Purdue University

NSF Awards · FY 2024 · 2024-09

In this project, funded by the Chemical Structure, Dynamics & Mechanisms B Program of the Chemistry Division, Xiaoliang Wei of the Department of Mechanical and Energy Engineering at Indiana University-Purdue University Indianapolis is investigating a new family of soluble, stable multi-redox macrocyclic organic (M2O) molecules that undergo reversible six electron electrochemical reactions. Despite the great progress so far, redox flow batteries are still facing critical technical hurdles including low energy density and/or short cycle life. Dr. Wei aims to address these challenges by developing highly soluble M2O molecules capable of six electron transfer with high stability via extended charge delocalization. The goal of this research is to use these molecules to achieve energy-dense, long-life redox flow batteries. Success of this project will increase the deployment capacity of renewable energies and improve the reliability and efficiency of our power grid. This project is highly interdisciplinary combining organic chemistry, electrochemistry, materials science and computational chemistry, and in this way, provides a well-suited platform for scientific training at all levels. Given the nature of the science, this project is expected to engage students considering careers in STEM (science, technology, engineering and medicine) that can benefit society. This project will explore novel multi-redox macrocyclic organic (M2O) molecules for use in aqueous redox flow batteries to achieve high energy density and long cycle life. The molecular design includes fused heteroaromatic structures with solvatable substituents to provide multi-redox activity, conjugative stabilization, and solubility. The relevant physicochemical and electrochemical properties of synthetic M2O compounds will be correlated with their molecular architectures to unravel the fundamental interplays. The objective of this project is to rationalize the design principles for the development of promising M2O candidates through answering the following questions: (1) What are the mechanisms for establishment of solubility limits and how do the substituents on M2O scaffolds affect the solvation structure and solubility? (2) What are the redox mechanisms of M2O molecules and how do the electrode microstructure affect their redox kinetics? (3) What are the decomposition pathways for unstable M2O molecules and what are the stability-controlling factors? 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

Environmental sustainability is vital to human society. Climate change, water scarcity, and declining biodiversity are closely intertwined threats. At the same time, technological solutions require manufacturing capability and steady supplies of materials. The twin threats of declining planetary resilience and U.S. supply chain risks must be addressed by individuals and organizations. The actions and practices of business enterprises play an outsize role in solving these problems, and therefore, the goal of this project is to develop a graduate program to fulfill an area of national need: integrating environmental sustainability into business. This National Science Foundation Research Traineeship (NRT) award to Purdue University and North Carolina Agricultural and Technological State University (NC A&T) will enable a commerce system that practices innovation for environmental sustainability. The project anticipates training 22 NRT-funded trainees and approximately 70 additional graduate students at both master's and doctoral degree levels. Fields of study include environmental and ecological engineering, materials engineering, civil engineering, computer science, mechanical engineering, industrial engineering, and architectural engineering. The NRT training and research outcomes will contribute significantly to an advanced STEM workforce in an area of critical national need. The education and training activities are guided by the Engineering for One Planet (EOP) framework, and include four components: 1) an outcomes-based curriculum; 2) doctoral dissertation; 3) at least one non-academic research experience at a for-profit organization aligned with the student’s dissertation topic; 4) formal, explicit training in communication, ethics, and teamwork in a project-based course. The technical rigor of a doctoral degree in engineering uniquely accelerates innovation over the entire business cycle of products and services, enabling optimal trade-offs among performance, environmental impact, and cost. The project team consists of ~20 researchers at Purdue University and NC A&T, who will conduct convergent research organized into three pillars: 1) Greening the Digital Economy, 2) Decarbonizing Steel and Electricity, and 3) Transportation. Key outcomes from each pillar include reducing the environmental impacts of computing, including data centers, in the context of the digital economy; utilizing lifecycle assessment methods for considering the consequential impacts of macro energy system transitions in producing large quantities of low-carbon fuels and materials; and developing frameworks and modeling tools that integrate techno-economic analysis and supply chain risk evaluation into environmental assessments of emerging technologies such as electrification, shared mobility, and e-commerce. These methods will impact multiple disciplines and help change the business paradigm around environmental sustainability. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Heart failure with preserved ejection fraction (HFpEF) is a debilitating disease with high morbidity, mortality and health care expenditures. Impaired physical capacity is the primary symptom and a strong determinant of prognosis and reduced quality of life (QoL). The prevalence of HFpEF is higher in women and increases with age. Women with HFpEF present with worse exercise intolerance, microvascular dysfunction, and QoL relative to men. However, older women remain consistently underrepresented in clinical trials. The specific problem is that very few therapies currently exist to improve functional performance and QoL in patients with HFpEF. For these reasons, HFpEF is recognized as the single greatest unmet need in cardiovascular medicine today. The objective of this proposal is to determine, for the first time, the benefits of home-based leg heat therapy (HT) on exercise tolerance and QoL in older women with HFpEF. This novel approach consists of custom engineered trousers, instrumented with a network of small flexible tubes connected to a portable water pump. Hot water is circulated through the tubes, evenly heating the buttocks, thighs and calves. The system is convenient for application in the home setting without supervision. These customized trousers were demonstrated to be safe, well-tolerated and to enhance exercise tolerance in elderly individuals with restricted mobility. In a preclinical model of HFpEF, we recently found that repeated HT enhanced skeletal muscle mass, microvascular function and treadmill running performance. Building upon our preliminary data, we propose to conduct a randomized, double-blind, sham-controlled clinical trial in 34 older women (≥60 years of age) with HFpEF to establish the effect of daily, home-based leg HT for 8 weeks on functional capacity and QoL. Patients randomized to the leg HT group (n=17) will be asked to apply the treatment daily for 90 min using water-circulating trousers perfused with water heated at 42°C. In the sham group (n=17), water at 33°C will be circulated through the trousers. The primary outcome is the change in exercise capacity during treadmill tests between baseline and the 8-week follow-up. Secondary outcomes include changes in perceived QoL and cardiovascular responses to exercise. In Aim#2, we will determine the tissue-level mechanisms by which HT affects muscle strength and exercise performance. We will assess skeletal muscle morphology (magnetic resonance imaging), leg strength (isokinetic dynamometry), microvascular oxygenation and blood flow (near-infrared spectroscopy, Fick equation), and mitochondrial respiration (31P-magnetic resonance spectroscopy). If the conclusions based on our preliminary data are substantiated, the proposed experiments will provide an evidence-based framework of feasibility and efficacy of a novel, straightforward approach to improve functional performance and QoL in patients with HFpEF. Given its accessibility, tolerability and ease of use, HT has the potential for rapid translation and application in the clinical setting, thereby opening new horizons for the non-invasive management of HFpEF.

