SUNY at Buffalo

NSF Awards · FY 2024 · 2024-09

Nontechnical Description The relentless incorporation of artificial intelligence is driving continued innovation in the design of modern computing systems. Traditional microelectronics have long been based upon silicon transistors, which has come at the cost of ballooning energy consumption. A new approach to microelectronics incorporating innovations in materials and novel electronic designs is therefore critically needed to enable energy-efficient and sustainable computing. The focus of this project is a new type of energy-efficient electronics that combines the distinct advantages of two very different classes of materials. The first of these is magneto-electrics, whose magnetization orientation can be switched both rapidly and with low energy consumption. The second is transition-metal dichalcogenides, two-dimensional semiconductors consisting of atomically thin sheets. These can be utilized as the conducting channel of a transistor. This project combines these materials to realize a “magneto-electric transistor.” These devices have high-speed and low-power operation and retains their programmed state when electrical power is removed. New types of microelectronic circuits suitable for energy-efficient computing are also developed, along with scalable approaches to wafer-scale manufacturing in a state-of-the-art foundry setting. These technical efforts are tightly integrated with an outreach program that provides broader impact by establishing viable models for workforce development for the semiconductor industry. Technical Description There is a crucial need for innovative co-design of new logic and memory solutions, simultaneously accounting for materials selection, device design, and circuit architecture to realize advances. In this program, scalable non-volatile CMOS+X devices and circuits are developed. These derive their functionality by integrating voltage-switched magneto-electrics with transition-metal chalcogenide transistor channels. The resulting transistors exploit proximity coupling between the boundary magnetism of their magneto-electric gate stack and carrier transport in a high spin-orbit-coupling channel. This allows substantial improvements in energy efficiency, making these transistors useful for data-intensive computation. This research effort is informed by a co-design philosophy that relies on understanding the microscopic properties of materials, on developing device concepts that can exploit the advantages of the materials, and on designing optimized circuits that lead to fast, energy-efficient operation. As such, this synergistic approach therefore involves all three areas of the technology stack. An integrated effort on workforce development results in broader impact, by providing engineers and scientists with professional development and training opportunities. Unique leverage for this program comes from access to state-of-the-art (300-mm) wafer-scale foundry (NY CREATES) facilities at the University at Albany. 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

NONTECHNICAL SUMMARY This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This project aims to understand the oxidation behavior of high-entropy alloy nanoparticles, which are a new type of alloys containing multiple elements in roughly equal proportions. Oxidation under industrial conditions negatively impacts the performance of these alloys, limiting their broader use. In this project, the team will employ a data-driven approach, combining experimental and computational datasets with targeted experimental synthesis. The goal is to develop reliable predictive models considering uncertainties in both types of data, to lead to a better understanding of the oxidation behavior of high-entropy alloy nanoparticles at the nanoscale. By gaining fundamental knowledge through this interdisciplinary effort spanning materials science, chemistry, and applied mathematics, the project has the potential to enhance the oxidation resistance of high-entropy alloy nanoparticles. It will also provide essential support for critical experimental studies to validate the data-driven models. This research opens new possibilities for innovative strategies to synthesize high-entropy materials, paving the way for exciting advances in future research and technological applications. The project provides comprehensive research training in materials chemistry and data science to graduate students within a collaborative and interdisciplinary research environment. The project will participate in a long program at the Institute of Mathematical and Statistical Innovation and organize a workshop centered around "Uncertainty Quantification for Chemistry and Materials Science". Leveraging the outcomes of the project, the team aims to propel and invigorate data-intensive research, particularly by integrating uncertainty quantification into predictive modeling within the domain of solid state and materials chemistry. TECHNICAL SUMMARY This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This project aims to gain a mechanistic understanding of the interplay among elemental segregation, migration, and oxidation resistance of high-entropy alloy nanoparticles by integrating experimental and computational tools with modern data science methods. The goal is to establish data-driven materials design strategies that allow precise control over the oxidation kinetics of high-entropy nanoparticles with composition design. The project will leverage existing experimental and computational datasets on thermodynamic and adsorption energetics, and combine this data with supplementary high-throughput first-principles calculations and hybrid molecular dynamics / Monte Carlo simulations, to develop machine learning models for elemental segregation and migration models in high-entropy alloy nanoparticles under oxidation conditions. A novel Gaussian Process regression model, which inherently includes uncertainty quantification and allows for intuitive interpretation, will be developed to predict oxidation behavior. Furthermore, the project will synthesize high-entropy alloy nanoparticles with specific compositions and characterize their structural and oxidation behavior, comparing the results with model predictions. This this research will provide experimentally validated fundamental knowledge regarding the structure-property relationships of high-entropy alloy nanoparticles under oxidation environments. Additionally, the project will establish a valuable suite of analytical and modeling tools for the field of solid-state and materials chemistry. These tools will enable an integrated approach to accelerate experimental-computational design of high-entropy alloy nanoparticles, facilitating theory-guided synthesis research of multicomponent material nanoparticles across a broader chemical space. 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

