Lehigh University

NSF Awards · FY 2025 · 2025-06

This Partnerships for Innovation – Mid Career Advancement (PFI-MCA) project enhances disaster response and recovery by improving the capabilities of machine learning and autonomous systems for rapid damage assessment. Frequent, and increasingly severe, natural disasters—such as hurricanes, wildfires, and floods—threaten human health, infrastructure, and natural systems. All natural disasters leave a path of devastation necessitating effective management to mitigate their adverse effects on human life. Any delay in the decision-making process intensifies human suffering and wastes valuable resources. To make well-informed decisions promptly, robust and scalable hazard projection and damage assessment are needed. Using the wealth of available data, including powerful machine learning and autonomous systems, and traditional numerical hazard and vulnerability models, this project aims to build a smart technology with the potential to address the complex problems of rapid response and recovery. The methodologies and findings from this project will have broader applications in fields such as remote sensing, healthcare, and autonomous systems. The project will also contribute to workforce development by designing new curricula, conducting hands-on workshops, and offering lecture series and conference tutorials to engage all Americans. The project aims to build a generalizable model for natural disasters based on large data from autonomous systems and numerical models. The model will address the complex problems of sustainable solutions for hazard projection and real-time damage assessment. While data-driven models are efficient, they often lack scalability as models trained on historical data and models trained on one hazard may not perform well in another. Conversely, numerical models, while widely used, are computationally intensive, which makes them less applicable to risk analysis. To overcome these limitations, this project will develop physics-informed machine learning models capable of operating across different geographic scales and disaster types by integrating physical principles with data-driven approaches. By combining insights from multiple disciplines, the project will create a unified model that captures the complex interactions among various disaster factors. Additionally, the project will focus on improving hazard modeling and its integration with post-disaster assessments to enhance decision-making. The novelty of the research lies in the development of advanced, physics-informed machine learning models aimed at improving vulnerability models and enhancing scalability and adaptability for real-time decision-making in post-disaster scenarios. 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

There is an urgent need for broadening computing education to prepare students for the Artificial Intelligence (AI) workforce, but achieving this vision faces major obstacles. These challenges include the complexity of computational problem-solving, inadequate teacher training, student retention, and the demands of self-paced learning environments where students must develop solutions using AI technologies with limited guidance. Specifically, students often struggle to independently develop problem-solving strategies and effectively manage their learning without consistent support. Self-regulated learning (SRL)—where students actively plan, monitor, and reflect on their own learning process—emerges as a critical skill for success in computing and AI education. While many learners struggle to develop these skills effectively, AI-assisted self-regulation through hybrid intelligence models offers a promising approach. This project leverages hybrid intelligence to develop Meta-Partner, an adaptive AI-assisted SRL solution. Meta-Partner enhances students’ self-regulation and metacognition through close, iterative human-computer collaboration. It empowers students to revise goals, adjust strategies, monitor progress, and enhance self-reflection with continuous AI support throughout the SRL cycle. Meta-Partner will be integrated into AIResolver, an existing online problem-based learning platform for AI literacy. A study with 300 high school and college students, using both quantitative and qualitative methods, will evaluate Meta-Partner's effectiveness. This project aims to promote AI education by making problem-solving platforms more accessible and engaging for learners developing self-regulation skills. 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 Faculty Early Career Development Program (CAREER) grant will fund research that looks to advance the capabilities of aerial robots by enabling aerial manipulation and transportation of flexible objects such as cables, rods, hoses, and plastic sheets, thereby promoting the progress of science, advancing prosperity and welfare, and securing the national defense. Current aerial robotic systems are mainly limited to the manipulation of rigid objects since aerial manipulation and transportation of flexible materials present unique challenges due to the dynamic and unpredictable forces involved, which remains an under-explored field. This research project will strive to solve these challenges by providing a novel methodology that integrates control systems and reinforcement learning to maintain stability, enabling fast learning, and ensuring time-critical recovery. The outcomes of this work intend to unlock transformative applications in construction, disaster response, and industrial automation. For instance, aerial robots could autonomously deliver and position cables and rods in construction projects, manipulate fire hoses in emergency scenarios, or deploy temporary plastic covers for roof protection during natural disasters. This project can promote scientific progress in robotics and benefits society through safety, efficiency, and cost-effectiveness. It has the potential to reduce human risks in hazardous environments and provide automated solutions to labor-intensive tasks. The educational and outreach components include the development of an open-source platform for aerial transportation – a collective effort with students from all levels – and collaborations with K-12 schools, supporting the next generation of scientists by encouraging early interest in programming and engineering. This research aims to make fundamental contributions to the field of aerial manipulation of flexible objects by developing a modular control architecture that progresses from stable behaviors to optimal performance. This framework enables aerial robots to continuously adapt and improve their manipulation capabilities, enhancing performance over time. The project begins with the design of an adaptive controller that ensures stability and provides real-time compensation for external forces without prior knowledge of an object's material properties. This controller establishes a robust baseline for reinforcement learning, which enables aerial robots to explore and optimize control strategies through interaction. By integrating adaptive control with reinforcement learning, the framework combines the reliability of baseline stability with the agility and efficiency of learned strategies. To address challenges associated with high-speed maneuvers and the inherent risks of real-world operation, the framework incorporates contingency strategies that allow the system to detect and recover rapidly from unstable states. The research progresses systematically, beginning with the manipulation of linear objects, such as rods and cables, and advancing to two-dimensional surfaces, including plastic sheets. These advancements will be evaluated in a construction-inspired testbed, where actual drones must repetitively transport objects. 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