NSF Awards · FY 2024 · 2024-09

The Miocene period (23 to 5 million years ago) had a climate much warmer than modern, with hotter temperatures, less ice at the poles, higher sea level, and greenhouse gas concentrations in the range likely over the next century. Models have had difficulty in reproducing these warm climates. Proxies for past climate are frequently used for comparing against climate models, but their interpretation may be complicated by poor understanding and representation of what environmental conditions they record. The project will evaluate the ability of climate models to predict past climate changes properly. This is important because these are the same models used to project future climate change. The project will be an international collaboration between a US modeling expert and a Swss geochemist expert. Broader impacts include support for a female early-career scientist and outreach activities in both Switzerland and the US. Climate models have difficulty simulating the weak meridional temperature gradients reconstructed from proxy records of past “greenhouse” climates. These models also normally fail to produce the right globally averaged temperatures when driven by CO2 concentrations reconstructed from proxies. The problem this poses is that: (a) either climate models are missing key physical processes that might be important for future climate prediction or (b) proxy interpretations incorporate deep, persistent biases. The goal of this study is to reconcile climate models and proxy interpretations for an extreme and well-characterized greenhouse climate in the Miocene period (23 to 5 million years ago). The project will use several methods: traditional paleoclimate proxy reconstruction and paleoclimate modeling methods as well as developing novel methods using proxy system models (PSMs). First, a PSM will be developed for coccolithophore-based CO2 and temperature (alkenone and clumped isotope) proxies, and then evaluated using preindustrial climate simulations and corresponding global proxy core top archives. Second, the PSM will be applied to the Miocene simulations. An initial and preliminary evaluation of whether the PSM reduced or improves model-data mismatch compared to traditional methods will be carried out. Third, new orbitally resolved records of phytoplankton isotope fractionation will be built to improve estimates of atmospheric CO2 levels. Additionally, new records of surface ocean temperature in high latitudes and tropical regions will be reconstructed using traditional and PSM methods to test if meridional temperature gradients were consistently flat in the Miocene. Fourth, a series of new Miocene simulations will be conducted using the Community Earth System Model Version 2 with estimated atmospheric CO2 levels, Antarctic ice sheet states and two eccentricity configurations. Finally, the simulated climates will be compared with the proxy results generated by the proxy system model for these Miocene climates. The effort will test the hypothesis that the apparent discrepancy between models and data in the Miocene (and perhaps more generally) is a matter of developing a better understanding of the paleoclimate proxies, not a failure of the climate models. If this hypothesis is invalidated the finger points directly at the climate models, which will be a more solid basis for future work. The results and methods developed in this work will be disseminated broadly and outreach efforts at the youth and K-12 levels will be made both by the Swiss and US teams. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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

