Tufts University

NSF Awards · FY 2026 · 2026-10

This award supports an on-campus summer research program at Tufts University on Directed, Intensive and Mentored Opportunities in Data Science. Each summer, 10 undergraduates from across the country come to Tufts and complete a 10-week research project with a faculty mentor. Projects cover a broad range of data science and machine learning, from theoretical foundations to applications in computational biology, healthcare, and cyber-security. Participants work closely with their dedicated mentor all summer to learn how to conduct research while doing research. The program's novelty is in exposing students to the unique high-touch mentoring culture at Tufts. The program's broader significance lies in its long-term goal: to strengthen the nation's science and engineering talent pipeline, especially reaching students who attend institutions with limited research opportunities. The program intends to accelerate participant ability and interest in becoming a professional data scientist and attending graduate school. The program's core philosophy is that sharp undergraduates can match the pacing and scope of how early-stage graduate students conduct research when given the right scaffolding. Data science projects are particularly suited to this endeavor, and the program's scaffolding has been refined over 5 previous summers. The program invites applications from all American undergraduates who have completed two college-level computer programming courses and demonstrated aptitude and interest in research. The program relies on experienced faculty mentors to help select their mentees and co-design an appropriate project via one-on-one apprenticeship. Beyond this immersive project, the students engage in weekly activities such as invited talks, training in ethical research and scientific communication skills, and professional development events. Participants present their findings at an end-of-summer symposium and are encouraged to pursue scientific publication. To support persistence in data science careers, the program mentors graduate school applications and connects current students to alumni from past summers and the broader Tufts 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 2026 · 2026-09

Machine learning has achieved state-of-the-art performance in image recognition, accelerated large-scale computational simulations, and unleashed the potential of generative modeling. The proliferation of machine learning has brought with it significant demands on computational resources (e.g., storage and energy) and reliance on data-driven models that lack accuracy guarantees. This lack of transparency has made it difficult to trust machine learning tools for high consequence tasks, e.g., drug discovery and cybersecurity. This project aims to bring a new level of scalability and transparency to machine learning using modern high-dimensional data analysis frameworks. These new methodologies, based on multidimensional matrices known as tensors, will uncover interpretable relationships in big data efficiently and will add new levels of flexibility to machine learning tools. By reframing machine learning using mathematically sound techniques, this project will provide a more reliable foundation from which big data, machine learning, and AI can accelerate innovation, drive economic growth, and benefit society broadly. Activities conducted under this project will foster growth in the next generation of computational mathematicians and data scientists, and prepare them to become leaders of an AI-enhanced workforce. This project will “tensorize” AI (TenAI) from two perspectives: (1) employ multilinear operations to exploit existing high-dimensional correlations in the data and models and (2) reveal or impose hidden multiway structures to bring new insights and flexibility to machine learning pipelines. Broadly, the tensorization strategies will take advantage of multiway correlations (e.g., spatio-temporal) to build more effective featurizers as well as efficiently approximate high-dimensional spaces through low-rank, interpretable representations. The new mathematical framework will bring theoretical and statistical insights across three major categories of machine learning: linearized (i.e., kernel methods), probabilistic (i.e., generative modeling), and fundamental (i.e., classification and regression). 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 2026 · 2026-06

This project is funded through the NSF Translation to Practice (TTP) program, which supports efforts to translate research discoveries into practical tools that benefit communities, industry, and society. For the TTP program, teams advance research results toward real-world deployment and adoption. Flooding is one of the most costly and dangerous natural hazards in the United States. Levees play a critical role in protecting more than 23 million people, millions of acres of farmland, and trillions of dollars in property from dangerous floods. However, many levees are aging, were not originally designed for today’s demands, and are monitored using outdated methods that rely on infrequent inspections and limited data. This research team develops a new, smarter way to monitor and manage levees using a “digital twin” which is a simulation of anticipated levee function that continuously updates using real-world data about levee performance. Combining advanced modeling with real-time information enables earlier detection of flood risks, better maintenance decisions, and improved emergency preparedness. The researchers partner with federal agencies, local levee districts, and industry partners to ensure the tools are practical, affordable, and ready for use. Ultimately, this project helps reduce flood risk, protect communities and infrastructure, and ensure taxpayer investments in flood protection systems are effective. This is the first levee-specific hybrid digital twin framework that integrates physics-based models with data-driven machine learning to provide probabilistic, near real-time predictions of levee performance. The researchers develop multi-scale models that simulate key failure mechanisms such as seepage, slope instability, and overtopping at both structural and regional levels. A central innovation is the integration of these models with monitoring data through recursive updating and machine learning–based error correction, improving predictive accuracy under changing environmental conditions. The framework also includes transfer learning methods to enable application to levees with limited data and optimization techniques to guide cost-effective sensor placement. A user-centered dashboard translates complex model outputs into actionable information for decision-makers. The system is validated using extensive field data and tested for scalability and operational use. The project results in improved risk assessment, enhanced decision support, and a validated pathway for implementing digital twin technology in levee systems across the nation. 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 2026 · 2026-05

This project seeks to understand how marine animal body size, and thus biodiversity, have been shaped by environmental change over Earth’s history. By assembling the most comprehensive database of fossil body size measurements spanning the last 575 million years, the project will test whether the appearance of new marine animals follows predictable patterns linked to environmental factors. While most paleontological work has examined why animal taxa disappear, this study focuses on how new taxa originate, filling a fundamental gap in understanding of biodiversity generation. The findings will improve forecasts of how today’s rapidly shifting environments may affect species emergence and ecosystem resilience, issues that intersect national interests in food security, coastal economies, and biodiversity. The project will (1) build on a standardized database of marine animal body size measurements, incorporating previously unpublished Ediacaran body-size data; (2) test whether size bias of origination differs among major taxonomic groups, varies through geologic time, and changes consistently under distinct environmental regimes; and (3) evaluate the influence of sampling completeness on observed selectivity patterns. Body size is the chosen metric because it is easy to measure and correlates with key animal traits such as metabolic rate and generation time. Statistical models will be applied to assess correlations between body size trends and proxies for marine anoxia, temperature, and other environmental variables. The resulting analyses will quantify origination selectivity, a dimension that has been largely undocumented, and will generate open-access data for the broader scientific community. The anticipated outcomes include improved predictive tools for assessing how future environmental change may shape biodiversity, and training the STEM workforce. 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 2026 · 2026-04

