Purdue University

NSF Awards · FY 2025 · 2025-07

The conference Frontiers in Stochastic Analysis will be held at Purdue University from August 11 to August 13, 2025. Following the success of the inaugural FISA conference, hosted at the University of Illinois Chicago in 2023, this event brings together leading experts in stochastic analysis to present lectures on a wide range of cutting-edge research topics. A key aim is to provide a platform for early-career researchers — including senior graduate students and postdoctoral scholars — to present their work and engage with the broader research community. Probability theory provides a fundamental framework for the study of random phenomena, many of which can be modeled using continuous stochastic processes or random fields. Stochastic analysis, a dynamic and growing area within probability theory, offers essential tools for investigating these processes. The research topics featured at this conference reflect several key areas of stochastic analysis that have seen intense development in recent years. The broad scope of topics is designed to expand the perspectives and interests of graduate students and early-career researchers. Additionally, the conference provides a platform for interaction between generations of researchers as well as across distinct areas of stochastic analysis. The website of the conference is: https://fisa2025.weebly.com This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

The objective of the Purdue Drug Discovery Training Program (PDD-TP) is to provide predoctoral students with a cross-disciplinary training at the chemical-biology interface as it broadly pertains to drug discovery. Modern drug discovery is rapidly evolving with new insights into human disease and new approaches for identifying therapeutic modalities. Drug Discovery involves a wide range of scientific disciplines, including biology, chemistry, and pharmacology, and utilizes a variety of experimental approaches, necessitating a multi- disciplinary approach to graduate training. The Purdue Drug Discovery Training Program brings together trainers and trainees from seven departments and four colleges to provide a comprehensive overview of the modern approach to drug discovery in biomedical research. Our overall objective is for these interdisciplinary scientists to gain broad understanding of the problems and approaches of modern drug discovery, the scientific skills to develop and implement rigorous research, and the communication skills to successfully engage with others in the community. To achieve that goal, the training program will utilize the following specific approaches: Trainees will take an integrated curriculum to expand their breadth of knowledge and understanding of experimental approaches. Trainees will participate in an embedding project outside their primary laboratory for cross-disciplinary experience and training. Trainees will participate in program-specific activities intended to enhance communication, critical thinking, leadership, management, mentorship, networking, and knowledge of academic and industrial drug discovery. Activities include formal presentations by trainees and trainers, organization of seminars in drug discovery, discussions of scientific ethics and responsible conduct, visits with scientists across a variety of careers, networking with alumni in drug discovery, and tours of on-campus and off-campus facilities. Trainees will have unique opportunities to engage experts in the evaluation of drug pipeline candidates and participate in a mock drug panel review. Quantitative assessments ensure that our training program is effectively increasing the scientific knowledge base of trainees, expanding their exposure and knowledge of careers in drug discovery, and providing them with the skills to responsibly conduct and communicate research related to drug discovery. We are requesting 3 new positions per year with 2 years of support. PDD-TP trainees will start the program during their second year of graduate school and stay in the program until graduation. PDD-TP trainees will graduate with a comprehensive skill set, exposure to the myriad of steps in drug discovery and pipeline development, and experiences that will continue to serve them long after they enter the biomedical workforce.

NIH Research Projects · FY 2025 · 2025-07

Summary Lipid nanoparticle (LNP)-based formulations are widely used for delivering macromolecular therapeutics, including mRNA. Biologics account for ~55-60% of the total current pharmaceutical product market, with all trends pointing to continued increases in market share. However, transfection efficiency of mRNA is severely hindered by various loss mechanisms, including rapid clearance, suboptimal cellular uptake, and incomplete endosomal escape. These processes reduce the effective delivery of mRNA to only a small fraction (~1-2%) of the administered dose. As a consequence, comparatively subtle patient-to-patient differences in loss can translate to large variability in therapeutic dosing, with corresponding variability in efficacy and side-effects. To improve transfection yields and better understand the intracellular barriers to mRNA delivery, particularly during the key step of endosomal escape, there is a pressing need for new tools capable of providing chemically selective, nanoscale insights in the intracellular fates of LNPs and their cargos within live cells. The goal of this project is to develop fluorescence-detected photothermal infrared (F-PTIR) microscopy to track the intracellular processes governing mRNA release from LNPs in real-time and with ultra-high spatial resolution, well below the optical diffraction limit. In brief, fluorescence from labelled mRNA will serve as a local temperature sensor. Upon IR absorption from the surrounding medium, local transient temperature increases result in corresponding reductions in fluorescence quantum yield. Change in fluorescence intensity as the IR wavelength is tuned enables IR absorption spectroscopy with a spatial resolution set by fluorescence imaging and heat transfer. Embedding fluorescently labeled mRNA within deuterated LNPs will yield IR spectra dominated by the CD stretching modes of isolated LNPs. Endosomal uptake will be tracked both by the physical position of fluorescence within single cells and by the changes in the lipid vibrational spectra (e.g., addition of CH-stretching modes from native lipids in endosome membranes). Tight timing control in combination with heat-transport modeling will be used to quantify distances between the fluorescence reporter and different IR absorbers. The relatively rare subset of internalized LNPs capable of releasing cargo intact into the cytosol will be identified by their corresponding change in local microenvironment (e.g., loss of CD stretches for mRNA solubilized within the cytosol) together with their 3D position and mobility measured by single molecule localization microscopy (SMLM). Once developed and validated, the proposed instrumentation can support informed optimization of therapeutic nanoparticle formulations designed to promote dosing yield and therapeutic efficacy.

