Purdue University

NSF Awards · FY 2024 · 2024-08

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Dick’s research group at Purdue University is developing new measurement tools to understand how chemistry changes in small volumes. These tools are essential to understand fundamental concepts for energy storage and conversion, biosensing, biochemical reactions, aerosol chemistry, and have ramifications to understanding the origins of life. Small droplets, such as those found in clouds, sea spray, and even tiny vesicles within cells, permeate nature. For centuries, chemists have assumed that chemistry occurring in large volumes like that of a coffee cup can be extrapolated to chemistry occurring within vesicles inside cells. The new measurement methods developed in this project will allow insight into curious chemistry and reaction acceleration in microdroplets. A particular emphasis will be placed on understanding the importance of the nature of the interface, be it microdroplets suspended in an immiscible liquid (emulsions) or microdroplets in gas (aerosols). Graduate, undergraduate, and high school students will be introduced to measurement techniques through a historical perspective by learning how to build their own instruments to corroborate (or refute) centuries-old observations. The instruments developed will be donated to local schools, and resources will be made available to include frontier measurement science in middle and high school curricula. Most studies of curious chemistry and reaction acceleration in microdroplets have dealt with microdroplets surrounded by gas. Electrochemistry is rather difficult to perform in gas, and this project develops new measurement methods to probe chemistry within single liquid droplets, where the microdroplet|gas interface is dominant. The project uses stochastic electrochemistry to probe reactions in single, sub-femtoliter droplets, and the electrochemical signal reports on the rate of the reactions occurring within the droplets. Stochastic electrochemistry offers high temporal resolution to ensure microdroplets can be probed on a droplet-by-droplet basis. Given that coulometry can be used to size individual droplets, this project offers a direct pathway to studying how reaction rates change as a function of droplet size with various interfaces and chemical reactions of interest. The project will enable detailed insight into the role the microdroplet interface plays in reaction acceleration, the spontaneous generation of reactive species, and the mineralization of hard materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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 mathematical study of turbulent flows requires models that can account for a large range of spatiotemporal scales, from small scale eddies to large scale coherent structures, the nonlinear interactions that are responsible for the transfer of energy across those scales, and statistical tools to account for the uncertainty and limited number of measurements available. Examples can be found in the study of atmospheric and oceanic flows, controlled plasma fusion, and other engineering applications. Due to their nonlinear coupling across a wide range of spatiotemporal scales, a rigorous analysis of these systems often becomes intractable and direct numerical simulations are likely to be expensive and inaccurate. The focus of this project is to develop a mathematical framework to derive tractable reduced-order models that effectively capture the dynamics of complex turbulent systems and apply them to complex systems of practical interest. This unified mathematical framework is based on the systematic integration of approaches from data assimilation, uncertainty quantification, and optimal control. The project will also provide training and research opportunities for undergraduate and graduate students. This project will develop a general framework for the formulation of self-consistent reduced-order closure models for turbulent flows, with theoretical justifications and application to relevant fluid flow systems. The unified reduced-order model is achieved through a precise decomposition of the state of the system into low-order statistical moments that characterize the dominant, leading-order coherent structures, coupled with the stochastic fluctuations modes accounting for the higher-order non-Gaussian statistics quantifying the multiscale feedback. The reduced-order model will form the basis for new multiscale data assimilation strategies with partial and noisy data. In addition, the reduced-order model will be used to formulate new mean-field control models to drive the fluid system to a desired coherent state. The resulting methods will be applied to several concrete models for geophysical flows and plasma physics. 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

Cybersecurity threats are the most significant challenge to information technology infrastructure in the digital age. The ransomware attack on Colonial Pipeline in Spring 2021 is just one example that highlights the urgent need for a well-trained cybersecurity workforce. The CyberCorps® Scholarship for Service (SFS) project at Indiana University-Purdue University Indianapolis (IUPUI) will provide diverse students with an educational experience that combines an interdisciplinary academic curriculum with real-world cybersecurity research and internship experiences. In addition, the project will promote diversity in the cybersecurity workforce by leveraging a variety of ongoing initiatives and interest groups at the university, and by partnering with a local community college in recruitment efforts targeting groups who remain underrepresented in the cybersecurity field. The combination of vital academic preparation with applied cybersecurity engineering experience will produce next-generation cybersecurity engineers uniquely situated to strengthen the U.S. government workforce's ability to respond to national security needs and protect its critical infrastructure. Over the past two years, IUPUI has built two new innovative degree programs based on existing strengths in computer and information technology and engineering, leading to a B.S. in Cybersecurity and a M.S. in Cybersecurity and Trusted Systems. Both programs are the first in the Indianapolis metropolitan region. The SFS project at IUPUI will provide 22 diverse students pursuing the B.S., M.S., or an accelerated B.S./M.S. program with access to a broad curriculum in cybersecurity, synergistic co-curricular activities, cohort-based mentorship, and internship experiences. The program will engage in active recruitment and outreach that promotes diversity through an array of ongoing initiatives at IUPUI, including: 1) an NSF REU Site on Enhancing Undergraduate Experiences in Mobile Cloud and Data Security; 2) an NSF S-STEM Urban STEM Collaboratory project; 3) an NSF Broadening Participation in Computing (BPC) initiative called STARS Computing Corps; 4) other existing cybersecurity research and education projects; 5) a collaboration with Purdue University’s Center for Education and Research in Information Assurance and Security (CERIAS) and Indiana University’s Center for Applied Cybersecurity Research (CACR); and 6) an existing partnerships with Ivy Tech Community College of Indiana. The program will also collaborate with IUPUI’s Office of the Vice-Chancellor for Diversity, Equity, and Inclusion; the Black Student Union, the IU-Minority Serving Institutions STEM Initiative; and the Center of Excellence for Women in Technology. This project is supported by the CyberCorps® Scholarship for Service (SFS) program, which funds proposals establishing or continuing scholarship programs in cybersecurity and aligns with the U.S. National Cyber Strategy to develop a superior cybersecurity workforce. Following graduation, scholarship recipients are required to work in cybersecurity for a federal, state, local, or tribal Government organization for the same duration as their scholarship support. 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