The New England Hardware Security (NEHWS) workshop brings together hardware security researchers, industry participants, and students in the New England area, to develop connections and collaborations among research groups from 10 universities. NEHWS 2026 is the sixth edition of a regional event with international visibility. There will be a panel hosting international participants to discuss the different views on hardware security validation around the world. AI and quantum computing are two key topics in this edition. For the first time, NEHWS 2026 has partnered with the Journal of Cryptographic Engineering (JCEN) to enable the best submitted work to continue as a special issue. This grant provides essential student travel support to enable U.S.-based students to attend NEHWS’26. Participation in this regional workshop is a critical part of a student's experience, offering opportunities to interact with senior researchers, present their original work, and be exposed to practical industry applications. By removing financial barriers to participation, this student travel grant will strengthen the collaboration among multiple universities in New England area and develop future workforce who will leverage AI and quantum to address the supply chain challenges in microelectronics and cybersecurity. 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 2026 · 2026-03

Many researchers across a myriad of scientific domains generate and analyze data. In order to make this data reproducible and reusable, it is important to also include metadata describing the context for the data. However, most current schemas for reading and writing metadata are optimized for machine use rather than directly accessible to citizens or scientists who are not expert programmers or data technicians. This project involves developing a toolset and a language, MEDFORD, that provides an easy and accessible structured approach for researchers who are not expert programmers to create metadata in a form that is easily human readable and writable. This metadata is then structured enough to be easily translated into popular metadata standards and included in databases that are FAIR (Findable, Accessible, Interoperable, and Reusable). The MEDFORD language and supporting toolbox will be tested for usability with scientists, students, and members of the general public across several scientific domains, including marine biologists studying coral reefs, and biologists studying the animal hosts of tick-borne diseases. The involvement of scientific experts in the collection and analysis of the metadata than accompanies the complex scientific data is crucial; however, many of the recommended practices and processes focused on making these data FAIR (findable, accessible, interoperable, and reusable), as well as replicable and reproducible, can be cumbersome and difficult to implement, particularly for users that are not experts in computer science. This project posits that increasing the widespread community adoption of processes around efficient, robust, trustworthy, and FAIR data and metadata will require a new focus on making these data easily human-readable, writable, and correctable, in addition to all the valuable past effort that continues to go into making them easy for machines and database systems to ingest, validate, and parse. Thus it is focusing on a critical but under-served piece of the problem of frictionless FAIR data and metadata collection for science. The proposed solution involves inserting an intermediate layer between unstructured human annotation and existing machine-parsable metadata standards - the MEDFORD language and parser. Further development of the MEDFORD language will be informed by principled user studies in scientific communities with different needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Fires at the wildland-urban interface damage property and infrastructure and release hazardous materials. After a fire, stakeholders must assess the safety of contaminated property and infrastructure. This project aims to improve assessment of post-fire property and infrastructure contamination and enhance the accessibility of these assessments for stakeholders. The project will advance fundamental understanding of contamination during wildland-urban interface fires, improve sampling and testing, and develop an AI-supported platform for access to testing data, thereby enhancing decontamination and economic recovery efforts. This project will address gaps in necessary post-fire property sampling and testing, along with better understanding the needs of residents. The project focuses on three research objectives: 1) examining the fundamental processes governing the fate and transport of wildfire contaminants in wildland-urban interface fires, and applying this knowledge to guide water and soil testing post-fire, 2) identifying residents’ needs, and 3) leveraging AI to assess how best to navigate multiple data sources and using this assessment to develop an interactive online platform to support decontamination and economic recovery efforts. Partners from fire-impacted communities will be engaged. Key project activities include analyzing contaminants generated during the burning of mixed household products and examining their transport into the plumbing system and through burned soils. Results will shed light on exposure risks after a wildfire. To capture community needs, residents impacted by specific fires will be interviewed to identify key factors influencing community priorities. Further, a tool combining large language model-assisted and rule-based methods will harmonize disparate data sources into a structured report for use by stakeholders. 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 2026 · 2026-01

Big data has revolutionized the kinds of problems we can tackle, enabling unprecedented personalization and innovation across commercial, scientific, and healthcare applications. The ever-growing amount of data has created a pressing need for new methodologies to reduce storage demands and extract representative features for downstream analysis. Many data, such as those arising in computer vision and imaging, neuroscience, networks (e.g., epidemic tracking, cyber security), and more, are natively represented as multiway arrays, or tensors. As a result, tensor-based approaches have become increasingly attractive for dimensionality reduction and feature extraction. However, many tensor-based approaches suffer from a so-called “curse of multidimensionality;” that is, that fundamental mathematical properties break down when applied to multiway data. Recent advances in tensor algebra have overcome this limitation by reframing tensors as mathematical operators rather than stagnant arrays of data. This project will take these advancements to the next level by learning the optimal mathematical operations required to drive down storage costs further while increasing the accuracy of tensor representations. The methods developed in this project will be useful for a wide range of high-impact applications, including precision medicine, climate simulations, and engineering. All algorithms and methods produced will be made available to the public in well-documented, open-source code. This project focuses on developing new methods to maximize the benefits of matrix-mimetic tensor frameworks- multidimensional frameworks that preserve linear algebraic properties. Such frameworks yield theoretical and empirical advantages over traditional matrix-based approaches and alternative tensor-based approaches. The matrix mimeticity arises from interpreting tensors as t-linear operators that multiply using tensor-tensor products. The choice of tensor-tensor product, given by an underlying tensor algebra, is crucial to representation quality, and thus far, has been made heuristically. This project will develop a unifying optimization framework to learn tensor algebras and efficiently represent multiway data with implicit correlations (i.e., relationships unknown a priori and thus challenging to capture heuristically). The learned tensor-tensor products will introduce algorithmic advantages (e.g., fast evaluations and low storage costs) while preserving theoretical guarantees of the matrix-mimetic framework. The main thrusts of this project are (1) to optimize tensor algebras by exploiting the coupling between matrix-mimetic tensor factorizations and tensor-tensor products, (2) to capture nonlinearity in multilinear algorithms by designing novel nonlinear tensor-tensor products, and (3) to extend the proposed algorithms using new, scalable strategies to increase the applicability of matrix-mimetic tensor approaches to massive multiway data 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.

