a robust economic and trust foundation via peer-to-peer and multi-party decentralized contracts including a robust contract network driven by economic dynamics, efficient methods to establish on-demand contracts, and highly robust and sustainable operations of the contract network, 2) a suite of methods for verifiability in decentralized environments by developing transparent data usage accounting, quality-of-service provisioning, and spectrum monitoring mechanisms, and 3) a sustainable market that encourages active participation in Opennect, including on-demand spectrum leasing, decentralized pricing for data plans, and crowdsensing-based spectrum access verification. The system is evaluated using large-scale simulations with real data, local testbed implementations, and scaled demonstrations on the NSF/PAWR-funded AERPAW testbed at NC State University. Overall, the project aims to advance the field of democratized wireless access by fostering trust, enabling verifiability, and promoting active participation through innovative market mechanisms. Its potential contributions include enhancing spectrum efficiency, expanding access to reliable Internet services, and supporting diverse applications in smart communities and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

National priorities to broaden participation in computer science require exposure to rich computing activities in elementary school. This will enable students to successfully navigate a computer infused world or envision themselves as future computer scientists. Currently, the scarcity of experienced computing teachers at the elementary level and a lack of coherent computing curricula that supports incremental knowledge building can hinder this growth. This situation restricts the opportunities for teachers to connect, collaborate, and support each other in fostering a robust computing education within the school district and beyond. This project aims to establish and study a new Research Practice Partnership between the University of Colorado Boulder, Colorado School of Mines, and the Aurora Public Schools to address these issues in one of Colorado’s most racially and linguistically diverse districts. This new Research Practice Partnership (RPP) project is focused on fostering the success of computing educators at the elementary level. These educators will collaborate with researchers and district administrators to adapt proven middle school physical computing lessons using the Micro:bit, making them accessible and engaging for elementary students. The Micro:bit enables students to create tangible computing projects that bring code to life in the physical world. In partnership with homeroom teachers, the team will develop follow-on lessons that align with students' interests, highlighting the relevance of computing throughout their educational journey. The research goals of this project are 1) to understand how participation in this cohort influences elementary STEM teacher capacity to teach computing concepts and their sense of belonging as computing educators both within the district and community at large and 2) to explore how the creation of this new Research Practice Partnership supports the integration of computing into elementary classrooms. Ultimately, this program aims to nurture a community of computing educators who are confident, capable, and passionate about supporting elementary students to discover the world of computing and become leaders in computing education within the district. Our research addresses two critical needs in computing education: the shortage of elementary computing teachers and the lack of a coherent, incremental student experience in elementary computing. The main research questions driving the study are 1) How do the co-design processes and routines develop elementary STEM teachers’ capacity to teach computing concepts and practices and their sense of belonging as computing educators? and 2) How do the proposed RPP partnership-building mechanisms help support the integration of computing into upper elementary classrooms? A design-based implementation research approach will be employed, interleaving co-design workshops, classroom implementations, and systematic data collection and analysis to investigate the research questions. Data will include pre-post identity and belonging surveys, video and artifacts from co-design workshops, reflective memos, exit tickets, and classroom observations. Over two years, the project will serve four elementary STEM teachers, four upper elementary homeroom teachers, and over 1000 upper elementary students. The project will produce two exemplar units that demonstrate how the storyline instructional model can effectively support elementary computing. A storyline is a sequence of lessons organized around student-generated questions from an anchoring phenomenon, promoting coherent, incremental knowledge building. This approach is designed to ensure that students understand how their current activities relate to previous and future lessons. Preliminary evidence will highlight the promise and feasibility of these units in engaging diverse elementary students in computing and fostering STEM interests. Additionally, the project aims to increase teachers' capacity to support students in computationally rich STEM investigations, creating a budding cohort of teacher leaders. Moreover, the project will provide a valuable case study on RPP formation using a recently developed framework, offering insights and best practices for the broader computing educational research community and helping ensure that the research-practice partnership between CU Boulder, Colorado School of Mines, and Aurora Public Schools matures and is ready for scale-up in future work. Resulting curricula, supports, and research findings will be shared with the district, and nationally with researchers and teachers via two high-traffic websites—OpenSciEd and Micro:bit Educational Foundation—and relevant conferences. This project is funded through the Computer Science for All: Research and Research Practice Partnership (RPP) 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 2024 · 2024-09