Many engineering and technology sectors, such as construction engineering and manufacturing, operate within an inherently complex and uncertain environment with dynamic operation conditions and potentially non-routine problems. To guarantee operational excellence, it is crucial to equip the workforce in these industries with advanced collaborative problem-solving (CPS) skills to collaboratively navigate challenges in the multifaceted working environment and develop innovative solutions. In response to this urgent demand, this project intends to advance knowledge in both learning science and technology to study the effectiveness of emerging artificial intelligence (AI) and robotics technologies in supporting inclusive, scalable, and effective CPS skill development. To achieve this, this project will explore proof-of-concept innovative learning technologies to transform the conventional in-person field-based lab environments into Hybrid-Flexible (HyFlex) field-based lab environments accessible to extensive remote and residential student populations for CPS skill development. The innovation of this project will revolutionize the existing fieldwork education and foster networked on-campus HyFlex field laboratories. Such a system would be akin to a "library of field labs with HyFlex mode,” enabling learners to access existing field-based labs established at different universities remotely or physically for fostering critical competencies in engineering and technology including CPS skills. The success of this project will significantly contribute to the pressing need to promote diversity and inclusion and broaden participation in developing CPS skills. Additionally, this project will also significantly advance understanding of innovative-technology-enabled solutions that effectively promote diversity, equity, inclusion, and accessibility in STEM education. The goal of this project is to develop new knowledge on the affordances of emerging generative AI (GAI)-enabled robot-mediated pedagogical technology called LILMR (Learner-In-the-Loop Multi-Robot system) for supporting scalable engagements of remote learners in innovative HyFlex field-based lab environments, facilitating effective development of CPS skills within a shared authentic experiential learning space with residential students. To achieve this goal, it is essential to answer three research questions (RQs). RQ1: What technological affordances are essential for effectively supporting the development of CPS skills in a HyFlex field-based lab environment for both remote and residential learners? RQ2: What technological, pedagogical, and logistical supports are needed to foster an engaging HyFlex field-based lab environment and promote the development of CPS skills? RQ3: How can the impact of emerging learning technologies on supporting the development of CPS skills for both remote and residential students in HyFlex field-based lab environments be assessed? To address these three RQs, the research team will establish a multidisciplinary research capacity and focus on three cohesive project objectives within a hybrid content-pedagogical-technological development space: 1) exploring an innovative GAI-enabled robot-mediated pedagogical technology and developing a LILMR system for enabling an inclusive HyFlex field-based lab environment for supporting the development of CPS skills. The expected deliverables will include the initial identification of essential technological affordances, the initial design of the proposed LILMR system, and the associated technological, pedagogical, and logistical strategies; 2) interactively deploying and refining the proposed LILMR system in different HyFlex field-based lab contexts. The expected deliverables will include the formulation of a suitable assessment framework for evaluating the impact of the proposed LILMR system in supporting the development of CPS skills, the refinement of the core functionality components initially defined for the LILMR system, the understanding of the affordances of the LILMR system, and the optimization of the associated technological, pedagogical, and logistical support strategies; and 3) conducting pilot studies. The expected deliverables will include the performance validation of the proposed LILMR system and associated technological, pedagogical, and logistical support strategies in supporting the development of CPS skills in fieldwork environments. Furthermore, the project’s results will be disseminated via courses, workshops, conferences, outreach, and collaborations. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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 I-Corps project is the development of a secure and automated construction contract risk identification technology that combines and leverages the state-of-the-art natural language processing, deep learning, and rule-based techniques to precisely extract risk from construction contracts. About 70% of construction projects end up in claims and disputes due to an improper understanding of the project requirements and conditions specified in contract documents and specifications. These claims result in delays and budget overruns, with millions of dollars spent on conflict resolution. Traditional contract review and risk assessment require deep expertise, extensive time, and manual efforts. The technology used in this project aims to improve the contract review process by automating the extraction of risks from construction contracts. The technology has the potential to save 60% to 70% of the costs associated with the traditional contract review processes and potentially reduce project cost overruns by 10-15%. This innovation will help project teams and stakeholders promptly address risks before construction begins, directly contributing to project successes, boosting commercial competitiveness of the construction industry through data-driven decision-making, and seizing emerging opportunities. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an innovative technology that harnesses the power of artificial intelligence to improve the analysis of construction contracts. At the core of the technology is a sophisticated neural network architecture designed and trained with state-of-the-art semantic Natural Language Processing (NLP) techniques and Large Language Models to comprehend the intricate language. The approach also considers multiple project specific parameters like stakeholders, demographics, geometry, and geography of the project, as they pertain to the specific situations of the site. This additional context enables early prediction of the potential risks before the construction starts. An NLP–based Automated Building Code Compliance Checking Framework serves as the technology foundation of this project. Moreover, the integration of advanced deep neural architecture, machine learning, and rule-based techniques enables the identification of complex risk patterns and trends that may not be readily apparent through traditional methods while maintaining the trustworthiness of the results. This ability enhances foresight and enables proactive risk management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Quantum computing could be a disruptive technology in dealing with challenging computational tasks, such as integer factorization, searching in databases, solving systems of linear equations, and various machine learning tasks with polynomial or exponential speed-ups over their classical computing alternatives. However, these algorithms are assumed to operate on fault-tolerant quantum computers, which are projected not to be available soon. Recent research and development efforts focus on devising algorithms relevant to practical applications on the qubit-limited, low-circuit depth, and noisy quantum hardware currently available, referred to as Noisy Intermediate-Scale Quantum (NISQ). Variational quantum approaches (VQAs) exploit variational quantum circuits (VQCs) of limited depth and reduced number of qubits, and become the leading candidates to showcase quantum advantage in the NISQ era. VQAs are hybrid approaches as a VQC operates in tandem with a classical computer to solve an optimization problem. Most existing VQAs either consider optimization problems without constraints or handle limited types of constraints in an ad-hoc manner. This project pursues a systematic framework for dealing with constrained optimization using VQCs, and it thus advances discovery at the intersection of quantum computing and optimization on three fronts. First, the proposed algorithms could markedly expand the applicability of VQCs to cope with large-scale optimization with applications in optimal resource allocation, signal processing, machine learning, and quantum physical sciences. Second, the proposed optimization models may constitute indispensable ingredients of the optimization software packages ten years from now, when some sort of quantum processing unit may have been integrated into our computing machinery. Third, the proposed analyses may identify the engineering applications VQCs could have a more profound impact and usher the design of more refined architectures as quantum hardware matures. The expected benefits to the industry are cutting-edge algorithmic solutions that offer value added to the nascent yet possibly disrupting technology of VQCs. This project proposes a comprehensive hybrid quantum/classical computing framework for solving optimization problems with constraints over binary and/or large-scale continuous decision variables using VQCs. The framework is termed Variational Quantum Eigensolver with Constraints (VQEC). The specific project objectives are to: i) Devise novel iterative algorithms for optimizing the parameters of a VQC to deal with constrained problems, featuring enhanced convergence while complying with the oddities of VQCs; ii) Chart the types of optimization problems (binary programs, linear programs, quadratically constrained quadratic programs, semidefinite programs) that could be handled by VQEC and modify VQEC algorithms accordingly; and iii) Analyze the performance degradation when solving an optimization problem in its variational quantum form over the VQC parameters rather than its original form. 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 PI and graduate students will use computer simulations to understand how light interacts with many atoms. The light will simultaneously interact with many cold atoms in a gas when the atoms are closer together than the wavelength of the light. Having many atoms interact simultaneously with the light leads to new effects that are nothing like when light interacts with one atom at a time. Some of these effects are interesting in their own right and some are possibly useful for quantum information science. Because of the quantum nature of this system, interesting and/or important effects can be amplified well beyond what would be expected from classical objects. The team will investigate three different combinations of light and atoms that could lead to the most interesting or useful effects. This project is also an ideal training ground for theoretically minded graduate and undergraduate students. All students develop their own programs to explain different aspects of a possible experiment and present their results to other scientists and the general public. They perform all tasks of physics research, growing as scientists in the process. The team of PI and students will simulate the collective interaction of many atoms with light, emphasizing many body interactions that can affect quantum-based transmission and manipulation. When atoms in a gas are cold and separated by distances of order the wavelength of the photon (or smaller), the photon interacts with many atoms simultaneously leading to qualitatively new behavior compared to when the atoms are hot or are widely separated. These new effects can serve as interesting advances relevant for quantum information science (QIS). Also, this many-body, open system leads to a richness in the physics that can be different from when many atoms interact through conservative potentials. In one group of projects, the team will explore the effect where photons interacting with an array of atoms leads to momentum and energy transferred to the atoms’ center-of-mass motion. As one example, the team will study how the photon recoil affects the lifetime of strongly subradiant states proposed as QIS elements: the phases of excited states can be chosen so the rate of photon emission is suppressed but this necessarily leads to forces on the atoms which modify the phases. In another example, the team will study the possibility for using the collective interaction with photons to enhance the cooling of atom arrays; atom arrays have been proposed as the starting point for quantum simulators and as elements in quantum computers. A second group of projects will investigate the behavior of light in waveguides interacting with many quantum systems for both quantum simulator applications and possible interesting sources of light. As one example, the team will investigate the character of the light after interacting with several transmons attached to a 1D microwave waveguide where preliminary calculations indicate interesting photon correlations. Another example uses many transmons attached to a 1D waveguide as a quantum simulator of an open many-body system. A last example uses atom arrays to couple to a nano-ring resonator as possible elements for transporting photons or generating interesting photon correlations. A third group of projects explores the possibility for experimentally accessible quantum simulator of open, many-body systems. The projects will explore aspects of a many-body system that is both driven and open to collective decay. As an example, in a symmetrical case, the steady state of a strongly driven gas qualitatively changes under infinitesimal perturbations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary The majority of breast cancer related deaths occur as a result of metastasis. Once the cancer cells metastasize, the 5-year relative survival rate for breast cancer patients drastically drops. During metastasis, a complex series of events is initiated by changes in the extracellular matrix (ECM) composition and architecture in distant tissues, where the metastatic cancer cells take root and form secondary tumors. These distinct changes in the premetastatic niche (PMN) facilitate tumor cell colonization, phenotypic heterogeneity of the cell population, and contribute to drug resistance frequently observed in metastatic tumors. Using an engineered model of the PMN, we have demonstrated that extracellular vesicles (EVs) facilitate dynamic changes in premetastatic tissues, and that blocking key events during PMN formation disrupts the metastatic process. Our preliminary studies indicate that ECM proteins transglutaminase 2 (TG2) and fibronectin (FN) play a significant role in establishing the PMN through an EV-dependent mechanism. However, fundamental gaps remain in identifying critical events within the niche that facilitate metastatic colonization of tumor cells, cellular plasticity, and drug resistance. The overall goal of this proposal is to identify how the dynamic changes within the metastatic niche make the distant tissues hospitable to metastatic cancer cells. Aim 1 will use our novel model of the PMN to identify how specific changes in the PMN facilitate colonization by disseminating cells. Aim 2 is focused on isolating specific effects of ECM dynamics and cellular constituents within the PMN that impact phenotypic heterogeneity and immunogenicity, giving rise to growth permissive and grow restrictive environments. Aim 3 will identify mechanisms by which key matrix components within the niche protect metastatic tumor cells from therapeutic agents. Our cross-disciplinary team encompasses expertise in biomedical engineering, metastasis, imaging, and EV biology. The proposed studies will establish novel mechanisms by which the PMN facilitates breast cancer metastasis, an essential step towards successful treatments to disrupt the metastatic process.