The most impressive and potentially dangerous expression of volcanic activity on Earth – commonly referred to as super-eruptions – involves explosive eruption of hundreds of cubic miles of volcanic ash in a single event. Super-eruptions produce destructive, fast-moving mixtures of ash and gas that can flow more than 100 miles along the ground. These pyroclastic flows leave behind deposits of ash and pumice that are referred to as ignimbrites. Ignimbrites cool very slowly, requiring decades to centuries to release their heat. During prolonged cooling, the ash particles can stick together, deform, and completely consolidate to make a welded deposit. Although we have been fortunate to not experience a super-eruption during human history, they are certain to occur in the future. On the other hand, hundreds of these large ignimbrites are preserved in the geologic record. Ignimbrites preserve important clues about how super-eruptions work. They can also point to geothermal and mineral resources that are often associated with the volcanic systems. This project will advance our understanding of super-eruptions and the deposits they leave behind. This work will train a Ph.D. student in studying volcanic processes and deposits. The project will also include an undergraduate student who is from a historically underrepresented group in outreach activities with the Buffalo Museum of Science. Despite the importance of large ignimbrites in helping to understand potential hazards and in locating resources, fundamental questions still need to be answered: • Do large-volume pyroclastic flows that travel long distances move in a manner akin to hot, turbulent flows with low concentrations (akin to super-intense sandstorms), or are they more like hot avalanches of concentrated ash particles? This is directly relevant for volcanic hazard mitigation. • Also relevant to hazards mitigation - how do the flows maintain high temperatures (between 1200-1800ºF) over distances of 50-100 miles or more? • How is the welding of a deposit (ignimbrite) produced and what is its quantitative relation with cooling and material properties of ash in the deposit? Such a quantitative understanding can aid in the interpretation of large ignimbrites related to natural resources. This project addresses these questions using as a case study the Peach Spring Tuff (PST), a well-preserved deposit that extends 80-110 miles to the east and west of its source volcano, which was located along the Arizona-California border (the volcanic system has recently been an active mining district). Flow- and deposition-related data will include deposit textures, sizes and types of ash, pumice, and other rock types, grading, internal contacts, and anisotropy of magnetic susceptibility. Welding- and cooling-related data will include deposit density, rock strength measured at outcrops, porosity, and permeability (both field and laboratory measurements), fracture characteristics, pumice deformation and other cooling-related textures, thermal remanent magnetism to estimate minimum temperature of the deposit, indicators of rate of cooling, and magma viscosity. Experiments will constrain the dependence of welding on pressure and temperature, aiming to produce a diagram showing conditions required to produce the observed PST properties and which can be applied to other deposits. Finally, computational modeling will use PST material properties to reproduce the thermal and welding history of the ignimbrite including the role of pore-gas pressure, with validation against the experimental results and other data related to emplacement temperature and cooling rate. The project will provide new insights into large-volume ignimbrites and welding in glassy volcanic materials, which has applications to a range of volcanic processes beyond ignimbrite welding, along with models that can be applied elsewhere. The results will inform our understanding of smaller-volume pyroclastic flows; these occur with greater frequency than large-volume eruptions, and as a result have significant hazards implications. This project will also: (1) train a Ph.D. student in application of field, experimental, and computational studies of volcanic processes and deposits; (2) provide internship opportunities for an undergraduate student in a STEM field who is from a historically underrepresented group; and (3) will be included in outreach activities such as with the Buffalo Museum of Science. 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

Moving into higher frequencies as proposed for 6G-and-beyond wireless networks can support future high-speed connectivity, Internet of Things (IoT), and new radio technologies. However, these advancements come with challenges. Despite massive investments in infrastructures and constant updates to wireless devices, the 5G networks have yet to deliver on their grand promises of ultra-fast speeds and stable connectivity. Unlike lower-generation networks (e.g., 3G and 4G), the performance of higher-frequency wireless networks (5G mm-wave, 6G-and-beyond) degrades significantly when the distance between the base-stations and the users increases or when the environment between the base-stations and the users is cluttered. To overcome these technical challenges, researchers are exploring the use of low-cost artificial surfaces to manipulate the wireless signal propagation environment and circumvent physical channel disruptions. This emerging technology, known as Intelligent Reflecting Surface (IRS), offers an exciting avenue for addressing the challenges posed by higher-frequency wireless networks, and provides a more efficient and economical solution that bolsters wireless connectivity. Despite their significant potentials, IRS technologies face deployment hurdles due to a gap between theoretical designs and practical implementations, primarily because of the disconnection between research conducted in the communications and signal processing (CSP) community and the electromagnetic (EM) community. The project aims to bridge this gap by considering the realistic behaviors of IRS hardware and ensuring adaptation to dynamic wireless signal propagation environments. The research outcome will lead to agile IRS reconfiguration that can optimize wireless network performance under varying and potentially unknown wireless channel conditions. The team will study a holistic design and implementation solution that can ultimately provide a new paradigm for future IRS-assisted wireless networks. The education and outreach plans of this project focus on recruitment, retention, and success of students in STEM. In addition, an annual industry seminar series will be initiated to share research progress and foster interactions and collaborations with local wireless industry. This project seeks to develop an end-to-end methodology that optimizes IRS design for wideband wireless operation, precise beam pattern generation, and long-term deployment cost reduction. By adopting a holistic design approach that bridges the gap between CSP and EM research communities, the project aims to make IRS technologies not only theoretically sound but also practical for real-world applications. The project plans to achieve this goal by envisioning a shift from the traditional one-size-fits-all IRS fabrication to a tailored approach. The main innovation of the hardware and fabrication is the IRS meta-atom that has a wideband response and full reconfigurability, achieved by metamaterial transmission lines terminated with the reflection-type amplifiers. This design then undergoes multiple layers of screening, design, and optimization to achieve practical performance as close to the theoretical ideal performance as possible while considering the realistic IRS behavior. Finally, the project integrates the fabricated IRS into a realistic wireless edge network through a revamped design of codebooks and novel machine learning techniques to ensure seamless and efficient 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.