Open-source releases of AI Image Generators have dramatically lowered the entry barrier to generating image content. Using commercial-grade hardware, anyone can generate realistic and aesthetically pleasing images with a few lines of code, or even no code. With this technological advancement, there is also an increasing responsibility to ensure that the technology does more good than harm to society. For example, AI image generators could be used to copy artists' work without their permission or create fake celebrity images. This project aims to develop techniques that prevent these harmful use cases, promoting a safer digital environment and mitigating AI image generator misuse. Additionally, the project will educate the public about the risks associated with AI image generators and raise awareness about privacy and the potential dangers of sharing their photograph data. To achieve these goals, the project categorizes harmful use cases into unintentionally and intentionally developed harmful capabilities. To address unintentionally harmful capabilities, the project develops methods to remove content from a trained AI generator (Thrust 1). Additionally, to prevent intentionally developed harmful capabilities, the project will develop techniques to "immunize" the AI generators (Thrust 2). This means it would be more challenging for a trained model to be fine-tuned on harmful concepts, e.g., formulated as a bi-level optimization framework. Finally, the project aims to efficiently solve the formulated optimization problems in Thrust 1 and Thrust 2. This includes studying the techniques and models that are well-suited to distributed modes of computation, as well as leveraging underlying structural properties, such as sparsity, to achieve further performance gains. 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

As raising the energy of particle accelerators for new physics becomes increasingly challenging, precision measurements emerge as a promising alternative to address fundamental physics questions. This research project aims to develop an extraordinarily sensitive spin-optomechanical system for studying fundamental questions in physics, such as the limits of quantum mechanics and the nature of quantum gravity. The research team will levitate rapidly rotating nanodiamonds with nitrogen-vacancy (NV) centers in high vacuum, and use them to investigate coherent dynamics and geometric phases, which are accumulated during rotation along a trajectory. This may potentially lead to the development of a nanodiamond matter-wave interferometer. This breakthrough could significantly advance precision measurements and topological physics. The project also includes a strong commitment to education and outreach, involving collaboration with industry in practical applications of quantum sensing, and integrating findings into academic curricula for training undergraduate and graduate students. Additionally, the PI and graduate students will engage the public through outreach events such as the "Quantum Open House" at Purdue University. In this project, the research team will directly load and trap nanodiamonds in high vacuum using an integrated surface ion trap, and explore quantum spin-optomechanical interactions between the motion of a levitated nanodiamond and its internal NV electron spin qubits for creating a nanodiamond matter-wave interferometer. The research team will systematically investigate the coherent dynamics and geometric phases of electron spin qubits in rapidly rotating nanodiamonds and using single electron spins to control the motion of these levitated nanodiamonds. Additionally, the research team will enhance spin coherence through rapid rotation and dynamical decoupling, which will contribute to the development of a nanodiamond matter-wave interferometer. This research also promises significant advancements in quantum sensing technologies using spin defects for potential practical applications in various 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.

NSF Awards · FY 2024 · 2024-08

This project assesses information sharing rules and practices in four International Environmental Agreements (IEAs) that govern global biodiversity conservation and sustainable use. Information sharing is critical to building trust and cooperative action among diverse actors and IEA member states in order to reach jointly agreed-upon global conservation objectives. In practice, however, IEA member countries follow a range of strategies for the selective sharing of information in order to promote different economic, social, or political objectives. At the same time, national funding agencies, non-governmental conservation organizations, and businesses are investing heavily in emerging technologies for monitoring and sharing data about global biodiversity conditions. Nonetheless, little is known about the current design and effectiveness of IEA information sharing platforms, or how IEA parties interact with global scientific data infrastructures in the context of meeting treaty obligations. In response, the project will advance knowledge of the complex landscape of global information sharing in conservation by examining the formal IEA information sharing rules and how they are mediated and operationalized through digital infrastructures by a variety of actors, including IEA Secretariats, government representatives, researchers, and conservation organizations. It will also map the data and decision-making linkages and gaps within and across the IEA information sharing platforms. Project findings will provide a systematic and holistic understanding of information sharing’s role in environmental governance and inform improvement and innovation in biodiversity resource management. The project uses a mixed-method approach to analyze the degree to which conservation-related data are exchanged on IEA platforms, how the level of exchange differs within and across IEA regimes, and how information sharing has been codified formally and in practice. This is accomplished by (i) using a standardized syntax called the Institutional Grammar to parse formal rules governing information sharing practices into core components and identify their rule type configurations by function (e.g., monitoring); (ii) examining the IEA platforms’ technical architectures and contents through a combination of IT staff interviews, data analytics, and database structure review; and (iii) interviewing key international and national decisionmakers to gain insights on information sharing perceptions and practices. The qualitative and quantitative data gained in steps (i) to (iii) will inform a Structural Equation Model designed to identify factors salient to the variation in actors’ information sharing propensities. IEA platforms offer a major opportunity to investigate long-standing assumptions about the importance of information sharing to effective resource governance. This project taps into that potential to investigate how the social and technical designs of IEA data infrastructures influence trust and transparency. Project results will include descriptive analyses of similarities and differences in the configuration of formal IEA information sharing rules, insights into IEA platform design, interconnectivity, and management of shared information, and the propensity of actors to engage with the rules and platform infrastructure and effectively share information. These results are also significant for recognizing and addressing equity and justice issues, e.g. for marginalized peoples and lower income nations. 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 overarching goal of this CAREER award is to understand how the chromatin motion in human cell nucleus responds to external mechanical stimuli. Chromatin is the mixture of DNA and proteins that form the chromosomes in the cells of humans and other organisms. The nanoscale movement of chromatin may modulate the interaction of DNA in the cell with regulatory molecules, which impacts many fundamental cell functions, such as DNA replication, DNA repair, transcription and, finally, gene expression. Understanding the mechanical regulation of the chromatin dynamics has the potential to power the engineering of living systems and to create new transformative strategies for the treatment of disease. This project will in particularly focus on the long-lasting question in nuclear mechanotransduction, i.e., if the change of chromatin motion in the nucleus is directly driven by the pico-Newton forces transmitted through the cell or is indirectly dominated by the diffusion throughout the cytoplasm. The preliminary data clearly concludes that both intracellular tensions and chromatin dynamics are mechanosensitive and are possibly connected. This project will design and develop a new imaging platform that can simultaneously quantify the intracellular tension and chromatin motion under mechanical stimuli. In addition, this project will develop an experiment to use a magnetic bead to mechanically disturb the live cell with high sensitivity and resolution. With this, it is expected to observe the spatiotemporal changes of the intracellular tension and chromatin motion in real-time. This project focuses on the dynamic signature of the chromatin, with an exquisitely sensitive imaging system and broadly applicable analytic tools for quantitative fluorescent imaging. In addition, this project will also develop a consortium that integrates high performance computational tools and cyberinfrastructure into biological research. This project will serve as a vehicle to increase the awareness of educational opportunities and career paths in computational biology and biophysics for high school students and undergraduates in related disciplines. An annual and free summer camp of bioimage informatics, which is part of the ACS SEED project, will be offered to high school students from economically disadvantaged families. The PI will integrate computational components into this project with the undergraduate teaching. Finally, an undergraduate research internship is developed on the IU-MSI initiative, which aims to attract underrepresented students from minority serving institutes, by providing short-term internship opportunities for the project. 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