NSF Awards · FY 2026 · 2026-01

Cosmic radiation on the earth’s surface over long timescales creates rare forms (isotopes) of many minerals. These isotopes are known as cosmogenic nuclides. Measuring the relative abundance of these minerals provides insights into current and past processes that have shaped the earth’s surface, including erosion, tectonic processes, glaciation, and sea level changes. The scientific data on these processes is normally collected and measured by independently working groups of scientists, so having the capability to share the data in a consistent way is extremely important for reproducing scientific results, reusing data for new research questions, and (as the coverage of the collected data on the earth’s surface becomes significant) tackling large-scale or even global research problems. The Informal Cosmogenic-nuclide Exposure-age Database (ICE-D) project enables this research by facilitating community access and engagement with the continually growing dataset of cosmogenic nuclide geochemical and field measurements used for exposure dating applications. The project expands the capabilities of prior work on ICE-D by implementing support for sophisticated surface processes such as dating now-buried surfaces, in addition to exposure dating. This project expands capabilities and trains a wider audience of geoscientists for the Informal Cosmogenic-nuclide Exposure-age Database (ICE-D) Project. ICE-D is a computational infrastructure project aimed at facilitating synoptic data discovery and analysis of geochronologic measurements that constrain numerous Earth surface processes. Transformative components of the project - the transparent computational middle-layer and users-as-developers model - are currently enabling higher-order analyses of cosmogenic-nuclide measurements that critically underpin several fields in Earth surface processes research, namely reconstructing past contributions to sea level fluctuations from ice sheets, assessing seismic hazards along major fault systems, and constraining global climate patterns that caused past alpine glacial fluctuations. Expanded capabilities targeted in this iteration of the project will additionally aid in analyzing fluvial landscape evolution processes, biological applications such as tracking species evolution and the dispersion of ancient humans across the world during the Quaternary, among other impactful Earth surface and biological processes. By centralizing the detailed datasets of cosmogenic-nuclide measurements - including field observations and laboratory measurements - required to compute geologically meaningful parameters from samples collected in a variety of environments worldwide, ICE-D removes several bottlenecks in the community. To further increase engagement, the project undertakes a workshop program to train the community of users to contribute their data, help maintain the database and ultimately use the database for synoptic analyses. Finally, project investigators institute an undergraduate research program aimed at training undergraduates as well as fostering engagement with the geoscience community. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Earth Sciences and the Division of Research, Innovation, Synergies and Education in the Directorate of Geosciences. 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 · 2025-11

This CAREER award involves emerging field of active fluids, which are a new class of liquid materials made up of densely packed suspensions of particles that can propel themselves by converting energy from locally available fuel into locomotion. Active fluids hold great potential for the development of new materials and products, but realizing this potential requires a quantitative understanding of the unusual material properties and transport mechanisms that these fluids exhibit, which can be much different than the properties of suspensions of inert particles. This project will combine theoretical analysis and numerical simulations to build a holistic computation framework for modeling, analysis, and control of active fluids in complex microfluidic environments. The project will provide undergraduate and graduate student training, create K-12 outreach opportunities, and support the development of a Virtual Reality package that will help interpret research results and enrich classroom teaching. The Virtual Reality package and demos will be available online to the general public, along with some of the open-source computation codes developed in the project, which will benefit both students and researchers in applied science and engineering. The physical properties of active fluids are fundamentally different from those of classical equilibrium systems. When suspended in a liquid, motile microparticles exert stresses on the ambient flows, which acts as a coupling medium for generating large-scale, unsteady collective dynamics. These concentrated systems often show common features, including ordering transition, fluctuating density, and force generation. The research in this project will take the next engineering step of learning how to manipulate active fluids by taking full advantage of their collective behaviors. The project consists of four research thrusts: (1) Develop a hybrid algorithm that combines penetration-free Stokesian dynamics particle simulations and coarse-grained active liquid crystal models; (2) Study the hydrodynamic instabilities and coherent flows; (3) Investigate non-equilibrium rheological properties and topological structures; and (4) Design active-liquid metamaterials for novel engineering applications. The hybrid algorithm will follow a bottom-up multiscale approach. The microscale discrete particle dynamics will be used to construct continuum kinetic models and new "polar" active liquid crystal models. The computational framework will permit researchers and practitioners to control active fluids by adjusting particle activity and interactions at the microscale, and by controlling and guiding constrained coherent flows at the macroscale. The numerical studies, together with supporting experimental verifications, will lead to quantitative understandings of the linkages between dynamics across scales, and possibly to new engineering devices for transporting fluids and particles. 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 · 2025-10