NSF Awards · FY 2024 · 2024-09

Invasive pathogens are an increasing threat to forests worldwide, causing tree mortality and reducing ecosystem health. To better predict the impact of pathogens on forests, fundamental knowledge of how trees physiologically respond to stress caused by pathogens is critical. Just like animals store fat, trees store carbohydrates for later use. Carbohydrate stores are then used by trees to support growth, but they can also be used to build chemical compounds that provide defense against pathogens. The relationship between a tree’s storage of carbohydrates and investment in defensive compounds remains relatively unexplored, but this information is key to understanding a tree’s resistance to pathogens. This project integrates two aspects of a tree’s physiology—carbohydrates and chemical defenses—to identify how trees buffer against stress caused by pathogens. Results will provide insight into the ability of Hawaiʻi’s ʻōhi‘a trees to resist a novel fungal pathogen that is causing widespread mortality. ʻŌhi‘a is the most abundant native forest tree throughout Hawaiʻi and an integral part of the island’s ecology, economy, and culture. However, without a better understanding of ʻōhi‘a tree biology and its response to stress, the impacts of the fungal pathogen will remain unresolved and disease mitigation measures will be hindered. This research will aid the conservation and management of tropical forests. Drawing on a shared interest in improving research with native knowledge and increasing the recruitment and retention of minority groups in the sciences, the team will support an authentic independent undergraduate research experience for Native Hawaiian and Pacific Islander students. In Hawaiian forests, a novel destructive fungal pathogen, Rapid ʻŌhiʻa Death (ROD; Ceratocystis spp.), threatens the keystone tree species Metrosideros polymorpha Gaud. (‘ōhi‘a). To protect these native forests, there is an urgent need to identify resistant phenotypes and the mechanisms responsible for enhanced survival. To meet this challenge and improve the ability to predict resistance of ʻōhiʻa trees to ROD, the team will couple field and greenhouse-based experiments to quantify the relationship between nonstructural carbohydrates (NSCs, i.e., sugars and starch) and the physical and chemical defense response to infection. In Aim 1, the team will identify the seasonal minima and maxima of both NSC reserves and constitutive defensive chemistry in different tree organs of mature ‘ōhi‘a trees. In Aim 2, the team will artificially manipulate stem NSC reserves in mature ‘ōhi‘a trees to directly test the role of NSC reserve status in determining the induced defense response and its role in providing resistance to ROD infection. In Aim 3, a greenhouse experiment will be conducted using ‘ōhi‘a saplings with genotypes across different putative resistance categories to assess genotype × environment effects on NSC-defense responses to ROD. These synergistic aims will enhance knowledge of ʻōhi‘a tree biology with crucial applications for its conservation and management in the face of unprecedented biotic stress. The knowledge gained will fundamentally change the way biotically-driven tree mortality is modeled and impact a range of disciplines from plant pathology to silviculture. The project will also provide an undergraduate research experience for Native Hawaiian and Pacific Islander 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 2024 · 2024-09

Galaxies come in wide variety of shapes, from great star-forming spirals like our own Milky Way galaxy to the red-and-dead elliptical-type systems. For some time, astronomers have known that this diversity is largely driven by environment, with galaxies in dense regions evolving differently from those in low-density voids. To understand this diversity we must look ~10 billion years into the past and survey large swaths of the universe at “cosmic noon”, when the universe was only ~1/4 its present size, star-formation and black hole activity peaked, and the great cosmic structures we see today were just beginning to form. By characterizing the galaxies' 3D environments, the team will quantify the relationships between a galaxy’s physical properties, such as its elemental composition, gas and dust content, stellar mass, and star-formation rate, with its environment. The team will also participate in a series of local education and outreach activities in Pennsylvania and Indiana, with a particular focus on engaging students in grades 4-12 to spark their interest in science. This program is aimed at elucidating how galaxies, quasars, and gas trace the cosmic structures of the 2 < z < 4 universe, and likewise, how dense environments within the large-scale structure influence their evolution. To do this, the program will combine the data products of three large surveys: imaging from the One-hundred-square-degree DECam Imaging In Narrowbands (ODIN) survey, wide-field integral-field spectroscopy of the Hobby-Eberly Telescope Dark Energy eXperiment and targeted spectroscopy from the Dark Energy Spectroscopic Instrument of up to several hundred thousand Lya-emitting galaxies at cosmic noon, residing in a wide range of large-scale environments. This unprecedented dataset will (i) confirm ODIN-detected cosmic structures and reconstruct the 3D shape of tens of massive protoclusters; (ii) identify rare astrophysical sources such as Ly-alpha blobs and active galactic nuclei (including quasars), in order to investigate their locations within the large-scale structure; (iii) infer the mean physical properties of galaxies as a function of large-scale environment based on their photometric and spectroscopic measurements; and (iv) study how intergalactic gas and galaxies trace the underlying matter distribution and each other. 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