With funding from the Chemical Synthesis Program in the Division of Chemistry, Professor Tong Ren and his team at Purdue University will study complexes of interesting photo-activity based on earth abundant metals. Chemists have made great strides in molecular photovoltaics using so called dye-sensitized solar cells, and in photoredox catalysis for more efficient production of medicines. Currently, both technologies rely heavily on rare precious metals such as Ru and Ir. Due to rarity and expense of these metals, the development of molecules with similar photon-capturing capability but based on earth abundant metals such as Fe and Co is highly desirable. However, most compounds based on earth abundant metals lose the captured solar energy (so called excited state) before converting it into electricity or using it to promote a reaction. Professor Ren and his team will extend the lifetimes of excited states by using a new family of macrocycles to support Fe and Co containing molecules, and hence improve the efficiency of solar energy utilization. Professor Ren and his team will work with the Purdue College of Science K-12 Outreach team, especially in introducing new teaching modules that reflect the heightened interest in sustainable chemistry. The PI and his coworkers will also play a proactive role in the Superheroes of Science, a program of Purdue College of Science K-12 Outreach, which develops a variety of science content for educators, including virtual labs for high school students. Organometallic chemistry of metal tetra-aza macrocycle complexes is a vibrant and evolving field. This project will seek to elucidate the impact of the tetraimine macrocycle TIM (2,3,9,10-tetramethyl-1,4,8,11-tetraazacyclotetradeca-1,3,8,10-tetraene) on the electronic structures, photophysical and photochemical properties of its 3d metal organometallic complexes. Fe(II)-TIM complexes will be synthesized and optimized as new metal-to-ligand charge transfer chromophores. The related Fe(III)-TIM alkynyl / aryl / alkyl complexes will be developed as ligand-to-metal charge transfer chromophores. As analogues of vitamin B12, Co(III) species containing Co-C(sp2) and Co-C(Sp3) bonds, such as [CoIII(TIM)RL]+ and [CoIII(TIM)(R)(R')]+, will be investigated with a focus on photo-induced Co-C cleavage reactions therein. While the primary focus of the project is the synthesis of novel Fe/Co(TIM) organometallics, the nature of excited states will be explored using both experimental (fs transient absorption and M edge XANES) and computational (CASSCF/CASPT2) approaches through collaborations with the groups of Vura-Weis at University of Illinois Urbana-Champaign and Vlaisavljevich at South Dakota (now University of Iowa), respectively. 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 award will support students from institutions of higher learning in the United States to participate to the 63rd IEEE Conference on Decision and Control (CDC), to be held in Milan, Italy, December 16-19, 2024, along with pre-conference workshops to take place on December 15, 2024. The CDC has been for over fifty years the world’s leading annual forum for scientific and engineering researchers who share an interest in systems and control theory, and in the foundations of automation technology. As in the past, the 63rd IEEE CDC will feature an extensive program of contributed and invited papers, tutorial sessions, as well as plenary and semi-plenary sessions and workshops. The conference brings together academic and industrial researchers and students who share and explore their latest research ideas and discuss directions for future development of the field in light of current and anticipated developments in applications and technology. The range of topics covered at the annual CDC is extremely broad, mirroring the varied research threads and applications of control and systems theory. The system-theoretic approach has played a critical role in the development of many contemporary infrastructures and technologies affecting everyday life. Today, for example, system and control-theoretic tools are central in the design, operation, and security of cyber-physical systems, where they can inform researchers about ways to operate large networks (e.g., power, communications, computer, transportation, and health-related networks). Intensive study is also underway on the relationship of control theory and machine learning. Students receiving travel funds from this award to attend the CDC will have many opportunities to interact with members of the professional community in a stimulating setting, and to exchange ideas with a broad group of professional colleagues. The large number of workshops before the conference and the interactive format of many of the presentations will allow additional opportunities for training, learning and for gaining experience in presenting technical results at a major professional conference. Special events will be arranged at the conference that focus on students and early career planning. Specific outreach efforts will be devoted to advertising the conference and the student travel award program to under-represented minorities. 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

Deeply Embedded Software (DES) is used to control a large and diverse set of software-intensive systems such as those used in homes, and for transportation, food, and energy systems. The proposed work addresses security vulnerabilities in DES systems, which have unique challenges due to the use of low-powered embedded devices and corresponding software. This project holistically addresses the problem by developing techniques to effectively secure existing DES systems while enabling easy development of new DES systems. The software security techniques and tools developed as part of the project may apply to billions of embedded devices. The knowledge created by this project will contribute to pedagogical materials to create awareness and increase graduate and undergraduate student community participation and K-12 outreach. This project seeks to transform the landscape of Deeply Embedded Software (DES) security and development by introducing a comprehensive set of modular techniques. The project develops novel solutions to tackle inherent challenges in DES, including diverse hardware and software environments, resource limitations, and legacy code. The project creates a method for hosting DES on Linux platforms and an approach for dynamic analysis of these rehosted applications through under-constrained random testing. Additionally, the project aims to create cost-effective memory safety improvements using pointer annotations, supported by necessary tools to automate annotation. The project also promotes the integration of the RUST programming language in DES development, facilitated by interactive type-matching techniques for seamless interaction with legacy systems and techniques for optimized utilization of existing RUST libraries or crates. The project will provide valuable training for graduate students, enable novel pedagogical approaches and material suitable for embedded systems and programming-related courses, and provide new computational thinking assessments and tutorials to broaden the participation of K-12 and undergraduate students in the broader community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