Microrobots have the potential to reach deep organs to deliver drugs or perform minimally invasive surgeries. But to realize such a vision, several scientific and technological challenges need to be resolved, key among them is the design of robotic systems tailored for efficient swimming and maneuvering in biological fluids. These fluids have unique physical and rheological properties that can facilitate or hinder cell movement. Inspired by the swimming motions of sperm cells, this project aims to develop, control, and analyze the motion of magnetically driven, sperm-like soft microrobots in nanofiber fluid suspensions with properties analogous to cervical mucus. Research thrusts of this NSF funded project will be tightly coupled with comprehensive educational and outreach activities, and are designed to educate and train future scientists and engineers from diverse backgrounds in interdisciplinary research at the intersection of dynamics and control, robotics, biomaterials, and fluid mechanics. The research activities will combine experimental and computational efforts to: (a) study fluid-structure interactions of magnetoelastic undulatory microrobots in artificial cervical mucus (ACM); (b) seek optimal swimming gaits and minimal feedback controllers; (c) exploit orientation-dependent swimming behavior to detect fluid properties and steer to the microrobot to specific sites. High-resolution 3D printing will be used to fabricate soft microrobots with larger number of degrees of freedom than their rigid counterparts, leading to greater motility as they negotiate obstacles in gel-like ACMs. Remote magnetic control will drive complex flagellum beating patterns to generate straight and turning motions. In accompanying computer simulations, Immersed Boundary methods will be used to resolve fluid-structure interactions of single and multiple microrobots in ACMs, and uncover their orientation-dependent swimming mechanisms. The data will then be used with state-of-the-art multi-objective optimization tools, to construct a minimal model-free control strategy. Successful completion of these research tasks will result in a new paradigm for microrobot design, analysis, optimization, and evaluation. 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 · 2025-10

This project aims to develop a novel, lightweight, and environmentally friendly battery that generates power using natural salinity gradients found in everyday fluids such as water, sweat, saliva, and urine. Unlike traditional batteries, which often rely on scarce and geopolitically sensitive materials like lithium, this new battery uses readily available and sustainable materials and operates without liquid electrolytes. Its dry, shelf-stable design makes it ideal for powering small, portable devices including wearable sensors, medical diagnostics, and ingestible electronics. The proposed osmotic battery harnesses nanoscale materials to create membranes that selectively transport positive and negative ions, enabling efficient on-demand energy generation. This innovation addresses the growing need for compact, safe, and eco-friendly power sources and supports NSF’s mission by promoting the progress of science and engineering, advancing environmental sustainability, and fostering health-related technologies. The project will also engage undergraduate students in cutting-edge research and integrate findings into engineering education, contributing to workforce development and STEM outreach. This project proposes the design and fabrication of an osmotic battery based on ion-selective membranes derived from two-dimensional nanomaterials, one for cation transport and another for anion transport—combined with ion-infused aerogel layers. These components form a dry, gelatin-based architecture that eliminates the need for bulky liquid electrolytes. The device generates power through salinity gradients established across alternating hydrogel compartments containing potassium chloride. This configuration is expected to deliver power outputs in the hundreds of microwatts while maintaining a total device weight below 10 grams, making it ideal for low-power and space-constrained applications. The proposed approach employs green chemistry techniques and offers advantages in scalability, safety, and storage. The project builds on the strong expertise of the research team in nanomaterials, bioelectronics, and low-power systems and is expected to produce fundamental insights into ion transport mechanisms in nanostructured membranes, as well as practical innovations in miniaturized energy harvesting technologies. 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 · 2025-10

While generative artificial intelligence (AI) offers tremendous benefits in reshaping software engineering, they raise pressing ethical and legal concerns, particularly around the widespread use of large-scale training datasets, often assembled through web scraping, that may contain copyrighted software code, or proprietary content without proper consent. The opaque data usage challenges existing intellectual property laws and complicates questions of ownership, attribution, and accountability. As generative AI becomes increasingly integrated into software development practices, this accountability gap undermines legal compliance, erodes trust in AI-driven tools, and hampers broader adoption of responsible AI. This project is motivated by the need to bridge this gap by developing new tools, legal insights, and accountability models to ensure that generative AI can advance responsibly while respecting copyright, licensing norms, and user rights. This project will contribute a comprehensive framework for responsible generative AI development. The proposed research focuses on (1) analyzing licensing inconsistencies and defining AI-relevant copyright interpretations, (2) uncovering memorized copyrighted code using novel prompt engineering techniques, (3) designing watermarking-based tools for identifying and mitigating unauthorized AI-generated code, and (4) developing a practical measure of accountability for generative AI in software development. In addition, the project will propose institutional frameworks including licensing, consent, and revenue-sharing strategies. Together, these efforts will guide legal, institutional, and technological interventions that promote ethical AI practices while supporting innovation. The outcomes will benefit policymakers, developers, and creators alike, ensuring that generative AI evolves in a way that respects legal boundaries and societal values. 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 · 2025-09

This I-Corps project is based on the development of a sprayable silk-based wound dressing designed to simplify and improve the treatment of burn injuries. Current burn care often relies on painful, multi-step dressing applications that require frequent changes, skilled labor, and specialized materials, which is especially problematic in emergency, resource-limited, or high-acuity environments. This technology is a shelf-stable, hand-held spray that forms a transparent, conformal film directly over the wound site, eliminating the need for adhesives or secondary dressings. The bandage stays in place and is slowly resorbed to avoid damage associated with traditional bandage removal. In addition, this bandage may be used to carry medications to reduce infection risk and shorten hospital stays. This technology may improve medical care as well as patient outcomes. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a silk-based hydrogel spray for wound care. The technology uses a silk fibroin formulation that undergoes aqueous aerosolization to instantly transition into a mechanically stable β-sheet, solid-state, porous matrix on contact with skin. The material forms a biodegradable, optically translucent dressing that becomes a protective yet breathable barrier, enabling continuous, non-invasive wound monitoring without removal. The silk matrix acts as a barrier against airborne bacteria, reducing infection risk while promoting angiogenesis and anti-inflammatory activity, while eliminating the need for dressing changes. This ambient-temperature process avoids the need for organic solvents, heat, or crosslinking agents. Unlike traditional dressings, this formulation adheres without external pressure, allows for real-time monitoring, and may be used to deliver antibiotics, growth factors, or biologics. In addition, this bandage is 100% biocompatible and may remain indefinitely without triggering an immune response while competing products that are reliant on silver-based antimicrobials are restricted to seven days of contact with skin. This technology may enhance patient outcomes, advance wound care, and minimize clinical burden. 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 · 2025-09