NSF Awards · FY 2025 · 2025-06

Semiconductors and chips are a crucial part of everyday life in the United States. The COVID-19 pandemic highlighted the problems caused by relying on imports for these essential components, affecting the economy, healthcare, and national security. To ensure national security, there is need to control all aspects of chip design, manufacturing, and integration. However, bringing chip manufacturing back to the US requires a significant increase in skilled workers, including technicians, engineers, scientists, and support staff. Chip manufacturing processes involve complex modeling and simulation tools, which are not commonly used in education today. Only a small number of universities in the US currently educate integrated circuit (IC) design engineers, and even tripling their enrollments won't meet the workforce demand. More universities need to be enabled to teach chip design. Many research institutions face challenges such as lack of Information Technology (IT), legal, and hardware support, educational materials, and experienced instructors. Chipshub will be a National Chip Design Hub for all US universities and colleges, providing education and training that is critical to bringing semiconductor manufacturing back to the US. Chipshub will overcome challenges that span (1) content/products: creation of appropriate and sharable educational and tool content; (2) infrastructure/market: secure, scalable, seamless, user-friendly, well-supported, and sustainable web delivery of the content; (3) users/customers: reaching, engaging, incentivizing, and training faculty to teach and/or conduct research in IC design; and (4) sustainability through community building and growth across partners, users, and content providers. Chipshub will robustly and sustainably meet the key requirements for a national chip design hub, including licensing, access, and maintenance of commercial and open-source electronic design automation (EDA) tools for end-to-end chip design and verification; secure cloud-based availability of process design kits that span from open-source nodes to emerging technologies; and paths to multi-project (i.e., shuttle) integration. Chipshub will leverage past NSF investment in nanoHUB infrastructure, plus multiple other investments at similar scale: NSF gateway infrastructure; DARPA and commercial funding of OpenROAD and Precision Innovations; and commercial investment in chip design software. Chipshub team members have individually been at the forefront of the quest for accessible infrastructure that can serve training and workforce needs. This project will aim to deliver Chipshub as an infrastructure that is built to last. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This REU Site award to Purdue University, located in West Lafayette, IN, will support the training of 10 students for 10 weeks during the summers of 2025-2027. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in the program. The Purdue Undergraduate Research Experiences for Plant Biology and Data Science (PURE-PD) program provides hands-on research opportunities via data-driven research projects in plant biology. Through mentorship and professional development, students will gain valuable transferrable skills applicable to careers in plant biology and related STEM fields to strengthen US workforce development. Student participants will learn how research is conducted, and many will present the results of their work at scientific conferences. The effectiveness of the REU site will be assessed using student feedback and by tracking the career path and publication record of program participants. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov. Developed as an interdisciplinary collaboration between the Purdue Center for Plant Biology and Libraries and School of Information Studies, the PURE-PD program aims to provide student participants with advanced training in basic plant biology and data science. Research projects in the PURE-PD program are diverse and include the regulation of nutrition signals, plant health monitoring, plant host-pathogen interactions, and the regulation of developmental processes, which collectively offer a significant and agriculturally relevant research environment for student participants. In addition, each student will be involved in professional development activities including research ethics, research data management and scholarly communication. The design of the professional development activities included in the program is evidence-based and will allow student participants and their mentors to master best practices in scientific research. Candidates will be selected based on a holistic review process involving REU leadership and faculty mentors. A comprehensive evaluation will be conducted at the end of each summer session for the iterative improvement of the program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This REU Site award to Purdue University, in West Lafayette, IN, will support the training of 8 students for 10 weeks during the summers of 2025-2027. A total of 24 students, primarily from schools with limited research opportunities, will be trained, which will contribute to developing the US STEM workforce. This program will develop practical and intellectual research skills under the general theme of protein analysis. The program will prepare students to pursue, and be successful in, advanced STEM degrees and/or enter research careers in industry. Students will conduct cutting-edge, mentored, independent research in a Department of Biochemistry lab. They will engage in hypothesis-driven experimentation using diverse analytical methods covering biochemistry, cell and molecular biology, and molecular genetics, while developing valuable critical thinking and communication skills. The program culminates with a presentation of research results at a campus-wide symposium. Many students will further present their work at external scientific conferences. Assessment will be achieved with a pre/post Likert survey designed by the Purdue Center for Instructional Excellence. Students should apply using NSF ETAP (Education and Training Application: https://etap.nsf.gov). Available projects in Department of Biochemistry labs cover a broad range of experimental approaches for protein characterization, including analytical chemistry (such as mass spectrometry), structural biology, enzyme kinetics, biophysics, molecular genetics, genomics (next-gen sequencing applications), microscopy and other cell biology methods. A variety of model organisms including yeast, fruit flies, and plants provide powerful and convenient systems to study protein function and regulation. Research is augmented with early learning modules and weekly group meetings covering fundamentals of protein structure and function, responsible conduct of research, lab safety, using primary literature, experimental design, research documentation, scientific communication, entrepreneurship, and preparation for graduate school. Participants will meet with biotech company representatives to discuss industry careers and hear from faculty about careers in academic research and teaching. Applications will be evaluated by a committee of faculty and staff, and selected using a holistic approach that includes academics, career interests, letters of recommendation, and personal statements. Participants will include rising juniors and seniors with demonstrated passion for experimental research and pursuing graduate degrees and careers in biochemistry- and molecular biology-related 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 2025 · 2025-06

This project explores how quantum technology can improve the way we measure and detect small changes in our environment, such as temperature shifts, pollution levels, or even tiny vibrations, across large areas like cities. Researchers at the University of Tennessee at Chattanooga will use a special kind of light, a so-called "squeezed light", to create a network that senses these changes more precisely than current methods allow. By testing this innovative approach on a real-world fiber-optic network in Chattanooga, built in collaboration with industry partners like the Electric Power Board (EPB) and IonQ, Inc., the project demonstrates how quantum science can move beyond laboratory experiments into practical, everyday use. Imagine a system so sensitive it could help monitor air quality in neighborhoods or ensure clocks worldwide stay perfectly in sync; those are the kinds of possibilities this work opens up. This effort funded by NSF will push scientific boundaries while offering real-world benefits. Beyond the technology, the project trains students and professionals in cutting-edge skills, preparing them for future careers in quantum information science and engineering. It also strengthens ties between universities and local industries, showing how federal investment can spark innovation, improve lives, and inspire the next generation to tackle big challenges with creative solutions. This research focuses on achieving sub-shot-noise-limited (sub-SNL) distributed quantum sensing using continuous-variable (CV) entanglement on a commercial metropolitan-scale quantum network. The team will construct a table-top CV-entangled network utilizing two-mode squeezed states, generated through four-wave mixing in atomic rubidium-85 vapor, to measure distributed phase shifts with sensitivity surpassing classical limits. Deep learning, specifically Q-learning, which is a reinforcement learning technique, will be employed to suppress excess noise without requiring pilot tones or training sequences, by adapting similar noise mitigation strategies from CV quantum key distribution (CV-QKD). This approach leverages homodyne detection and real-time phase estimation to optimize local oscillators across the network, addressing noise introduced by beam splitters and environmental interactions. A single-mode squeezed light source at the telecom wavelength of 1570 nm will extend this methodology to the EPB Bohr-IV Quantum Network, a software-reconfigurable fiber-optic infrastructure deployed by IonQ, Inc., featuring a hybrid ring/spoke topology with scalable quantum nodes. The project’s intellectual significance lies in its novel integration of machine learning (ML) with CV quantum sensing, offering the first practical demonstration of sub-SNL distributed sensing on a deployed commercial metro-scale quantum network. Through partnerships with Arizona State University and industry collaborators like EPB and IonQ, Inc., this work advances quantum information science and engineering, providing a scalable framework for future quantum networking applications and contributing to both theoretical and experimental progress in the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-06

