Grants

Jefferson Public Library

Expand the existing Robotics Programs and S.T.E.M. days to increase program participation and offer children and teens in the community fun, engaging, and educational opportunities.

NIDCR - National Institute of Dental and Craniofacial Research

PROJECT SUMMARY Idaho State University (ISU) is a resource-limited, regional, R2 doctoral-granting institution in a rural, Mountain West state. As the state’s designated lead for training in the health professions and a participating member of the statewide Idaho INBRE program (NIH IDeA Network of Biomedical Research Excellence), ISU has significant potential for growing its biomedical research portfolio. To build toward a stronger future in biomedical research, ISU needs a clear picture of the institutional capacity and research environment. A needs assessment and an action plan that specifically hones in on strengthening the biomedical research enterprise has never happened at ISU, and it is much needed. Our long-term goals are to strengthen biomedical research infrastructure at ISU and to create a research environment where faculty and students in the biomedical sciences can flourish. The objective of BRING-IT: Biomedical Research Investing in Growth and ISU Talent is to develop an institution-wide and data-informed action plan that will guide improvements in biomedical research capacity. University leadership will champion the action plan, which will be developed by institutional collaborators at different levels of the research enterprise. To attain the objective of this application, we will create a practical roadmap, with both short-term and long-term goals, for strengthening the future of biomedical research at ISU in ways that will lead the institution and faculty to be more competitive for NIH funding. Institutional leadership is committed to seeing through implementation of the action plan. The overall impact will reverberate across the university, with ISU becoming better positioned to secure individual research project grants (e.g., R01s), as well as capacity-building grants (e.g., COBREs and CTRs). Outcomes are expected to have a positive impact on faculty research support and professional development, student training opportunities, institutional research culture, and research infrastructure.

NSF

Viruses are effective at delivering DNA or RNA to cells. They protect the DNA or RNA inside a small capsule constructed of proteins. Replacing the DNA or RNA normally carried by these particles with therapeutic molecules makes them potential drug delivery candidates. The shapes of the proteins that create the capsule can be changed to increase the internal volume of the capsule or to fit together more loosely to allow small molecules to enter or exit the capsule. Or, the amino acids on the outer side of the capsule can be changed to alter the way they interact with different cell types. How the proteins self-assemble has not be studied extensively despite its importance for the capsule's use in drug delivery. The objective of this project is to understand how the cell produces and assembles these capsules. This project will also engage K-12 teachers and create and share teaching materials that integrates biology, biochemistry, engineering principles, and computational modeling. This goal of this project is the production of engineered virus-like particles (eVLPs). These are nanometer-scale particles that mimic the delivery properties of viruses but cannot replicate and are not infectious. The intention is to engineer eVLPs that contain mammalian genetic circuits capable of autonomous and programmable control of gene expression. Computation will guide design of individual biomolecules and genetic circuits. A primary emphasis is designing a system to dynamically direct the efficient self-assembly of eVLPs. In some ways, this approach borrows lessons from how natural viral systems have evolved to produce similar particles, but in this case the control system is focused on optimizing manufacturing (rather than viral replication). It will bring a new type of characterization to these important systems. This work will generate new genetic parts, biomanufacturing-ready cell lines, computational models, and conceptual frameworks enabling bioengineers to extend these approaches to investigate and optimize biomanufacturing of other complex biologics. 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

Macrophages are immune cells that play critical roles in fighting disease, healing tissues, and maintaining overall health. Their behavior can shift in ways that either help or harm the body, depending on their environment. Histone lactylation, in which lactate, a small molecule metabolite produced in the body, changes how genes are turned on or off, may help explain how these cells change roles. This project will use synthetic biology to create precision tools that can add or remove lactylation marks on DNA-packaging proteins. These tools will help scientists understand how lactylation affects macrophage behavior and could lead to new ways to control immune responses in diseases like cancer. Course module development and support of undergraduate researchers will help to grow the biomanufacturing workforce. This project investigates the functional role of histone lactylation in macrophage polarization. Lactylation has been linked to macrophage transitions from inflammatory (M1) to anti-inflammatory (M2) phenotypes, particularly within the immunosuppressive tumor microenvironment. The central hypothesis is that modulating histone lactylation can selectively control macrophage phenotype, independent of external stimuli. In Phase I, lactylation writers and erasers will be created. These are enzymes capable of adding or removing lactylation at specific sites on histones. First, a detailed map of histone lactylation across macrophage polarization states and external stimuli will be established. Site-specific lactylation tools will be generated by engineering a dCas9-p300 fusion system to promote lactylation at H3K18 near the Arg1 gene and evolved using a yeast-based screening platform for increased lactylation selectivity. Histone deacetylase complexes using CRISPR-Cas9 strategies to remove lactylation will be designed and implemented, and the impact on macrophage phenotype assessed. Key milestones for Phase 1 include validation of lactylation writers and erasers with confirmed selectivity over acetylation and demonstration that these tools can meaningfully regulate macrophage phenotype. In Phase 2, the functional impact of these new lactylation tools in preclinical cancer models will be evaluated. Studies will employ lactylation readers and writers to quantitate how lactylation influences tumor-associated macrophage repolarization and how these tools can improve outcomes of chimeric antigen receptor macrophages, using both in vitro and in vivo tumor environments. In total, this project will advance fundamental understanding of macrophage immunometabolism, provide versatile tools for lactylation engineering, and lay the groundwork for durable macrophage-based therapies for cancer and other immune-mediated 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.