Recent research suggests that meaningfully integrating engineering design into K-12 science instruction leads to a deeper understanding of science and engineering content and practices among students. This project will examine middle school students' learning of earth and physical science concepts, their functional understanding of engineering design, and their development of STEM interest and agency as they engage in newly developed integrated STEM curriculum units that incorporate community-based service projects. The goal of this project is to develop cognitively grounded, research-based integrated STEM units that lead to robust learning of science and engineering content and practices while cultivating students' STEM interest and agency. In collaboration with middle school teachers and their students, researchers will iteratively co-design and refine two units in which students investigate how local environmental conditions, such as water quality, temperature variability, and land use patterns, affect both human and ecological health. The iterative development of the curriculum and instruction will draw upon resources from NASA's Earth System Observatory and leverage the expertise of local scientists and engineers to support students' learning. Middle school teachers and students from rural, suburban, and urban schools across multiple states will be involved in the project. With asset-based instructional features, the units will be adaptable to a wide variety of school contexts. The project involves a mixed-methods design, combining qualitative and quasi-experimental techniques for data collection and analysis. The team will develop measures of and analyze data on students' learning of science and engineering, as well as their development of STEM interest and agency. Data from each project year will be used to develop, implement, and revise the program. The project also includes professional development for middle school science teachers, focusing on enhancing pedagogical strategies and knowledge of science and engineering content and practices. This project is funded by the Discovery Research PreK-12 program (DRK-12), which seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers through the research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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

Hail is the most consistently damaging hazard of severe thunderstorms, producing losses in the U.S. alone exceeding $10 billion per year over the past 14 years with impacts on homeowners, business owners, aviation, agriculture, transportation, and renewable energy producers. The In-situ Collaborative Experiment for Collection of Hail In the Plains field campaign, or ICECHIP, will improve radar detection and monitoring of hail, provide critical ground-truth information for materials science, and improve the nation’s capabilities to predict hailstorms and their impacts. Current hail forecasting methods struggle to forecast more extreme hail events, and it is difficult to connect radar-observed storm characteristics with specific hail characteristics such as concentration or extreme sizes. Very few observations of hail characteristics other than maximum dimension are even available, nor knowledge of how those characteristics could change during a storm. ICECHIP, the first U.S. hail-focused field campaign in over 40 years, will use modern instrumentation and numerical modeling capacity to provide a long-awaited advancement in hail science. Mobile radars, unpiloted aerial systems, lofted drifters and probes, laser scanning technologies, high-resolution cameras, and more traditional field observations such as atmospheric conditions and surface hail size will all be used to obtain synchronized and comprehensive observations of hailstorms, the hailstones they produce, and the damage they cause. Researchers will deploy a fully mobile network for 6 weeks across the Front Range and Central Plains, gathering observations from a wide variety of hailstorms and hail types. This first-of-its-kind dataset will be instrumental in improving radar-based hail detection, hail models and forecasting, and resulting warnings through diverse collaboration among academic, government, private sector, and international partners. ICECHIP will promote educational efforts through training of 32 undergraduate and 20 graduate students across 10 U.S. universities. ICECHIP will address 5 major research themes, each corresponding to a current significant gap in hail science. In Theme 1, which focuses on hailstone growth and fall behavior, advances in digital photography will be used to explore little-observed microscale hail processes such as tumbling, melting, and shedding with the aim of reducing the uncertainty in microphysical parameterizations and hail growth models. ICECHIP’s comprehensive observations will be used to validate newly developed hail trajectory models and parameterizations in Theme 2, which concentrates on in-storm hail trajectory and convective updraft relationships. Those models will then be used to explore how thunderstorm updraft characteristics and evolution can modify in-storm hail trajectories and surface hail production. Environmental impacts on hail processes and predictability will be examined in Theme 3 by quantifying model hail forecasting skill, with a particular focus on environmental wind, moisture, and temperature profiles. In Theme 4 the surface properties of hailstones and associated impacts will be investigated by linking the internal and environmental storm characteristics to predictable variations in observed hailstone properties (size, shape, density, and strength) in both space and time within the swath and through new insights derived into the impact of wind on hail impacts. These properties will be linked through laboratory and model experiments to different damage modes of materials and structures. Finally, in Theme 5, which focuses on relationships between hailstone physical properties and growth processes to radar observations, radar-based detection of hail size and concentration will be better established through comprehensive ground truth validation that includes the natural variability of hail properties. Radar indicators of updraft width will be linked directly to increases in hailstone mass and damage potential at the surface. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Abstract Despite advances in automation, manual material handling (MMH) tasks continue to be commonplace in the workplace. To design and control the high burden of musculoskeletal disorders (MSDs) during MMH tasks, several assessment tools have been proposed to monitor injury risk factors such as posture, repetition and force. However, several fundamental limitations of current tools affect their effectiveness in controlling injury risk factors. First, tools currently used in practiced are typically cross-sectional and observer-based. These manually observed snapshots of the jobs are insufficient for capturing the complex exposures (e.g., varying frequencies, loads, hand-load couplings) that workers experience daily in MMH. Second, worker force exertion is a key causal factor for MS injuries, and unlike repetition and posture, force exertions are “invisible” such that even trained ergonomists have difficulty assessing without interfering with the workers or the hand-material interfaces. Finally, current tools are not practical at scale for capturing the individual exposures of every worker at every task (e.g., time and labor cost of trained analysts to directly observe or videotape the workers). This proposal aims to address the need for automated systems for predicting workers’ hand, wrist, and forearm injury risk in manual material handling (MMH) tasks. We propose innovative low cost, sensitive, and scalable triboelectric glove system with advanced deep-learning techniques. The proposed tool will be the first all- in-one glove system that can automatically assess all metrics required for the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Value (TLV) for Hand Activity Level (HAL). Three studies are proposed: 1) material engineering to produce reliable triboelectric sensors at scale, 2) iterative, user-centered design of multi-modal glove sensors for MMH, 2) experiments to train artificial intelligence models for predicting HAL TLV, and 3) demonstration of the technology in real work environments. The expected immediate outcomes of this work will result in a novel system that can impact two critical challenges in managing workplace hand/wrist MSDs: 1) scalable, automated assessment methods that can continuously monitor the high varied MMH tasks and 2) enable practitioners to assess the “invisible” force exertion risk factor that current relies on techniques such as force matching or manually weighing objects. This work aligns with NIOSH’s overall strategic goal of reducing occupational musculoskeletal disorders and have cross-sector impacts, specifically the immediate goal of reducing musculoskeletal disorders with emerging technologies.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Only about 20% of pancreatic ductal adenocarcinoma (PDAC) patients are expected to survive one year from diagnosis, with the overall 5-year survival rate reported at a devastatingly low 8%, which is the lowest 5-year survival rate of all major cancers. A hallmark of PDAC is the dense extracellular stroma called the desmoplasia, which can occupy up to 90% of the tumor volume. The desmoplasia acts as a physical barrier preventing drugs from reaching the tumor cells. Additionally, the desmoplasia acts as a barrier that prevents immune cells from infiltrating the tumor, limiting the efficacy of immunotherapies. Systemically administered stromal targeting therapies have provided a promising route for reducing desmoplasia and allow both cells and drugs to enter the tumor more readily. However, these agents have a wide range of off-target effects, which limits the dose that the patient can receive. The overall goal of this proposal is to advance a local controlled release platform for intratumoral delivery of hyaluronidase, allowing for locally elevated concentrations of the enzyme without systemic involvement. Aim 1 will focus on the use of a multi-scale modeling approach to define the parameter space of implant design needed to achieve a uniform distribution of hyaluronidase within excised human tumors. Aim 2 is focused on the use of medical imaging to characterize the effects of stromal targeting agents on tumor blood flow and drug accumulation. Aim 3 will characterize the effects that stromal targeting therapies have as immunomodulatory agents for improved immune surveillance and evaluate treatment efficacy. The proposed studies will establish a novel approach for targeting tumor desmoplasia, improving mass transport into the tumors, and enhancing immune cell infiltration into the tumors.