The broader impact of this Partnerships for Innovation – Research Partnership (PFI-RP) project is in enhancing the understanding of drug interactions within the body by providing detailed spatial information on how chiral drugs behave in different tissue environments. Chiral drugs are medications where the molecules have a specific three-dimensional arrangement of atoms that affects their biological activity. The imaging technology goes beyond traditional methods by allowing scientists to see exactly where and how drugs interact with tissues, which is crucial for developing safer and more effective medications. The integration of novel detection schemes will enable quicker and more sensitive assessments of drug candidates, speeding up the development process. This technology will help ensure that drugs are effective and minimize harmful side effects, ultimately contributing to the advance of personalized medicine. Furthermore, this project will foster innovation and entrepreneurship among students. The project aims to address a critical need in pharmaceutical research by developing an advanced imaging instrument that operates in the mid-infrared spectrum. This instrument will allow for non-destructive, spatial analysis of complex chiral drug interactions within biological tissues. Understanding chirality, or the geometric property of molecules that results in non-superimposable mirror images, is essential because the enantiomers of chiral drugs can have vastly different effects. Some enantiomers can cause unintended and harmful side effects, so it is vital to study these interactions in detail. By leveraging recent advancements in single-element detection schemes and metamaterial assemblies, this project will transform traditional chiroptical spectrometers into imaging systems. This innovation will provide new insights into drug behavior, enhancing the safety and efficacy of drug development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This incubation project will advance integrated approaches to artificial intelligence (AI) ethics education in undergraduate science, technology, engineering, and mathematics (STEM) programs. The research team will seek to transform materials from the ethics bowl competition, which is traditionally an extracurricular activity focused on research, consultation, collaboration, and debate, into structured classroom tools. This will help instructors to cultivate the understanding that students have of AI ethics. The project aims to build a strong community foundation and develop capacity for larger educational initiatives. The project will create and evaluate instructional resources like case studies, instructor guides, and active learning assignments. These tools will provide practical, scenario-based learning experiences to enhance student skills in ethical reasoning, teamwork, and communication. The team will develop and pilot sample resources with participants during a professional conference, workshop the resources at a two-day meeting, and refine and disseminate the resources after the workshop. The project focuses on STEM ethics education, specifically AI ethics. Project goals are to (1) recalibrate STEM education to incorporate ethical reasoning with professional competencies like teamwork, research, and communication, addressing a critical gap in AI education; (2) equip STEM faculty with a new pedagogy to engage students in ethical discourse and analysis; and (3) set new benchmarks in AI ethics education by providing a replicable model for integrating ethical decision-making into STEM disciplines. The impacts of the project include enhancing AI ethics understanding among students, expanding the reach and inclusivity of ethics education by co-creating materials with a broad collection of institutions, and producing deliverables like pedagogical materials, online resources, and community engagement platforms. The project will involve a collaboration with government, industry, and community partners. The curriculum produced will address current ethical challenges in AI and equip students with relevant skills for various professional settings. This project is funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The field of robotics has experienced a remarkable transformation from its early, classical phase to its contemporary state, characterized by significant artificial intelligence (AI) advancements and expanded applications. Correspondingly, there is an increasing need for workforce development with the modern AI-powered robotics knowledge. This RET site at Colorado School of Mines will introduce key modules of modern AI-powered robotics technologies to high school teachers and community college instructors, who will adapt pre-prepared curricular modules for their student populations and courses, creating a significant impact on the introduction