This award supports the Research Experiences for Undergraduates (REU) site at Lehigh University, which will provide ten undergraduate students each summer the opportunity to participate in ten weeks of research across a diverse range of physics fields. These fields span from fundamental areas, such as String Theory, Nuclear Physics, and Astrophysics, to classical domains like Atomic and Molecular Physics, and Statistical Physics, as well as areas with practical applications in technology, energy, and health, including Condensed Matter Physics, Optics, and Biophysics. The program involves research, weekly faculty seminars and lab tours, a student symposium at the end of the summer, other professional development opportunities, and various social functions that foster the interaction of undergraduate students with faculty and graduate students. A group housing arrangement greatly enhances both the academic and social experiences of the participants as it helps the students to develop a strong group identity. Program participants gain research experience at a critical point in their undergraduate careers, as well as an excellent preview of graduate school. The students make substantial contributions to specific research projects, with many presenting their findings at key conferences such as the American Physical Society Meeting, and some achieving publication of their work in referred journals. Since the beginning of the program at Lehigh more than two-thirds of the participants have gone on to graduate studies in science and engineering, and most of the remaining students obtained jobs in fields of science and technology. The program plays a critical role in empowering promising students to pursue careers in science and other technical fields, guided by ongoing evaluation and assessment for continuous improvement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Non-technical summary Gene editing tools like CRISPR are transforming biology and medicine. However, ensuring their safety and precision remains a critical challenge. Delivering CRISPR only when and where needed would significantly enhance its effectiveness while minimizing unintended effects. This project explores how biomaterials can be used to enable precise control over CRISPR's location and timing. Biomaterial-guided CRISPR delivery has the potential to enhance CRISPR’s stability, regulate its release, and target its activity to specific locations, offering several advantages over conventional delivery methods. Despite its potential, significant gaps remain in understanding how the physical and chemical properties of biomaterials influence CRISPR delivery, uptake, and editing efficiency. This research will address these gaps in knowledge by exploring how tuning the composition of biomaterials and nanoparticles impacts CRISPR delivery. The specific objectives will examine how material properties like charge and porosity control CRISPR release and activity and how cell-material interactions influence CRISPR uptake and function. By systematically studying these factors, this project will provide new insights for designing safer and more effective CRISPR delivery systems, advancing gene editing technology for clinical and research applications. In addition to its scientific goals, the project integrates education and outreach efforts to increase public understanding of how biomaterials can enhance CRISPR’s safety and effectiveness and engage students in STEM. By combining cutting-edge research with educational initiatives, this project seeks to drive innovation in gene editing while promoting equity and diversity in science and engineering. Technical summary Spatial and temporal control over CRISPR delivery is essential for increasing its efficacy and safety. Harnessing three-dimensional (3D) biomaterials as platforms for material-guided CRISPR delivery is a promising strategy to enhance CRISPR efficacy and minimize off-target effects across a wide range of basic and translational applications. Biomaterial-guided CRISPR delivery offers specific advantages due to enhanced CRISPR cargo protection, defined spatial location, and precise control over temporal action. Despite these advantages, there is a gap in knowledge regarding how material physicochemical properties and cell-material interactions determine CRISPR delivery rates, cellular uptake, and editing function. This CAREER program aims to understand how the physicochemical properties of nanoparticles and 3D biomaterials, along with material-cell interactions, control CRISPR function. The central hypothesis is that by tuning the formulation and properties of nanoparticles, the charge and porosity of hydrogels, and cell-material interactions, we can control CRISPR's temporal action, editing efficiency, and off-target effects. The proposed work combines a highly tunable alginate hydrogel strategy with novel peptide-based CRISPR delivery and emerging synthetic biology tools to investigate three specific objectives following a multi-level approach: (1) at the CRISPR delivery level, determine the effect of peptide-based nanoparticle formulation and physiochemical properties on CRISPR ribonucleoprotein (RNP) uptake, activity and functionality; (2) at the material properties level, decouple the individual contributions of material charge and porosity on the temporal action of CRISPR nanoparticles; and (3) at the cell-material interaction level, establish the impact of cell adhesion and proliferation on CRISPR cell uptake and editing efficiency. Additionally, this CAREER project integrates an educational and outreach program focused on: (1) increasing scientific literacy on how biomaterials can enhance the safety and efficacy of gene editing, and (2) promoting STEM interest and retention among students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Quantum computing (QC) may revolutionize computations in the future as well as expand the types of problems that can be solved efficiently. Chemical engineering, and in particular process systems engineering (PSE), will benefit from a QC revolution. PSE relies on mathematical models and decision-making problems concerning physicochemical systems. These problems are challenging to solve with classical (i.e., non-quantum) computers. The aim of this project is to investigate how novel QC methodologies can be used to solve problems in PSE. The successful completion of the project will showcase the possibility of quantum computers eventually outperforming classical computers in solving complex decision-making problems in PSE. Such an achievement would have far-reaching implications for other sub-fields within engineering, such as supply chain management, signal processing, machine learning, and operations research. Additionally, this project will contribute to expanding the QC workforce to include much needed experts in QC with backgrounds in chemical engineering and operations research. The objectives of this proposal are three-fold. First, while most of the optimization-oriented QC research is dedicated to the solution of optimization problems with only discrete-choice decision variables, this project will first focus on deriving and analyzing quantum optimization techniques for problems with continuous choice decision variables. Second, akin to what has been successful in classical optimization, this research aims to use these quantum continuous optimization techniques as the fundamental block to address the solution of specially structured mixed optimization problems arising in process systems engineering (PSE). The hybrid combination of strengths from both QC and classical hardware will be key for the successful implementation of the proposed approach. Third, the investigators aim to tailor and evaluate the performance of the derived quantum optimization techniques on mixed-integer optimization problems arising in PSE, particularly those arising from a structured approach to modeling interconnections between the array of process operations. To accomplish these three objectives, researchers will address the fundamental problem of building a complexity and performance framework for quantum algorithms for the solution of continuous optimization problems, aiming to characterize and analyze their performance not only in theory, but also in computational practice. In addition to designing and analyzing hybrid quantum algorithms, researchers will develop specialized software and numerical techniques to test the derived strategies and obtain crucial feedback for computationally effective algorithmic design. The successful completion of the proposal’s objectives will lead to a better understanding of the potential of QC to help in the solution of application-relevant mathematical problems and have a direct impact on the capacity of quantum computers to outperform classical computers in the solution of complex decision-making problems in PSE and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Artificial microswimmers modeled after microorganisms hold great potential for biomedical applications such as minimally invasive surgery and targeted drug delivery. Most biomedical applications require multiple microswimmers, and their activity is complicated by the non-Newtonian properties of bodily fluids. Hydrodynamic interactions and fluid rheology both significantly affect swimmers’ swimming performance, and extrapolation of collective behavior using existing models is not possible. Understanding the physics of this type of swimming will enable successful biomedical applications. The researchers will use a novel framework that combines experimental, numerical, and machine learning methods to understand fluid dynamics in environments closer to real-life applications. The overarching goal of this project is to develop a comprehensive understanding of the emergent behavior in finite microswimmer clusters in Newtonian and complex fluids. The researcher will conduct computational and experimental analyses in Newtonian and complex fluids on microswimmer clusters of different populations and packing densities, performing a prescribed gait and positioned in prescribed formations. The project will study the emergent behavior of finite clusters of autonomous robotic swimmers endowed with machine learning capabilities experimentally for similar test cases without prescribing a gait to be performed. The experimental results will be compared with patterns predicted by AI-coupled simulations. The experimentally validated computational tools and flow visualization techniques will be utilized to elucidate the physics governing the emergence of the patterns. The integrated education and outreach plan will engage undergraduates in STEM through workshops, research involvement, and structured mentorship. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

