University of Connecticut

NSF Awards · FY 2025 · 2025-02

This NSF CAREER project aims to enhance electric power grid operators' situational awareness, improve dynamic model quality, and enable online controls to ensure secure power system operation with high penetration of inverter-based resources (IBRs). The project will bring transformative changes to the use of measurements for dynamic state estimation, model deficiency diagnosis and calibration, and measurement configuration, thereby enhancing system reliability and security. This will be achieved through innovative dynamic estimation theories and algorithms that leverage the increasing diversity of sensors and communication infrastructure, as well as advancements in robust estimation, uncertainty quantification, optimization, and data analytics. The intellectual merits of the project include i) a generalized, computationally efficient derivative-free observability theory, with observability indices tailored for dynamic systems with black-box models, ii) integration of Bayesian inference with robust estimation to develop novel nonlinear dynamic estimation methods and iii) a scalable Bayesian framework for dynamic parameter estimation and uncertainty quantification. The broader impacts of the project include developing the next generation of robust dynamic estimation paradigms for IBR-dominated power systems, and industry-academia collaborative initiatives to promote industry-driven research, course renovation, and training to equip students (including K-12 students) with unique experiences in sustainable energy technologies, data analytics and power engineering. The rapid deployment of inverter-based resources (IBRs) and battery energy storage is changing the dynamic landscape of electric power grids. Traditional steady-state-based static state estimation, used in current energy management systems, is insufficient for capturing these dynamics in real-time operations. This project addresses the critical need for improved dynamic observability and reliable models for system reliability analysis and decision-making. The research objectives include i) developing a generalized derivative-free state and parameter observability theory for black-box and hybrid dynamic systems, overcoming limitations of linearization-based and Lie-derivative-based theories for IBR-dominated systems, ii) fusing robust statistics with estimation and optimization to create nonlinear dynamic estimation methods capable of addressing black-box IBR models, control mode switches, current limiters, anti-windup constraints, unknown controls, and multi-timescale dynamics and iii) designing observability-informed, scalable parameter estimation and uncertainty quantification algorithms to continuously refine power system dynamic models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project focuses on establishing a coherent learn-from-knowledge paradigm that achieves concurrent optimization in hybrid remanufacturing, by exploiting the shared target of additive manufacturing and machining. Integrating the strengths of both systems, hybrid remanufacturing is particularly promising for rejuvenating damaged or obsolete parts synergistically, thereby increasing efficiency, reducing costs, and improving sustainability. However, current state-of-the-art practices often overlook the intricate interactions between two distinct technologies, which leads to compromised part quality and low throughput. This project addresses this challenge by creating a holistic framework that integrates data from various stages of remanufacturing, including part specifications, machine logs, sensing, and expert knowledge, and repair protocols into a unified knowledge representation. By offering rich semantic representation and advanced reasoning capabilities, this project paves the way for enhanced efficiency and reliability for system-level optimization in hybrid remanufacturing. Validation and testing of generalizability will be carried out both in laboratory setups and within integrated hybrid machine networks through collaboration with two industry partners. The anticipated outcomes include the development of computational models for knowledge representation and fusion, as well as automation and decision-making algorithms to facilitate system integration of additive manufacturing and machining as inter-connected components. This research will disseminate findings through education and outreach activities, not only emphasizing the participation of underrepresented groups but also providing workforce training for local small- and medium-sized manufacturers to promote resilient, adaptable, and sustainable systems. The overarching goal of this research is to establish a new framework of knowledge representation that leads to the concurrent optimization of hybrid manufacturing at the system integration level, which is facilitated by the cognitive principles producing unified and distilled knowledge. This framework bridges connectionist and symbolic approaches within knowledge graphs, systematically representing structured domain schemas and providing semantic richness to address data multi-modality, sparsity, and semantic heterogeneity in hybrid remanufacturing. The technical thrusts in this project are: (1) creating new cognitive encoders to unify multi-modal data from remanufacturing workflows into knowledge graphs, while integrating them as a retrievable memory into the semantic relationships; (2) advancing knowledge fusion through cognitive operations that facilitate data-driven reasoning, knowledge fusion, distillation, and online knowledge discovery; and (3) developing a symbiotic multi-objective optimization that leverages the memory-enabled knowledge graph to guide hybrid remanufacturing tasks. Collectively, these breakthroughs will be synergistically integrated into hybrid remanufacturing systems with insights from industry 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 2025 · 2025-01