NSF

Cells sense and respond to their environments. Immune system cells sense and respond to viruses, bacteria, and even cancer cells. Most sense and response circuits involve several intermediate molecules acting as a cascade. This takes time and makes it difficult to engineer these circuits to respond to new inputs or to generate new responses. Existing circuits often overlap and interact. This makes efforts to modify them subject to possible negative side effects. To avoid these problems, it is proposed to develop a class of molecules that sense and respond directly to environmental cues that can be engineered and can infiltrate existing cells to change their responses. For example, this might be a way that cells in an existing tumor could be directed to break apart and die. This effort will focus on a class of proteins referred to as intrinsically disordered proteins (IDPs). Unlike most other proteins, they do not have a fixed conformation and can rapidly and dramatically change shape in response to stimuli. Because they can be designed to interact with signal molecules and cellular proteins, they could act as a rapid switch for cell metabolism in response to a specific signal. Outreach at the local Southside Chicago Science Festival will make science relatable and help attract students to STEM careers. Supporting undergraduate researchers will grow the synthetic biology workforce. Molecules that drive cell-specific responses based on defined inputs can increase the safety and efficacy of bioengineering and biomedical technologies. This synthetic biology project will establish a new class of dynamic molecules that drive defined, gated output signals based on endogenous, cell-specific input triggers, where the sensing and output functions are entirely contained within a single molecule. Binary interactions between these “smart molecules” and either one of two targets – an effector and sensing target - are weak, but cooperative interactions in the ternary complex are strong; thus, if a function is linked to any of the binary interactions, dependence on cooperative ternary complex formation creates an AND gate for that function. This project will establish this new mechanism by developing dynamic cooperative molecules that elicit a controlled response (i.e., apoptosis) in mammalian cells based on the coincidence of two binding targets – either an exogenously expressed model protein or endogenous proteins specific to a target cell type, such as HIF1a. The novel AND gate-induced molecular technology will define a new mechanism for cell engineering and molecular design and pave the way for a new class of therapeutics with broad potential to impact human health. 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

Immunoglobulin G (IgG) is a type of antibody. It is primarily active in controlling infection in body tissues. It actively binds viruses, bacteria, and fungi. Nanobodies (Nbs) are fragments of IgG. Some nanobodies exhibit anti-cancer activity. Bacteria can be modified to produce Nbs and one of these, E. coli Nissle 1917 (EcN), has been shown to selectively colonize tumor tissue. This project will develop two gene circuits in EcN. The first will sense signals in the tumor microenvironment and respond by synthesizing antitumor NBs. The second circuit will sense the presence of the artificial sweetener erythritol and will break open the bacterial cell, releasing the Nbs and killing the bacteria and the tumor cells. If successful, this could be adapted to a range of solid tumors. which are very difficult to treat with conventional therapies. The project includes extensive educational and outreach components, offering hands-on research opportunities for students and trainees at multiple levels. The technical thrust is to program a probiotic strain with multi-input genetic circuits. These circuits couple therapeutic nanobody expression with regulated release. The system targets two immune checkpoint receptors, PD-1 and LAG-3. It is designed to operate by applying exogenous erythritol to induce bacterial lysis and release of the therapeutic Nbs into the tumor microenvironment. Erythritol, artificial sweetener not naturally present in tumors, offers a reliable external trigger to control timing and dose. The approach will be validated in mouse models using in vivo bioluminescence tracking of bacterial populations. The research also aims to establish a robust biocontainment system utilizing integrases or toxin-antitoxin systems to enhance the stability and reliability of the genetic circuits for biocontainment purposes. The project will achieve scientific advancements through the design of genetic circuits, the development of microbial chassis, and the application of microbial immunotherapy. 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