Processes at the ice-ocean interface of marine-terminating glaciers play a critical role in determining the rate of ice sheet mass loss and the depth at which meltwater enters the ocean. Submarine melting along glacier ice faces, traditionally thought to be governed by the strength of subglacial discharge, also influences iceberg calving rates. However, emerging evidence reveals the presence of energetic dynamics elsewhere along the ice face, driving turbulent flows that remain poorly understood and underrepresented in existing models. These dynamics challenge current parameterizations of melt and freshwater flux, underscoring the need to directly validate and improve these frameworks. Specifically, there is the need to accurately represent their role in amplifying feedback loops and nudging the climate system toward potential tipping points relating to accelerated ice loss and disrupted ocean circulation. This project will integrate direct measurements of submarine melt rates and near-ice boundary-layer dynamics at Greenland’s marine-terminating glaciers with numerical simulations to improve the next-generation climate models. Beyond the importance to society and the scientific community, the work will provide mentorship and support for early career researchers, post docs, graduate and undergraduate students, and outreach with a local community as part of a conversation about their changing icy landscape. This proposal will support development of a robust, observationally grounded model for submarine melt prediction at Greenland glacier termini. Current melt parameterizations have largely been formulated for limiting cases where shear or convection dominates and assume simplified geometries and idealized ice and ocean forcing. The investigators recently developed instruments that directly measure the evolving ice boundary and demonstrated that melt is controlled by the interplay between fjord currents, turbulent eddies and near-boundary buoyancy that interact with a complex three-dimensional glacier-ice interface. Moreover, flow along the boundary were found to be significantly more energetic, with melt rates higher than predicted by current theory. This work hypothesizes that a skillful (unbiased) scale-aware melt parameterization will require an improved accounting for all sources of kinetic energy and how they drive the turbulent and diffusive ice-ocean boundary layer. Thus, the investigators propose a focused yet comprehensive set of small-scale measurements of submarine melt and the ice-ocean boundary layer across distinct turbulent regimes. These and larger-scale measurements will be integrated with a suite of numerical simulations to characterize submarine melt rates as functions of temperature, subglacial discharge, fjord dynamics, and other key factors, ultimately providing a framework generalizable to diverse glacier 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 2025 · 2025-09

With the support of the Chemistry of Life Processes Program in the Chemistry Division, PI Dr. Joshua Kritzer and co-investigators Dr. James Baleja and Dr. Yu-Shan Lin from Tufts University will use a stapling strategy to produce stable collagen-like assemblies that incorporate known interaction sites for human collagen-binding proteins. Collagen is the most abundant protein in mammals, making up 25-35% of all protein in the body. Collagen plays important structural and signaling roles in health, growth, and development, but it is challenging to study because its unique assembled structure is very difficult to produce using defined systems. What is currently known about collagen is largely derived from experiments with processed natural collagens or synthetic peptides, but these systems are a tangle of interconverting structures. This project describes a new and elegant method for locking synthetic peptides into collagen-like structures using chemical bonds or “staples”. The team will use these hyperstable collagen mimics to map protein binding sites and understand cellular signaling of collagens at a much broader scale than previously attainable. The new methods are simple and require only commercially available building blocks, allowing rapid adoption by the larger chemical biology community. The project also supports the development of professional skills workshops for graduate students to prepare them for academic jobs, industry research positions, and other science careers. Intramolecular cross-links (“staples”) have been pursued for decades as a means of restricting peptide conformation. In preliminary work, the Kritzer Lab demonstrated that stapling using the artificial amino acid 4-mercaptoproline (4-MP) provides a solution to the longstanding problem of how to stabilize collagen triple-helical structure. 4-MP staples are powerfully stabilizing for collagen triple helices, producing melting temperatures as high as 85°C. This approach is much more compact, synthetically efficient, high-yielding, and modular than all prior approaches to stabilizing collagen-mimetic peptides. These hyperstable collagen-like assemblies provide new opportunities to address critical questions in collagen biophysics and biology, including the straightforward production of heterotrimeric collagens in a programmable manner, understanding the functional effects of sequence variation among native collagen sequences, and effects of sequence and structure on the binding of collagen receptors including integrins, platelet glycoproteins, and DDR tyrosine kinases. The unprecedented small size and high yields of 4-MP-stapled collagen-mimetic peptides will also allow this project to produce the first-ever collagen arrays. Such arrays could comprehensively expand the field's understanding of the “human collagenome” including features important for protein binding, cell signaling, cell adherence, and cell migration. Overall, this project from PI Dr. Joshua Kritzer and co-PIs Dr. James Baleja and Dr. Yu-Shan Lin seeks to uncover molecular mechanisms of collagen-receptor interactions that will greatly accelerate chemistry and biology research on collagen and the extracellular matrix. 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 · 2025-09

Oxygen is crucial for macroscopic life, yet the causes and repercussions of its accumulation in the atmosphere are poorly understood. A key question for resolving the trajectory of planetary habitability is if chemical shifts recorded in ~2.4-2.0 billion-year-old rocks reflect global-scale oxygen changes or regional-local conditions. To answer this question, rock cores from Gabon, which hosts the best-preserved sedimentary archive across this interval, will be analyzed for possible chemical imprints of oxygenation. This project serves the national interest by promoting the progress of fundamental science that identifies how Earth became habitable. Synergistic outreach objectives include initiatives such as community tables at farmers’ markets from Northeast-Midwest USA to enhance public scientific literacy and undergraduate curriculum development to support an American STEM workforce that is globally competitive through improved education. This interdisciplinary project applies stratigraphy, paleomagnetism, geochemistry, and geochronology to assess whether extreme geochemical shifts in the wake of the Great Oxidation Event (GOE) reflect global, regional, or local-diagenetic conditions. Laterally correlative drill cores across shallow-to-deep paleoenvironments in the Francevillian sub-basins of Gabon will be applied to test the hypothesis that, in the Paleoproterozoic, there was a prolonged overshoot in O₂ coeval with a widespread perturbation of the carbon cycle. The objectives are: 1) Create a detailed stratigraphic framework and isolate primary magnetizations to examine facies and latitudinal climate-belt controls; 2) Assess a variety of isotopic and geochemical criteria in carbonates to determine if a primary “oxygen overshoot” is preserved; and 3) Apply U-Pb zircon geochronology/geochemistry and carbonate Rb-Sr isotope compositions to evaluate if geochemical shifts are of global relevance. The results will facilitate detailed tracking of the dynamics of oxygenation and concurrent environmental changes in the wake of the GOE—including extraordinary disturbances to the carbon cycle—that are key for deciphering this tipping point in the trajectory of planetary habitability. 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 · 2025-09