PROJECT SUMMARY Fluorination of an organic compound affects physicochemical properties, which in medicinal settings perturbs pharmacodynamic, pharmacokinetic, distribution, and/or metabolic profiles both in vitro and in vivo. Thus, the ability to selectively install fluorinated groups, such as sulfonyl fluorides (SO2F) and aryl fluorides (ArF), under mild conditions, is essential for accessing new therapeutics and biological probes. SO2F are privileged functional groups that act as covalent warheads to evaluate substrate-protein interactions and elucidate residues in the enzyme binding site. As a result, this group serves as a significant tool in medicinal chemistry and chemical biology. Furthermore, ArF are common motifs seen in FDA approved drugs and drug candidates due the ability to improve a compound’s drug-like properties. However, the unique physical properties of fluorinated substrates and/or reagents typically perturb fundamental organic reactivities, which can complicate synthetic sequences to access fluorinated compounds. Thus, many routine organic reactions simply do not work in the presence of fluorinated reagents or with fluorinated substrates. Additionally, the unique properties of fluorinated substrates enable new reactivities that cannot be achieved by the respective non-fluorinated counterparts, which provides opportunities to develop innovative strategies for accessing medicinally relevant substructures. In this proposal, we aim to synthesize these fluorinated substructures using electrochemical synthesis rather than utilizing traditional methods of synthesizing SO2F and ArF, which includes harsh reagents and expensive photocatalysts. Previously underutilized by organic chemists, electrochemical synthesis has seen a revival within the past few decades due to its sustainability, unique reaction tunability, and mild conditions. In the proposed research, we aim to synthesize a more diverse scope of sulfonyl fluorides. While there have been many methods for synthesizing aryl sulfonyl fluorides, the generation of alkyl and vinyl sulfonyl fluorides is underexplored. We also aim to use electrochemical synthesis to create a direct method for synthesizing aryl fluorides without the need for leaving groups. Aim 1 will deliver optimized conditions for aryl, alkyl and vinyl sulfonyl fluorides alongside computational studies to provide insight into our proposed mechanism. Aim 2 will involve optimizing electrochemical conditions to selectively generate ArF. Once optimized conditions are delineated, we will generate a varied substrate scope of selectively fluorinated (hetero)arenes. Development of the proposed strategies will enable medicinal chemists to access new and unique biological probes and therapeutics through facile late-stage fluorination of drug candidates and yield diverse aryl, alkyl and vinyl SO2F, thus broadening the field of both medicinal and electrochemistry simultaneously.