Synthetic biology can be used to create and improve treatments for many diseases. Genetic techniques are used to modify T cells to create CAR-T cells that can kill some types of cancer cells. Gene therapy offers the promise of curing genetic diseases for which there are no treatments. A significant problem with these technologies is the variability of gene delivery to the targeted cells. Viral particles are often used to deliver the desired genes. A cell could be infected by a single viral particle, or many, meaning the cell could receive one or many copies of the gene to be expressed. The variability in the resulting response of the infected cells means the treatment results could vary widely. The objective of this project is to develop protein circuits that can self-regulate. This would remove the effect of infection variability on cellular performance, and thereby even out the therapeutic effectiveness. The reproducibility this would introduce would accelerate the development process for new biotherapeutic strategies. This project will develop dosage-controlled synthetic circuits by implementing proteolysis-based incoherent feedforward loops (IFFLs). The primary goal is to create self-regulating circuits that maintain consistent performance regardless of delivery variations. The approach combines proteolytic regulation with secreted protein engineering. The research will proceed in two phases: first, establishing the technical foundation using synthetic reporters to develop and characterize the basic circuit components, followed by demonstrating functional feasibility using cytokine outputs. An iterative strategy of computational modeling and experimental validation to optimize circuit design and performance will be employed. The methodology includes developing proteolytic control mechanisms, engineering secreted protein systems, and implementing circuit-on-circuit dosage control through careful characterization and tuning of individual components. The experimental approach will systematically progress through several key stages. Initially, individual proteolytic regulatory elements will be designed and optimized, establishing their kinetic parameters and dose-response characteristics. These components will then be integrated into IFFL circuits, carefully validating their function using fluorescent reporters. Computational modeling will guide the design process and help predict circuit behavior under various conditions. The project will advance to testing with therapeutic proteins, specifically cytokines, as output molecules. This phase will include extensive characterization of circuit performance in therapeutically relevant cell types and delivery vectors. 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.

Elizabeth Menges

Bringing Cyprus' Rich Legacy to Cambridge

NSF

This project aims to serve the national interest by expanding undergraduates' access to research experience by embedding a long-term ecological research study in a large-enrollment introductory biology lecture course. Mentored research experiences are fundamental for training future science professionals and have been linked to students' persistence in STEM. However, access to research mentors is limiting and mechanisms for recruiting students into research favor those with exemplary academic records. As a result, many students may not benefit from the training and skills development that comes with research experience, and students are likely to be overlooked who have enormous creative potential despite grades or standardized test scores that suggest otherwise. Research is increasingly becoming a feature of undergraduate biology laboratory courses, but these require significant resource inputs (staffing, supplies, safety equipment) and serve far fewer students than their 'lecture-based' counterparts. This Level 2 Engaged Student Learning project seeks to study the effectiveness of embedding research in a large-enrollment introductory biology lecture course. Students will test hypotheses by collecting and analyzing data as part of a multi-year ecological study that documents seasonal change in local campus trees. The timing of autumn change in plants is highly consequential for both natural and human-centered systems, including seasonally dependent economic sectors such as agriculture and tourism. This project plans to develop technological tools and instructional materials to support both instructors and students in implementing the project in diverse contexts and proposes to develop a digital platform for integrating local campus data with a national database accessible to anyone with internet access. Student learning outcomes will be evaluated to inform the development and revision of tools and materials, and to identify specific features and conditions of the research experience that maximize student learning and affinity for science. Course-based Undergraduate Research Experiences (CUREs) have been linked to multiple positive STEM outcomes. Mechanisms underlying CURE outcomes are not well understood but are hypothesized to be mediated through students' development of core science skills and concomitant gains in science self-efficacy and affect. This project adapts a CURE framework to a large lecture context and evaluates student outcomes resulting from participation in a long-term study of autumn phenology in local campus trees. Phenology (the study of recurrent natural phenomena) in plants is a high-priority research focus. Minor shifts in the timing of plant processes (e.g., leaf color change, leaf fall) can disrupt species interactions that regulate large-scale ecosystem processes, such as biogeochemical nutrient cycling and carbon sequestration. This project seeks to develop technological infrastructure to support students in collecting high-resolution data using their mobile devices and visualizing data through an interactive app that enables comparisons among tree species, over different scales of space and time, and in relation to local weather variables (e.g., precipitation, temperature, etc.). Students have the opportunity to develop analytical and systems thinking skills by interacting with complex real-world data and asking questions for which the answer has not been pre-determined. Instructors receive support through the development of instructional materials and technological tools that facilitate project implementation. A partnership with USA-National Phenology Network has the potential to establish a long-term and sustainable solution for maintaining a secure and accessible data infrastructure that expands the reach and impact of student-collected data by linking it to a repository of phenology data used by scientists, economists, and policymakers globally. This project enhances STEM education by leveraging evidence-based and high-impact practices, providing a rich context for teaching and learning about complex systems, and engaging students in authentic inquiry that is relevant at local and global scales and in natural and managed ecosystems. Proposed research tests hypotheses about student learning and specifically explores the role of original data collection as a contributor to CURE outcomes. An evidence-driven design approach will inform development and revision of instructional materials and a digital infrastructure that together, will support broad dissemination in diverse educational contexts including under-resourced institutions. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Ukiah School District

Supply students and the community with up-to-date and efficient technology tools that complement the new high speed fiber optic internet being installed at the school.