Processes that involve the transport of electrically charged species in fluids consisting of multiple phases, such as liquids and gases, are fundamental in energy production, energy storage, healthcare, and manufacturing. Important examples include energy storage systems such as batteries, hydrogen production, medical diagnostic devices, and manufacturing processes. Despite their importance, accurately modeling these processes remains challenging due to their complex interactions and varied scales ranging from microscopic interfaces to large-scale systems. Addressing these challenges can significantly enhance the performance, efficiency, and affordability of critical technologies, particularly in energy production and storage. This project develops an accessible, advanced simulation framework that enables scientists and engineers to effectively model and optimize these vital electrochemical processes. By simplifying complex computational challenges, the project accelerates innovations across several crucial sectors, benefiting society through improved energy technologies, healthcare applications, and industrial processes. Educational activities and community training are integral components, aimed at increasing STEM participation and training a workforce skilled in cutting-edge technologies. This project develops an architecture-agnostic computational framework called FASTEST (Framework for Advanced Simulation of multiphaSe ElecTrochemical Systems). FASTEST provides scalable, robust, and accurate simulation capabilities for the complex, multiscale dynamics of multiphase electrochemical systems. FASTEST employs a domain-specific language (DSL) to enable domain experts to focus on scientific modeling while computational experts optimize performance and scalability. The framework combines scalable adaptive meshing, implicit numerical methods, and architecture-aware portability, leveraging modern computing resources such as multicore CPUs and GPUs. It addresses longstanding computational challenges in the modeling of electrochemical systems, such as stiff equations, multiscale adaptivity, and implicit solvers, with optimized numerical algorithms and iterative solvers. FASTEST improves computational speed and accuracy compared to current commercial and open-source solutions. The project emphasizes the development of robust numerical methods, scalable parallel algorithms, and a user-friendly interface to facilitate widespread adoption and application. Comprehensive validation, verification, and benchmarking efforts ensure accuracy and reliability, supporting broad applications in energy production, energy storage, manufacturing, and bioengineering. The outcomes advance simulation-based understanding and optimization of multiphase electrochemical systems, fostering innovation across multiple critical technological domains. This project is co-funded by the Office of Advanced Cyberinfrastructure (OAC) and the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI). 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 · 2025-08

A key measurement in modern biology research is measuring changes in the levels of specific proteins in live cells. However, many of the methods by which proteins are detected, such as Western blots, are hopelessly antiquated and provide only a small snapshot of information. This project envisions a new method for biologists to measure changes in live cells. Specifically, the method is designed to measure the rates of changes, the duration of the effects, and the return to a normal state, all in one experiment. These measurements are enabled by a new type of “turn-on biosensor” which can monitor protein levels in real time. Prototypes for this new type of biosensor will be developed using computational design, machine learning, and directed evolution. The initial application will be monitoring levels of beta-catenin, a master regulator of cell growth and specialization. Beta-catenin levels are known to change in response to chemical signals released by nearby cells, making it an ideal system for exploring the design and applications of the new turn-on biosensors. The new biosensors described in this Tools4Cells project not only provide much richer biological information than the current state-of-the-art, they will have broader impact on the field as simpler, more quantitative, less expensive, and more sustainable. Fluorescent biosensors have been pursued for decades, but nearly all current strategies rely on split binding domains or domains with large conformational changes upon analyte binding. However, there are very few such domains, limiting applications to relatively few analytes. Also, current biosensors require extended washout steps and other manipulations, and their sensitivity is severely limited by high background from the “always-on” fluorescent proteins and dyes. This project develops turn-on biosensors (TOBs) which fluoresce only when the analyte is bound. TOBs use a modular binding protein to recognize the analyte, which ensures generalizability. For initial applications, the binding protein will be a nanobody to allow selective recognition of a specific target protein. The nanobody is fused to a variant of the self-labeling protein HaloTag, which allows covalent installation of a synthetic, environment-sensitive dye. TOBs will be engineered using computational methods and directed evolution to minimize dye fluorescence in the absence of analyte and to maximize fluorescence in the presence of analyte. TOBs that report levels of the transcription factor beta-catenin will be developed and tested in cell culture models of beta-catenin activation and inhibition. These critical pathways are known to be mediated by changes in the cellular levels of beta-catenin, but this method will demonstrate real-time measurements of these changes. Once developed, TOBs will be readily adaptable and useful to a large proportion of the cell biology 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 2025 · 2025-08

Quantum scientific computing, a rapidly growing interdisciplinary field, integrates classical numerical methods with quantum technologies to tackle challenges across physics, chemistry, biology, engineering, industrial applications, and beyond, and has the potential to vastly outperform classical computers in solving such problems. In this project, the overall goal is to develop, analyze, and implement quantum and quantum-inspired numerical algorithms that leverage both well-studied classical methods and state-of-the-art quantum techniques to overcome bottlenecks in numerical simulations of complex problems, such as "the curse of dimensionality," which restricts the development of efficient methods and solvability for problems in high-dimensional space. The quantum methods developed in this work have the potential to translate into advancements in many practical applications, e.g., machine learning, data science, optimization, and electric grid simulations. All techniques will be implemented in an open-source software package and will be made available to the scientific community. The main focus of this project is to develop, analyze, and implement quantum finite-element methods (FEMs) for solving partial differential equations (PDEs). While the discretization steps of FEMs can be implemented optimally on classical computers, solving the resulting large-scale and often ill-conditioned linear systems remains the most computationally intensive step. To overcome this challenge, the project explores the use of quantum algorithms and investigates the conditions under which quantum methods offer polynomial versus exponential speedups, clarifying optimal cases for a quantum advantage. This work has two main research objectives: (1) To develop efficient and provable quantum-inspired classical FEMs for solving high-dimensional PDEs; and (2) To develop practical quantum FEMs for solving PDEs on both near-term and far-term quantum computers with theoretical guarantees. In the first objective, quantum principles are introduced into classical algorithms, creating quantum-inspired classical algorithms that achieve comparable speedups for FEM applications on classical computers. In the second objective, hybrid quantum-classical approaches are first developed for near-term quantum computers, which exist today. The long-term goal is then to extend these methods to fully quantum FEM implementations on far-term large-scale quantum computers, which have yet to be developed. This involves incorporating quantum techniques into adaptivity and mesh refinement, as well as developing structure-preserving quantum FEMs. The goal is to produce new fundamental theory and advanced numerical methods for solving PDEs in 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 Awards · FY 2025 · 2025-07