and integration of modern AI-powered robotics into the high school and community college curriculum. The experience will produce a lasting partnership among collaborating high schools, universities and industry to produce a highly skilled workforce poised to strengthen the Colorado advanced robotics industry. In addition, the site will generate new knowledge about how to best prepare teachers to instruct and guide students to develop AI-powered robotics solutions. The goal of the RET site is to prepare high school teachers and community college instructors to integrate modern AI-powered robotics into high school or community college science, technology, engineering and math (STEM) curriculum through Colorado School of Mines (CSM) faculty and graduate student research mentorship. The complexity of modern AI-powered robotics that merge computer vision, speech communication, and autonomous navigation can make its teaching and learning challenging for 9-12 and community college students. This RET site will use a modular, application-driven approach to break down and simplify these advanced concepts into independent modules and exposing teachers with these modules repeatedly in cutting edge robotics researches, such as robotic exoskeletons, bio-inspired dog robots, multi-finger robotic hands, swarm robots, and in various industrial applications, such as construction, mining, space, medical, and assistive living. These opportunities will provide teachers the knowledge and experience to educate students about the different career pathways and opportunities in future robotics industry and about the potential of AI-powered robotics as the new industrial revolution to impact individuals’ lives. Teachers will practice new content and research knowledge through micro-teaching and creating knowledge transfer through building cross-disciplinary connections. As a result, an innovative STEM curriculum will be created to enhance modern robotics knowledge in an approachable and translatable manner. The program will also include professional development activities and curriculum development opportunities for the use of the modern robotics technologies in the classroom. Follow-up academic year engagement between the teachers and CSM will include students in a robotics competition among participating schools and tours of CSM robotics labs, and guest lectures from industrial representatives to students in the classrooms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Robotics is critical to many areas of national interest, such as search and rescue, space exploration, agriculture, and mining, as well as healthcare, therapy, and education. Because these areas come with acute ethical risks, roboticists must be trained to attend to those risks, and to do so in a way that is equitable, responsible, and inclusive. This project aims to addresses this challenge by producing (1) a comprehensive framework for cultivating ethical and responsible robotics researchers and engineers, (2) a grounded understanding of the gaps in existing robotics research ethics training and education curricula; (3) educational modules that address those gaps; (4) an assessment of the impacts of the modules on student learning, (5) an understanding of the cross-cultural impacts of the modules, and (6) insights on how the modules provide learning opportunities for senior researchers. The project team will draw on its prior work to inform and develop a framework for advancing how students understand, value, and engage in responsible and equitable research and design. The team will seek to answer three key research questions. First, which design frameworks might best support the training of ethical and responsible roboticists? Second, how can those frameworks be productively used in training ethical and responsible roboticists? Third, what benefits will educational modules have across different cultures and at different career stages? Research question one will be addressed using ideation workshops to develop a comprehensive framework. Question two will be addressed by first performing a Training Needs Analysis. Then the team will develop modules that address the identified gaps, are informed by the team’s framework, and are refined through a three-stage curricular design process. Question three will be addressed using mixed-methods analysis of classroom activities. In this final phase, the modules will be deployed and evaluated in a set of high school, undergraduate, and graduate classrooms. This project is jointly funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences and the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Small particles are ubiquitous in nature and many times they can sit on the surface of liquids, such as water, or between two liquids, such as when oil and water meet. These particles then impact how molecules move between the air and the water, or the oil and the water, and thus influence many natural processes. This collaborative project will help answer important questions about how sheet-like particles organize on the surface of liquids, and how the organization alters the movement of molecules from a gas to a liquid, or a liquid to a liquid. This knowledge is important for creating materials for sustainable foods, pharmaceuticals, and coatings, and for helping to design and build better particles. The proposed work involves two research groups, one in chemical engineering at Colorado School of Mines, and one in materials science and engineering and