2420517 (Shah), 2420518 (Yip), and 2420519 (Soh). This project aims to develop the fundamental knowledge necessary to enable an innovative and integrated bio- and physico-chemical process that can effectively recover critical metals from municipal solid waste (MSW) landfills within associated leachates. Globally, over two billion tons of MSW are generated per year, with expected daily per capita waste increases of nearly 20% by 2050 from high income countries such as the US. This trajectory motivates the project vision to inform technical innovation and drive a paradigm shift in the way MSW and associated leachates are stewarded and ultimately re-envisioned and valued. Fundamental advancements are necessary for prototyping innovative solutions in waste management and metals recovery, with potential for near term national impact and longer term impact in developing countries where waste production will likely triple over the next several decades. Project outcomes will foster informed decisions in how to recover and valorize critical materials from landfills—a process that can be powered by recycling and reusing landfill-generated methane—to ultimately impact grand challenge areas of reducing waste and curbing climate change. The project will also provide the critical first steps of research that can directly impact US national security interests and strengthen US economic competitiveness by extracting marketplace resources from landfill waste streams and thereby mitigate vulnerabilities to supply chains. The research targets enabling a more circular economy and sustainable future. To recover critical metals from landfill waste streams, the project’s goal is to develop the fundamental knowledge behind an integrated biochemical and physicochemical process using microbes to promote in-situ metal leaching into the leachate stream and recovering metals from the leachate stream via ion selective membrane separations. Project objectives are to: (1) characterize the currently unknown critical metal content in existing landfill leachates in the US, (2) examine the role and use of in-situ biofilms for bioleaching, (3) elucidate the complex chemistry of metals in landfill leachate samples, (4) advance membrane science and technology for selective ion separations, and (5) integrate life cycle (LCA) and techno-economic (TEA) assessments to inform process development. Outcomes will advance knowledge to: (1) establish baseline values of total critical metal concentrations in US landfill leachate samples, (2) characterize and promote bioleaching performance of microbiomes in US landfills, (3) elucidate the complex speciation chemistry of metals in real and simulated landfill leachates that directly inform separation and recovery efforts, (4) fabricate ion-selective membranes with tailored chemistries to demonstrate enhanced specificity of ion separation for recovery of critical metals from leachates, and (5) evaluate cost and sustainability metrics for iterative process development and quantification of the economic value and environmental impacts of the overall recovery process. 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

Quantum education is considered a strategically important investment and a major priority for workforce development in the United States. Engaging and inspiring K-12 students at an early age is essential to spark curiosity and foster comprehensive knowledge about quantum technologies and their applications. However, there are limited educational programs and opportunities for middle school students to learn fundamental quantum concepts. This project aims to enrich middle school science education by integrating fundamental quantum concepts into the curriculum. This initiative will develop and pilot interactive educational materials, including simulations and hands-on activities in partnership with science teachers across six diverse schools in Indiana. By making abstract quantum concepts accessible and engaging through real-world applications, this project serves the national interest by fostering scientific curiosity and literacy among young students (i.e., 1040 students per year). This innovative educational effort is in collaboration with ten science teachers, which ensures that the program is deeply integrated into the existing curriculum and sensitive to the academic needs of students. This project will promote educational diversity and help prepare a quantum workforce of the future. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. The project is structured around design-based research (DBR) methodologies, guiding the creation, testing, and refinement of educational materials across various stages. The initial phase involves drafting curriculum materials, such as lesson plans and assessments, in close collaboration with expert science teachers. Subsequent phases focus on iterative feedback cycles with partnering middle school teachers and the real-world classroom implementation of these curricula. The educational content specifically targets introducing students to complex quantum concepts like quantum randomness and superposition through engaging science units such as radioactive decay and light. Interactive technologies, including simulations and hands-on experimental kits, will be used to explain these advanced topics and make them tangible for students. For example, students will explore quantum key distribution through hands-on activities designed to teach the principles of quantum communication. Data collection methods will be comprehensive, involving pretests, posttests, and delayed posttests to measure the impact of the lessons on student learning and engagement. This data-driven approach will allow the team to assess the effectiveness of the curriculum, understand student engagement levels, and refine teaching strategies based on empirical evidence. Moreover, recognizing the importance of accessible knowledge dissemination, the project team will create web-based media resources that describe the quantum revolution conceptually and intuitively. These resources will cater to a broader audience, filling the gap in the existing literature and nurturing interest in quantum technologies beyond the classroom. The project’s potential contribution lies in its capacity to significantly enhance science education by embedding fundamental quantum concepts into middle school education, preparing students for advanced studies and careers in STEM 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.