Imaging cells deep within living things is challenging because light is scattered as it travels through tissues. Two-photon (2P) microscopy is a technique that uses longer wavelengths which are less prone to scattering to allow for deeper penetration into the tissue while maintaining the ability to visualize single cells. However, the depth of penetration is limited. This project uses an ultra-compact chip-scale device based on optical nanostructures to overcome the depth limits of 2P microscopy. This technology could enable tissue imaging at faster speeds over larger volumes using a chip-scale microscope. This technology will enable new observations of cell behavior to improve our fundamental understanding of biology, leading to improved diagnostics and therapeutics. This project focuses on the development of photonic-integrated-circuit two-photon microscopy (PIC-2P), whereby light is delivered from a minimally-invasive nanophotonic chip with cellular-scale thickness (<50 µm). This technique overcomes the fundamental imaging depth of 2P imaging by delivering light orthogonally with respect to the image collection, obviating the effects of scattering and absorption from superficial layers, and leading to a signal-to-background (SBR) many orders of magnitude higher than traditional 2P imaging. This project aims to demonstrate feasibility of PIC-2P imaging in scattering and absorbing specimens and to characterize the anticipated imaging depth improvements. The team combines expertise in nanophotonic devices and multiphoton label-free tissue microscopy. Goal 1 develops a low-loss, packaged biocompatible fiber-to-chip coupler that can efficiently deliver multiphoton pulses to the PIC using co-design of micro-optics and a PIC coupler. Goal 2 develops a dispersion-compensated, low-loss PIC-2P waveguide platform for 2P pulsed light delivery through fundamental investigations of dispersion and power handling properties of dispersion-engineered waveguide systems. Dispersion management techniques based on waveguide and cladding cross-section optimization and transverse and longitudinal nanostructuring of the waveguide will be used to tune the dispersion throughout the system. Goal 3 integrates the PIC with a free-space 2P microscope to characterize the image depth improvements of PIC-2P vs 2P imaging in phantoms with varying physiologically relevant scattering and absorption properties. 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 · 2025-07

NSF CAREER: The Impact of Power on Active Learning in Learning Assistant-supported General and Organic Chemistry Lectures The Faculty Early Career Development (CAREER) program is a National Science Foundation-wide initiative that supports early-career faculty in advancing research and education. The U.S. STEM workforce plays a critical role in driving innovation and economic growth, to ensure that these goals are met, additional initiatives must be implemented in courses within STEM curricula to ensure student readiness and preparation. Active small-group learning supported by students who took the course previously and return as Learning Assistants (LAs) is beneficial for undergraduate students in gatekeeper general and organic chemistry lectures. However, variation in student learning for different student groups is understudied and has been hypothesized to be connected to students’ relationships with each other and the subject matter. This CAREER project studies the impact of student relationships on their learning as it occurs in the moment of their interactions with each other in LA-supported general and organic chemistry lectures. This research investigates dynamic power relationships amongst students and between students and instructors (professors and LAs) as well as students and concepts that are co-constructed in interaction. Specific research goals of this project are to: (R1) describe patterns of how power relations impact learning as it occurs in the moment of student-student interactions in active learning lectures; (R2) investigate how differences in LA facilitation of student learning impact these power relations; (R3) characterize how the way different professors in the study design their classes impact these power relations; and (R4) describe how any factors that go beyond the classroom level impact these power relations as student-student interactions and the classroom they occur in are embedded in greater society. The research is guided by a combination of theory on power, learning, facilitation, and classroom systems. Multiple strands of data will be collected and qualitatively analyzed, including videos of LA-facilitated student-student interactions and interviews with professors, LAs and students. The research goals connect tightly to education goals aimed at developing and implementing activities for instructional teams of LA-supported active learning classes that promote self-reflection and noticing of power dynamics as well as stimulate discussion of how facilitation and class design might be used and changed to disrupt power relations that hinder learning. Additionally, this project will develop guides for instructional team members of how to bring these activities to their instructional teams including navigating power dynamics within instructional teams during implementation. Through the connection between research and education, this CAREER project will provide knowledge and concrete activities for instructors including faculty instructors and LAs that can transform gatekeeper STEM classes to be taught in ways that are more beneficial for all students’ learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Nontechnical description: Photonic integrated circuits (PICs) that use light in the visible wavelength range can enable scalable optical interfaces for control and readout of information from cells, atoms, and ions. Miniaturizing these systems to a chip-scale will allow for optical techniques in several fields that impact society, such as biomedical devices, quantum computers, and portable displays, to be scalable, portable, low-cost, and mass manufacturable enabling widespread dissemination. However, in contrast to traditional infrared telecommunication applications where PICs already play an important role, visible applications require optical wavefront shaping capabilities. Current wavefront shaping techniques rely on table-top optics or chips with large waveguide systems, which grow in terms of footprint, optical loss, density of optical routing challenges, and electrical control power. This project addresses these challenges by developing single waveguides capable of creating miniaturized 3D optical patterns using nanoscale wavefront shaping. The project includes educational activities for K-12, undergraduate, and graduate students that will contribute to building the workforce in semiconductors and chip-scale technologies. Visible PICs are visually captivating and important for many societally-relevant applications (e.g. neurotechnology), making them an ideal platform to demonstrate the power of chip-scale technologies to students. The plans also include an annual immersive early-exposure science day for middle school students (Nano Day) as well as an interactive, hands-on lesson that will be delivered to local public schools through the STEM Ambassadors program. Technical description: The proposed research develops a platform to enable scalable, nanoscale wavefront shaping using multimode PICs through amplitude and phase control of transverse spatial optical modes. Here we leverage the highest resolution optical structure within waveguides for sub-wavelength reconfiguration of optical patterns within waveguides and wide-angle beam steering, tunable focusing, and structuring of free-space beams from multimode waveguides. Goal 1 is to experimentally validate the theoretical framework for multimode wavefront shaping. Goal 2 develops compact mode superposition control devices based on high-resolution thermo-optic and electro-optic refractive index modulation. Goal 3 develops a compact out-of-plane 3D emitter for dense integration using multimode PICs and demonstrates a system application for neural stimulation. The work proposed here will lead to ultra-compact fully integrated chip-scale beam steering devices capable of producing grating-lobe-free complex beam structuring over a wider field of view than previous methods. In turn, these structures for manipulating optical modes will also allow new system architectures in PICs that leverage the spatial degree-of-freedom beyond a few higher order modes to make a leap towards very-large-scale integration like their electronic counterparts. 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 · 2025-07