NSF Awards · FY 2024 · 2024-09

This award will establish a new framework for digital twin modeling of urban energy delivery strategies. This framework is grounded in logistics and platform economics and leverages both model-based and data-driven approaches, along with a living lab and industry collaboration. The project aims to address fundamental challenges in integrating electric mobility into urban infrastructure, ensuring efficient energy delivery while promoting sustainability and economic viability. The project's significance lies in its potential to enhance urban energy efficiency, reduce greenhouse gas emissions, and support the integration of renewable energy sources in a manner that inclusively serves all communities. By fostering collaboration between academia and industry, this research will contribute to the advancement of science and technology in urban energy systems. Additionally, it will support education by providing students with hands-on experience and exposure to interdisciplinary topics in energy and mobility with real-world data. The project also aims to promote diversity in STEM fields by engaging a broad range of students and researchers. The research consists of three thrusts designed to address the complexities of urban energy delivery through digital twin modeling. The first thrust will design a three-sided market modeling framework that captures the interactions between travelers, mobility providers, and energy providers. This framework will incorporate mechanisms from logistics and platform economics to understand and optimize the dynamics within electric mobility markets. The second thrust focuses on developing a scalable digital twin environment to support comprehensive analysis and decision-making for these ecosystems. This environment will integrate both model-based and data-driven approaches, enabling the simulation and evaluation of various strategies and their impacts on urban energy delivery. The third thrust aims to create an "energy delivery playbook" by identifying diverse strategies through industry collaboration and testing them in theoretical and living lab environments. This playbook will provide guidelines and best practices for implementing effective energy delivery solutions in urban settings. The research will establish fundamental bounds on the achievable performance of three-sided electric mobility markets and develop tools and algorithms with guaranteed performance metrics. By combining theoretical analysis with practical application, the project will offer robust solutions to enhance the efficiency and sustainability of urban energy systems. The integration of this research into education through a dedicated software platform will further extend its impact, offering students valuable experience and fostering the next generation of infrastructure experts. 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

Mathematical literacy and statistical reasoning are critical for making day-to-day decisions in a world that is quickly moving online and increasingly data driven. For example, many lifestyle choices such as smoking, eating meat, or using a seatbelt while driving require individuals to think and reason about the probability of different outcomes rather than simple cause and effect. Unfortunately, a large percentage of students have misconceptions about probability and statistics that can lead to incorrect reasoning. More concerning is that these inaccurate ideas often persist even after taking courses in statistics. One promising line of research demonstrates that mathematics instruction that utilizes visual representations, authentic problem-solving, and physical manipulatives can help address these concerns when these components are connected through instructor and student gestures. However, less is known about how gestural strategies apply to statistics education, and even less is known about how to apply these strategies to online and remote learning environments. Therefore, this project aims to conduct a large-scale study using a diverse sample of high school and undergraduate students to examine how prompting learners to gesture when learning statistics content in video-based learning environments may improve their understanding of statistical concepts and procedural knowledge. The main objective of this project is to evaluate an approach to designing video learning environments (VLEs) for teaching statistics concepts that use gestures during instruction and cue learners to perform gestures. Using a mixed methods approach, researchers will employ design-based methods to create VLEs that incorporate instructional gestures, cue students to perform these gestures, and monitor and provide feedback about student gestures during statistics instruction using webcams that are standard on most computers. Researchers will also utilize randomized control trials to assess the impact of VLE design quantitatively on conceptual understanding of statistics concepts, procedural knowledge about how to solve statistics problems, transfer of understanding to future learning, and the impact on self-regulated learning. Case-study methods will examine which features of gesture effectively scaffold student learning and enhance transfer during online instruction. The project will contribute to our understanding of embodied learning, specifically the impact of metaphorical gesture on self-regulated learning and conceptual understanding of foundational math concepts. It will also contribute to an emerging body of literature situated at the junction between gesture and video-based, online learning. Researchers will make the resulting VLEs freely available to educators and researchers on the project’s website, where they will also post a design guide highlighting the gestures and other pedagogical features that were successful as well as those that were not. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This Level II project focusing on STEM Learning and Learning Environments is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce 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.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT International Lowe Syndrome Symposium; Aguilar (PI) The International Lowe Syndrome Symposium, scheduled for November 2024 at Purdue University, aims to transform the landscape of Lowe Syndrome (LS) research and care. This rare genetic disorder, marked by early mortality and a lack of effective treatments, demands urgent attention and collaborative efforts. The symposium will unite an international assembly of scientists, clinicians, patients, and families, fostering an inclusive and multidisciplinary approach to understanding and treating LS. Our primary goals are to advance scientific research, enhance clinical practices, promote diversity and inclusivity in medical research, and empower junior investigators in the field of LS. A focus on diversity, equity, and inclusion ensures broad representation and accessibility, enhancing the symposium's impact across various communities. The Indiana Clinical and Translational Sciences Institute (Indiana CTSI) infrastructure will be used as a vehicle to engage a regional and national network, in order to attract a broad range of participants to the symposium. A central initiative is the "Bring a Junior Colleague" program, supported by R13 funding, which is designed to engage and develop emerging scientists and clinicians. This includes a “Poster Competition” to encourage innovative research and provide young investigators with a platform for visibility and professional growth. The 2-day meeting agenda also includes current updates from key investigators, clinicians and patient families through talks and panel discussions. The symposium's outcomes are expected to be far-reaching: sharing cutting-edge research, establishing strong networks for ongoing collaboration, raising public awareness, and identifying new translational opportunities. Ultimately, the symposium seeks to improve clinical outcomes and enhance the quality of life for those affected by Lowe Syndrome. In summary, the International Lowe Syndrome Symposium stands as a crucial convergence point for accelerating progress in LS research and treatment, advocating for patients and families, and cultivating the next generation of dedicated researchers.