Bristol

Bristol Fire House #2 Roof Replacement

Bristol

Bristol Fire House #2 Roof Replacement

NSF

Assembling reinforcing bar is one of the slowest and most labor-intensive tasks in reinforced concrete construction. Furthermore, when design requirements dictate complex and congested reinforcement for concrete elements, constructability and concrete quality can be compromised with potential implications on the capacity of a structure. The goal of this BRITE Pivot project is to understand the fundamental structural behavior and constructability of optimized and additively manufactured (also known as 3D printed) steel reinforcement for concrete structural elements that are prone to reinforcement congestion. This novel reinforcing steel will lead to automation, high construction quality, speed, reduced reliance on manual labor, elimination of reinforcing bar congestion, and structural efficiency. The findings of this project will enable engineers to think beyond grid-like reinforcement layouts and re-imagine reinforcement as a freeform structure tailored to function. The project will allow the principal investigator to gain knowledge on mechanical properties and processes of additively manufactured steel to transfer it to structural engineering for advancing reinforced concrete design, construction, and response. The project will study topologically optimized, free-form, additively manufactured steel reinforcement for concrete lateral load resisting elements that undergo load reversals. Shear wall coupling beams that often require challenging-to-build reinforcement in diagonal patterns are selected as the application to demonstrate the concept. The research scope includes nonlinear analyses and topology optimization to identify viable reinforcement shapes, understanding of processes that are suitable for manufacturing optimized steel for reinforced concrete applications, characterization of mechanical properties of additively manufactured steel, quantification of bond between additively manufactured steel and concrete considering inherent surface undulations of additively manufactured steel, understanding the cyclic response of shear wall coupling beams reinforced with topologically optimized steel through laboratory testing, and documenting the tradeoff between the higher cost of additive manufacturing and construction/structural efficiency. The project will initiate collaborations with engineers and researchers leading the metal additive manufacturing field in Europe and the US. In addition to training a PhD student, the project will enhance classes related to concrete structures and deliver interactive outreach activities for middle and high school 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

This BRITE Pivot award supports research that contributes new knowledge and novel robotic solutions related to the repair of aged electric vehicle (EV) batteries, thereby promoting the progress of science, and advancing prosperity and welfare. EV battery repair currently relies heavily on skilled manual labor, requiring extensive expertise and effort just to dismantle a battery before the repair process can even begin. This challenge arises from the complex design and the uncertain conditions of aged batteries, as well as the significant safety risks associated with handling high-voltage components. This project seeks to solve this challenge by leveraging emerging artificial intelligence (AI) technologies, especially large language models (LLMs), to significantly enhance robotic capabilities. By providing an efficient robotic repair solution that is both economically viable and safe for human workers, this project has potential to extend the automotive lifespan of EV batteries, manage the growing volume of aged EV batteries, address skilled labor shortages, maximize the use of critical materials before recycling, and promote a sustainable, long-term, domestic, and circular battery supply chain. A series of workshops and webinars will be organized to provide training opportunities for next generation workforce in robotic EV battery repair and remanufacturing. Existing automation solutions for EV battery repair are highly customized, expensive, and often require extensive reconfiguration and human intervention to address the associated complexities and uncertainties. This research aims to create novel battery repair solutions with greater flexibility and adaptability in robotic task planning, motion planning, and manipulation by leveraging emerging AI technologies. The project looks to develop a fine-tuned, multimodal LLM tailored for robotic EV battery repair. This model seeks to enable more flexible, human-like task planning beyond rigid, pre-programmed sequences that struggle to handle complex battery repair processes. Furthermore, the research seeks to create a new motion planning framework to incorporate real-time human guidance for coordinated planning among heterogeneous robots. The research also includes activities that look to design a specialized robotic gripper, integrated with a modified residual reinforcement learning algorithm, to handle interlocking structures commonly found in EV batteries. Collectively, these foundational advancements seek to enable adaptable EV battery repair encompassing disassembly, replacement, and reassembly, and will lay the groundwork for a new paradigm in the EV battery remanufacturing industry. 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