NSF Awards · FY 2024 · 2024-09

The complexity of natural hazards, how they are exasperated by our changing climate, and the ways society reacts to these hazards requires an interdisciplinary approach to research the branches across the geoscience disciplines, engineering and the social sciences. Undergraduate students at the beginning of their career have not yet mastered a single discipline of expertise and thus are often not prepared for research opportunities that are interdisciplinary. This research experience for undergraduates site seeks to develop undergraduate experience in interdisciplinary research to give them the tools to identify societal problems and how to solve them using a variety of research methods. Students will have primary and secondary advisors who are domain experts in different fields to work on a research project based on climate and geohazards. By having two faculty advisors from different fields, students are much more likely to progress in interdisciplinary research. All mentors are members of the University of Buffalo’s Center for Geological and Climate Hazards. The students will spend 10 weeks at the University of Buffalo working on a hands-on research project as part of a team and participating in weekly professional development workshops with a cohort of their peers. The primary goal of this project is to equip undergraduate students with the skills needed for interdisciplinary research with a focus on climate and natural hazards. Student participants will be active researchers in current, highly socially relevant scientific research with experts in the field. Students will also learn science communication and presentation skills, how to identify potential mentors, how to apply to graduate school, and the basics of proposal writing. The projects cover topics such as glacier lake outburst floods, lake effect snow causes and impacts, driving behavior in snowy weather, managing agriculture to mitigate the impact of climate change, long range transport of wildfire smoke and the impact on air pollution, shoreline erosion along Lake Erie, and threats of massive forest die-off due to rapid environmental change. The project will use formative, summative and longitudinal 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 2024 · 2024-09

The broader impact of this I-Corps project is based on the development of an artificial intelligence technology to enhance the efficiency and effectiveness of online security measures. The technology analyzes emotional weighting in natural language to detect violent motivations within social media content in real-time. By identifying violent intentions early, the goal is to prevent harm and protect individuals and communities. Real-time analysis also has the potential to enhance safety and security, enabling law enforcement agencies and security personnel to respond swiftly to threats. Social media platforms can use this technology to automatically flag and remove harmful content, maintaining a safer online environment. Lastly, identifying violent language can also help direct users to mental health resources or crisis intervention services. This solution could improve how security threats are identified and managed and provide a scalable solution to address the pressing need for improved social media security while contributing to a safer digital space by proactively addressing violent motivations. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on research that identifies the moral and emotional motivations that drive violent behavior. Previous research demonstrates accurate detection of users with strong moral motivations and their intended targets, thus the ability to identify violent actors via these specific motivators. This technology is based on an artificial intelligence (AI)-driven solution to detect nuanced indicators of violent motivation online. By analyzing text, images, and videos, this technology goes beyond traditional sentiment analysis, creating a more proactive approach to detecting the propensity for violent behavior online and deterring actual violent behavior in the real-world. By developing an application that analyzes social media content based on these research findings, this solution could address a critically important gap in current social media security measures. 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

Robotic-assisted surgery has the potential to offer enhanced overall surgical performance and greater precision compared to traditional surgical methods. However, surgeons’ mental workload remains a concern in robotic-assisted surgery due to increased complexity of operations, leading to unexpected human errors and unsatisfactory surgical outcomes. As robotic-assisted surgery rapidly advances with more complex technology, it is critical to prevent surgeons’ mental overload to ensure surgical task performance and patient safety. Neuro-adaptive technology represents an innovative solution for reducing human mental workload by enabling the context-awareness of robots to offer adaptive interventions within response to variations in human cognitive states. However, the adoption of the neuro-adaptive technology in robotic-assisted surgery remains largely unexplored. This gap highlights a fundamental research opportunity in understanding the advantages and limitations of neuro-adaptive technology to enhance surgical outcomes. This EArly-concept Grant for Exploratory Research (EAGER) grant supports research to design neuro-adaptive technology for robotic-assisted surgery. Introducing such an innovative technology to robotic-assisted surgery has the potential to transform traditional teleoperation into a more collaborative human-robot interaction. This, in turn, has the potential to improve patient health, identify and mitigate cognitively demanding procedures or operative conditions, and to reduce costs associated with adverse patient outcomes. This research aims to design neuro-adaptive robotic-assisted surgery by enabling the robot's awareness of the surgical context, with the aim of understanding: (1) how to monitor different surgeons’ workload levels, (2) how to understand the cause of such workload, and (3) how to perform interventions. An artificial intelligence-powered multi-sensing system will be investigated to monitor workload levels on a personal basis for surgeons with varying skill levels. A context-awareness architecture that synthesizes visual and auditory data will be used to identify the cause of mental overload and initiate proper interventions and a prototype of the researched neuro-adaptive technology will be designed and validated. By leveraging multi-modal sensor data, human factors modeling, and artificial intelligence, the ultimate goal of this project is to refine the implementation of these life-saving remote surgery techniques, ensuring that they are more effective, adaptable, and safe. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project examines the impacts of natural disasters on community resilience from a geographical perspective. Natural disasters heterogeneously disrupt both the mobility of residents and the short-term needs that they exhibit. This research concurrently examines spatial variation underlying changes in mobility patterns and the attempts by residents to elicit aid from government agencies and other community residents via social media postings. The findings inform the approaches used by first responders and municipality authorities to mitigate the effects of natural disasters. The project also contributes to the training of graduate students while building capacity for analogous geographical research by developing and disseminating reproducible methods for geospatial analysis of the effects that natural disasters have on communities. This multifaceted project concurrently examines three aspects of the human responses to natural disasters. First, as reflected in the anonymized records of mobile phone users, spatial variation in the disrupted patterns of mobility is considered as it relates to demographic and socioeconomic variation across neighborhoods. Second, the project examines spatial disparities in the extent to which residents request assistance from municipal authorities. Third, the researchers study the geography of help-seeking behavior among social media users and the roles of digital platforms in facilitating the support of volunteers. This project is empirically grounded on a compelling complement of datasets, engages theories in disaster resilience and disparities, and leverages a combination of methods in geographical and statistical analysis, geospatial artificial intelligence (GeoAI), and network modeling. Insights from this project and the developed methodological frameworks have the potential to inform future disaster studies on disaster-caused human mobility disruptions, community-initiated responses, and the roles that social media play in shaping these responses and spatial disparities. 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