NSF Awards · FY 2024 · 2024-09

Data labeling, the process of assigning labels or annotations to data points, is crucial in supervised machine learning for training models to make accurate predictions in various applications. Labels refer to the output variables that the machine learning model aims to predict or classify. For instance, in genetic and genomic studies, labels may refer to traits or the presence of diseases, and accurate data labeling is essential for training models to understand the relationships between genetic information and various traits or diseases. The labeling process is resource-intensive, requiring domain expertise, advanced experimentation techniques, and rigorous quality control to ensure accuracy. Consequently, labeling all data points in a large dataset is often impractical due to resource limitations. Therefore, selecting an informative subsample from the large pool of data points to label becomes a critical and challenging problem. This project aims to develop subsampling methods for labeling large and high-dimensional datasets. The anticipated results will be applicable to genetics, biology, and medicine. Graduate and undergraduate students will be involved in the project and exposed to these results, which will also be incorporated into university courses. The project aims to develop advanced subsampling techniques for data labeling, particularly for large and high-dimensional datasets. Optimal subsampling approaches for both continuous and binary labels will be developed to enhance the predictive performance of models trained on the labeled subsample. Additionally, sequential sampling plans will be investigated. The project also emphasizes fairness in the trained models, striving to develop subsampling methods that ensure a balanced representation of multiple demographic groups in the labeled data, achieving consistent accuracy across all demographic groups. This project will contribute substantially to advancing subsampling methodologies in statistics and data 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 · 2024-09

Maize breeders have significantly increased crop yields by optimizing plants for higher planting densities. Further improvements in crop productivity per unit of land can be achieved by modifying the structure of individual plants and their arrangement in fields. Our current technologies allow for scanning large quantities of plants, but the acquired data is often underused. The goal of this project is to use the scanned data to create a virtual model of maize (its digital twin), which will capture the maize geometry, reflectivity, and function. The digital twin will be able to simulate real plant growth and response to the environment by careful verification against the measured data. The digital twin will be used in hypothetical scenarios of changing climatic conditions to answer "what-if" questions, providing answers for better plant architecture and planting distributions. By using AI and automatic optimization, this project will attempt to identify genetic markers and candidate genes governing variation in the same traits, enabling efforts to breed or engineer plants with optimal canopy architectures. This innovative approach will advance our understanding of plant biology and contribute to meeting global food demands. This project takes an important step towards in silico optimization of maize canopy architecture. We propose to develop innovative data processing and advanced visualization tools to generate fundamental knowledge applicable to agriculture to advance food needs. Our tools will reconstruct maize into its digital twins (plant ideotypes), simulate configurations of individual plants and plant populations differing in leaf canopy-related traits, and evaluate how plant traits perform in varying environments. We will use the vast amount of gathered data from phenotyping facilities and gantry to reconstruct 3D plants into their simulation-ready digital twins, fine-tune computer simulations to visualize and optimize the plant structure and function and identify optimal canopy architectures for given sets of conditions. This work will be combined with genome-wide association study for leaf canopy architecture traits derived from 3D reconstructions of real populations to identify markers and candidate genes, enabling efforts to breed or engineer plants producing optimal canopy architectures. The results of this work will strongly impact agronomic and plant genetic research in both the public and private sectors. There is a critical need for models to predict how plant varieties will respond to different environments. The 3D interactive application will allow experimenting with complex situations at interactive frame rates on a standard desktop computer, something never achieved before. It will be connected to existing data pipelines that provide vast amounts of (often unused) data. We will develop a set of novel algorithms that reconstruct 3D maize plant shapes and functions from input data from varying sources (RGB, depth, point clouds). The developed system will also generate synthetic data suitable for AI training (labeled sets of plants and 3D geometries with proper lighting). The project will partner with The National Data Mine Network, an NSF-funded initiative and the Computer Science department at Purdue University to engage and recruit students in phenotyping, data analysis, algorithmic design, and 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.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT This project's primary endpoint focus will be efficiently rescuing toxic cyanide exposure using three distinct animal models. None of the current countermeasure options meet the requirements for the risk scenarios and objectives in PAR 22-209. There are continual threats of chemical exposure to human populations. Health and safety measures in the workplace cannot fully mitigate the chances of unexpected or malicious use of chemical toxicants. Safe and effective countermeasures to a cyanide chemical exposures that can deploy in a variety of challenging emergency settings represent a continual unmet medical need. In addition to survival, mitigating the sequel morbidities of toxic chemical exposures means another challenge for next-generation countermeasures. For metabolic poisons that reach high levels of exposure like cyanide, approaches to reverse the insult's effects are warranted to mitigate more prolonged-term effects. A team of investigators with experience in discovering and developing countermeasures and therapeutic agents and expertise in medicinal chemistry, pharmacology, toxicology, discovery pharmaceutics, in vivo phenotypic screens, and complimentary animal models will pursue milestones established from 3 specific aims. Aim 1: To improve and down-select next-generation platinum (II)- based countermeasure agents hydrogen cyanide scavengers with properties that meet the criteria for developability. Aim 2: To evaluate combinations of the metabolite glyoxylate with platinum (II)-based scavenger agents. Aim 3: To evaluate the efficacy and safety in a porcine model for cyanide intoxication of lead candidate platinum (II)-based agents alone and in combinations with the metabolite glyoxylate.