This BRITE Pivot award supports research to enable high-fidelity computer simulations of granular materials with unprecedented scalability and efficiency by fundamentally reimagining the discrete element method (DEM). Granular materials – spanning soil and powders to lithium-ion battery particles – are central to many natural and engineered systems. DEM has become an essential tool across disciplines for modeling granular materials, offering critical insight into how microscopic interactions among individual particles translate into the macroscopic behavior of granular systems. It effectively bridges particle-scale mechanisms and bulk material response. However, for nearly five decades since its inception, prohibitively high computational costs have constrained the scale and duration of DEM simulations. This research addresses the long-standing challenges by eliminating key computational bottlenecks, aiming to enable real-time, large-scale simulations on standard computing platforms. These advances will broaden access to particle-scale modeling and accelerate discovery across civil and natural hazards engineering, mechanical and materials science, energy systems, additive manufacturing, and other fields where granular materials are central. The outcomes will advance fundamental scientific understanding, improve predictive capabilities in engineering, and enhance the performance and resilience of complex particulate systems. To achieve this, the project introduces a novel computational framework that integrates artificial neural computations into DEM, enabling simulation speeds five orders of magnitude faster than conventional techniques. This approach will support real-time simulation of systems with up to a hundred million sand-sized particles. The framework, termed DEMIAN (Discrete Element Method Infused with Artificial Neural computations), is built upon four key innovations: a perturbation network for estimating long-range particle interactions with reduced computational overhead; a particle geometry space representation that efficiently encodes various particle shapes and sizes; an on-demand contact force strategy that avoids costly per-timestep updates; and optimized floating-point operations to accelerate computation and reduce memory usage while maintaining simulation fidelity. Training data will be generated using an impulse-based DEM approach that has already shown significant performance gains over conventional approaches. Together, this research will define a new standard for high-speed, high-fidelity, and large-scale simulations in granular materials 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.

NSF

This BRITE project explores how emerging quantum computing can enhance the design of high-performance engineering structures. As products such as aircraft components, automotive frames, and 3D-printed parts become more complex, engineers rely on computational tools to optimize material usage and performance. However, traditional methods struggle to keep up with the scale and complexity of modern design problems. This research seeks to develop a new hybrid computational approach that combines quantum and classical computing to improve design speed, efficiency, and quality. The project will also create educational materials to help students and professionals understand how quantum computing can be applied to real-world engineering challenges. This research introduces a hybrid quantum-classical approach to topology optimization that strategically applies quantum computing to the most computationally demanding steps in the design process. The project focuses on three main contributions, seeking to solving large, sparse linear systems using quantum methods; estimating design sensitivities via quantum gradient estimation without relying on adjoint solvers; and updating material distributions using variational quantum circuits optimized through quantum natural gradient descent. These research tasks are intended for near-term quantum hardware and will be implemented and tested on both simulators and available quantum processors. By targeting numerical bottlenecks of classical topology optimization, the proposed framework aims to improve solution efficiency, scalability, and robustness. The research outcomes will include a validated set of hybrid algorithms, performance benchmarks on relevant structural design problems, and accessible computational tools to support broader adoption in engineering and scientific communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Sodium-based batteries are emerging as a promising alternative to lithium-ion batteries, which face high costs and limited lithium supply. Sodium is more abundant; in the US alone, there are 23 billion tons of soda ash that could be used to produce sodium. Sodium batteries can be more cost-effective, perform better in cold weather, and may endure more charging and discharging cycles. While sodium-based batteries offer many advantages, one key area of improvement at room temperature is addressing dendrite formation, which can impact safety and performance. To overcome this problem, liquid sodium-potassium anodes have been proposed. However, there has not been a viable manufacturing process to fabricate liquid metal anodes. This BRITE Pivot award supports fundamental research aimed at developing a scalable manufacturing technology for liquid metal anodes in sodium batteries. If successful, it will pave the way for large-scale production of next-generation rechargeable batteries that are low-cost, high-capacity, and reliable—unlocking new economic opportunities in the advanced manufacturing sector. This award supports an integrated experimental and theoretical study to address key challenges in a new battery manufacturing process, including ion diffusion limitations, irregular electrodeposition fronts, and the precise control of multi-metal deposition ratios required to form desired liquid metal alloys. This new process involves complex electrochemical phenomena that are not yet well understood. While redox reactions in electrodeposition have been extensively studied, achieving the desired alloy composition by simultaneously depositing different metals remains challenging — particularly due to diffusion limitations in porous materials. Experimental studies will explore the effects of both electrical and geometrical process parameters. Multiscale modeling techniques, including density functional theory and kinetic Monte Carlo simulations, will be employed to investigate underlying mechanisms such as nucleation and growth under heterogeneous conditions. Research enabled by this award is expected to advance understanding of fundamental phenomena in complex electrochemical systems and lead to both new scientific insights in electrochemical processes and materials for energy storage. 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