This project will investigate a novel high-current conversion technology to promote various distributed energy resources (DERs)-related applications, including battery energy storage, electric vehicle fast charging, hydrogen production from solar and wind power, and data centers. With the rapid growth of energy demand from DERs, there is a significant challenge to directly supply a huge DC current, up to a few kiloamperes or even higher. From the power systems perspective, this project aims to provide capability to support high-power demand response with high efficiency and fast speed. From the power electronics perspective, it suggests a solution to satisfy the high-current requirements with high power density and low cost. This project aims to alleviate the existing technical gaps and contribute to the development of the next-generation high efficiency, compact, and low-cost high current conversion technology. The project is expected to benefit the renewable energy and transportation electrification industry and contribute to the advancements of the US both technically and economically. This project will leverage the state-of-the-art wide bandgap (WBG) semiconductor devices to develop a modular multiphase interleaved high current conversion technology for various DERs and loads. To satisfy the high current needs and overcome existing challenges, the project proposes to study high-efficiency resonant circuits, integrated magnetic coupling, and modeling and control of power converters. A key technology is the compensation circuit topology design, including inductors, capacitors, and their interconnections to create resonance. A unique innovation of this project will be the development of novel compensation topologies to achieve a high-current source output property, which is different from a conventional voltage-source output. In this way, the expected high-current capability could be realized to satisfy many DER applications. The developed high frequency soft-switching strategy would significantly reduce the size of passive components and contribute to high power density in practical use. Meanwhile, education efforts will also be integrated into the research to ensure that the research achievements in this project will benefit students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Computers can now generate text that closely resembles human writing in a wide variety of domains, from essays to poetry to computer code and even to movie scripts. However, there is no way to know today exactly how these technologies will change the ways that we teach, work, and learn. Thus, people tend to imagine different possible futures, and these imagined futures can shape their thinking about generative AI. This project studies the futures that college educators imagine around generative AI by examining discussions of classroom policies. For both introductory writing courses and introductory computer programming courses, the project team will analyze discussions among educators about how and why to form policies around using or prohibiting generative AI tools. Analyzing these discussions can help reveal the futures being imagined around generative AI and how those imagined futures are influencing our actions in the present. This project will also share results of the researchers' analysis with instructors who participate in the research, helping them to build a better sense of the space of possible policies they might use in their own classrooms. The project includes two main lines of research activity. First, discussions about educational policies around generative AI will be collected from a range of online sources, including opinion pieces (e.g., in the Chronicle of Higher Education), social media discussions (e.g., in academic Reddit groups), and others. The researchers will analyze these discussion data using computational topic modeling to identify textual patterns indicative of latent suppositions and beliefs about possible sociotechnical futures. Second, researchers will conduct a series of qualitative interviews with instructors of two types of introductory college courses: courses on writing and composition, and courses on computer programming. These interviews will ask instructors directly about the futures that instructors imagine around generative AI, as well as how those imagined futures relate to their own course policies. The interviews will also include a reflexive component, where preliminary results from the above computational analysis are shared with participants. Doing so both serves as a member check on the results, i.e., comparing the research team's interpretations to those of the instructors themselves, and offers instructors an opportunity to reflect upon and contrast their own policies and imagined futures with those of other instructors. The results of this project will help lay a foundation for future research examining beliefs and policies about generative AI in a variety of application domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The broader impact/commercial potential of this I-Corps project is the development of wireless power transfer (WPT) technology. Compared to the existing power systems, the proposed technology may increase power transfer efficiency while reducing cost and weight. This may benefit a wide variety of applications including the charging of low-power consumer electronic devices and high-power electric vehicles (EVs). The proposed technology is thin enough to be integrated into an electronic device without increasing its size and thickness. In addition, in EV charging applications, it may significantly improve the mobility of EVs by removing the charging cables and providing convenience to customers. Each wireless charging pad works as an independent power system with a secured data-link service for customers to support intelligent charging demand and management. The applications may be expanded to biomedical implants to eliminate wire-connected chargers for patients and bring benefits in healthcare area. The proposed technology also may be applied in high-voltage power systems to support the penetration of renewable energy and distributed energy resources with high-voltage isolation capabilities. This I-Corps project is based on the development of wireless power transfer (WPT) technology based on magnetic resonance. The proposed technology uses a resonant circuit topology design and a special magnetic coupler structure implementation. It has been demonstrated to achieve multi-kW power transfer with over 97% efficiency across a distance up to 8 inches, which represents state-of-the-art performance. In addition, the magnetic coupler structure is optimized to confine the magnetic fields within a limited area and reduce magnetic field emissions to the surrounding environment. The technology is designed to satisfy the safety requirements proposed in both the IEEE C95.2 standard and the international commission on non-ionizing radiation protection (ICNIRP) guideline. The proposed WPT technology targets high power, high efficiency, long distance, and compact (high power density) applications, spanning from low power to high-power systems and may be used in the power, energy, and biomedical areas. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to identify trends in the demographics and turnover behavior of the STEM teacher workforce. It focuses on specific remedies and investments needed to retain prospective teachers in high-need schools, especially rural communities, and to improve outcomes for their students. The project includes two complementary studies, one using the National Teacher and Principal Survey (an update from the earlier Schools and Staffing Survey) and a second, using longitudinal administrative data from the states of Kansas and Missouri. The project will explore a range of questions, including: how STEM teacher demographics, turnover intentions, and actual turnover may have changed nationally due to the COVID-19 pandemic; and how the Great Recession of 2007-08 and the COVID-19 pandemic may have influenced teacher outcomes. This body of research will produce insights to inform actionable recruitment and retention practices for high-need school districts and future research focused on teacher labor markets. The conceptual framework and proposed analyses build upon a model of teacher turnover that suggests three main categories of factors that drive teacher turnover: teacher factors, school factors, and external factors and events. Leveraging this framework, the investigators group the research questions and analyses into three broad themes: STEM teacher characteristics, the school and student characteristics in which STEM teachers are employed, and contemporary secular trends that impact STEM teachers. They also consider the interplay between the STEM teacher characteristics and the school context in which STEM teachers work. The investigating team will employ descriptive and regression analyses to answer the research questions. Across interconnected lines of inquiry, the researchers will balance national generalizability with comprehensive state-specific application to inform current and future practice, policy, and research. This project is supported by the EHR Core Research(ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent issues in STEM education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Cardiovascular aging, which involves various structural and functional changes in the vascular, valvular, and ventricular systems, is a significant risk factor for heart diseases and associated morbidities. These conditions typically progress silently and become noticeable only in advanced stages, making early detection and intervention crucial. Traditional diagnostic methods, which rely on symptoms to guide further testing, are increasingly challenged by a growing patient population and a short-staffed clinical workforce. This project aims to transform aging care by developing personalized digital twin technology that will integrate data from wearable devices, echocardiographic measurements, and advanced cardiovascular modeling. This initiative will enhance the monitoring and understanding of cardiovascular aging through noninvasive methods, allowing for better therapeutic interventions. The digital twin technology will have broad applications in healthy aging care and disease monitoring. Additionally, the project will provide educational opportunities in mathematics, scientific computing, and biomedical science, promoting diversity and inclusion in STEM fields. Outreach efforts will emphasize the importance of healthy lifestyles and scientific literacy to the broader community The central theme of this project is to utilize computational and animal models to develop a physics-based personalized digital twin for monitoring and understanding cardiovascular aging. By integrating subject-specific simulations, artificial intelligence, and multiscale noninvasive data, the digital twin will enhance insights into cardiovascular health. The project will focus on developing a data-driven, physics-informed digital twin for real-time monitoring and prediction of aging-related cardiovascular diseases, using mechanics-based markers. Leveraging advanced modeling techniques, scientific machine learning, and noninvasive measurements, this project aims to fill significant knowledge gaps and create a transformative tool for personalized healthcare. The development of novel algorithms for handling multiscale, multimodal data is anticipated to enhance the understanding of mechanical changes associated with cardiovascular aging. Expected outcomes include a physics-based digital tool with predictive capabilities, validated against animal models, offering the potential for early detection and intervention in aging-related cardiovascular diseases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Human tissues are highly organized structures with specific collagen matrix arrangements and the resulting mechanical properties varying from point to point. The effects of such heterogeneity play an important role for tissue function and failure. However, fundamental challenges present in understanding how heterogeneity affects the growth and remodeling of various tissues and the related mechanical performance of these tissues, due to limited knowledge on understanding of the multi-scale interactions between cell signaling and heterogeneous matrix remodeling and their roles in tissue function. The central theme of this proposal is to develop a novel bio-mathematical framework that dynamically integrates physics-based and d ata-driven modeling approaches with experimental measurements, to capture cell-matrix interactions in tissue multi-scale behavior and function. We aim to improve the fundamental understanding of the underlying biological and mechanical implications o f matrix heterogeneity, and advance towards our ultimate goal of providing a digital twin model for in-vivo tissue degeneration on aneurysm growth. The team, formed by three Pl/Co-ls with complementary expertise on scientific machine learning, experimental tissue biomechanics, and biological tissue modeling, plans to ad vance on theoretical, computational, and experimental as pects towards the knowledge of fi broblast-medi ated extracellular matrix (ECM) remodeling via three specific aims: (i) A multi-scale data-driven model will be developed to concu rrently couple our intra-cellular signaling network model, a novel agent-based cell population model, and a peridynamics model of tissue mechanics; (ii) E nriched by advanced multi-task/multi-modal operator learning techniques, an information-theory based model adaptation-experimental design integration pipeline will be developed; and (iii) New quantitative knowledge on long-term fibroblast-mediated ECM remodeling will be obtained and validated through both in-vitro and ex-vivo experiments. As a result, our combined in-vitro/ex-vivo experiments and multi-scale model will help identify for the first time how heterogeneity affects tissue remodeling and the related mechanical behaviors of tissue. The identified key features and optimized model forms enable the learning from small data regime, providing a step-stone towards the digital twin modeling f or in-vivo aneurysm growth. The proposed model will have significant impacts on transforming our understanding of the roles of cell functi ons for determining tissue structures/mechanics, and thereby enabling model-based virtual screening to identify and optimize therapeutic interventions. Our multi-scale model will of fer a powerful prediction tool to the multi-scale biological mechanisms in many other bio-tissue systems, such as blast-induced traumatic brain injury, aging of cartilage tissue, and bone reconstruction.