Computational techniques are impacting the way in which scientific and engineering research are conducted. Impressive computational advances have been made in different scientific areas including biology, medicine, and drug discovery. Given the great importance of these areas in society, it is essential to advance the state of the art in all three topics. Such advances can be made when scientists come together to discuss the best possible diagnosis tools, treatment procedures, and drug therapies for various diseases. The International Conference on Computational Advances in Bio and Biomedical Sciences (ICCABS) unites leading scientists in academia and industry serving as a collaboration platform. These collaborations are expected to result in the solution of challenging problems in all three fields. Topics to be discussed include, but are not limited to, biological big data analytics, biomedical image processing, biomedical data and literature mining, and biological modeling and simulation. The funds provided by this award will be used to support the travel expenses of trainees including US-based undergraduate and graduate students, and post-doctoral fellows. Students funded by this award will learn from a state-of-the-art scientific and educational conference program, an opportunity to present their work and receive feedback, and an opportunity to network with peers and other members of this broad community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Despite recent successes, Artificial Intelligence (AI) has also shown to be lacking in areas such as safety. Therefore, it is imperative to prioritize safety as a fundamental aspect across various domains of AI research. Notably, due to the ubiquity of network (graph) data, network learning techniques have strongly impacted AI in the past decade, being widely deployed across applications such as social networks, healthcare, and cybersecurity. However, real-world networks often include challenges like data issues and unforeseen environmental hazards, resulting in risky AI techniques and unsafe outcomes when these networks are employed. Existing studies for safe network learning lack versatility, efficiency, and comprehensive integration across multiple safety dimensions. They struggle to ensure timely, safe predictions in practical scenarios and fail to holistically address data, model, and usage aspects. These challenges collectively hinder their capability and effectiveness, and there is currently a lack of a holistic framework to adequately tackle safety issues in network learning. To bridge this research gap, the goal of this project is to design, develop, and evaluate a novel Safety-centric Network Learning framework (SNL) for safe decision-making on networks in the wild. The project outcomes will substantially impact network learning research and offer advanced solutions to address challenges across diverse domains such as public health, cybersecurity, and social media. Additionally, the project will foster interdisciplinary collaborations and facilitate technology transfer to industry. The project outcomes will be made publicly accessible and broadly disseminated. Moreover, the project will integrate research with education through novel curriculum development and student mentoring activities with an emphasis on underrepresented groups, aiming to train and educate future generations in effectively developing and utilizing AI while also ensuring AI safety. The project will involve comprehensive efforts to develop SNL that prioritizes the critical safety dimensions of reliability, stability, and explainability, and further encompasses the crucial aspects of data, model, and usage in a general, efficient, and integrated manner. Formally, SNL will provide reliable network data and network learning models (reliability) while generating stable and consistent outputs (stability) accompanied by easily understandable usage explanations (explainability). Specifically, the research components that engage innovative theories, algorithms, and models in this project are fourfold. First, design novel network learning algorithms to identify and generate reliable network data that are minimally impacted by data and environmental issues. Second, create new network representation learning models and training strategies to promote efficiency and reliability in learning network representations. Third, devise innovative data-to-model optimization theories to ensure the stability of network learning. Finally, develop novel generative learning methods to advance the usage explainability and output receptivity of network learning. The unified framework allows seamless collaboration and mutual reinforcement among different research components. Through the convergent research program, the project will not only make significant advancements in network learning and AI safety research but also shed novel insights to tackle various societal challenges, ultimately benefiting society at large. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Nitrogen is a critically important nutrient for plant growth. However, current understanding of how nitrogen availability influences plant form and function are based on plants with similar growth forms and means of photosynthesis. Tropical epiphytes, which grow on other plants and obtain water and nutrients from the canopy rather than soil, are very diverse but have been excluded from studies connecting nitrogen use to photosynthesis, growth and ecology. This project will investigate the tropical bromeliads (Bromeliaceae, 3500+ species), a family with many epiphytes and different means of photosynthesis, as a model for understanding how nitrogen scarcity influences physiology, growth and nitrogen cycling through ecosystems. Broadly, this work will use experiments and surveys to test how nitrogen availability influences how plants photosynthesize, invest in leaves versus flowers, and decompose in different habitats. Including tropical epiphytes will provide a more robust framework for understanding the evolution of plant diversity. The project will engage researchers from high school onward in an interdisciplinary team spanning botanical gardens, a liberal arts college and a research university to train a new generation of scientists in collaborative, greenhouse-based research, with conservation applications in Florida where many threatened bromeliads grow. Plants grow in response to their environment, resulting in varied morphologies, physiologies, and life histories. Categorizing their forms into functionally integrated suites of traits helps answer why species grow where they do depending on how they obtain and allocate limiting resources. One such framework describes a tradeoff between fast-growing, resource-acquisitive traits and slow-growing, resource-conserving traits. This fast-slow continuum hypothesis applies to diverse lineages and across ecosystems. Yet, it was developed based on terrestrial, C3 photosynthetic plants, and has largely excluded other photosynthetic pathways and growth forms. The diverse neotropical Bromeliaceae, which has been understudied for functional traits, includes multiple evolutionary origins of CAM photosynthesis in epiphytic habitats where nitrogen (N) is especially limiting. This project interrogates connections between N limitation and tradeoffs in plant functional traits, taking into account the roles of photosynthetic pathway and growth form. Specifically this project will test 1) the physiological and molecular responses to N limitation in C3 and CAM bromeliad species, assess 2) long-term effects of N limitation on allocation to functional traits, including vegetative vs. reproductive growth, and determine 3) how N, photosynthetic pathway, and functional traits affect leaf litter quality and N-cycle feedbacks in terrestrial versus epiphytic habitats. These goals will be achieved via interdisciplinary work combining experimental studies, morphology, ecophysiology, ecology, and genomics. The research project will engage scholars and scientists from high school onwards, will provide exceptional research experiences for students at a primarily undergraduate institution, and will train a new generation of scientists in interdisciplinary, collaborative science. Statement of Merit Review: This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The broader impact of this I-Corps project is the development of a seaweed-based coating to preserve the freshness of perishable produce. Currently, about one-third of fresh produce is lost post-harvest, equating to about 40 billion pounds of food, $50 billion in revenue losses, and 8% of greenhouse gas emissions annually. These losses pose a significant threat to both U.S. and global food security. The goal for the technology is to develop a carbon-negative, edible coating derived from seaweed that can extend the shelf life of perishable produce by twofold or more. This technology may reduce food loss across the supply chain, from farmers to retailers, increasing profits for stakeholders and potentially enhancing consumer intake of fruits and vegetables. The aim of this project is to reduce spoilage of highly perishable and fragile produce grown in the U.S. such as berries, mushrooms, peaches, and others. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a produce preservation technology. This technology uses a seaweed-based edible coating and manufacturing process and a precision coating process that applies the coating to fresh fruits and vegetables to extend their shelf-life. The seaweed extracts are derived from farmed seaweed using a green biorefinery method that eliminates the use of harsh chemicals. The coating is formulated into an edible coating ingredient that is shelf-stable. The coating material is easily dissolved in water prior to the use and applied using a precision spray technology that is quick drying (up to 4x faster than dip coating) and reduces material usage (<20% material compared to dip coating). In addition, the coating is compliant with the Food and Drug Administration's Generally Recognized as Safe requirements, and U.S. Department of Agriculture-Organic standards. Lab results show that the coating reduces the weight loss from respiration and fungal spoilage in strawberries, each by more than 50% compared to the no-coating control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Infectious diseases are a major global health concern. Rapid and accurate detection of the presence of disease-causing RNA is crucial for effective diagnosis and control of these diseases. The CRISPR-Cas13 system is a cutting-edge technology for RNA detection. However, the currently used Cas13 enzymes can become unstable and lose their effectiveness during long-term storage and in field applications. This project aims to improve the stability and sensitivity of a heat-resistant version of the Cas13 enzyme, making it more reliable and sensitive for detecting RNA. This includes enhancing the enzyme's ability to recognize and cut RNA and combine it with advanced electrochemical devices to create a highly sensitive and stable detection method. The proposed scientific advancements are closely connected to educational outreach activities. The project will involve high school and community college students, particularly from underrepresented backgrounds, in biological and bioengineering research. Students will receive training in experimental techniques, data analysis, and scientific writing. Additionally, high school students will be introduced to CRISPR technology through a biotech academy and integrate the