NIH Research Projects · FY 2026 · 2024-08

Abstract Metals play a critical role in our health. However, the study of the association between metal exposure/intake and human health has been hampered by a lack of technology to non-invasively quantify body burden of metals. Accurate quantification of metal body burden is important because most of the diseases related to metal exposures develop over time, and cumulative metal body burden is the best biomarker for individual cumulative metal exposures. Metal body burden is also the most relevant biomarker to determine the toxic threshold of a metal, to evaluate the efficacy of potential treatments of metal-related diseases, and to provide a method for early diagnose of metal toxicity. Metals cumulates in storage organ such as bone. However, it is challenging to non-invasively detect metals in bone. Our lab has been continuously conducting research in the in vivo quantification of metals and trace elements in human body. In this project, we propose to develop and validate a highly sensitive working prototype in vivo neutron activation analysis (IVNAA) system to quantify manganese (Mn), sodium (Na), aluminum (Al), potassium (K), and magnesium (Mg) in human bone. The feasibility of the technology has been demonstrated by our preliminary work. In the proposed project, we will solve the existing technical challenges to meet the sensitivity and reproducibility required for real-world applications, as well as the challenges to extend the technology to broader communities. Monte Carlo (MC) simulations will be used to design an advanced deuterium-deuterium (DD) neutron generator and a highly efficient U-shaped NaI (Tl) gamma-ray detector, and to design the moderator, fast neutron filter, reflector, and shielding components for the neutron irradiation system, as well as the background reduction component for the gamma-ray detection system. The system will be set up and tested for sensitivity (detection limit) and reproducibility once the final design is determined. Robust data analysis method and calibration procedures will be developed and tested. We will test the system's sensitivity and reproducibility using bone equivalent phantoms and human cadaver bones, validate the system of measuring multiple metals against ICP-MS, and improve the design of the system for the comfortability of the human subject and easy operation. We will also apply the system in a small local population to verify utility for human health study applications and sufficient sensitivity and apply the system in a non-local population to verify its ability for wide adoption in human health studies. The proposed research is expected to result in a valuable tool for the study of relationships between chronic metal exposure and health, nutrition and health, and metal toxicity. It also has great potential to become a clinical tool to diagnose metal toxicity or diseases related to metal exposure and trace element intake. This R01 proposal is highly responsive to PAR-22-127, Focused Technology Research and Development, with the goal of supporting “the development of technologies with demonstrated proof-of concept that have remaining significant technical challenges to full implementation and broad utility”.

NSF Awards · FY 2024 · 2024-08

This project serves the national interest by expanding access to rapidly growing geospatial tools and datasets to empower social science scholars working on some of the most pressing problems. In the realm of social science research, a wealth of valuable data with geospatial attributes is readily available, such as microdata from the US Census and American Community Survey, remote sensing data from NASA and USGS (satellite imagery, land use, urban cover, etc.), and field data generated through mobile and drone-based sensors. Such resources can have broad applicability to a wide range of fields such as public health, consumer science, urban planning, human-environmental interaction, political science, criminal justice, and disaster management, among others. However, literacy in geospatial technologies, data, and ethics has not been a standard component of social science curricula, resulting in a serious and growing gap in methods and training. Many researchers in these related fields lack proficiency in computer programing and the use of advanced cyberinfrastructure (CI), geospatial analysis, and relevant data skills, hindering their ability to derive meaningful insights from publicly available datasets. To bridge this gap, targeted training programs are essential, empowering social science researchers to navigate geospatial and cyber technologies and integrate them into their methodologies responsibly. By investing in education, interdisciplinary collaboration, and ethical considerations, this project will enable researchers and practitioners to unlock the full potential of geospatial data creation, curation, analysis, visualization, and interpretation in transformative and impactful research practices and data-driven policy making. Increased geospatial and CI literacy is the first step towards enabling the application of modern data and computationally intensive methods in impactful social science research. In this project, a multidisciplinary team of experts in geography, data science, anthropology, advanced CI, and technology pedagogy will develop a comprehensive set of training modules that can be integrated into instructional programs for undergraduate and graduate students, librarians, and faculty from two and four-year degree granting institutions. The project comprises two primary sets of activities: first, the development of a comprehensive curriculum of instructional materials for working with computational social science datasets; and second, the integration of these materials into various delivery mechanisms targeted to diverse stakeholders. The comprehensive curriculum will take the form of five broad modules that impart competencies related to the acquisition, analysis, and management of social science datasets along the data lifecycle. Each module will be designed to provide a general overview of the relevant CI tools using scenarios and datasets relevant to social science students and will be split into individual learning units with associated difficulty levels: beginner, intermediate, and expert that will assist with course organization for audiences at all levels. In addition, the researchers will employ a range of delivery mechanisms to integrate the developed curriculum into existing and novel learning pathways, targeting students as well as researchers and librarians who will integrate these learning materials in their own institutional programs. Using a train-the-trainer approach along with instruction on pedagogical methods, curriculum design, and online dissemination methods, the project will enable participants to scale and sustain these training activities in their respective home institutions as they train the next generation of social science scholars. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Social, Behavioral, and Economic Sciences Directorate and 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 · 2024-08

This EArly-concept Grants for Exploratory Research (EAGER) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. This project works to discover more efficient ways of using energy, to mitigate global climate change and to promote U.S. national security through greater energy independence. The project targets the optimization of energy consumption in data centers, which currently consume ~ 2% of the U.S.'s energy use and is projected to grow substantially. The project’s objectives are to develop techniques to improve the energy efficiency of software, so that less energy is spent in data centers. The scientific innovation of the project is in the application of an emerging technology, Large Language Models (LLMs), to this task. Additionally, the principal investigators will use a systems thinking conceptual framework to identify patterns and understand interconnections needed to develop new educational material related to this topic. This material will be presented at a workshop in the second year of the project and made freely available online. More generally, the possible contribution is to catalyze a shift toward effective energy-efficient computing, both in software engineering practices and in STEM education. With funding from an EAGER award through the NSF-wide Clean Energy Technology initiative, the project investigates approaches to improve the energy efficiency of software and fill two gaps in the current energy-aware toolkit: energy-aware tools to support software implementation, especially for data center software; and energy-focused educational material for university students and workforce development. Towards the first goal, the project studies the application of LLMs in developing energy-efficient software solutions for data centers. The principal investigators evaluate the hypothesis that LLMs can significantly improve software energy efficiency with targeted prompting and feedback from modeled and real energy data. Towards the second goal, the project assesses the effectiveness of systems thinking as a conceptual framework for energy efficiency. They hypothesize that systems thinking, with its emphasis on holistic reasoning across levels of abstraction and timescales, is a better basis than the prior conceptual frameworks. To validate these hypotheses, the project is structured around three thrusts: measuring the efficacy of LLMs in fostering energy-efficient programming, creating pedagogical materials, and disseminating our insights through a workshop. The research specifically contributes (1) a framework for automated energy-optimization of software, including a catalog of energy-efficiency LLM prompts combined with a hierarchy of energy measures; (2) empirical data on the effectiveness of LLMs in developing energy-efficient software for use in data centers; and (3) educational materials on energy efficiency for software in data centers, including the first incorporation of LLMs and of systems thinking into this line of pedagogy. If successful, the project will advance the state of the art in energy-aware software engineering and pedagogy. 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