NSF Awards · FY 2025 · 2025-06

Interactive dynamics, where individuals or particles influence each other and the overall system, are important in fields like biology, physics, materials science, and social sciences. These dynamics often involve rare events that can have large impacts, such as changes in protein structures or genetic evolution. Inaccurate predictions of these events can hinder our understanding of biological processes, drug design, and our ability to prepare for extreme events. This project seeks to improve our ability to predict and control these rare but critical events by identifying key patterns in energy landscapes and developing new methods for estimating fluctuations and controlling extreme events in complex spaces. The insights gained will not only help in predicting extreme events but also improve our understanding of various interactive behaviors, such as material design and social opinions. Educational initiatives will promote interdisciplinary learning and advance the growth of applied mathematics and related fields. This project aims to predict and control rare, significant events in interactive dynamics on complex configuration spaces. The goal is to advance the theoretical understanding of Hamilton-Jacobi equations (HJEs) and control theory for multiscale interactions by addressing key challenges, including non-uniqueness of stationary solutions, fluctuation estimates in multiscale interactions, and singular optimal control in probability spaces. Theoretical developments will be applied to practical problems, such as rare event simulations, to enable more predictable dynamics in complex systems. First, the investigator will develop a systematic method to select stationary solutions to HJEs, providing a uniformly converging vanishing viscosity approximation and a global energy landscape. Second, a novel decomposition for species concentration and reaction fluxes in non-equilibrium multiscale reactions will be introduced, along with a singular limit framework for fluctuation estimates. Third, rigorous justifications for the singular limit of variational solutions for HJEs with state constraints will be achieved via the equivalence between optimal control and optimal path measures for transition problems in infinite dimensional spaces, particularly in the presence of boundary singularities. The research outcomes will be disseminated through conferences, publications, and online platforms. The approaches developed will also be integrated into undergraduate and graduate teaching, offering research opportunities for graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Many bacteria harbor “microcompartments” to help them use unusual food sources, yet very little is known about how these microcompartments are built from their protein components, or how they work. Because both beneficial and harmful bacteria use microcompartments, understanding their assembly and mechanisms of action could allow us to engineer bacterial strains to combat chemical pollution and carbon dioxide surpluses, or conversely to design antibiotics less likely to promote widespread resistance. The project combines cryo-electron tomography, mathematical modeling and simulation, and biochemical assays to develop a model of how these large protein assemblies are built, mature, and function. This research is part of an increasing trend toward interdisciplinary science, yet many students struggle to cross traditional scientific divisions. Therefore, the project also includes an education plan focused on developing the necessary skills to analyze and interpret the various types of research data in the molecular life sciences. Group-based projects with raw data will be developed for a large enrollment Bioanalytical Chemistry class. “Best practices” methods will be published for use by other educators. The best-studied and simplest microcompartment known is the alpha-carboxysome, which allows ocean bacteria to utilize CO2 to build sugars for metabolism. The carboxysome has a protein shell and contains just two enzymes: carbonic anhydrase that converts ocean bicarbonate to CO2, and Rubisco that removes the carbon from CO2 for downstream metabolism. Carboxysomes are so stable that they are passed from cell to cell, living far longer than the cell that originally made them. In addition, they are so efficient that cells without them die even if they still have the enzymes in the cytoplasm. How the microcompartment is assembled and the enzyme components organize to become a stable, efficient carbon-fixing machine will be studied. The lessons learned from this detailed study will then be applied to the characterization of a more complex system, the propanediol-utilization compartment that allows Salmonella to thrive in the mammalian gastrointestinal tract. Taken together, these studies of beneficial and pathogenic microcompartments will both provide new insights into prokaryotic metabolism and create blueprints of powerful machines whose redesign could lead to new technologies or whose disruption could treat mammalian illness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Current and future US engineering workforce demands require research to better understand how to support the professional formation of all engineering students. The number of enrolled engineering students nation-wide had the sharpest decline in a generation. Further complicating the problem is the decreased math and reading scores across the US since the pandemic, adding an additional filter of who can enter into engineering. Projected national shortages of engineers are in the tens and hundreds of thousands of workers in some sectors. Simultaneously, fewer young people are entering into four-year degrees. Once a student has enrolled in an undergraduate engineering program, they become a valuable asset for meeting the workforce demands and need support to continue in their professional formation. However, researchers have found that some subgroups of students are at a particularly high-risk of leaving engineering. Among those subgroups of at-risk learners are nonbinary engineering students. Researchers know very little about factors supporting or hindering nonbinary students engineering professional formation. This project serves to help understand how these students leverage identity-specific strengths from their communities, known as community cultural wealth, to succeed in their academic careers. This novel, transformational EAGER proposal will explore their community cultural wealth—that is, for example, how these students sustain hopes and goals, successfully navigate their majors, receive support from family-style relationships, leverage their social network, transgress expectations and resist negative stereotypes and microaggressions—as a means to thrive in engineering. We will interview twenty nonbinary engineering students at various stages of their academic careers using narrative inquiry. Through this project, we aim to raise awareness of the unique assets of the nonbinary engineering community so that engineering students feel affirmed and heard, and engineering educators may design inclusive education practices and advocate on behalf of the nonbinary community in engineering. This project outcomes will result in the development of resources that can be shared to support the professional formation of nonbinary students, as well as the broader engineering student population. The purpose of this asset-based qualitative study is to investigate how nonbinary engineering students leverage their community cultural wealth to support their wellbeing, belonging, and persistence during their professional formation. We are guided by two research questions: 1) How do nonbinary engineering students access community cultural wealth within engineering and queer communities, and 2) how do nonbinary engineering students mobilize their cultural capital to support their wellbeing, sense of belonging and persistence? We will interview 20 engineering students at various stages of their professional formation using composite narrative inquiry and critical incident technique. By interviewing students at various stages of professional formation, we will explore how capital is accrued and how different forms of capital impact students’ persistence at differing stages of their identity development. Our findings will generate new knowledge about how nonbinary students draw upon their personal assets and those of the LGBTQ+ and ally communities during their professional formation. Nonbinary participants will benefit from being heard, affirmed, and seen throughout the interview process, and from reading the narratives of other nonbinary engineering students leveraging their assets to persist, belong and thrive in engineering. To reach students outside of the study, we will disseminate the composite narratives to LGBTQ+ STEM focused social media and professional organizations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Plate tectonics is a unifying theory for the Earth sciences and explains how the Earth’s surface and subsurface has changed through time. It proposes that the Earth’s outermost layer, the crust, is composed of multiple rigid plates that move across Earth’s surface. Plate movement is largely driven by subduction, which is the tectonic process by which oceanic crust is pulled into the Earth’s interior. While decades of research have helped understand how subduction drives plate motion, induces volcanism, and is related to hazardous earthquakes, the processes through which subduction starts remain largely unknown. Without this knowledge, the theory of plate tectonics remains incomplete. This grant is focused on understanding the process of subduction initiation by studying an ancient subduction zone that is exposed in southern Alaska along the Shelikof Strait. This area preserves sedimentary and volcanic rocks that were deposited prior to, during, and after subduction started. The formation of this new subduction zone in the Late Triassic led to the development of the Talkeetna volcanic arc. The preservation of such a complete record of subduction initiation is rare and will allow the team to better understand the processes that caused oceanic crust to begin to sink into the Earth’s mantle. The characterization of these rocks will be the focus of an ambitious PhD student’s research under the direction of the two PIs at Purdue University. The grant will also include the production of Earth science classroom materials and the production of popular science education videos that will be posted on YouTube. To accomplish the proposed research goals, the PIs will conduct a multidisciplinary study of Late Triassic sedimentary and volcanic strata that are exposed along the Shelikof Strait. These rocks are the oldest known portion of the well-studied Talkeetna arc and record the events preceding, during, and immediately following subduction initiation in this area. The exceptional preservation of these strata will allow the team to test two end-member hypotheses that have emerged as possible subduction initiation mechanisms: forced and spontaneous. Forced initiation occurs when compression leads to crustal shortening and the development of structures along which negatively buoyant oceanic lithosphere can sink into the mantle. Spontaneous initiation occurs when density unstable oceanic lithosphere begins to sink into the mantle along a pre-existing lithospheric weakness. Both end-member processes make specific predictions for the nature of pre-, syn-, and post-subduction initiation sedimentary and volcanic strata. Forced initiation should lead to uplift and unconformity development prior to initiation. Spontaneous initiation will lead to sudden extension coeval with initiation. Typically, the record of these processes is obscured by subsequent deformation and magmatism in modern and ancient arcs. However, the Late Triassic strata along the Shelikof Strait remain relatively undeformed and are suitable for testing these models. To fully understand this record, the PIs will make new geologic maps of the area, produce new geochemical measurements and radiometric age determinations, and measure stratigraphic sections of sedimentary and volcanic strata. Once integrated, these data will provide a remarkably continuous record of subduction initiation processes over geologic time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-05