This BRITE Pivot project supports research that intends to advance the development of next-generation smart fabrics that can sense, adapt to, and interact with their environments autonomously. While current smart fabrics integrate sensors or actuators into textiles, they typically depend on external hardware or human intervention to function, limiting their potential for true autonomy, energy efficiency, and lightweight design. This research seeks to address a fundamental challenge: how to design textile materials that can physically respond to environmental changes—such as heat or light—without relying on traditional electronic controls or power systems. By embodying physical intelligence directly into the material and structure of the fabric, the project aims to create self-regulating textiles that change shape or stiffness in response to stimuli, enabling applications in wearable healthcare, rehabilitation, human-machine interfaces, and responsive clothing. The research supports the national interest by promoting the progress of science and engineering through interdisciplinary innovation across advanced manufacturing, smart materials, textile engineering, and soft robotics. It also fosters workforce development and public engagement through educational outreach, including programs for K-12 students. By reducing the complexity and cost of smart fabric systems, this work has the potential to impact diverse fields such as personalized medicine, assistive technologies, responsive architecture, and sustainable fashion design. The objective of this BRITE Pivot research project is to uncover the fundamental mechanisms that govern physically intelligent architected fabrics constructed from liquid crystal elastomer-based fibers. The approach in this project combines direct ink writing of liquid crystal elastomer fibers with controlled twisting and weaving to create fabric structures that autonomously adapt their shape and mechanical properties in response to external stimuli. The research is organized into three thrusts: (1) scalable fabrication of high-performance, thermally responsive fibers and their integration into textile architectures; (2) mechanical modeling to understand the coupling between material properties, fiber geometry, and fabric architecture; and (3) actuation studies to characterize and optimize adaptive behaviors such as bending, curling, or stiffness tuning. The project seeks to contribute new theoretical frameworks for modeling complex, twisted fiber structures, and generate predictive tools linking material and structural design to functional output. It also explores how embodied physical intelligence can reduce the need for traditional sensing and control systems. The knowledge gained looks to establish design principles for a new class of intelligent fabrics, with potential to transform how responsive materials are used in wearable and soft robotic 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

Strongly correlated materials are an emerging class of materials where electron-electron correlations play an important role. The strong electron-electron correlations give rise to unique mechanical, thermal, chemical, optical, and electrical properties with applications in lightweight high-strength alloys, energy conversion, catalysis, and porous materials for sensing and separations. Despite their importance, the mechanical characterization of these materials including their properties, stress-strain behavior, surface mechanics, and dynamical response under loading remain elusive. The challenges in characterizing these materials originate from the lack of methods that can accurately determine the electron-electron correlation energies and scale reasonably with the number of electrons on classical computers. Quantum computers offer a potentially attractive approach to tackle these difficult problems. This BRITE PIVOT award supports fundamental research that seeks to develop algorithms to understand the mechanics of strongly correlated materials on quantum computers. The education and outreach plan includes training opportunities in the highly interdisciplinary areas of quantum computing, mechanics, and correlated materials, undergraduate research mentoring, a workshop for graduate students and postdocs pursuing careers in research, and public dissemination of project outcomes, especially a video module on introductory quantum computing concepts. Understanding the mechanics of correlated materials is challenging because of the strong role electron-electron interactions play in the total energy of a material. Quantum-mechanical methods such as the density functional theory are widely used to compute electron-electron interaction energy. However, density functional theory can be inaccurate for strongly correlated materials and beyond density functional theory methods such as, for example, coupled cluster, are needed. While beyond density functional theory methods can treat electron-electron interactions more accurately, they exhibit exponential scaling on classical computers and are not suitable for correlated materials. Quantum computers are a promising solution for correlated materials as they exploit superposition and entanglement principles and can potentially perform calculations in a computationally efficient manner. To accurately determine the electron-electron correlation energy and the total energy of a material, this research will perform computations that will use hybrid classical and quantum hardware and a fragmentation-based framework that integrates variational quantum eigen solver with post Hartree-Fock electronic structure methods like coupled cluster. Once the energy and its variation with strain is understood, fundamental mechanics studies focusing on mechanical properties, anharmonicity, stress-strain behavior, failure, surface adsorption, dynamic behavior under loading, and other phenomena will be performed. The knowledge generated looks to relate electron correlation effects to fundamental mechanics of this emerging class of materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