Plastic products are essential for our daily life and underpin an expanding, multi-trillion-dollar global market across numerous industry sectors. However, the resulting plastic waste presents an emerging threat to environmental health and public safety. Large plastic debris gradually breaks down to nanometer-sized particles in the environment and the long-term effects of these ultrafine plastic particles remain largely unknown. This project supports fundamental research to understand the interactions of nanoplastic particles in the environment with microorganisms, which provide insight into the nanoplastic accumulation in the human food chain. The research team will combine theoretical and computational modeling with cutting edge experimental techniques to discover the relationship between particle shape, cellular entry process, and cell responses. This new knowledge gained at the nano-bio interface will enable the development of a rational hazard and risk assessment framework for nanoplastics. The educational activities of this project will motivate and train a competitive workforce for developing sustainable solutions to the plastic waste problem. The project will provide multidisciplinary and inter-institutional research experiences for graduate and undergraduate students via step-wise progression and highly structured mentoring. The team will create a symposium at professional conferences to stimulate growth of a research network on nanoplastics. The goal of the outreach is to engage local middle and high school students and to enhance the awareness of the global plastic crisis in the broader community. The goal of this project is to investigate the interactions of novel synthetic models of environmental nanoplastics with green microalgae on the single-cell level as the starting point to understand how they accumulate in the environment and to which there is subsequent human exposure. To address the need for representative surrogates of environmentally released nanoplastics, this research will create polymer-coated nanocrystals with well-defined non-spherical morphologies and surface enhanced fluorescence. A microfluidic platform will be developed for in situ and in vivo experiments of nanoplastic uptake and toxicological responses of Chlamydomonas reinhardtii cells. The team will apply the membrane elasticity theory and coarse-grained simulation to predict adsorption and internalization of anisotropic nanoparticles by lipid membranes on different length scales. The integration of experimentation and multiscale modeling will allow the testing of key hypotheses that irregular surface morphology influences individual uptake and collective internalization of environmental nanoplastics. The project activities will be accomplished by integrating graduate students through collaborative and structured mentoring, as well as by providing course-based research experiences to undergraduate students, particularly those from groups traditionally underrepresented in science and engineering. The educational component of the project will also support the development of a topical symposium at the ACS Northeast Regional Meetings, a webinar series, and a plastic pollution exhibition at the annual National Engineers Week Community Day to engage broad research and education communities around nanoplastics. 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

Typical university classrooms use traditional grading practices where points are allocated to assignments, mistakes result in point deductions, and assignment scores are combined using some form of weighted averaging to determine grades. Often, student behaviors are assessed within grades as well, including timeliness, effort, and conformity to standards. Several alternative and equitable grading practices have been proposed to combat the implicit bias often embedded in these practices. However, a significant barrier to adoption of these equitable grading practices has been the lack of tool support for educators. Virginia Tech and University of Buffalo will develop and integrate educational tools to deploy equitable grading practices (EGP), which have been shown to engage students more deeply with the learning process, to provide more equitable feedback to students about their learning. The project will bring equitable grading practices into the hands of many more educators because of the integration with already popular online learning tools and the most common learning management systems (LMS). Because these learning management systems are used by all levels (high school, community college, university), this technology integration will make adopting these practices much easier at all levels. This project will provide educators with the tools necessary to transform grading practices in their classrooms towards equitable grading practices and thereby better support student learning and achievement of course outcomes. The current set of tools available for instructors to use when adopting equitable grading practices is lacking in several respects. The project will resolve these technology issues by (1) designing a concise and expressive way for instructors to describe the way they want results from learning tools to be mapped into their chosen grading strategy; (2) developing a Learning Tools Interoperability (LTI) “proxy” for equitable grading practices that can serve as a software adapter that uses the instructor description to map results from any LTI-based educational tool into an LMS in an EGP-supportive way; (3) devising corresponding practical strategies for using learning management systems (Canvas) as the umbrella to integrate equitable grading practices seamlessly into existing courses; and (4) building a pathway for learning tools to directly support these approaches, research and develop proof-of-concept integrations of EGP-based grading and feedback processes into a selection of existing community learning tools for practice exercises, automatically graded assignments, and electronic textbooks. In addition, this project will address the following educational research questions that investigate the impact of equitable grading practices as a teaching innovation [RQ1] How does student engagement with and use of educational learning tools (such as autograding tools, homework practice tools, e-textbook tools, etc.) change with the use of EGP integrated tools? [RQ2] Are EGP-driven changes in the feedback given to students on assignments from these tools associated with any changes in student behavior or achievement? [RQ3] Is achievement of course learning objectives measured more consistently and uniformly under EGP supported by tools, compared to traditional grading practices? This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and 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 2024 · 2024-09