Altered circadian rhythms and sleep patterns are a hallmark of both aging and neurodegenerative diseases including Alzheimer’s disease (AD). Disruptions in circadian behaviors are observed at an early stage of AD and emerging evidence suggests that these might not simply be a symptom of disease, but could also contribute to pathogenesis. Notably, these disruptions in circadian behaviors are also observed in both aging Drosophila and in fly models of AD, indicating that Drosophila provides a genetically tractable model system in which to identify the conserved mechanisms underlying AD-associated circadian disruption. Circadian rhythms are controlled by a molecular clock that consists of a transcription-translation feedback loop that requires the rhythmic deposition of chromatin marks for its proper regulation. The deposition of these chromatin marks is mediated by enzymes that use key metabolic intermediates as substrate, providing a connection between the metabolic state of a cell and its epigenome. During aging, we and others have observed changes in the metabolic pathways that produce the donor molecule required for histone and DNA methylation, S-adenosylmethionine (SAM) – referred to as one-carbon metabolism. In the aging Drosophila head, we observe changes in one-carbon metabolism including an increase in levels of S-adenosylhomocysteine (SAH), which inhibits the activity of methyltransferases. Similar changes in SAM and SAH levels have been reported in clinical samples from AD patients, suggesting that these metabolic changes are a common feature of aging and neurodegenerative disease in both flies and humans. In both flies and mice, the circadian clock is necessary to prevent age-dependent retinal degeneration and neurodegeneration, suggesting that disruptions to circadian rhythms in AD could indeed contribute to pathogenesis. Here, we propose to expand our lab’s current aging studies into the context of AD by testing if AD, like aging, leads to changes in one-carbon metabolism that alter the methylation capacity of peripheral clock cells leading to epigenetic changes that disrupt circadian rhythms. Based on our preliminary data, we will focus on two enzymes that control the ratio of SAM to SAH in flies: Glycine N-methyltransferase (Gnmt) and Adenosylhomocysteinase (Ahcy). We propose that the changes in the SAM:SAH ratio observed in aging and in AD patients could be generated by increased expression of Gnmt, which is induced by inflammation, and/or by decreased activity of Ahcy, which is inhibited under exposure to oxidative stress. Notably, Gnmt plays a similar role in one-carbon metabolism to that of an enzyme that is also upregulated in the brains of AD patients: nicotinamide N-methyltransferase (NNMT). We will determine how AD-associated tau alters circadian gene expression, one-carbon metabolism, and histone methylation, and use genetic approaches to test if Gnmt, human NNMT, and Ahcy are necessary and/or sufficient to explain the impact of tau on circadian behavior. These studies will provide the basis for expanding our work on metabolism, epigenetics, and circadian behavior from the aging eye into the context of neurodegenerative disease with a particular focus on AD.

NSF Awards · FY 2025 · 2025-05

Programmable Small Molecule Biosynthesis This project aims to develop new bioplastics with diverse properties. The team will computationally design complex enzymes within engineered bacteria to produce novel, sturdy bioplastics that can withstand high temperatures and be easily recycled into their original components—or biodegraded and composted if discarded in a landfill. These advances will strengthen supply chain resilience through the bioeconomy and open new directions in domestic manufacturing. Beyond scientific advancements, the initiative will include outreach to undergraduate students to train the next generation of biotechnologists, the development of new workshops on protein design and its real-world applications, and will provide open-source tools for future innovations in biomanufacturing. This interdisciplinary effort seeks to engineer polyketide synthases (PKSs) to produce monomers for polyhydroxyalkanoates (PHAs) with enhanced thermal stability and recyclability. The focus is on creating hybrid PKSs capable of synthesizing gem-dimethylated PHAs (gdPHAs) through advanced computational protein design, addressing the longstanding challenge of slow turnover rates and poor domain interactions in engineered PKSs. Researchers will identify and recombine PKS domains to enable programmable biosynthesis of 3-hydroxyacids with tunable properties, leveraging deep learning-powered design tools. These tools will optimize enzyme functionality, reprogram active sites, and enhance transient protein-protein interactions within PKSs. Experimental design-build-test-learn cycles will validate the designs. Engineered PKSs integrated with PHA synthases will enable bacterial production of PHAs resistant to high temperatures and suitable for depolymerization, allowing either biodegradation or recycling. The project will develop retrosynthesis workflows for PKS pathways, computational tools for protein design, and methodologies for high-GC DNA synthesis. By making these innovations broadly accessible, the work will advance enzyme engineering, support STEM education, and foster domestic biomanufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Machine learning, particularly through deep neural networks, has revolutionized society, transforming fields such as computer vision and natural language processing and profoundly impacting daily life. In chemical engineering and process systems engineering (PSE), neural networks have made significant contributions across various scales—from designing new molecules for drug discovery to simulating chemical plant operations. However, a key limitation remains: the “black-box” nature of these models makes them difficult to interpret, often resulting in outputs that do not adhere to essential physical laws. This lack of reliability is especially concerning in safety-critical applications like process design and control, where compliance with strict physical constraints is crucial. To address this, a promising solution called physics-informed neural networks (PINNs) has emerged, embedding physical laws within neural networks to improve their accuracy and reliability. Still, PINNs have limitations, particularly in enforcing nonlinear and logical constraints that are common in PSE. This project proposes developing optimization-inspired neural networks (OINNs), a new class of neural architectures that integrate optimization principles to rigorously enforce physical and logical constraints. This approach not only enhances model reliability but also aims to broaden the utility of machine learning in engineering and beyond. In addition to the scientific advancements, the project includes educational initiatives to equip the next generation of chemical engineers with foundational AI and optimization skills. The Principal Investigator (PI) will redesign an existing data science course at Purdue University, emphasizing AI's role in engineering and integrating machine learning with core chemical engineering principles. The PI will recruit undergraduate researchers through the Research Experiences for Undergraduates (REU) program. Moreover, the PI will engage K-12 educators and students through outreach programs, including the development of a video game and working with K-12 teachers. These activities aim to broaden public understanding of engineering and inspire diverse young learners to explore STEM fields. This project proposes the development of optimization-inspired neural networks (OINNs) that incorporate strict, physically meaningful constraints directly within their architecture. These networks will be designed to address limitations of physics-informed neural networks (PINNs), which often rely on “soft constraints” that do not rigorously enforce physical laws and may not be suitable for safety-critical applications. The OINN architecture integrates optimization theory, enabling strict adherence to both linear and nonlinear constraints, as well as embedding domain-specific knowledge and accommodating uncertainties in parameters. To achieve these goals, the research will leverage optimization techniques from linear programming, conic programming, and mixed-integer programming to build layers that represent various physical and logical constraints. The OINN framework will be benchmarked against PINNs and other models in process design, process control, and chemical structure prediction, assessing improvements in prediction accuracy, interpretability, and constraint satisfaction. Through this approach, the project aims to establish a new approach for reliable and explainable machine learning models in process systems 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.