The objective of this BRITE Pivot project is to support research studying computational methods to manage traffic more efficiently when disruptions occur due to accidents, cyber infrastructure failures, or sudden changes in driver behavior. These disruptions lead to nonequilibrium traffic, which is common but difficult to formulate, predict, or control, especially across large transportation networks. Traditional tools often fall short in aiding response promptly. This project seeks to integrate quantum computing with classical methods to build smarter systems that can predict and manage sudden traffic changes in real time. In addition to reducing congestion, improving road safety, and cutting emissions, the results look to advance broader goals of economic productivity and sustainability. Research conducted in association with this project tackles the growing challenge of managing traffic systems that are increasingly complex and connected. With more vehicles relying on real-time information and communication technologies, traffic is shaped by not only nearby vehicles but also signals from across the network (e.g., downstream road conditions or rerouting guidance). This invites the question, namely, how we leverage the power of quantum algorithms and machine learning to better predict traffic jams, reroute vehicles, and improve the reliability of our transportation systems. To answer this question, the project seeks to design new algorithms that can process vast amounts of information and make prompt, informed decisions under rapidly changing system dynamics. New ways to represent traffic as a complex, high-dimensional system will be explored, drawing inspiration from quantum systems. The developed tools could be extended to infrastructure management, emergency response and other applications. The project also trains students in emerging technologies and prepare the future STEM workforce for the quantum era. 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

This BRITE Pivot research project aims to develop new engineering tools to better understand how biological signals (like gene and protein expression) interact with mechanical signals (like tissue structure and stiffness) in the body. These tools will allow scientists to combine and visualize biological and mechanical data at the cellular and subcellular levels, offering new mechanobiology insights into how these signals work together. The research focuses on the uterus, specifically investigating how exposure to endocrine-disrupting chemicals (EDCs), such as propylparaben (PP)—a common preservative found in everyday products like lotions and cosmetics—may affect fertility and pregnancy. Growing evidence suggests that long-term exposure to EDCs may trigger inflammation and fibrosis (the buildup of collagen), which can interfere with embryo implantation, a crucial step in pregnancy. Tools that look to be developed could also be used to study inflammation and fibrosis in other organs, including the lung, kidney, and liver. By advancing engineering tools and applying them to urgent public health challenges, this research seeks to advance the fields of mechanobiology and mechanics and contributes more broadly to understanding tissue dysfunction across multiple organ systems. This research aims to bring powerful and emerging tools in molecular and cellular biology—spatial omics and multiplexed protein profiling—into the field of mechanobiology. The goal is to integrate and co-register these complex biological data with mechanical and imaging data obtained by second harmonic generation (SHG), atomic force microscopy (AFM), and nanoindentation. This effort seeks to create a new multi-scale framework for understanding mechanobiology by mapping spatial relationships among molecular signals, tissue microstructure (e.g., collagen fiber organization), and mechanical properties at cellular and subcellular resolution. The project focuses on uterine tissue, which exhibits complex signaling and remodeling, to investigate how exposure to endocrine-disrupting chemicals (EDCs), specifically propylparaben (PP), alters tissue structure and mechanics. While the uterus serves to set up the framework, the experimental and computational approaches that look to be developed can be broadly applicable to other heterogeneous tissues influenced by mechanical and molecular disruption, such as the lung, liver, and kidney. 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

This BRITE Relaunch award supports research that enables the manufacturing, modeling, and control of fiber-reinforced actuators leveraging advances in additive manufacturing and artificial intelligence, thereby promoting the progress of science, and advancing prosperity and welfare. Fiber-reinforced actuators are common in soft robotics since they closely mimic biological muscles. One challenge with fiber-reinforced actuators is that significant variability can exist between actuators if manufacturing methods are not precisely controlled. Additionally, the actuators displace nonlinearly and are difficult to control. This project will solve this challenge by utilizing 3D/4D printing, in which 3D structures are printed with electronic capabilities that allow them to sense physical phenomena, to repeatedly fabricate, characterize, and test fiber-reinforced actuators. Data obtained will inform physics-informed artificial intelligence models, which will then be integrated with advanced control strategies to enable more precise control of soft robotic actuators. The methods developed could address a fundamental gap in soft robotics related to control for applications ranging from biomedical to space systems. In addition, a multifaceted approach to generating excitement about STEM disciplines is planned that includes K-12 outreach activities, undergraduate research and teaching experiences, and development of a freely available, online workshop curriculum. The field of soft robotics offers significant potential for advancing how robots interact with humans. Fiber-reinforced, pneumatic artificial muscle (PAM) actuators and sensors could serve as transformative technologies for various applications. Control of fiber-reinforced PAMs remains challenging due to inadequate mathematical models of the nonlinear dynamics associated with the actuators. Furthermore, although many designs have been presented in the literature, each PAM is slightly different and performs differently. As a result, empirical or numerical mathematical models derived from experimental data often do not adequately capture the nonlinear dynamics of the actuators and are not broadly applicable to a variety of actuator designs. The research aims to study how advances in 3D/4D printing, additive manufacturing, strain sensing fibers, and physics-informed artificial intelligence modeling can be used to develop robust mathematical models of PAMs. The use of 3D/4D printing will lead to more reproducible fabrication and subsequently a more robust dataset that characterizes actuator performance. The data will further be used to create a physics-informed neural network model, which will then be used to develop advanced control strategies. The outcome would be a novel, data-driven approach that integrates repeatable manufacturing with AI to transform modeling and control of fiber-reinforced actuators. 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