All animals rely on their ability to sense and respond to their constantly changing environments to survive. Because they do not have eyes or ears, C. elegans (small roundworms) depend heavily upon their ability to taste and smell chemical information in their soil environment to find food and avoid danger; animals must move towards chemicals that indicate a food source while avoiding chemicals that indicate potentially harmful environments. Although these senses are key to the survival of animals in the wild, little is known about how naturally occurring genetic diversity affects chemosensory responses, or how adaptive forces might shape the response profiles of animal populations worldwide. Using C. elegans as a model, this project will dissect the mechanisms by which genotype and environment influence how animals interact with their surroundings; the findings will benefit researchers working in organisms ranging from invertebrates to humans. In particular, understanding the naturally occurring range of nociceptive sensitivity could provide new ways to manage aversion and pain across species. Graduate students and undergraduates, will participate in these studies in the lab of the PI. A summer research experience for undergraduates will provide opportunity for independent research and professional development. Funds from this grant will also support a summer stipend for undergraduates from a Liberal Arts College that does not itself have undergraduate research. A fundamental goal of neuroscience is to understand how genes and the environment come together to shape animal behavior, as it is a crucial evolutionary determinant of survival. However, in the case of chemosensation, much of what is known comes from the study of laboratory strains of animals. While these approaches have led to foundational discoveries, they are limited in their ability to couple behavioral variation between individuals of the same species to habitat and evolutionary history. For C. elegans, the isogenic laboratory strain named N2 has been used for chemosensory experiments for over 60 years. ~400 wild isolate strains have recently become available through the Caenorhabditis Natural Diversity Resource (CaeNDR), along with whole-genome sequence data and habitat isolation information, making genome-wide association (GWA) studies now possible. We will use this set of CaeNDR strains and tools to examine natural variation in attractive and aversive chemosensory behaviors between geographically and/or genetically distinct populations of C. elegans. The ability to combine the accumulated knowledge of chemosensory mechanisms in the N2 strain with that of the recently available wild isolate strains positions C. elegans as an ideal system in which to dissect the mechanisms by which genotype and environment influence how animals interact with their surroundings. 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

Flagella and cilia are thin hair-like cellular structures which play an essential role in many basic life processes. By beating rhythmically, flagella and cilia move fluid in the local environment of cells. This biological function enables pulmonary mucus clearance in airways and the transport of ovums from the ovary to the uterus for example. Malfunction of flagella and cilia can lead to a group of serious human disorders, ciliopathies, which cause a heavy economic and disease burden on society. However, despite the ubiquity and importance of flagella and cilia, fundamental biomechanics underlying the fluid transport of beating flagella and cilia are still poorly understood. Particularly, the detailed flow field induced by beating flagella and cilia remains unresolved. Combining state-of-the-art microscopy techniques with data-driven machine learning, the research team aims to address this difficult biomechanical problem. This research will investigate the flow field of healthy flagella as well as those of mutant flagella associated with ciliopathies using synergistic experimental and numerical modeling efforts. A potential solution to remedy the flow deficiency of malfunction flagella will be researched. In addition to the training and research opportunities for undergraduate and graduate students, the project will produce appealing scientific videos and demonstrations to enhance the undergraduate curriculum and enrich outreach activities at the local communities of the two principal investigators. As a generic model for the morphology and dynamics of flagella and cilia, green algae Chlamydomonas reinhardtii, will be studied in this research program. Optical microscopy will be used to track the three-dimensional (3D) fluid flow around the beating flagella of a single alga at micron scales with sub-millisecond temporal resolutions. Both wild-type and mutant algae of different swimming modes will be investigated. The mechanical efficiency of flagellar dynamics will be analyzed based on the 3D flow field. Moreover, using the experimental flow field as a basis of reference and taking advantage of modern machine-learning algorithms, the team plans to develop a numerical model of maximal simplicity that can quantitatively capture the algal flow. The model will facilitate the study of the optimization and synchronization of flagellar dynamics and the collective dynamics of algal suspensions. Through the collaborative experimental and modeling efforts, the missing link between the flagellar dynamics and the resulting microscopic fluid flow will be revealed by this research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This workshop, set to take place in the spring 2025, at the National University of Singapore, aims to bring together approximately 50 artificial intelligence (AI) experts from the United States (US) and Southeast Asian nations (including Singapore, Malaysia, India, Vietnam, Indonesia, and Thailand). The objective is to facilitate discussions on enhancing collaboration in responsible AI research between Southeast Asia and the US. This interactive workshop will focus on: (1) identifying research gaps and opportunities in responsible AI research across countries to promote potential collaboration between Southeast Asia and the US; and, (2) discussing responsible, use-inspired foundational AI development that can serve in providing crucial elements of future economic development and societal well-being. The multinational workshop will be held at the National University of Singapore (NUS), a globally recognized institution with a strong commitment to fostering advancements in AI. Key members of the workshop steering committee include leadership and faculty from NUS and Nanyang Institute of Technology, as well as members of industry and government. Insights from the workshop discussions will be captured in a white paper which will include the essential takeaways from the event, providing: (i) a snapshot of the collective knowledge and (ii) recommendations for future directions of research collaboration between US and the Southeast Asian countries in the field of responsible AI. The workshop project is co-funded by the Division of Information and Intelligent Systems (IIS) within the Directorate for Computer and Information Science and Engineering (CISE) and the Office of International Science and Engineering at NSF. 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 Greenland Ice Sheet (GrIS) is an important component of the global sea level budget – when the ice sheet loses mass, global sea level rises, and vice versa. Well-documented changes in global land ice have shown that, currently, the GRiS is the single largest contributor to global sea level rise. Improving our understanding of how the Greenland Ice Sheet changes with global temperatures is critical to forecasting future changes to sea levels. Data on the history of this interaction over millennia allow scientists to more fully understand these interrelated processes. This dissertation research will investigate how the Greenland Ice Sheet changed over the past 40,000 years. This interval encompasses large swings in the global climate system, including Ice Age Conditions and warm interglacial conditions, as we have today. This research will be led by a doctoral student at the University at Buffalo, who will collect samples from the Greenland Ice Sheet to test new methods to shed light on the timing of past ice history of the Greenland Ice Sheet. This dissertation research will apply a novel analysis in glacial geology - luminescence rock surface dating - to further constrain the timing of the extent northeast Greenland Ice Stream (NEGIS) over millennia. These data will provide critical insight into the relationship between the extent of the ice sheet over time and how sea level may have changed during different climate intervals. This project will provide invaluable research training for an early career scientist, who will leverage connections with two organizations: Asian-Americans and Pacific Islanders in Geoscience and Polar Impact to share this research and encourage other minority students in field science. 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