NSF Awards · FY 2024 · 2024-08

This research project aims to advance quantum technology by exploring the potential of Rydberg excitons—unique, large artificial atoms within semiconductors that exhibit strong interactions with light. These Rydberg excitons could lead to significant advances in quantum computing and secure communications by enabling new ways to manipulate and entangle light particles. The primary activity involves developing new theoretical models to understand and control how Rydberg excitons interact with their surroundings, particularly with crystal impurities. This work will not only deepen our knowledge of these interactions but also guide future experiments and technological applications. Additionally, the project will contribute to the training of graduate students and postdoctoral researchers in cutting-edge quantum mechanics and semiconductor physics, fostering the next generation of scientists in this exciting field. Furthermore, the project includes outreach activities such as developing an educational computer game to help students and the public learn about quantum phenomena in a fun and engaging way. This project addresses a critical gap in the theoretical understanding of Rydberg exciton polaritons, which are promising candidates for scalable quantum systems due to their strong light-matter interactions and controllable exciton-exciton interactions. The research will develop a coupled-channel scattering theory to elucidate the interactions between Rydberg excitons and crystal impurities, quantify polariton-polariton interaction strengths, and calculate polariton scattering in structured photonic environments. These theoretical advancements will provide a comprehensive framework to interpret experimental results and explore the potential of Rydberg exciton polaritons in optical quantum technologies. By establishing scattering theory as the appropriate method for these systems, the project will address significant loss mechanisms due to charged defects in high-purity semiconductors and reveal the long-range nature of Rydberg polariton interactions. The findings will also demonstrate how photonic band engineering can control these interactions, advancing the field of quantum optics and the development of solid-state quantum information processing platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project aims to serve the national interest by enhancing conceptual learning in quantum and semiconductor physics using interactive visualizations and simulations. This Engaged Student Learning, Level 3 project will conduct the first large-scale investigation into the design of educational tools and design features that can significantly improve conceptual understanding in quantum and semiconductor physics in particular, and STEM in general. The project will develop more inclusive tools for a diversity of use cases and assess their utility and effectiveness across different educational environments. While interactive visualization and simulation tools have made progress in mitigating certain student misconceptions, there is a lack of comprehensive studies examining the design efficacy of these tools. Existing research in this area is constrained by both limited size and scope. This project will expand and refine the interactive visualizations and simulation tools previously developed by the team with a focus on aiding the conceptual understanding of quantum mechanics and semiconductor principles among undergraduate students. Additionally, the project will carry out extensive studies on the effectiveness of these tools across diverse educational environments. As a consortium of researchers spanning five diverse universities and colleges, the team will investigate the following research questions: 1) To what extent can such tools change undergraduate students' conceptions of Quantum Mechanics and Semiconductor Physics and 2) How does the design of such tools affect students’ conceptions of these topics? The team will conduct two large-scale mixed-methods controlled studies using a concurrent triangulation design. Both quantitative and qualitative data will be collected and analyzed. Analysis methods include multilevel modeling, repeated measures multivariate analysis of covariance, and qualitative content analysis. 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.

NIH Research Projects · FY 2025 · 2024-08

Phospholipase C b (PLCb) enzymes increase intracellular calcium in response to diverse extracellular signals, regulating numerous processes including cell proliferation and survival. Dysregulation of their expression or activity contributes to pathophysiological conditions such as heart disease, cancer, and addiction. All four PLCb isoforms bind to the cytoplasmic leaflet of the plasma membrane where they hydrolyze phosphatidylinositol-4,5- bisphosphate (PIP2). They are activated via direct interactions with the heterotrimeric G proteins Gaq, and in most cases, by Gbg. All PLCb isozymes have two elements that profoundly autoinhibit PIP2 hydrolysis: a lid that blocks access to the active site, and a helix in the proximal C-terminal domain that binds to the catalytic core. G protein binding and recruitment of the lipase to a membrane surface has been proposed to displace all the autoinhibitory elements, resulting in activation. However, recent cryo-electron microscopy (cryo-EM) structures of G protein–PLCb3 complexes bound to model membranes reveal that this is model is insufficient. In these structures, the catalytic core fails to engage the membrane and the active site remains blocked. Thus, the mechanism of activation and the molecular basis of isoform-specific responses to G proteins remain unresolved. Furthermore, G proteins may also increase PLCb activity by altering their behavior on the membrane surface, an aspect that cannot be assessed via structures or cell-based assays alone. To address these gaps, we propose a synergistic and interdisciplinary combination of functional and structural studies, cell-based assays, and single molecule microscopy to investigate the structure and regulation of the four PLCb isoforms. In Aim 1, we use functional analyses to test hypotheses derived from new structures of PLCb complexes. We will also determine cryo-EM reconstructions of the four PLCb isoforms in solution, and a subset in complex with G proteins on model membranes known to promote activity. Aim 2 utilizes bioluminescence resonance energy transfer (BRET) and signaling assays to monitor the location and disposition of PLCb isoforms within a cell and measure the kinetics of G protein-dependent activation in response to receptor stimulation. In Aim 3, single molecule total internal reflection fluorescence (TIRF) microscopy will be used to dissect the contributions of the PLCb regulatory domains, PIP2, and G proteins to the kinetic behavior of individual lipase molecules on model membranes. Strong preliminary data is included in support of each aim. Our work will not only reveal new mechanistic insights in PLCb regulation but allow us to identify regulatory features unique to each isozyme that could be targeted by novel selective chemical probes.

NSF Awards · FY 2024 · 2024-08

The Purdue Site of the Center for Bioanalytic Metrology (CBM) works in collaboration with partner Sites at Indiana University-Bloomington and Notre Dame to address two objectives: (1) to deliver best-in-class analytic metrology tools and expertise enabling the development of powerful new precompetitive technologies across the pharmaceutical, biotechnology, food/nutrition/agriculture, and energy sectors; and (2) to test applications of new instrumentation to cutting-edge chemical and biochemical problems. These objectives contribute to the national welfare by supporting the development of advanced industrial technologies across all four sectors. In addition, CBM provides compelling opportunities to invigorate human resources through access to hires of Center-trained students and opportunities for continuing education of existing staff. CBM operates under a unified site model, in which projects, independent of location, are associated with any member company with a significant interest in them. In addition, CBM operates by identifying the most timely and important problems of interest to its members and devising projects to address them. This approach is accomplished in a yearly cycle that starts with member companies identifying their most pressing Gaps, Needs, and Opportunities (GNO). These are used to solicit proposals, and the most timely and responsive proposals are selected for funding. CBM’s research groups projects into five thematic areas - (1) overcoming performance limits, (2) point-of-use technologies, (3) ML/AI data science & automation, (4) chemical imaging, and (5) enabling research technologies. The grouping of project themes recognizes a natural organization of the research carried out within CBM that reflects the strengths of the individual sites, but each theme contains projects of interest to members in all four industry sectors. The Purdue Site has representation across all five project themes, with strong IAB support in Chemical Imaging. CBM research provides longer-term, larger-scale, and more cost-effective solutions to pre-competitive industry measurement science problems than those that can be achieved in-house or through contract research organizations. In addition, CBM provides industry members with compelling opportunities to invigorate human resources through access to hires of Center-trained students and opportunities for continuing education of existing staff. Broader impacts of the CBM include: increasing US economic competitiveness, increasing the number of partnerships between academia and industry, and contributing directly to the development of a diverse, globally competitive STEM workforce. This award is co-funded by the following Programs: Industry University Cooperative Research Centers Program in the Division of Engineering Education and Centers - in the Directorate for Engineering, and the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry, Directorate for Mathematical and Physical 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.