Engineering design is a highly iterative process and there are many paths toward a solution. As such, the process of learning engineering design is complex and multi-faceted. As industry and manufacturing continue to advance in complexity and in data-driven decision-making, engineers and engineering education must adapt the way in which engineering design is considered. In data-driven decision-making, the need for integration of science-based and economic decision-making will be critical. Engineers must create designs weighing both domains and the trade-offs between them. Traditional education in this area focuses on engineering design from the perspective of technical skills and knowledge-based design. However, research on the integration of these topics in an economic context is minimal. In this work, we will use an economic approach to analyze how students see design goals, trade-offs, and how they learn and integrate scientific concepts throughout the complex design process. This work will follow a population of first-year undergraduate engineering technology students through a semester-long process of learning about energy, energy transfer, and designing an energy-efficient home. In this process, students will learn how to build a computer-aided design (CAD) model, how to apply and assess renewable energy components such as solar panels, and how to assess the cost and benefit of these components in terms of energy, cost, and return on investment. These efforts will support the formation of professional engineers for the future workforce through complex design learning. The work will also increase the community of researchers conducting engineering education research through the mentoring and support of a new researcher in this domain. Results from this work will be shared in publications and education conferences, further disseminating the results and expanding the community. This project will utilize a mixed method approach assessing written qualitative data and quantitative data generated from a computer-aided design software program to assess the following research questions. RQ1: How do students apply their energy knowledge to inform their designs of energy-efficient houses? RQ2: What is the relationship between students’ design thinking strategies and their economic decision-making? RQ3: What are the characteristics of engineering technology students’ design thinking strategies as they engage in the challenge of building an energy-efficient house? RQ4: What conceptual learning of energy concepts do students develop as they engage in the challenge of building an energy-efficient house? RQ5: What are the patterns of student-produced economic and energy-efficient designs? The results of these questions will help educators understand students’ design process dynamics, their scientific learning of energy concepts, how they approach economic design making, and complex learning in design. This project focuses on undergraduate engineering technology students, who are an understudied population. The results from this work will support the professional formation of engineers through the application of deep technical and professional skills, knowledge, and abilities in both formal and informal settings/domains. This project also investigates how engineering teaching and learning can be supported by cyberlearning innovations. The intellectual merit of the project resides in its contribution toward discipline-specific learning theories that can describe complex learning in engineering practice. 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

An array of forces act on the Earth's crust in the Arctic (on the margins of Russia, Alaska, and Canada), giving rise to this region's active faults and earthquakes. Some of these forces are from sideways pushing by tectonic plates and from drag by the Pacific Plate as it slides beneath the Alaska crust. Other forces result from gravity acting on the high mountains and from buoyancy as the crust slowly recovers its shape following the loss of heavy ice sheets that once weighed the region down. New data from high-precision GPS sensors and seismometers now suggest that flow of the Earth’s mantle causes drag along the base of the crust, and also contributes to faulting and earthquakes. Flesch, Elliott, and their students will analyze all available GPS data from the region to make a detailed map showing how points in the surface are moving right now. They will make computer models representing the Arctic region as blocks bounded by active faults, and use this with the GPS velocity map to estimate how fast these faults are slipping, which is important for understanding earthquake risk. After this, they will develop more sophisticated computer models to understand which forces are most important for causing observed surface movements and fault slip rates, and how big these forces are. This project will determine a synoptic regionally internally consistent multi-tectonic plate scale velocity field by incorporating both GPS and mid-oceanic ridge spreading rates in the Arctic that represent motions over hundreds of thousands of years. This work will incorporate the most recent seismic analysis providing constraints on the geometry and strength of the lithosphere, perform instantaneous and time dependent 3-D geodynamic simulations of Alaska and the Arctic. Observed surface motions will be compared to the predicted velocities from the geodynamic simulations at the volume surface to address the questions: (1) How does gravity acting on topography in the Arctic and opening at the Gakkel ridge contribution to force balance in Alaska? (2) How do southward directed mantle tractions beneath northern Alaska distribute in central and southern Alaska? Does the generated uplift in the McKenzie mountains provide a torque responsible for rotation of the Bering plate? (3) Is there an incipient subduction zone forming at continental margin north of Alaska? 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