research findings into university courses. The goal of the project is to combine mechanism-based protein engineering and cutting edge electrochemical devices to generate next-generation RNA detection tools for infectious disease diagnosis. The project will leverage the CRISPR-Cas13 system, which has shown great promise as next-generation diagnostics for in vitro RNA detection owing to its high specificity, programmability, and fast reaction rate. The collaborative project aims to first investigate the structure and mechanism of the recently discovered thermostable Cas13. Leveraging the mechanistic understanding, rational engineering of the thermostable Cas13 will be performed to produce new variants with superior thermostability and protease resistance, as well as enhanced target sensitivity and reaction speed. The engineered Cas13 variants will be combined with innovative electrochemical devices to enable ultrasensitive and robust RNA detection of various pathogens derived from clinical samples. Successful completion will provide superior RNA detection tools for medical and research applications, alongside novel insights into the Cas13 nuclease mechanism. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Engineering projects and technological developments have been at the core of social and economic development globally. A typical underlying assumption is that engineers' work contributes to improving public welfare and protecting and mitigating harm to the natural environment. Consequently, professional codes of ethics integrate such assumptions but do so by relying mostly on incentivizing the conduct of "responsible" engineers towards the public, clients, and peer engineers at the expense of understanding or addressing the broader ethical impact of engineering on society and the public interest. These broad ethical principles do not routinely figure into engineering decision-making. Furthermore, research has shown that engineering students' focus on public welfare declines as they move through their undergraduate education, and that social issues are still remote for the average engineering student. A human rights-based approach to engineering work and education can help address this lack of broad ethical principles in the field, by harmonizing existing engineering work with human rights core principles of distributive justice, participation, consideration of duty bearers, accountability, and indivisibility of rights. This new and innovative approach can equip a new generation of engineering students with knowledge and tools to identify connections between their profession and the well-being of the people and to advance national health, prosperity, and welfare. Given the dearth of existing systematic evidence of the effectiveness of teaching human rights to engineering students, this project aims to evaluate the effect of pedagogical innovation on changes in student attitudes toward human rights and the societal impact of engineering. The project will employ a quasi-experimental design approach to test the hypothesis that learning modules focused on human rights can positively impact engineering students' attitudes toward human rights and their awareness of the social impact of engineering on society. The research will be carried out by a highly interdisciplinary team including expertise in engineering, human rights, and education. The project will develop, deploy, and evaluate the efficacy of human rights training modules in English and Spanish for integration into engineering courses. Findings from this project are relevant to broader human rights education in the STEM fields of engineering, sciences, and math. This research also stands to impact engineering education in the United States by foregrounding macro-ethical issues in engineering and developing curricula that can be used at scale by colleagues at other institutions who are interested in adopting a human rights approach to engineering education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This grant supports research that contributes new knowledge related to a novel manufacturing process called Form-Fuse. This process creates electronics that geometrically conform to rigid 3D surfaces. Such conformal electronics are critical for emerging automotive, aerospace, robotics, biomedical, energy, and environmental applications. Compared to existing manufacturing techniques Form-fuse can realize superior electrical performance for geometrically complex surfaces and access to a wider array of materials. This award supports fundamental research to understand key mechano-electrical phenomena in Form-Fuse. It can positively impact the production and performance of advanced electronics that are critical to the nation’s prosperity and security. This multi-institutional project involves several disciplines including manufacturing, modeling, machine learning, and design and will further broaden the participation and education of diverse underrepresented groups in manufacturing. A critical limitation of existing manufacturing techniques for conformal electronics is the inability to achieve high electrical performance for complex surface geometries without sacrificing size- and material-scalability. The Form-Fuse process involves printing of nanoparticles on flat polymer sheets, forming this assembly to match the shape of the targeted 3D surface, fusing the nanoparticles using light, and attaching this final assembly to the targeted 3D surface. Using multiple intermediate forming stages overcomes the above limitations of the state-of-the-art manufacturing methods. This research will address key knowledge gaps on the physical mechanisms that drive electrical performance in Form-Fuse. The research team will perform experiments to characterize the impact of the polymer’s thermomechanical history on electrical performance, establish physics-based models to reveal and predict the deformation mechanisms that drive electrical performance, and create physics-guided techniques for rational and scalable design of process parameters and intermediate stage geometries. These tasks will create the scientific foundation for understanding and scaling the Form-Fuse process in a cost-effective manner. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This is a travel grant to support student travel to Co-NEXT 2024, to be held in Los Angeles, California. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This Engineering Emerging areas of Advanced Manufacturing (ENG-EAM) award supports research that will focus on establishing systemic and robust resilience to cyberphysical attacks on connected digital manufacturing systems. Digitization and connectivity are the cornerstones of modern manufacturing, but these very qualities allow cyberphysical attacks to negatively impact part performance by stealthily altering the digital representations of geometry, process plans, and/or in-situ sensing signals. This has the potential to pose a significant threat to societal well-being, economic stability, and national security by introducing defective parts into electronics, spacecraft, planes, automobiles, biomedical devices, and energy components. The state-of-the-art practice of dealing with such attacks by sacrificing productivity, yield, cost, and connectivity to ensure part performance critically limits pervasive and trustworthy adoption of Industry 4.0 and digital manufacturing. This research project will create and validate a novel computational paradigm called Smart-Recover that actively assures every part’s performance despite cyberphysical attacks and with minimal loss in productivity, yield, connectivity, or cost-effectiveness. The research will be complemented by developing a multi-institutional manufacturing cybersecurity education program for workforce development across high school, undergraduate, and graduate educational levels. The specific goal of the research is to establish the mathematical basis for the Smart-Recover paradigm, which combines pre-fabrication correction of attack-altered geometric models with stoppage-free in-process mitigation of defects created by attack-modified process plans and attack-distorted in-situ sensing signals. To this end, the research objectives include the creation of techniques for: (1) pre-fabrication computational reconstruction of only the attack-altered features of the digital geometric model; (2) in-process remodification of process plans to disrupt formation of local defects induced by atypical attack-driven alteration of exogenous process parameters; and (3) in-process restoration of defect prediction accuracy for attack-altered sensor signals at speeds necessary for local defect mitigation. The research team will further explore the generalizability and collective interaction of these elements of Smart-Recover with stealthy and system-spanning cyberphysical attacks via two manufacturing testbeds. These advances will be achieved via innovations at the convergence of geometric design, machine learning, in-situ sensing, and physics-based modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Biodiversity Research Collections at the University of Connecticut is one of the few collection facilities in the country with considerable holdings of parasites. This project aims to stabilize and curate a series of large collections of parasites collected over the last 80 years from a wide range of vertebrates across the United States and around the world. These specimens and their detailed associated data will be made publicly available through several on-line platforms. Given that many of their hosts cannot be readily sampled for parasites today, in part because many are now considered threatened or endangered, these collections serve as irreplaceable records of the parasites of these vertebrates. The historical nature of these collections will help inform our understanding of broader changes in parasite faunas over time. The comprehensive nature of their associated data makes these collections valuable assets for furthering our understanding of fundamental questions in parasite ecology and evolution. To connect this unique resource with broader themes of science in school curricula and civic themes in particular, a competition to formally designate a Connecticut State Parasite will engage middle school students in museum science and, familiarize them with the legislative process required to transform an idea to a bill and finally to a law. Opportunities to gain skills in collections management, databasing, host and parasite diversity, and science communication will be provided to a curatorial postdoctoral fellow, and graduate and undergraduate students. The primary goals of this project are to stabilize, curate, and provide access to ca. 128,000 specimens of parasites (e.g., nematodes, tapeworms, flukes, ticks, lice, and fleas) from vertebrates (e.g., fish, sharks, stingrays, birds, mammals, lizards, and amphibians) in nine historical collections. Stabilization activities will include remounting and reattaching labels to microscope slides and replacing archival vials and lids. Specimens will be re-curated and re-organized to facilitate accessibility. Host identifications will be updated to align with current taxonomy. All specimens will be accessioned and databased along with their associated data in the existing Lawrence R. Penner (LRP) parasite database and shared with data aggregators. A new module will be added to the LRP database to house images of original host specimen necropsy sheets associated with parasite specimens. In collaboration with the Connecticut State Museum of Natural History, on-line access to information on 25 iconic Connecticut vertebrate species and four of their parasite