NIH Research Projects · FY 2025 · 2024-09

Abstract In the US, there are 7.3 million older adult immigrants (inclusive of refugees), and by 2060 the US’s older adult immigrant population is anticipated to increase to 22 million. Compared to other older adults in the general population, older refugees have a disproportionate burden of challenges related to past pre-resettlement traumas, language barriers, family stress, social isolation, employment, and access to health care. These challenges and stressors, in turn, are associated with poor mental health. Unfortunately, older refugees receive limited attention from providers, and there is a dearth of evidence-based interventions designed to meet the needs of this vulnerable population. Our goal is to use a community-based participatory research approach (CBPR) to conduct an exploratory sequential mixed methods longitudinal study of mental health of older adult ethnic-Nepali Bhutanese with a refugee life experience resettled in the US. First, we aim to use qualitative methods to explore social support as a protective factor for mental health among older Bhutanese and develop a conceptual model that will be evaluated and refined over the course of the study. Second, we will use a 3- year, longitudinal design to quantitatively assess the role of post-resettlement risk and protective factors in the relationship between pre-resettlement experiences and mental health among 200 Bhutanese aged 50 and older. We will use a longitudinal study design to assess past exposure to war-related traumas/stressors in the country of origin (Bhutan) and refugee camps (Nepal), current bio-psycho-social risk and protective factors, and mental health outcomes at three-time points. Third, we will qualitatively explore the caregiving experience of Bhutanese older adult caregivers and quantitatively examine the relationship between family caregiver mental health and mental health. A family caregiver of each older adult will be enrolled in the longitudinal study (N=200) and interviewed at the same three-time points as the primary study participant. We will assess the psychosocial functioning of these caregivers over the duration of the study. Accomplishing these aims will increase our understanding of the impact of forced displacement and immigration on long-term mental health and inform multi- level interventions to address the psychosocial functioning of aging refugees resettled in the US. This has the potential to reduce health disparities among aging immigrants from racial/ethnic minority groups.

NIH Research Projects · FY 2024 · 2024-09

Transcriptional profiling of proliferative skeletal muscle mononucleated cells coupled with broadband electrical cytometry towards diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome PROJECT SUMMARY / ABSTRACT Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating, acquired disease affecting up to 2.5 million Americans. With increasing evidence that a proportion of patients with COVID-19 experience prolonged convalescence and chronic symptoms similar to ME/CFS, it is suggested that the incidence of ME/CFS will increase significantly. Currently, no single biomarkers or pathognomonic signs have been identified for diagnostic measures. Instead, diagnosis is based on clinical symptoms after exclusion of other possible etiologies known to cause fatigue, a method that does not prescribe adequate sensitivity or specificity. Since clinical symptoms suggest that skeletal muscle is a major and consistent target of the pathology, and proliferative skeletal mononucleated cells (SMMCs) are excellent indicators of muscle disorders, we hypothesize that proliferative SMMCs from of ME/CFS patients are distinguishable from those of healthy individuals on the molecular and cellular level. Proposed work will investigate gene expression, functional pathways and electrical characteristics of single SMMCs from ME/CFS and healthy donors to identify a matrix of molecular and biophysical markers to enable future development of diagnostics. Single-cell mRNA profiling will identify differential gene expression describing alterations that occur in ME/CFS samples. Using differential biomarkers identified by scRNA-seq, subpopulations of SMMCs unique to ME/CFS will be sorted to study changes in protein expression and cell function. Furthermore, an impedance cytometer recently developed by our team will be used to measure single cell electrical spectra, and disease-specific signatures will be identified by machine learning. The molecular, cellular and electrical characteristics will be further correlated with each other to provide a comprehensive understanding of the SMMC pathology in ME/CFS, an untapped subject. The proposed single- cell transcriptome analysis of SMMCs from ME/CFS patients represents the first study of its kind and will greatly contribute to the fundamental knowledge of the role of proliferative SMMCs in ME/CFS dysfunctions. Compared to molecular approaches, proposed impedance cytometry captures a ‘big picture’ of the multitude of changes contributing to abnormalities in ME/CFS SMMCs. As specific molecular markers have not been identified for diagnostic measures, the holistic electrical characteristics of single cells offer a unique perspective of global changes in SMMCs and hold great diagnostic potential in the future. Integration of electrical and biological studies of SMMCs will further allow interpretation of the impedance spectra to promote sensing specificity.