Memory is an important cognitive ability. Its significance is often made most apparent by its disruptions—from everyday misremembering to post-traumatic stress disorder and to the onset of Alzheimer’s. Explaining, treating, and possibly ameliorating these memory malfunctions requires an understanding of how the brain retains memories in the first place. Finding the engram—the neural mechanism of memory retention—has been a guiding project for neuroscience since its earliest days. But while it has long been assumed that there was an engram, only recently with the development of new tools and technologies have specific engrams been identified and activated. These exciting discoveries raise questions as to how the engram as a concept should be updated, expanded, or revised to accommodate them. A more detailed framework for the engram concept can help both scientists and the broader public understand the significance of recent discoveries and how they can be used to make further progress in treating memory disorders. This project in the philosophy of neuroscience employs methods from biology and the history and philosophy of science that have been used to understand the gene concept (i.e., the changes it’s undergone from Mendel’s peas, to DNA, to genomics) to build a comparable framework of understanding for the engram: how neuroscientists working at different times, with different tools, research questions, and populations think about this basic unit of memory. The project includes updated trainings in opto-genetics and other new intervention technologies, alongside close collaboration with leading engram neuroscientists, to ensure the framework is well-grounded and representative. By offering context for recent discoveries in the neuroscience of memory, this project demonstrates the importance of the intersection between neuroscience and philosophy for understanding features of the mind and cognition and for promoting science literacy and engagement with the broader public. 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 PI, graduate, and undergraduate students will provide theoretical support for the experimental search for asymmetry between matter and antimatter by the ALPHA collaboration. The current understanding of the laws of physics predict perfect symmetry between matter and antimatter which is in tension with the observation that the universe is almost universally composed of matter. Any difference between hydrogen and its antimatter counterpart, antihydrogen, would challenge our understanding of the universe. The PI, graduate, and undergraduate students will perform calculations of basic processes in the ALPHA experiment to improve the precision of measurements of antihydrogen leading to more stringent tests matter/antimatter symmetry. In addition to the insights about fundamental physics, this project is an ideal training ground for theoretically minded graduate and undergraduate students. All students develop their own programs to explain different aspects of the ALPHA 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. This research project will provide theoretical and computational support in the experimental search for differences between the hydrogen atom and its antimatter counterpart, antihydrogen, by the ALPHA collaboration. Basic theoretical principles predict that the properties of hydrogen and antihydrogen are identical except for some trivial sign changes. Several properties of the hydrogen atom have been measured to extraordinary levels of precision. The goal is to reach this same level of precision in antihydrogen measurements, enabling high precision tests of matter/antimatter symmetry. While this is fundamentally an experimental undertaking, theoretical and computational treatments of the ALPHA experiment increase the precision and accuracy of measured properties of antihydrogen. In addition, the simulations help improve the experiment. The PI, graduate, and undergraduate students will focus on simulating basic processes in the experiment including laser cooling, non-linear stochastic heating of antihydrogen, predictions of spectra in strong fields, and sympathetic cooling of plasmas. During this project’s time frame, we will greatly improve the antihydrogen measurements by increasing the number of trapped antihydrogen by more than 10X through improving the plasma conditions in the experiment. These improvements will lead to: at least 10X improvement in the spectral precision of the 1S2S transition, the ability to measure other transitions (e.g. 2S-2P or 2S-4P) for an independent determination of the antiproton radius and antihydrogen Rydberg constant, improved measurement of gravity on antihydrogen using ideas from non-linear dynamics, and precision measurement of the 1S hyperfine splitting. Antihydrogen synthesis, trapping, and measurements lie on the boundaries between atomic, plasma, and experimental particle physics and it cannot be studied properly without using tools from all these 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.

NSF Awards · FY 2024 · 2024-08

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Carlos Larriba-Andaluz and his group at Indiana University-Purdue University in Indianapolis are working on improving the understanding of how ionic compounds may be separated and characterized in the gas phase via a technique known as Ion Mobility Spectrometry (IMS). Understanding how this separation occurs is not only vital to analytical chemistry, but also to aerosol science and plasma physics. One of the reasons that IMS has become quite relevant is that it is able to distinguish ions that have the same mass but different shapes, known as isomers. Distinguishing isomers is extremely important - small changes in shape can result in drastic changes in chemical and physical properties. In fact, IMS systems have arrived at such sophistication that the existing theory is incapable of describing some of the observed separation capabilities. The Larriba-Andaluz group is working to fill this knowledge gap through novel theoretical approaches and numerical tools. The team is engaging undergraduate students, including members of underrepresented groups, through summer courses. They are also developing online IMS training materials to captivate both new and seasoned users. Ion Mobility Spectrometry (IMS) is becoming one of techniques most employed in combination with Mass Spectrometry (MS). As IMS systems become more sensitive and accurate, commonly used modeling and theory approximations have been unable to explain some recently observed capabilities, including separations of isomers, isotopologues, and even isotopomers. Interchange of energy between translational and internal degrees of freedom accompanying ion-molecule collisions is a potential contributor to these phenomena which has not yet been incorporated into existing theories. The Larriba-Andaluz group is investigating the mechanism and role of this energy exchange under conditions of varying electric field and temperature. Specifically, they are developing 1) higher-order ion mobility approximations using two-temperature theory, followed by the inclusion of ion energy calculations and inelasticity effects, and 2) models of mobility and energy balance using an in-house Molecular Dynamics-Monte Carlo hybrid code. Comparison of experimental results at different fields and temperatures is being used to predict the effect of inelastic contributions. The resulting insights are expected to help explain the remarkable separation ability of ion mobility spectrometry and to broadly advance the field of ion mobility. 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

Project Summary/Abstract: To understand the function and dynamics of neural circuits that control behaviors and cognitions, we need to observe the neuronal network at a cellular resolution over large spatial scales across multiple brain regions. Although two-photon fluorescence microscopy (TPM) combined with genetically encoded function indicators has provided a paradigm shift in neuroscience by enabling cellular resolution functional imaging deep in brain tissue, current TPM technologies still fall short of achieving whole-neocortex coverage. In this project, the engineering team at Purdue and the neuroscience team at UCSD will join the force to develop the Light Pipe Microscope (LPM) and enable ultra-large-scale whole-neocortex calcium imaging. Compared to conventional TPMs, LPM offers three advantages. First, LPM offers unparalleled signal collection capabilities over a large imaging field of view, which translates to superior imaging quality, depth, and extremely low photobleaching for long-duration continuous recording. Second, LPM features an extremely compact body, which allows multiple LPMs to be densely or sparsely packed to simultaneously image the curved brain surface. Third, LPM can be constructed with low-cost components and is fully compatible with common lasers, scanners, data acquisition systems, and software, which will facilitate broad dissemination. In particular, the single LPM and the dual LPMs can be constructed as add-on components to the existing two-photon imaging systems with a small fraction of the cost of a commercial TPM. They will convert the existing two-photon systems for ultra-large-scale cellular resolution functional imaging of mammalian brains. Through the proposed development, we will have the fully developed and thoroughly validated LPM, each with a large FOV and unparalleled signal collection efficiency. A low-cost ($4,800) single unit (as an add-on) can convert the widely used two-photon scopes into mesoscopes with unmatched signal levels. The utilization of two or more LPMs will provide unprecedented ultra-large-scale two-photon imaging coverage, which will transform the study and understanding of mammalian brains.