chemistry at Texas A&M. Undergraduate and graduate students from both institutions will share knowledge across the different disciplines while they perform research. They will gain the foundational skills required to be leading scientists in the STEM workforce. The goal of this project is to understand the relationship between the structure of 2D particle films at fluid-fluid interfaces and the mass transport across the films. Preliminary work indicates a complex and unknown relationship between particle area concentration and permeability, and microscopy data reveal that 2D particles form heterogenous films with structure that depends on area coverage and particle oxidation. The central hypothesis is that permeability across 2D particle films will be governed either by film heterogeneity or tortuosity depending on particle area concentration. The researchers will probe this hypothesis by combining theoretical transport models with experiments visualizing graphene oxide (GO) film structure and experiments quantifying interphase mass transport. This collaborative work leverages expertise in fabricating particles and organizing nanosheets at interfaces, as well as development of an array of techniques for visualizing 2D particle film structure at fluid-fluid interfaces with microscopy. The PIs will support the development of undergraduate and graduate student researchers and will jointly develop and implement a half-day workshop on particles at interfaces to be held in association with the ACS Colloid & Surface Science Symposium. Graduate students will be trained in laboratory skills, critical thinking, data analysis, and dissemination of research results, and they will participate in joint meetings between lab groups to facilitate the exchange of knowledge and expertise. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

In general, complex flows - those observed in supersonic-to-hypersonic combustion and propulsion, fusion technologies, and astrophysics - involve multi-material mixing and span a broad range of space and time scales. Fluids participating in such flows have a wide range of molar masses, and in many cases, the flow is highly compressible. It is still a challenge for current engineering tools to predict the key flow physics that arise due to the compressibility and large material property variations. A stronger fundamental understanding of these effects on turbulent flows will significantly increase our ability to model the flow physics accurately, such as the rate of turbulent mixing that occurs in complex multi-material flows, and to perform numerical simulations of such flows with a decreased computational expense. These gained abilities will have a direct impact on the improvement and development of many high-tech products in the space, energy, and defense industries. Therefore, the focus of the proposed study is to quantify the coupled large molar-mass ratio and compressibility effects on the gravitationally driven turbulent flows. The project will also deliver an educational component by generating content for undergraduate- and graduate-level courses. It will also support outreach activities to promote interest in fluid dynamics and turbulence, and more broadly in STEM among local middle-school students. Multi-material turbulence has so far mostly been studied with quasi-incompressible and Boussinesq flows with small variations in material properties. The proposed project aims to describe flow compressibility effects on Rayleigh-Taylor unstable turbulent mixing with large density variations beyond the Boussinesq approximation and the incompressible assumption. Novel direct numerical simulations of buoyancy-driven flow that resolve all spatial and temporal scales will be performed at large density ratios (>2) with highly compressible fluids using the adaptive mesh refinement to optimally deploy computational resources. Unique statistical tools will be developed to quantify the non-Boussinesq turbulent compressible mixing dynamics. The proposed simulations and statistical analyses will be used to establish a deeper understanding of turbulence transition for non-Boussinesq flows, and in particular, the small-scale flow topology of the compressible active-scalar mixing. In addition, the findings of this research are expected to inform new sub-grid-scale models and strategies to decrease the computational cost of the multi-physics complex fluid-flow simulations and validate the reduced-order models for these complex flows. This project is jointly funded by Fluid Dynamics program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The deformation and breaking of Earth materials are crucial processes occurring at across time and space, particularly in earthquake-prone regions. Earthquakes that are a result of tectonic forces, frequently occur in zones containing fine-grained, crushed and ground-up rock fragments called gouge. Therefore, the deformation of this gouge material is critical to understand the earthquake process. Traditional mechanical techniques do not consider the complexity of gouge materials as observed in field studies, overlooking the heterogeneities visible in high-resolution images. The consequences of shape simplification are not well understood, especially for gouge materials formed by the grinding of rocks during prior earthquake events. Consequently, modeling the interactions of such materials in fault gouge zones remains a significant challenge. The community lacks a comprehensive understanding of