NIH Research Projects · FY 2026 · 2025-04

Project Summary Glioblastoma (GBM) is the most aggressive type of cancer that occurs in the brain. While functional anti- cancer therapeutics, such as emerging chimeric antigen receptor (CAR)-T and natural killer (NK) cell therapies, have been developed to treat various cancers, their therapeutic applications for brain cancers have been impeded by the blood-brain barrier (BBB) and immunosuppressive tumor microenvironment. Due to their native ability to cross BBB and penetrate the brain parenchyma, neutrophils have been recently used as carrier cells to deliver imaging and therapeutic drugs into brain tumors. Recently, the investigators engineered a GBM-targeted CAR-neutrophils from human pluripotent stem cells (hPSCs) for the first time and demonstrated their superior antitumor activities in animal models of GBM as compared to CAR-NK cells. However, the limitation of such approaches is the expensive and strenuous in vitro process required for generating the engineered cells. Currently, a significant gap remains in our understanding and programming of tumor-associated neutrophils within the tumor microenvironment (TME) in vivo. In this proposal, the investigators will harness the power of synthetic biology, unbiased machine learning, next-generation sequencing, murine and canine models to interrogate tumor-associated neutrophils and develop new strategies to program them towards antitumor effector cells. Our preliminary data shows that synthetic nucleoside-modified messenger RNA (modRNA) could be specifically delivered to circulating neutrophils via lipid nanoparticles (LNPs) or exosomes and produce effective antitumor CAR-neutrophils directly in vivo. The central hypothesis of this proposal is that modRNA CARs specific for glioma cells will direct neutrophils to remodel TME and extend the lifespan of tumor-bearing animals. To test this hypothesis, we will first develop and optimize neutrophil-specific CAR constructs with machine learning algorithms in Aim 1. Then in Aim 2, we will determine the antitumor activities of combinatory CAR-neutrophils and radiation or CAR-T cell therapy in murine models. In Aim 3, we will evaluate the safety and therapeutic efficacy of in vivo produced CAR-neutrophils in pet dog patients with spontaneous glioma. It's expected that this study will lead to the establishment of a novel in vivo neutrophil programming platform, providing proof-of-concept for modRNA CAR-neutrophil immunotherapy.