NSF Awards · FY 2024 · 2024-09

Reducing the carbon and energy footprint of the chemical industry will require developing new chemical transformations that use non-fossil carbon sources to make high-volume commodity chemicals. To that end, the project explores the feasibility of catalytically converting ethanol and methanol (both potentially manufactured via green technology) to acrolein – a chemical intermediate used in the manufacture of acrylate polymers. The project represents a collaborative effort between Lehigh University and the Hamburg University of Technology under joint funding from NSF and the Deutsche Forschungsgemeinschaft (German Research Foundation, DFG). The research explores molecular level details of how the chemical reaction progresses over an iron molybdate catalyst, obtaining information related to the site on the catalyst surface where reactions occur (active site) and the sequence of reaction steps to form the product (the reaction pathway). The molecular-level insights will aid in the discovery of novel catalytic materials and optimal reactor operating conditions, thereby reducing process energy requirements. Taken together, the use of green feedstocks, combined with energy-efficient catalysts and intensified process technology, has potential to greatly decrease the carbon footprint of acrylic polymer manufacture. In addition to the technical aspects, the collaborative project will catalyze an international exchange of ideas, methodologies, and educational materials, thereby fulfilling the broader objectives of the NSF-DFG research initiative. The project supports a synergistic and unique US-German collaboration to develop a novel spatial- and time-resolved analysis of catalytic systems whereby variation in the structure of the catalyst and the concentration of surface reaction intermediates, both along the reactor length and over time, is leveraged to obtain underlying information about the catalytic active site and the reaction pathway. The project combines research expertise at Hamburg and Lehigh to employ: (1) operando molecular spectroscopy techniques in the modulation excitation spectroscopy (or MES) mode to understand the fast transients of the catalytic system and elicit kinetically relevant surface intermediate species and reaction pathways; (2) a novel compact profile reactor (CPR) coupled with spectroscopy and mass spectrometry - a unique set up not currently available in the US - to understand the slow evolution of catalyst active sites, surface and gas phase species, and reaction rates along the reactor bed; and (3) spatial (CPR) and temporal (MES) data of the catalytic system along with hierarchical modeling tools (Density Functional Theory (DFT), microkinetic modeling, and optimization methods) to develop a mechanistic model of acrolein synthesis on FeMoOx catalysts from non-fossil methanol and ethanol. This work will allow deep contrasting spatiotemporal studies to elicit molecular insights about the catalyst and aid in the development of tools to create mechanistic models from DFT, MES, and CPR data that will be applicable to a wide range of catalytic reaction systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This collaborative research project will explore how deep-sea hydrothermal vent ecosystems use the important nutrient element nitrogen, and how these environments influence the broader deep-ocean nitrogen cycle. Despite the intense biological activity at hydrothermal vents, their role in deep-ocean elemental cycling remains poorly understood. A key challenge is that previous studies of nitrogen cycling in these systems have been conducted under atmospheric pressure, whereas hydrothermal vent organisms live under extreme high-pressure conditions. This discrepancy raises concerns about how pressure changes might have affected previous nitrogen cycling experiments conducted on samples from these systems. To address this, this project will employ specialized equipment to conduct nitrogen cycling experiments at deep-sea pressures. By doing so, the investigators will enhance understanding of hydrothermal vents' contributions to the oceanic nitrogen cycle. Additionally, the project will support two Ph.D. students and a Postdoctoral Scholar who will take on a key leadership role. Leveraging the recent merger between Arizona State University (ASU) and the Bermuda Institute of Ocean Sciences (BIOS), the team will also collaborate with the Director of BIOS Education and Community Engagement to create ship-to-shore virtual field trips for classrooms in the U.S., and develop a lesson plan which will be shared with high school educators. Current understanding of the role that deep-sea hydrothermal vent ecosystems play in nitrogen (N) cycling in the ocean is plagued by inaccuracies arising from microbial N-cycling rate measurements made under atmospheric pressure, previously unmeasured N-cycling pathways, and overlooked contributions from characteristic vent habitats. This project will address these issues using 15N-labeled tracer incubations to measure rates of loss, recycling, and microbial assimilation of bioavailable N across reduction-oxidation (redox) gradients at two distinct seafloor vent habitats. The investigators will perform incubations on low-temperature vent fluids emanating from bare rock and faunal assemblages. They hypothesize that the more gradual redox gradients within faunal assemblages will favor N-recycling processes (i.e., nitrification and dissimilatory nitrate reduction to ammonium) that have not been measured at the seafloor. The team will measure rates of N-cycling processes in parallel under atmospheric and in situ pressures at the Endeavour hydrothermal vents, where previous evidence has indicated a tight coupling between reductive and oxidative processes. Molecular genetic data produced from environmental samples and tracer incubations will be used to identify the relevant microbial taxa involved in the different pathways of N cycling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

In this project, a participatory modeling approach is taken to guide construction of a human judgment platform that generates temporal forecasts of the trajectory of an infectious agent. It is posited that to learn about the behavioral dynamics of experts three key features must be considered: (1) the factors that an expert uses to make decisions, (2) the accuracy by which any human and expert can predict an epidemic, and (3) how a set of forecasts can be combined to more accurately model the future to improve decision making. Work in the field of infectious disease modeling and human behavior typically concentrates on the public, often overlooking experts who make decisions that influence the general population. No work to date has explored constructing a human judgment forecasting platform that can collect temporal forecasts from individuals, methodology specific to combining human judgment temporal forecasts into an ensemble, and focusing on the characteristics of public health decision processes which impact downstream general population behaviors. Not only does this project advance the science of infectious disease forecasting, but it has the potential to benefit several populations who are at high risk for adverse outcomes due to influenza. The goals of this proposal are divided into three tasks. Task 1 involves recruitment of experts in the modeling of infectious disease, public health officials, and infectious diseases clinicians. From this population, cultural norms/needs are established and thought processes associated with infectious disease decision making are identified. In Task 2, a novel human judgment platform is constructed that experts and a lay audience can use to generate temporal forecasts of the trajectory of an infectious agent. This serves as a testbed to measure the performance of expert and lay temporal forecasts and compare human forecasts to computational model forecasts. Task 3 involves implementation and comparison of the performance of algorithms for combining human judgment forecasts; understanding the properties that are in common between human judgment and computational forecasts; and building a novel algorithm trained on traditional surveillance and augmented by human judgment temporal forecasts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Biological macromolecules work together in a complex network to carry out essential cellular functions in living organisms. Understanding this complex network of interactions at the molecular level by developing necessary computational tools is an all-encompassing goal of our laboratory. This proposal aims to continue such efforts particularly on CHARMM-GUI for the biomolecular modeling and simulation community, G-LoSA-related tools for structural systems pharmacology, and GlycanStructure.ORG for glycan modeling and simulation and glycan binding site prediction. First, CHARMM-GUI has become an essential web-based cyberinfrastructure for constructing complex biomolecular simulation systems. We will further develop and expand its functionality to support more force fields, QM/MM interface for enzymatic catalysis reactions, more advanced simulation methods, API development, and DOI assignment. Second, we will continue to expand the application of our local-structure centric computational toolset, G-LoSA (Graph-based Local Structure Alignment), for the studies of protein-ligand interactions at the proteomic level. Our local structure refinement method will be extended to include protein-protein binding interfaces to further elucidate conserved local surface regions of protein-protein interactions that are crucial in biological processes. Third, our glycan modeling and simulation toolset will be extended to glycan binding pose prediction, glycan binding site prediction, and glycan binding site optimization for rational design and refinement of a known or potential glycan binding site. The successful completion of this project is expected to provide a large and unique scope of research software tools for the biomedical research community to carry out innovative and novel biomolecular modeling and simulation research for the prevention and treatment of human disease.