how these complexities influence ruptures and critical factors in fault zones. This project addresses these challenges by producing realistic models from the limited information available from raw data and images and using more realistic shapes for conducting micromechanical modeling. The findings can bring about new insights into the mechanical properties of gouge zones and enhance the predictive capabilities of models when representative models are used within a realistic micromechanical model. The project integrates education and outreach efforts, engaging graduate, undergraduate, and high school students. The project also aims to build stronger partnerships between academia and industry, with broad impacts across multiple fields, including mechanical modeling, soil science, powder technology, materials science, biology, and physics. Geomaterials undergo significant changes as a natural process that takes place at different scales, particularly in areas with seismic activity where earthquakes occur. The focus of this proposal is on the gouge zones as they often represent materials with complex heterogeneity. These regions typically contain complex rock formations due to tectonic forces and surrounding geological structures. Conventional computational approaches often simplify the complexity of these materials and ignore the complex frictional and interlocking characteristics. In addition to computational barriers in numerical methods, the lack of representative shape models limits our understanding of the mechanical behavior of such materials. At the same time, our understanding of how these complexities influence fault rupture, critical zone factors, and transport properties is still incomplete. This proposal addresses these gaps by incorporating relevant complexities in building realistic models. Subsequently, these are incorporated into a micromechanical model in which the displacement of gouge materials in faults can be quantified. This project will utilize high-performance computing to build models for use in analyzing the interactions of gouge materials under seismic conditions. This project will enable large-scale domain generation, capturing the true complexity of gouge materials and evaluating their mechanical behaviors. The expected outcomes of this research will enhance our understanding of the mechanical properties of fault gouge zones and lead to the development of more predictive models. These results can also inform large-scale finite-element methods and experimental tests by providing more representative constitutive models and informative experiments, respectively. These models will improve our ability to simulate fault behavior during earthquakes, providing valuable information for earthquake hazard assessment and mitigation strategies. This project provides opportunities for the mentorship of and outreach to populations with disabilities by involving them in research. The results of this research will also be included in the graduate and undergraduate courses taught by the PI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With the rapid adoption of electric vehicles (EVs) on the road, there will be numerous new job opportunities for EV maintenance and repairs for the next several decades. However, there is a significant shortage of adequately trained automotive technicians in the United States who are well-prepared to maintain and repair EVs. The existing automotive technicians have limited career development opportunities due to the fact that they have full-time jobs and limited time and resources to acquire the knowledge and skills needed for maintaining and repairing EVs. In addition, EV maintenance and repair require professional knowledge from multiple domains, which makes it challenging for existing training methods to create immersive and effective learning experiences for automotive technicians. The broader impacts of this project include the development of a globally competitive EV workforce, broadening the full participation of minorities and underrepresented populations in the EV industry, promoting future EV adoption to achieve global sustainable goals, and enhancing the future designs of EVs through the partnerships between academia, industry, local communities, and public agencies. In this project, an interdisciplinary team of researchers will work with multiple industry and educational partners to explore and test the practical foundations of an experiential learning approach for helping existing automotive technicians upskill their knowledge and skills for future EV technologies. This project will: (1) Understand existing EV training workflow and identify automotive technicians’ training needs through co-design and survey; (2) Develop a multi-stage experiential learning pipeline for existing automotive technicians; (3) Propose an effective and scalable experiential learning curriculum for automotive technicians to identify and solve real-world EV problems via online learning, hands-on learning, factory visits, and co-op/internship. This project aligns with the NSF ExLENT Program as it seeks to support experiential learning opportunities which range from fundamental theory to hands-on applications of EV diagnosis, maintenance, and repair. These opportunities exist for individuals from diverse professional and educational backgrounds, and seek to increase their interest in, and their access to, career pathways in emerging EV technology fields. 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.