NSF Awards · FY 2025 · 2025-04

This research develops statistical methods for analyzing the relationships between the many variables resulting from psychometric studies. These relationships typically are represented in the form of graphs, with the nodes denoting the attributes and the edge strengths denoting the relationships among them. Due to the computational intractability of these graphical models, existing methods rely on approximate techniques that generally result in less efficient estimates and do not allow for the probabilistic quantification of uncertainty. This project develops tools to mitigate these issues and enable analyses that are simultaneously statistically efficient and computationally tractable. The developed methods are applied to study the relationships between symptoms of psychological diseases, latent skills, and attributes. As a part of the project, graduate students are trained, and publicly available software is made available for the broader scientific community. Additionally, the investigators design advanced courses that incorporate major research findings from this project. This research develops a scalable computational toolbox that allows for likelihood-based inference for exponential family graphical models. Probabilistic graphical models used to study relationships between multiple variables often involve an intractable normalizing constant, which precludes both maximum likelihood and fully Bayesian inference. Approximate methods, such as those based on pseudo-likelihood or score matching, provide the usual workarounds. However, full likelihood-based inference, when feasible, is statistically efficient. This project develops statistical tools that allow full likelihood-based inference for intractable graphical models that are both computationally and statistically efficient. Crucially, this also includes fully Bayesian procedures that produce automatic uncertainty quantification. These tools are extended to cover a broader class of models that are obtained by marginalizing over certain variables in the graph. These new models allow for nonparametric dependence structures between variables building on basic parametric models and are useful for item-response theory. Finally, new longitudinal models are developed to study the evolution of relationships between variables over time. A key ingredient in these longitudinal models is the decoupling of time-varying and subject-specific effects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This project aims to develop novel algorithmic solutions to advance the optimal scheduling of distributed energy resources (DERs) in power distribution grids. The project will bring potentially transformative changes in real-time DER orchestration as the proposed solutions waive low-observability challenges in distribution grids and unlock the full potential of DERs controlled by machine learning (ML) models. These two goals are achieved by identifying the most critical data streams for orchestrating DERs in real time and by integrating ML models into grid scheduling. Regarding intellectual merits, this project: i) introduces feature selection into grid optimization, ii) leverages contemporary differentiation tools, iii) develops algorithms for grid optimization using generalized load models; and iv) draws parallels between grid optimization and empirical risk minimization. Regarding broader impacts, this project: i) explores converging ideas at the intersection of power systems, optimization, and ML, which could bring potentially significant technological innovation in the power industry ecosystem; ii) advances knowledge in sensitivity analysis, feature selection for optimization and control, and resilience against data attacks to optimization input parameters; and iii) reaches out to undergraduate students through hands-on activities, seminars, and discussions on career opportunities in STEM. Optimally scheduling distributed energy resources (DERs) in distribution grids entails communicating with thousands of customers in near real-time to read load demands and pass them as input data to the optimal power flow (OPF) problem. At the same time, DERs increasingly deviate from traditional constant-power models as their injections are specified by data-driven control rules or policies, which often give rise to dynamics by interacting with the power grid. In this context, this project aspires to develop OPF formulations that are data-frugal and seamlessly accommodate grid-adaptive power injection models for DERs. The particular objectives of the projects are to: 1) Reduce data communication for a grid operator to orchestrate DERs, by strategically identifying subsets of customer or grid meters that are most influential in making effective OPF decisions; 2) Use machine learning tools to transform distilled data so that when fed into the OPF, they yield near-optimal near-feasible decisions; 3) Expedite the calculation of sensitivities (partial derivatives) for the inverse power flow (PF) mapping and equilibrium states under DER-induced grid dynamics; and 4) Devise novel algorithms for solving the OPF under generalized power injection models, when convex relaxations are deemed inadequate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This I-Corps project is based on the development of an advanced decision-support system that optimizes workforce allocation in healthcare settings. The system addresses critical staffing shortages by dynamically assigning medical professionals across multiple facilities based on real-time demand, patient needs, and workforce availability. By reducing understaffing and reliance on expensive temporary workers, this solution enhances operational efficiency, improves patient care, and mitigates burnout among healthcare workers. Beyond healthcare, the technology has potential applications in other industries that require dynamic workforce distribution, such as emergency response, retail, and logistics. By leveraging advanced analytical techniques to improve decision-making in complex, resource-constrained environments, this project contributes to economic sustainability and workforce resilience. The commercial potential lies in its ability to provide scalable, data-driven staffing recommendations, offering significant cost savings and efficiency gains for large organizations that face fluctuating demand and constrained labor resources. 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 a predictive and prescriptive analytics system that integrates machine learning with optimization techniques to generate real-time staffing recommendations. Unlike existing workforce management tools that focus on single facility, this technology continuously adapts to changes in demand, hospital conditions, and workforce availability across facilities. The research behind this project builds on state-of-the-art methods from operations research and machine learning to develop a decision-support system that optimally balances workforce distribution while maintaining operational efficiency and resilience. The research underlying this system has demonstrated its ability to reduce staffing inefficiencies and improve resource allocation through simulation and pilot testing in real-world settings. By enhancing the adaptability and responsiveness of workforce planning, this project advances the field of decision analytics and has the potential to drive widespread adoption of intelligent resource management solutions across multiple sectors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Many computer security applications necessitate the creation and deployment of updated or modified versions of existing software. In fact, the ability to deploy security-related software updates is now considered essential to guarantee a device's security. Unfortunately, in many scenarios, such as devices running legacy applications for which the source code is unavailable, reliably creating, deploying, and verifying software updates remains challenging. As a result, many devices are left unpatched, even when known to be vulnerable to security weaknesses. To address this issue, this project will develop a principled and comprehensive methodology for developing, deploying, and verifying software patches. In particular, this work will ease software patching in scenarios where the original software source code is unavailable, allowing the deployment of security patches in millions of devices currently left unpatched. Additionally, this project will produce educational materials and conduct security competitions to develop and assess software patches. To achieve its goals, this project will start by developing differential binary-analysis techniques to compare original and patched binaries, focusing on the modifications introduced by the patch. These techniques will then be used to efficiently assess the security enhancements introduced by a patch while detecting potential unintended side effects. In parallel, the project will develop reliable approaches to patch binary code at scale. Finally, the project will implement tools to generate human-readable representations of patch modifications to aid analysts in understanding the impact of patches and to update Software Bills of Materials (SBOMs). Collectively, these efforts will establish a comprehensive, human-in-the-loop pipeline for defining, generating, and verifying software patches at the binary level, thereby enhancing the security and reliability of current and future software systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-04

Project Summary The mis-regulation of chromatin drives both cancer progression and chemotherapy resistance. For example, alterations in the trimethylation of histone 3 at lysine 27 (H3K27me3) is associated with numerous cancers; however, H3K27me3 is also important in numerous normal cellular processes as well. H3K27me3 marks are targeted by the chromodomains of the chromobox (CBX) subunits of Polycomb Repressive Complex 1 (PRC1), which compacts chromatin and represses transcription. There are five CBX paralogs (CBX2,4,6,7,8) that have differential expression in cancer. While CBX6 and CBX7 are often decreased in cancer, CBX2 and CBX8 are frequently increased in cancers. Chromodomains for several CBX paralogs (CBX4, 6, 7) have been successfully targeted with small molecules and peptides that display moderate potency and selectivity. To develop CBX2 and CBX8-selective inhibitors, our research team developed a robust and sensitive assay for affinity-based enrichment of ligands from DNA-encoded libraries and utilized these assays for selections of focused DNA- encoded libraries against a panel of chromodomains. The selective CBX8 inhibitors developed using this approach have validated the CBX8 chromodomain as a therapeutic target in MLL-AF9 leukemia; however, their low cell permeability limits their utility in targeting CBX8-specific function in these cancers. In this grant, we will develop improved CBX8 inhibitors using DNA-encoded libraries selected and designed for increased cellular activity compared to current probes. Hit molecules will be resynthesized off-DNA and tested for affinity and specificity in vitro, as well as in cells. Cellularly active CBX8 chromodomain inhibitors will be used to validate the selective inhibition of CBX8 as a viable, non-toxic therapeutic strategy in vivo.