Long-duration energy storage (LDES) technologies provide numerous societal benefits, including enhancing grid stability, enabling greater use of renewable energy, and reducing the dependence on fossil fuels. LDES technologies transform intermittent renewable resources, such as solar and wind power, into dispatchable power sources. Among the available options for LDES are redox flow batteries (RFBs), which store energy electrochemically and feature significant operational flexibility, modularity, and cost advantages. Nonetheless, key challenges remain in developing energy carriers for RFBs, including cost and performance barriers stemming from fundamental limitations in their chemical properties. This research will focus on designing new chemical compounds, called biphasic charge carriers, that can store electric power, and link the fundamental chemical properties of these charge carriers to their function in small-scale working batteries. The goal is to develop a clear set of design principles for a new generation of batteries purpose-built for storing solar and wind power. This research project integrates hardware prototypes under active development by a startup company founded out of one of the participating laboratories, enabling further potential for societal impacts through commercialization and entrepreneurship. Additionally, a new training curriculum for undergraduate students will be developed to learn the fundamentals of electrochemistry and battery science. Work will be undertaken to deploy a series of educational outreach activities encompassing the development of low-cost hardware and software tools to support broader dissemination via the delivery of new laboratory courses at the participating universities, education research literature, and digital media. This project will develop design rules for inorganic charge carriers for redox flow batteries. The overarching objective is to overcome hurdles related to energy density and materials availability by developing redox-active molecules that store charge both as soluble units and in the solid phase. This feature opens opportunities to explore new RFB designs, beyond aqueous transition metal complexes, via the development of biphasic charge carriers, wherein soluble forms of a given molecule are used as mediators to shuttle charge to or from the solid form of the same molecule. The project encompasses hypothesis-driven studies directed at controlling molecular solubility across multiple charge states via ligand modification, alongside detailed investigations of charge transfer at interfaces between the electrode and the electrolyte and between the electrolyte and the solid-state charge carrier. Successful completion of this work will yield lab-scale biphasic battery systems with promising functional properties that can be fully rationalized from basic physical properties, entailing extensive opportunities for further development. 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