NSF Awards · FY 2024 · 2024-09

Cell engineering often focuses on a small number of genes. CRISPR technology has helped sharpen that focus. The secondary effects of cell engineering are more widespread and difficult to anticipate or measure. These effects are critical to efforts to design and optimize biomanufacturing processes and to produce safer therapeutic cells. The main objective is to understand the impact of gene activation on stem cell metabolism. Outreach to increase retention of students from underrepresented and at-risk backgrounds will strengthen the STEM career workforce. CRISPR application to stem cell biomanufacturing is constrained by suboptimal delivery vectors. These vectors cause unexpected off-target effects. Preliminary data demonstrates the potential of CRISPR-mediated activation (CRISPRa) to upregulate key regulatory genes of mesenchymal stromal cells (MSCs). A fundamental gap in knowledge remains: the effect of specific gene upregulation on the gene network interactions that dictate the completion of complex cellular functions such as differentiation. The goal is to integrate non-viral CRISPRa and artificial intelligence to understand the impact of cell manipulation and gene activation on stem cell differentiation. Two specific objectives are proposed: (1) Decouple the effects of cell transfection and CRISPRa on MSC therapeutic capacity, and (2) bridge the gap between single-gene manipulation and complex cellular behaviors by predicting the downstream effects of CRISPRa on gene expression profiles. The completion of these objectives will be fundamental for establishing standardized high-throughput protocols to enhance the therapeutic output of engineered stem cell products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to revolutionize the understanding and prediction of human mobility by leveraging the vast data generated through advanced urban infrastructures and commercial systems. Understanding real-time population density and how human mobility is affected by crises can significantly improve early interventions and response strategies. By analyzing large-scale human mobility data from sources like smartphones, payment systems, cellular towers, and transportation networks, this project seeks to overcome the limitations of existing research, which often relies on biased, incomplete, or noisy data from single domains. This project aligns with NSF’s mission to promote the progress of science by providing mathematical and statistical algorithms for the analysis of large spatiotemporal datasets with applications to quantitative models of human dynamics. Additionally, this work will include educational and outreach activities, promoting diversity and inclusion within the scientific community and beyond. The project will address two fundamental challenges: spatiotemporal data heterogeneity across various domains and privacy concerns during cross-domain collaboration. To achieve this, the investigators will design domain-invariant spatiotemporal modeling techniques for human mobility prediction, develop new frameworks for collaborative learning across various domains without compromising data privacy, and create algorithms for threat detection, targeting, and mobility prediction during crises. This research will utilize real-world data from two cities, incorporating 13 types of mobility data. The intellectual merit of this project includes the development of a spatiotemporal nonlocal neural operator model, a unified framework for federated unsupervised graph learning, and domain-aware anomaly detection models for threat scenarios. Beyond human mobility prediction, the technological advancements will contribute to a wide range of spatiotemporal modeling applications. The methods and tools developed will be made available as open-source software, and the investigators will conduct various dissemination activities to share the findings with both academic and broader communities. The educational framework integrates outreach activities to ensure that diverse groups benefit from the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Better model systems are needed to improve our understanding of how tissues develop, function, and regenerate in order to design more effective biomedical therapies. Synthetic hydrogel matrices have been widely used in these applications and have significant advantages as fully defined systems that can be tailored to specific biomedical applications through the inclusion bioactive ligands within matrices having tunable viscoelasticity. However, the extracellular matrix (ECM) that surrounds cells in tissues contains numerous proteins and biopolymers that are dynamically modified by cells, and which aspects of cell-ECM interactions are most important for recapitulating physiological adhesions is an area of active research. All adherent cell types use both syndecans and integrins to mediate cell attachment to the ECM and measure the local viscoelastic properties. However, most hydrogel systems are often functionalized with a single integrin-binding RGD ligand covalently attached to the polymer network, which does not bind syndecans and cannot be re- arranged by cells. We propose to develop a platform technology that can be used to better mimic cell-ECM interactions found in tissues. This will be done by including dynamic ligands for both integrins and syndecans within a hydrogel having tunable viscoelastic properties. We have developed a platform that utilizes interpenetrating networks of covalent and multiplexed non-covalent polymers to enables us to independently tune the mobility of multiple adhesion ligands in addition to both the stiffness and stress relaxation of the hydrogel. We hypothesize that including dynamic ligands for syndecans will lead to cell-matrix adhesions that better recapitulate those found in tissues, and this will increase osteogenic differentiation of human mesenchymal stem cells within viscoelastic matrices. We will test this hypothesis in two aims. The First Aim utilizes a multiplexed system containing multiple discrete self-assembling peptide nanofiber networks, each which can be functionalized with ligands for either integrins or syndecans having tunable mobility. Different ligand combinations and mobilities will be tested the number and size of focal adhesions will be quantified, in addition to the extent of actin network formation. The Second Aim will utilize covalent and non-covalent networks to tune the viscoelastic properties of the hydrogel to understand how syndecans and integrins combine to transduce mechanical signals that drive cell behavior. We will culture hMSCs in gels having different viscoelastic properties and ligand compositions to understand and quantify how dynamic syndecan ligands increase osteogenic differentiation of hMSCs. The PI has significant experience designing dynamic, viscoelastic hydrogel matrices to target specific cell-matrix interactions. This proposal will both help develop a highly modular engineering platform that can be applied to a range of tissue systems, while also uncover design rules for recapitulate physiologically relevant cell-matrix interactions within synthetic systems.