NIH Research Projects · FY 2025 · 2025-03

Project Summary. Exposure to childhood adversity is one of the strongest risk factors for depression across the life course, increasing risk by at least twofold. Although these exposures are clearly harmful, there is substantial variation in how people respond to adversity; not all children who experience early-life adversity go on to develop mental health problems. This finding raises the question: Are there modifiable factors early in life that protect against the effects of adversity, contribute to resilient biological processes, and prevent new onsets of depression? Family- and community-level factors, including maternal social support, parenting behaviors, grandparent involvement, peer support, and school quality, are established promotive factors for depression, even among children with a history of adversity. Emerging research also suggests DNA methylation (DNAm), a well-studied epigenetic modification, may function as a pathway to explain the biological embedding of these promotive factors. Yet, prior studies in this field have been small, cross-sectional, and focused mostly on candidate genes. As such, our interdisciplinary team seeks to considerably advance these insights by identifying the extent to which DNAm mediates, or partially explains, the effect of these positive life experiences on risk for depression across childhood to adulthood. We will study these relationships in two birth cohorts: the US-based Fragile Families and Child Wellbeing Study (FFCWS) and the UK-based Avon Longitudinal Study of Parents and Children (ALSPAC). Both cohorts are rare in containing repeated measures of positive life experiences, childhood adversities, DNAm, symptom and/or diagnostic markers of depression risk, and indicators of positive adaptation. Leveraging our team’s track-record of identifying adversity-linked sensitive periods for DNAm and depression, we will capitalize on these data to pursue three aims. In all aims, we will model risk (i.e., childhood adversity exposure) and positive life experiences simultaneously, which few studies have done before. In Aim 1, we will characterize the time-dependent effects of positive life experiences on depression from childhood to adulthood. In Aim 2, we will investigate the time-varying impacts of positive life experiences on DNAm patterns and trajectories. For Aims 1 and 2, we will use a two-stage structured life-course modeling approach (SLCMA) our team developed for high-dimensional data. In Aim 3, we will evaluate the extent to which these DNAm patterns explain the relationship between positive life experiences and depression using statistical mediation. In sum, this study will: 1) identify modifiable positive life experiences that shape DNAm and depression patterns, 2) determine if there are sensitive periods when these experiences are more influential in shaping these outcomes, and 3) generate insights about promotive and protective biological mechanisms that could lead to new targeted interventions that benefit all children and mitigate the effects of adversity for those who are exposed.

NSF Awards · FY 2025 · 2025-03

Space infrastructure plays a critical role in socio-economic development-enabling scientific discoveries and advancing communications, remote sensing, geophysical and astrophysical applications. The exponential growth in the launch of space objects in orbits around the Earth over the last few years has made space more congested and has contributed to increased space debris. Additionally, the increased government and commercial interest in lunar and Mars missions pose both potential benefits and risks to safe and sustainable space operations. The primary goal of the Center for Research in Emerging Sustainable Space Technologies (CRES2T), a partnership between the Pennsylvania State University, Texas A&M University, and Purdue University, is to research novel concepts and associated technologies that enable safe and responsible use of space for humanity. CRES2T researchers will investigate the intricate relationship between hardware and software design, autonomy, artificial intelligence, and modeling and simulation to enable safe in-space assembly, service and manufacturing (ISAM) while addressing the unique challenges posed by the harsh space environment. The secondary goal of this center is to stimulate the next-generation workforce by training the next-generation workforce in this critical area of national need. CRES2T activities have the potential to impact the new global space economy, create new jobs, and advance our nation’s economic, scientific, technological, and national security interests. The CRES2T activities will focus on an ecosystem where researchers not only conduct research and development of individual technologies related to sustainable space operations but also focus on integrating different technological advances in a seamless manner to accelerate transitioning of these advances to commercial entities. The research thrusts and the operation of CRES2T are formulated to address the complex and rapid commercial pulls involved in developing space technology. Purdue University’s College of Engineering identified the emerging space sector as a critical area for its entire College of Engineering. Through the Purdue Engineering’s Cislunar Initiative, Purdue is leveraging its existing strengths in mission design, space debris analysis and propulsion to advance access to cislunar space; characterize and enable the utilization of resources from the Moon and near-Earth objects; and conceive the infrastructure necessary for cislunar space development and habitability. As the orbital economy grows, new challenges are also emerging in space traffic management and policy, and defense of the nation’s space-based assets. Purdue team will work closely with other sites (Penn State and Texas A&M) to test these technologies in a seamless manner and developing the US operational workforce through student internships, annual workshops, short courses, and virtual tutorials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This award supports the convening of a two-day workshop that brings together anthropologists, ethnobiologists, and conservation scientists to further the science of cultural adaptation, ecological change, and conservation in mountain regions across the world. The workshop will be held in association with the 2025 meeting of the Society of Ethnobiology. This workshop provides a novel approach to uniting the biological, social and environmental scientific approaches to understanding the variations in human adaptation in mountain regions worldwide. The workshop results in a number of tangible societal benefits, including: supporting collaborations between universities, government institutions, and members of local communities; supporting students and faculty in EPSCoR jurisdictions; informing pathways for grassroots solutions to changing land-management and conservation challenges developed by community stakeholders that center local knowledge and community implementation; providing a model for collaborations between experts and local knowledge holders and for future partnerships that result in knowledge and resource sharing, student training, and ongoing multinational scientific investigations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

This Major Research Infrastructure (MRI) grant acquire an advanced metal additive manufacturing (AM) system that will support research to advance knowledge in metal AM, contributing to scientific progress and enhancing national economic prosperity. Additive manufacturing, often called 3D printing, enables the creation of complex components that are difficult or impossible to produce through traditional methods. The acquired equipment will enhance the structural performance of AM parts, develop sustainable energy solutions, and improve engineering education through hands-on experiences with advanced technologies. These advancements will benefit society by fostering innovation, sustainability, and workforce development in critical industries such as aerospace, energy, and healthcare. The interdisciplinary nature of this research, which involves fields like manufacturing, materials science, and sustainable engineering, will also support the education and training in engineering and STEM. The technical research facilitated by the advanced metal AM system will address several critical areas, significantly advancing knowledge in AM. The system will enable the development of advanced qualification and certification methods to enhance the reliability and structural integrity of AM components. Efforts will focus on developing novel post-processing strategies to improve fatigue resistance. Performance-based topology optimization for intricate geometries will be conducted to enhance robustness under extreme conditions, including fluid-structure interactions. Furthermore, innovations will include supersonic fluid mixing systems, high-efficiency heat exchangers, advancements in photovoltaic cells, and catalyst bed optimization for water purification reactors. Through innovative research, education, and training programs, this project will contribute to both fundamental knowledge and practical applications, driving impact in sustainable energy, workforce development, and the competitiveness of U.S. industries. 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.