NSF Awards · FY 2024 · 2024-08

The Standard Model of particle physics is the most successful physical model describing the structure of the known matter of the universe and the interactions between these particles. Still, we know that the Standard Model is not complete, in that there are several features of the universe that it cannot explain. These include the asymmetry between matter and anti-matter, the identity of dark matter and dark energy, and the anomalous magnetic moment of the muon. A variety of theoretical models that extend the Standard Model, so-called beyond Standard Model (BSM) physics, have been proposed. Precision atomic, molecular, and optical physics measurements can be used to test the predictions of the Standard Model in small scale, table-top experiments, validating the standard model in this energy range, or helping elevate one of the various proposals for BSM physics. The goal of the proposed work is to precisely measure the strength of an extremely weak optical interaction in atomic cesium. This interaction is permitted only through the influence of the weak force interaction between electrons and the cesium nucleus, or between the nucleons themselves. A precision measurement of the strength of this interaction provides a precise value of the weak charge of the cesium nucleus, which in turn is used to determine a quantity known as the electro-weak mixing angle. The goal of the present program is to improve the precision of the value of the electro-weak mixing angle in the low energy range, testing the level of agreement with the Standard Model prediction, and possibly discriminating between various BSM models. In addition to its impact on our understanding of the universe, this program provides a valuable laboratory experience for graduate and undergraduate students. Students gain strength in physics knowledge and technical skills in lasers and optics, vacuum techniques, electronics, data acquisition and analysis, and technical writing, which they can apply to solving other important technical problems in industry or academics. One set of measurements proposed in this program will be carried out on the optical transition from the ground 6S state to the excited 7S state in atomic cesium. The weak force interaction, which is not symmetric upon spatial inversion, slightly mixes atomic states of opposite parity (S- and P-states, for example), and introduces a weak electric dipole (E1) transition moment between the 6S ground state and 7S excited state. Using a two-pathway coherent control technique, and applying a uniform static electric field to the atoms, the investigators will drive the transition concurrently with two optical interactions, one a linear interaction with 539.5 nm light (the weak-force allowed transition and a Stark-induced transition); the second a two-photon interaction with light at 852 nm and 1470 nm. The laser beams are phase locked to one another, assuring mutual coherence between the optical interactions. The interference between the strong two-photon interaction and the weak linear interactions can be controlled by varying the optical phase difference between the various laser beams, resulting in modulation of the total excitation rate. Precise measurement of the amplitude of the modulation allows a precise determination of the ratio of the E1 moment for the transition and the Stark transition polarizability. The goal of the measurement is to reduce the uncertainty of this ratio to a value approaching 0.1%. A second measurement is based on the extremely weak E1 transition between hyperfine components of the ground state. The largest contribution to this nuclear-spin-dependent interaction is due to the anapole moment of the nucleus, which results from a parity-odd nuclear current produced by the weak force interaction between nucleons. 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

Currently, researchers generate large amounts of data that capture many features of physical plants and bring that data into computer systems in order to understand and reveal complex biological processes and interactions. However, extracting meaningful information from this data requires advanced technical skills such as algorithm development, programming, and statistics. The VR-Bio-Talk project will develop a life-like virtual reality (VR) visual analytics system, which will be controlled by voice. The user will be immersed in a field of scanned plants and will be able to interact with it verbally. For example, the command “show me all plants older than four weeks and their average leaf area.” will display only the correct plants and their leaf area as a label and a graph above them showing how the value is changing over time. At its core, this project will develop novel algorithms for artificial intelligence (AI)-based interaction and advanced processing, reconstruction, and visualization of large plant datasets. The overall aim is to bridge the domain gap of current data analytics systems. The anticipated impact will be support for the development of a data-enabled biology workforce capable of advancing the understanding of plant biology and contributing to innovations by deriving insights from data using novel systems and algorithms for interaction with large phenotypic data. Special attention will be given to including potentially disadvantaged users through built-in robustness to accents and support of learners with limited English proficiency and through VR data interaction designed to be accessible to users with limited motor skills. The novel AI-based voice-controlled VR interaction and visualization algorithms will have a broad impact that extends beyond the life sciences. This project has three main aims. (1) Development of novel AI-based algorithms for the reconstruction of the vast, rich, but often underused data from phenotyping facilities into plant digital twins that respond to the environment by providing highly detailed 3D geometry and light interaction. In particular, the project will use the rich data acquired by the University of Arizona field scanner. The plants will be rendered with high visual plausibility and photorealism. They will also be rendered in more salient false colors as needed for analysis and AI training. (2) The second aim is the development of a voice-controlled VR user interface that interprets complex compound commands. The interface will be connected to an AI-based voice recognition system and voice-to-text encoder, which will generate code and executable commands. The control will be tuned to respond to a wide variety of accents and commands. (3) The third aim will focus on the deployment and evaluation of a set of carefully designed experiments with participants ranging from novices to experts. The users will use intuitive, natural interaction via dialogue with an AI-enabled data analytics system. 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 grant provides travel support funds for undergraduate and graduate students and early career faculty to attend the 2024 ASME International Mechanical Engineering Congress and Exposition (ASME-IMECE) in Portland, Oregon, 17-21 November 2024. The travel funds will support 20 undergraduate and graduate students to present at a special poster session and 30 Assistant Professors to attend the ASME-IMECE and participate in the inaugural Mechanical Engineering Rising Star Celebration, expected to attract hundreds of participants. Both events will be open to all eligible conference attendees. The travel award selection will consider the inclusion of members from underrepresented groups and diversity of institutions represented by the students and faculty, and the variety of programs within engineering. This grant aims to benefit the nation by educating a skilled and diverse engineering workforce prepared to provide transformative solutions to the challenges in their fields. Participant support is expected to enhance students' professional, scientific, and technical development as they present their NSF-funded research projects at the largest mechanical engineering conference in the nation. Students are expected to improve their communication skills through discussions of their work with top researchers. Participants will also have the chance to attend various technical presentations, keynote and plenary sessions featuring technological pioneers, and network with potential mentors, colleagues, and employers. For faculty, particularly untenured and underrepresented members, the conference provides a unique opportunity to network and form crucial connections with Rising Stars in Mechanical Engineering, offering long-term career benefits. This project is funded by programs within the Division of Civil, Mechanical, and Manufacturing Innovations (CMMI) and the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET). 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.