Effective civil protection during effusive volcanic eruptions requires an understanding of the lava’s ability to flow (its rheology). This is because forecasting methods for lava flow paths and velocities use models of the lava’s flow properties (i.e. how viscous it is) to estimate what areas the lava flows may affect and how fast lava may reach certain areas. The current understanding of the flow properties of natural lavas is largely based on controlled laboratory experiments on small volumes of analogue materials or remelted rocks and is limited by the fact that bubbles (a fundamental component affecting flow properties of natural lavas) cannot be contained in laboratory experiments. This project is motivated by three main points: 1) An incomplete understanding of lava flow properties. 2) A lack of viscosity measurements of lavas in their natural state. 3) The need for shorter response times between eruption and lava flow-path forecasts. The project will tackle these challenges by: 1) Building two new and unique field tools to measure the viscosity of lava while it flows. 2) Using these devices on active lava flows to generate the most complete dataset of natural lava viscosity ever measured (i.e. containing bubbles, crystals and melt). 3) Performing two-phase (melt plus crystals) laboratory experiments at the conditions relevant to the natural environment (characterized during fieldwork). 4) Developing a pipeline for field viscosity measurements of active lava to be used in lava flow forecasting efforts at volcano observatories. This project will entail working with volcano observatories in the US, Iceland, France, and Italy to gain safe and effective access to active lava flows. Combined, the field and lab measurements help to characterize the effect of bubbles on three-phase lava flow behavior by comparing the field measurements (with bubbles) and lab measurements (bubble-free). This has been an open challenge in understanding how lava flows for decades. The team and facilities that are part of this project are in a unique position to do this for the very first time. The project results can be used during effusive eruptions in the future to help guide decision making in civil protection efforts. Project results will be incorporated into the University at Buffalo EarthEd program, providing content for K12 educators serving underrepresented communities, and promoting science literacy. It also aims to contribute to science education through the development of a museum showcase. This project supports two early-career researchers and one PhD student. It involves international collaborations (USA, Italy, France, Iceland) in academia and at volcano observatories.   Accurate forecasting of lava flow paths and advance rates is crucial to hazard mitigation, civil protection, and management of eruptive crises. This task has been hampered by an incomplete understanding of multiphase (melt+crystals+bubbles) lava rheology. Field viscosity measurements of lava are extremely rare, commonly done using uncalibrated devices and have never been tied to laboratory data. This highlights the dire need for more, and better in-situ measurements. While the understanding of the flow properties of lavas has advanced significantly over the past decades, two core limitations have always remained: 1) Accurate reproduction of natural emplacement conditions (scale, textures, fO2). 2) The inability to maintain three-phase suspensions in the lab (bubbles escape on experimental timescale, limiting measurements to two-phase crystals-melt suspensions). Measuring the viscosity of lava in the field removes both limitations. Importantly, when combined with bubble free lab measurements it has the potential to quantify the effect of bubbles on multiphase lava rheology. Further, in-situ field measurements have the potential to enable the optimization of near real time lava flow forecasting by providing accurate viscosity data from the field directly to the modelers. This project takes a holistic approach to lava rheology to advance our understanding of flow properties and the emplacement of natural lavas. It does so by deploying two field rheometers and pairing the data with laboratory experiments that mimic emplacement conditions. This enables the team to gather the first ever suite of rheological data that ties laboratory rheology to actual field data. Doing so achieves three fundamental goals: 1) Deploy two new and unique field rheometer prototypes to generate the most comprehensive dataset of natural lava rheology ever measured. 2) Perform two-phase (melt+crystals) laboratory experiments at the temperatures, shear rates, and fO2 of lava measured in the field. Combined, the field and lab measurements enable them to deduce the effect of bubbles on three-phase lava rheology by contrasting the field measurements (with bubbles) and lab measurements (bubble-free). This has been an open challenge in understanding lava rheology and the team and facilities that are part of this project are in a unique position to do this for the very first time. 3) Develop a pipeline for rapid lava viscosity measurements to inform lava flow forecasting efforts done at volcano observatories. The large impact that combined field and lab measurements can have on scientific understanding of lava flow properties paired with the transfer of this knowledge to eruption management teams and the availability of already trained experts set this project up for success and to address some of the major limiting factors to advancing the understanding of lava flow emplacement for decades. Transfer of knowledge to relevant staff at volcano observatories is done via demonstration and training campaigns at the USGS Hawaii Volcano Observatory (HVO), the observatory of Piton de la Fournaise (OVPF), The University of Iceland (HI) and INGV Catania. The project results can be used during effusive eruptions in the future to help guide decision making in civil protection efforts. 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 Standard Model of particle physics has been a successful theory, agreeing with decades of experimental observations involving weak, electromagnetic, and strong interactions. The discovery of the Higgs boson at the LHC was further confirmation of this success. However, the Standard Model remains an incomplete theory. Precise measurements of the properties of the Higgs boson at the LHC could provide insight into new physics beyond the Standard Model. This research focuses on exploiting the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) to search for new particles, study the decay of Higgs bosons, carry out precision measurements of other Standard Model (SM) processes, use jet substructure to reconstruct high-momentum objects, and extend the capabilities of the CMS particle-tracking detector to increase its acceptance and tolerance to high collision rates. New cutting-edge machine learning (ML) and artificial intelligence (AI) techniques will be used for data analysis, detector operations, and reconstruction of collision events. The research activity will also foster collaboration with high school teachers and students through the QuarkNet and Science Olympiad outreach efforts. This research will search for subtle signatures of Beyond Standard Model (BSM) physics using advanced methods for identifying rare signals and addressing systematic uncertainties. These include detailed studies of the properties of the Higgs boson, extending the search program for BSM physics, and systematically improving techniques to maximize the physics potential. This research will also extend the capabilities of the CMS tracking detector to handle conditions at the High Luminosity LHC, extend its geometric coverage, and add tracking information to the far-forward particle flow algorithm. In addition, studies will be performed on the properties of highly Lorentz-boosted SM Higgs bosons decaying to bottom quark-antiquark pairs to fully explore the Higgs coupling to quarks, as well as to search for new phenomena in unexplored signatures at higher masses, such as new heavy vector bosons. This program will also develop and maintain object reconstruction for heavy- and light-flavor jets, as well as perform measurements of SM physics processes including production of W/Z/gamma in association with heavy flavor jets, and detailed understanding of the quantum chromodynamic evolution of jets. 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 ability to imitate is critical for learning to speak or sing. Not yet understood is why this skill comes easily for most but is quite difficult for others. Although speech and song imitation involve coordinating the same motor and auditory systems, there are important differences in the signal, the context in which production is used, and possibly individual differences in ability. This project tests whether these differences, though striking, emerge from a common system for vocal motor control. Differences across speech and song may involve adapting this common system to communicative demands. The idea that speech and song rely on a shared representation has important implications in therapeutic settings, supporting the use of interventions that use melody and song to bootstrap speech rehabilitation. The specific hypothesis underlying this project is that people adapt vocal motor control based on the acoustical structure of the pattern they are producing, as well as the contextual demands of the current situation. The work evaluates whether producers “reweight” the importance of different acoustical parameters (e.g., pitch stability, rate of production) based on these factors. Acoustic parameters are manipulated along a speech-to-song continuum (e.g., the amount of variability within a tone), as well as contextual information that may signal a more speech-like or more song-like context (e.g., vocal synchronization with another person versus turn-taking). Participants vocally imitate pitch patterns under these different circumstances or rate whether an auditory pattern sounds more like speech or more like song. The issue is whether speech/song differences reflect a continuum rather than a strict categorical distinction in both perception and production. Also assessed is whether vocal pitch control varies across features associated with speech and song based on the surrounding context. 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 support from the Environmental Chemical Sciences Program and the Chemical Measurement and Imaging Program in the Division of Chemistry, Emanuela Gionfriddo at the University of Toledo and her team will study the partitioning of organic pollutants in multi-phase environmental systems. Organic pollutants are continuously released to the environment by industrial and agricultural activities and use of pharmaceuticals. These pollutants can alter environmental processes and have far-reaching effects on humans and wildlife. Upon their release, organic pollutants partition with natural and anthropogenic substrates in the environment, establishing complex multiphasic equilibria that affect their chemical behavior. This research focuses on characterizing the molecular interactions that drive these partition equilibria. This research has the potential to improve understanding of the transport, distribution, and fate of organic pollutants in the environment and biota. The Gionfriddo team uses micro-extraction methodologies to probe the distribution of organic pollutants in heterogeneous systems. Of particular interest are emerging classes of organic pollutants, such as pesticides, pharmaceuticals, and perfluoroalkyl substances and their degradation products, and how they partition and interact under naturally occurring environmental conditions. These research objectives are integrated with efforts to promote public engagement in science, technology, engineering and mathematics (STEM) disciplines. Summer research activities will introduce members of underrepresented minority groups to scientific research and career opportunities through mentoring and hands-on experimentation. Emanuela Gionfriddo and coworkers will study the molecular behavior of organic pollutants partitioning onto natural polymers using task-specific micro-extraction devices. This will improve our understanding of the molecular mechanisms of organic pollutants and microplastics interactions under naturally occurring environmental conditions. Partitioning phenomena of organic pollutants among anthropogenic polymers, aqueous media, and aerosols will be quantified. The use of tunable micro-extraction methodologies is expected to enable precise assessment of the dynamics of partitioning processes in multicomponent systems without disturbing the equilibria under investigation. This aims to provide solutions for the study of environmental chemistry processes and separation science strategies to evaluate phenomena of transport and magnification of organic pollutants in distinct environmental compartments. 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