species represented by specimens in the collection will be provided to classes of middle school students. Classes engaged in the Connecticut State Parasite challenge will compete in making a case for their favorite parasite and will learn about the legislative process from experts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The University of Connecticut, the University of Wisconsin-Madison, and the University of Illinois at Urbana-Champaign are conducting a research study of the barriers and solutions that physics graduate students and faculty experience in non-traditional post-secondary training and workplace settings. The lack of full inclusion of people with disabilities in the STEM workforce is a missed opportunity to realize the full potential and talent of the entire U.S. population. Opportunities to advance knowledge about physics postsecondary training setting and workplace barriers and solutions for faculty and graduate students with disabilities will lead to increasing the engagement, academic career retention, and career advancement of faculty and students with disabilities in STEM. Such success is essential for building and advancing a robust U.S. STEM workforce. The research team is engaging with an expert advisory board, an objective evaluator, a postdoctoral research scholar, and graduate students to contribute to the project work. The research includes the collection, analyses, and interpretation of qualitative and quantitative data that are informed by robust theoretical frameworks and conceptual models. Findings will be share with the general public as well as researchers, educators, and administrators. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). The project is funded by the Directorate for Social, Behavioral and Economic Sciences’ Office of Multidisciplinary Activities, the Division of Equity for Excellence in STEM’s Education Core Research (ECR), the Division of Equity for Excellence in STEM’s Alliances for Graduate Education and the Professoriate (AGEP), the Division of Equity for Excellence in STEM’s Louis Stokes Alliances for Minority Participation program (LSAMP), the Division of Undergraduate Education’s Improving Undergraduate STEM Education (IUSE), and the Division of Equity for Excellence in STEM’s Eddie Bernice Johnson Inclusion across the Nation of Communities of Learners of Underrepresented Discoverers in Engineering and Science (INCLUDES). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Protons and neutrons, collectively known as hadrons, are composite particles made of quarks, which are the fundamental, structureless building blocks of matter. Despite moving within the proton at nearly the speed of light, quarks are confined to the microscopic interior of the proton by the strong force, the most powerful of the four known fundamental interactions in nature, as described by quantum chromodynamics (QCD). A key challenge in modern nuclear physics is to provide a quantitative understanding of hadron properties through the internal dynamics of quarks within the framework of QCD. The research supported by this project seeks to establish the foundations for describing the structure of hadrons theoretically. The outcomes of this research project will advance knowledge of hadron structure theory and will provide theoretical support for ongoing science programs at state-of-the-art experimental facilities, as well as for future projects such as the future Electron-Ion Collider at the Brookhaven National Laboratory. The broader impacts of this project include the training of undergraduate and graduate students and postdoctoral researchers. Additionally, it supports a summer bridge program providing research opportunities for undergraduate students from underrepresented groups in the physics department at the University of Connecticut. The objective of this project is to advance the current understanding of the nonperturbative properties of hadrons in QCD, as described in terms of form factors, parton distribution functions, generalized parton distributions, transverse momentum dependent parton distribution functions, and transverse momentum dependent generalized parton distributions. The latter provide an attractive overarching umbrella concept that unifies all the aforementioned hadron properties. These functions serve as powerful tools for describing high-energy processes within QCD factorization frameworks and provide access to a multitude of previously unexplored nucleon properties, such as proton mass and spin decompositions, as well as distributions of energy, internal forces, and orbital angular momentum within the proton. The theoretical research supported by this project focuses on investigating the nonperturbative properties of these functions, including studies of polarized helicity sea quark distribution functions, exploration of chiral symmetry breaking effects in nucleon structure, and analyses of generalized transverse momentum dependent distribution functions within effective field theoretic approaches. The methods employed in this project encompass phenomenological studies, investigations in effective theories and QCD-inspired models. Additionally, the formulation of quasi parton distributions is explored to provide complementary insights and support ongoing lattice QCD studies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The theory of cluster algebras is a highly active research area in mathematics that was initiated in 2002. The original motivation came from representation theory, a branch of modern algebra which is concerned with studying the symmetries of an algebraic structure rather than studying the structure itself. Representation theory has numerous applications in physics and chemistry as well as in other mathematical fields. Cluster algebras capture fundamental underlying combinatorial patterns that occur