Coral symbioses are comprised of animal, algal, and microbial members. The primary association, between the coral animal and its photosynthetic algal symbiont, is crucial for the coral host; most tropical corals are highly dependent on their algal symbionts and will bleach and subsequently die if this symbiosis is disrupted by poor environmental conditions, e.g. warming ocean waters. The Northern Star Coral, Astrangia poculata, a common species found in temperate coastal waters of eastern North America, exhibits a flexible symbiosis with its algal symbiont. Adjacent colonies, subjected to the same environmental conditions, can be found with few to many algal symbionts; some colonies appear stark white, seemingly bleached, whereas others are brown in color, containing photosynthesizing algal symbionts, and yet both types of colonies can be healthy. This unique aspect of the biology of A. poculata with its algal symbiont, Breviolum psygmophilum, will be used to examine how symbiosis affects the energy corals devote towards the basic tasks required of all organisms: development, growth, body maintenance, and reproduction. Given rapidly deteriorating ocean conditions for most corals worldwide, understanding how corals acquire and use energy with and without their symbionts, in different environmental conditions, is imperative. The project will support the education and research training of numerous undergraduate students from diverse backgrounds, and will support outreach efforts to engage the public in temperate coral symbiosis and conservation. While tropical corals exhibit one of the classic examples of mutualisms in biology, the fitness costs, benefits, and tradeoffs of coral-algal symbioses are challenging to explicitly test because their symbiosis is obligate. Astrangia poculata, a common subtidal coral found in temperate coastal waters of eastern North America, may permit this type of testing because it exhibits a facultative symbiosis with its algal symbiont, Breviolum psygmophilum. By their very nature, facultative symbioses are evolutionary, ecological, and physiological puzzles that are intriguing at many levels of biological inquiry, and their energetic ramifications may be best disentangled using integrative and comparative approaches. There are certainly fitness costs, benefits, and tradeoffs to different symbiotic states, but unless the benefits are perfectly balanced across states, one would predict convergence towards or away from symbiosis. In A. poculata however, multiple symbiotic states persist in nature, immediately adjacent to each other in physical space. This study will examine symbiosis from an energetic perspective, quantifying the effects of symbiotic state on reproductive investment and larval performance, important life history characters and measures of fitness. The investigators will also construct, parameterize, and validate a comprehensive dynamic energy budget model for A. poculata - B. psygmophilum, the first such model in any system to explore energetic acquisition, expenditure, and needs. Broader impacts will include coordination with existing institutional programs to facilitate the transition into research labs of underrepresented first-year undergraduate students, empowering women in science, and outreach efforts to engage the public in coral symbiosis and conservation. 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 award supports research that looks to create a proof-of-concept robotic factory for the automated design, fabrication, and testing of novel soft actuators, powered by pressurized air. Such actuators are important for food handling, wearable devices, surgical tools, and other applications requiring the safe manipulation of easily damaged items. The robotic factory can print an actuator from multiple polymer feedstocks covering a wide range of mechanical properties, in order to custom tailor the component to the needs of the user. Combining multiple materials to achieve a desired result is a challenging process even for human experts, made more difficult when producing novel parts for one-off tasks. Therefore the robotic factory looks to embed an intelligent design capability, using high-fidelity simulations to test and evolve millions of possible solutions. Even the best simulations depend on accurate knowledge of physical parameters. Therefore, after a part is manufactured, the robotic factory looks to test it against the simulator predictions. If the performance is acceptable, the part is used. Otherwise the simulator is recalibrated and the process repeats. With each cycle, the algorithm can explore an increasingly rich design space. To extend the number of iterations that can be performed without human intervention, failed parts are recycled and the material reused. The robotic factory intends to draw on previous results to continually improve the parts it makes. Future generations of robotic factories will produce increasingly complex devices, up to and including fully functional soft robots. This project seeks to transform the design and discovery of novel soft components and devices. The system, called EvoFab, will be an autonomous robotic factory that combines innovative evolutionary algorithms with fully automated fabrication and in-situ characterization to design, manufacture, and test pneumatically powered soft components. EvoFab looks to design and simulate parts, which will then be 3D printed, probed, and placed in a pneumatic test fixture, all without human intervention. Material from failed parts will be recovered in an integrated recycling process. This collaborative project has five innovative research threads: (1) a fabrication-aware evolutionary design system that rapidly and efficiently searches a design space for responsive configurations; (2) simulations of designs that minimize differences between numerical predictions and real behaviors; (3) an automated fabrication pipeline that prints and (4) characterizes those designs; and (5) an integrated recycling process that maximizes untended operation. 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.