NIH Research Projects · FY 2024 · 2024-08

Project Summary Our proposed project will build upon previous research which has shown an association between oral health and systemic health, including the relationship between heart disease and periodontitis. We will benefit from recent methodological advances in computational genomics and be the first study to provide causal evidence about the postulated relationship between poor oral health and heart disease using nationally-representative, longitudinal data. Specifically, we will construct polygenic scores using a GWAS-by-subtraction genomic structural equation model that are suitable for use in instrumental variables estimation. To accomplish our Aims, we will use longitudinal files containing 11 years of data (2006-2016) from the Health and Retirement Study (HRS). This longitudinal database will provide over 22,000 older Americans producing a sufficiently large sample size to overcome prior analytical obstacles. Aim 1. Determine the strength of genetic risk factors for poor oral health. The working hypothesis is that individual genetic composition is a strong predictor of poor oral health. Aim 2. Determine the causal effect of poor oral health on the probability of heart disease (HD). The working hypothesis is that variation in oral health due to individual genetic composition can be used to identify the causal effect of poor oral health on HD.

NSF Awards · FY 2024 · 2024-08

Biological membranes separate living cells from the environment and create intracellular compartments as a hydrophobic barrier. They are composed of diverse lipids and form dense environments with thousands of transmembrane and peripheral proteins. These proteins are engaged in essential cellular functions, including metabolism, energy generation, signal transduction, solute transport, vesicle trafficking, cellular motility, recognition, adhesion, differentiation, and proliferation. Advances in experimental methodologies and artificial intelligence algorithms have led to a surge in membrane protein three-dimensional structures and in silico models that are available to researchers. Nonetheless, a comprehensive understanding of membrane protein organization, including facilitating the dynamic interactions within and between cells remains elusive. This gap in our understanding impedes our ability to decipher membrane protein functional roles and regulatory mechanisms in the context of the cells, organs, or organisms in which they work. The proposed BioMembHub cyberinfrastructure (https://biomembhub.org) is designed to advance, expand, and unify databases and web servers for structural modeling and analysis of proteins, peptides, and small molecules in lipid membranes of varying molecular compositional complexity. BioMembHub is distinguished by its integration of physics-based methodologies, bioinformatics techniques, and the deep learning capabilities of the widely used AlphaFold system. BioMembHub will be easy-to-use and thus serve not only as a valuable research resource for scientists and educators, but as an educational platform for students and an instrument for public engagement with cutting-edge biomembrane research. The goal of the BioMembHub collaborative project is to create an integrated platform consisting of seven web servers and three large databases, which would enable exploration of the structural and dynamic aspects of biomolecules in membranes using both implicit and explicit membrane representations. The suite of web servers, namely TMPfold, FMAP, PPM, OPRLM, and TMDOCK, enables all-atom modeling and analysis of folding, stability, conformational positioning, and molecular interactions of proteins and peptides in membranes. PerMM and CellPM web servers calculate membrane permeability coefficients and translocation pathways across lipid bilayers of small molecules and peptides. The OPM database includes the massive set of experimental structures of membrane proteins and peptides from the RCSB Protein Data Bank (PDB) positioned in membranes by PPM. The Membranome 3.0 database serves as repository for thousands in silico models of single-pass transmembrane proteins from six proteomes. The PerMM database collects experimental and calculated permeabilities data for five hundred small molecules. With execution of the project, the OPM/OPRLM database will be significantly advanced by streamlining its update procedures and broadening the dataset of membrane proteins and peptides with known structures aligned in flat or curved membranes. The Membranome(X) database will be expanded by incorporating single-pass transmembrane proteins from 20 proteomes and a novel collection of protein complexes. These complexes will be modeled using the AlphaFold Multimer methodology and validated by employing TMDOCK. The sustainability and expandability of these resources will be improved to ensure their long-term utility and relevance to the scientific 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

A large portion of human-induced carbon dioxide emissions, and the heat that they trap within Earth’s climate system, are being absorbed by the global ocean. The long-term impacts of this absorption on climate and ocean circulation are poorly understood. Forecasts of 21st-century climate change suggest that global warming will lead to a more stratified ocean and weaker global ocean circulation, which would have major ramifications for regional and global climate. However, recent studies suggest that ocean circulation was stronger, not more sluggish, during warm intervals earlier in Earth’s history. This potential discrepancy warrants a better characterization of ocean conditions during warmer-than-present climates of the past. To do so, the study will focus on intervals in the geologic past during which atmospheric carbon dioxide concentrations were similar to today, and temperatures were several degrees warmer. The most recent geologic interval that meets this criterion is the Pliocene Epoch (5.33 to 2.58 million years ago). The primary goal of this project is to generate paleoceanographic records to test models suggesting that there was a strong overturning circulation in the North Pacific Ocean during the Pliocene Epoch. This award will support the careers and training of five early-career scientists, and train and mentor high school and undergraduate students. The goals of this proposal are to: 1) test the recent hypothesis that there was North Pacific deep-water formation and an active Pacific Meridional Overturning Circulation present at times during the warm Pliocene; 2) trace the regional distribution of ocean ventilation; and 3) refine the use of redox, temperature, and productivity proxies in bulk sediments and foraminifera for reconstructing Pliocene North Pacific Ocean ventilation, nutrient availability, and water mixing. A multi-proxy approach will examine intermediate to deep ocean circulation patterns across two key geologic intervals: the mid-Pliocene warm period through the intensification of Northern Hemisphere glaciation (~2.5 to 3.3 Ma) and the early Pliocene (~4.9 to 5.1 Ma). This project will generate new records of i) redox and productivity proxies from bulk sediments, ii) redox, productivity, and water mass mixing proxies from benthic foraminifera, and iii) productivity and temperature proxies from planktic foraminifera, at four sites (IODP 882, 883, 887, and 1208) that cover a range of depths and locations across the North Pacific Ocean. In parallel, the study will analyze a series of coupled climate model simulations with active biogeochemical and d13C cycling. The model tracer, circulation, isotope, and ventilation age results will aid in the interpretation of the newly generated data. This multi-proxy approach, underpinned by climate model analysis, will help ensure the feasibility of the proxy-inferred dynamics. 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.