throughout representation theory. Quite remarkably, these patterns turn out to be present as well in a number of other branches of mathematics and physics that had previously seemed mostly unrelated. This project will contribute to the development of cluster algebras and their relations to other areas, in particular to knot theory and representations of algebras. The project will involve graduate students in the proposed research. The project has several objectives. The principal investigator will develop a fundamental connection between cluster algebras and knot theory that realizes important knot invariants as specializations of cluster variables. The centerpiece of this project is the construction of a cluster algebra from an arbitrary knot or link, such that the cluster algebra contains a cluster in which each cluster variable specializes to the Alexander polynomial of the knot. The second objective is to study Cohen-Macaulay modules over 2-Calabi-Yau tilted algebras. These are non-commutative algebras that are associated to the clusters of a cluster algebra via categorification. One overarching goal is the classification of 2-Calabi-Yau tilted algebras that admit only finitely many Cohen-Macaulay modules. A third aim is to study maximal almost rigid modules, a new concept in representation theory inspired by the PI’s previous work on Catalan combinatorics. In this project, he PI will show that the triangulations of a surface with marked points and dissection correspond bijectively to the maximal almost rigid modules over an algebra associated to the surface dissection. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The Creating Inclusive Scientific Societies through Policies and Practices (CRISSPP) project brings three universities, University of Michigan, University of Connecticut, and University of North Texas into a partnership to develop, implement, and assess a set of evidence-based guidelines and practices for scientific organizations (beginning with Psychology) to promote inclusion and minimize systemic exclusion. The research literature indicates that academic exclusion includes social, informational, and epistemic exclusion, and professional societies can play a central role in members’ academic careers, facilitating the dissemination of their scholarship and providing opportunities to establish prominence within the field. The guidelines and practices will help professional societies create and sustain positive disciplinary environments that lead to success for all faculty. The project will empower organizations to shape individual members’ experiences of inclusion/exclusion and the organization’s climate in four critical areas: governance, awards, conferences, and publications. The CRISSPP guidelines and practices will (1) conduct climate surveys and audits, (2) construct interventions (to include transparency audits, toolkits, including guidelines and rubrics as appropriate, commitment to optimal processes, pathway development (for governance), educational workshops and, (in the case of conferences), a brief daily online climate assessment tool), and (3) assess the overall impact of these interventions on the organizations and on members’ sense of belonging. The guidelines, practices, and lessons learned will initially be shared within Division 9 of the American Psychological Association and up to nine additional partner organizations in psychology, reaching over 20,000 members. This partnership will be evaluated internally and externally, formatively and summatively, to improve the guidelines and practices for other organizations and identify implementation issues that may need to be addressed. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions.  Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate.  ADVANCE "Partnership" awards provide support for the adaptation and adoption of evidence-based strategies to academic, non-profit institutions of higher education and non-academic, non-profit organizations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Soot emissions from combustion devices and fires have been plaguing humans for centuries, with repercussions on health and climate. Understanding the process by which soot forms remains a challenging topic in combustion research because of difficulties in describing the critical steps involved. The most sophisticated approaches to study soot formation in flames entail detailed measurements of gaseous soot precursors and soot particles with multiple, complementary diagnostics that follow the entire evolution from parent fuel molecule. At the other end of the diagnostic spectrum, the sooting tendency is described by a simple sooting index, without the assessment of soot production rates necessary for modeling soot in engine-relevant conditions. This proposal seeks a middle ground, aiming to quantify the soot production rate while maintaining the simplicity of single index characterizations. The study will impact the design of practical engines, the reduction of the environmental footprint of combustion, and, indirectly, air quality, public health, and climate. The research goal is to quantify soot production rates of several relevant fuels in a simple but fundamental manner, without ad hoc assumptions. The approach involves establishing opposed jet gaseous diffusion flames, doping them with a few thousand parts per million of pre-vaporized practical fuel components and measuring soot volume fraction through pyrometry. The experimental work is complemented by numerical simulation of the flames structure to accurately describe the velocity and temperature fields. These data enable the quantification of the soot production rate from the soot governing equation. In this study, fuels to be tested are common components of Jet fuels and Diesel fuels, and their surrogates, including: iso-octane, n-decane, n-hexadecane, iso-cetane, toluene, 1,2,4 tri-methylbenzene, methyl-naphthalene and decalin. They will be tested individually at first and then in blends mimicking the sooting tendency of jet fuel and Diesel fuel. The developed database will allow a quantitative comparisons of different fuels in highly controlled environments either by keeping a constant temperature-time history, which affects soot formation critically, or by varying peak temperature over several hundred degrees and pressure in the 0.1-3.0 MPa range. The quantified soot production rates will be converted to sooting yields and their explicit dependence on temperature, pressure, strain rate, and local mixture fraction will be established. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With this AccelNet Design Track award, PIs and their US and international partners will form a global clean hydrogen Network of Networks (NoN): Research and Education Accelerated by Connections in Clean Hydrogen (REACH2). The network will be composed of researchers from academia, national labs and industry, across the globe. Clean hydrogen is entering a pivotal moment of ever-growing interest, with its potential to decarbonize the fields of energy generation, transportation, manufacturing and a number of chemical and industrial processes. However, close to 95% of the current global hydrogen supply is produced from fossil fuels by steam reforming processes that emit large amounts of CO2. The grand challenge is to enable a paramount shift and scale-up in clean hydrogen production, mainly by water electrolysis, and its use (in fuel cells to produce clean energy and other applications). Our network will enable a coordinated and collaborative global effort with the goal to collaboratively address unmet fundamental research gaps and accelerate science in clean hydrogen technologies, water elecrolyzers and fuel cells, as well as to facilitate networking, education and training of next generation diverse talent and qualified workforce, benefiting society as a whole. REACH2 Network will advance the following scientific areas by facilitating coordinated and synergetic collaboration, by providing tools and models for knowledge, data, samples, and facilities sharing, and by utilizing expertize of each network member: (1) We will collaboratively study, develop, and implement process automation, advanced data science, machine learning and artificial intelligence approaches in fabrication, testing, characterization, experimentation and data correlations for fuel cell and electrolyzer systems; (2) We will focus on accelerating scale-up fabrication science and on understanding processing-structure-properties-performance correlations in these systems by using above tools; (3) We will focus on rational design and synthesis of next generation functional, highly-performing and stable catalysts and electrolytes for these systems, and study their properties, interfacial phenomena, performance and degradation mechanisms. The final and most important focus of REACH2 NoN is to enable effective and systematic networking, scientific exchange, sharing of ideas and knowledge, and synergistic training and education of next generation leaders in clean energy. During the Design phase of our Network, we will have three main planning goals that will enable launch of the REACH2 NoN and its successful implementation: (1) Design and establish the Network roadmap, with mission, goals, models, and activities to promote collaborative synergistic research innovation and scientific discovery; (2) Plan and establish a suite of shared and publicly accessible tools and platforms for data sharing, storage and processing; (3) Design shared training programs for development of future workforce and next generation of experts and leaders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The Cluster Algebra Summer School takes place at the University of Connecticut June 17-21, 2024. It is aimed at graduate and advanced undergraduate students, and it comprises four mini-courses on different recent developments in the theory of cluster algebras and related topics. This theory is a relatively young branch of mathematics. The initial motivation was to gain an understanding of certain positivity properties in representation theory, a branch of modern algebra. The theory quickly developed deep connections to a variety of disciplines in mathematics and physics, and it is a highly active research area. Cluster algebras are commutative rings equipped with a combinatorial structure that groups its elements into certain subsets, called clusters, which are related to each other via an intricate apparatus called mutation. This structure turns out to be very natural, in the sense that it is present in a large number of mathematical designs. The four mini-courses are on the following topics. (1) Cluster structures on Richardson varieties and their categorification, which focuses on a relation between representation theory and cluster algebras in the setting of algebras arising from Grassmannian varieties. (2) Cluster algebras and Legendrian links, a mini-course on a connection between cluster algebras and symplectic geometry, especially the contact structure on positive braids. (3) Maximal almost rigid modules, a new type of modules over gentle algebras that correspond bijectively to triangulations of surfaces. (4) Cluster algebras and knot theory, which is on a fundamental relation to knots and links that gives new insights into both areas. All courses are on recent advances in the field and are taught by researchers who are directly involved in these developments. This summer school will help to prepare a diverse group of junior mathematicians to work in this important field. The url for the website of the school is https://egunawan.github.io/cass24/. 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.