Chronic venous insufficiency is a vascular disease in which damage to leg veins due to high blood pressure reduces efficient blood flow back to the heart. Currently, understanding of the mechanical and biological processes responsible for venous valve damage is limited. This BRITE Relaunch project aims to provide the first data on how the cells of the venous valves respond to mechanical forces associated with high blood pressure and tissue damage. In addition, the research seeks to develop computer models to simulate these processes at the tissue and cellular levels. This work will add to fundamental knowledge and looks to broadly impact the design of replacement valves, on heart surgery using veins, and more. It intends to provide data on cell signaling to help improve therapies for chronic venous insufficiency and other vascular diseases, such as deep vein thrombosis. The Principal Investigator will engage students through seminars, workshops, and online resources. The project will also support education, outreach, workforce development, and entrepreneurship initiatives, including activities for K–12 students and those at Primarily Undergraduate Institutions. In this BRITE Relaunch project, objective 1 looks to generate and share the first mechanobiological characterization of venous valve leaflet endothelial and interstitial cells, including baseline in-situ and in-vitro data and responses to physiologic and pathologic biophysical stimuli (e.g., cyclic stretch, fluid shear stress). Datasets will cover morphological analysis, growth kinetics, immunofluorescent staining (focused on mechanotransduction proteins), PCR-based mRNA quantification, Western blot protein levels, and atomic force microscopy measurements of cell stiffness. Objective 2 seeks to provide the first data on early mechanobiological effects of hypertension on ex-vivo cultured venous valves. These datasets look to build on Objective 1 by adding biaxial mechanical testing and confocal scans to map protein expression changes to stresses and strains under normal and elevated pressures and flows. Objective 3 intends to develop and openly share multiscale FSI models to explore venous valve degeneration. These new tools and insights intend to create the first cellular-level basis for understanding venous valve degeneration and its role in chronic venous insufficiency, benefiting both science and society. Together, these efforts intend to significantly advance the biomechanics and mechanobiology of venous valves and set the stage for future breakthroughs in venous disease prevention and treatment. 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

An increasing number of engineering innovations rely on functional surfaces that transmit mechanical forces, thermal fluxes, electromagnetic fields, sensing signals, and other physical information. Although soft polymers are excellent candidates for synthetic adhesives and coatings on functional surfaces, it is unclear why surface roughness, often due to manufacturing processes, damage, or design topography, sometimes compromises the polymer’s ability to fully bond with the surface, thereby disrupting its function. This BRITE Relaunch award supports fundamental research that seeks to elucidate the multiscale mechanisms of adhesion, friction, and delamination on non-smooth polymer surfaces. Insights from this research will promote technological advances in areas critical to the strategic interests of the United States in aeronautics, biotechnology, microelectronics, and smart materials. Synergistic partnerships with research and educational institutions across the US will cultivate STEM student training and professional development, increase participation in STEM education, research and innovation, and foster collaborations with local high schools and predominately undergraduate institutions to reach a broader population. Non-smooth surfaces have long been thought to improve adhesion with soft polymers by increasing surface energy and delaying delamination failure. Recent studies, however, have shown that surface roughness leads to adhesion hysteresis wherein parts of the surface come in and out of contact. Hysteresis reduces the effective contact area and can cause stress concentrations triggering local damage progression. This project seeks to characterize adhesion, friction, and delamination on non-smooth interfaces with soft polymers using multiscale computational models. The methodology looks to leverage atomistic-level simulations to inform a continuum-level interface formulation for bonding with soft polymers that will account for adhesion hysteresis, stress concentrations, and delamination patterns on surfaces with random topologies. Experimental tests on 3D-printed samples will be conducted to validate the numerical models. The methods intend to will facilitate understanding of interface mechanics and functional surface design for a wide range of engineering applications. 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.