While the research on securing IoT software and systems has made significant progress in recent years, educational offerings in this area have not kept pace. This lag can be attributed to several factors, which include a lack of wide access to IoT software and the essential infrastructure needed to develop cybersecurity curricula and teaching materials, and a lack of active learning platforms for students such as IoT specific Capture-the-Flag (CTF) systems. Moreover, existing CTF platforms have many pedagogical, functional, and inclusiveness limitations. To mitigate current educational shortcomings, this project will design and host a next generation CTF platform. It will have profound broader impacts, including: (1) enhancing the education and training of the next generation of cybersecurity researchers in topics related to IoT software and systems security; (2) preparing educators and practitioners who will have deep theoretical understanding and practical skills in IoT software and systems security; and (3) involving partner institutions in the project that will utilize the project's outcomes to enhance their cybersecurity curricula. This project will advance the state of knowledge in IoT software and systems security education pedagogy and platforms. The key intellectual merits include the following. (1) Student-centered pedagogy in software and systems security education that will involve students in designing CTF and defensive challenges, while tracking and supporting students' progress by automating the feedback process. (2) Inclusive pedagogy in software and systems security education. (3) Development of the PwnIoT.Academy, a next generation student-centered CTF platform. (4) Development of IoT CTF and defensive challenges for different architectures and software platforms. (5) Collection of extensive data on student learning, which will enable a better understanding of the capabilities of the platform as well as identification of persisting challenges to cybersecurity education and workforce development. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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 Graduate Student Symposium (GSS) is a day-long, student-organized event held during the Spring and Fall National Meetings of the American Chemical Society (ACS). This activity has been conducted since 2005 under the guidance of the Graduate Student Symposium Planning Committee of the American Chemical Society (ACS), typically with base funding from ACS supplemented by funding from other organizations. A team of students from the University of Buffalo has been selected as the Graduate Student Symposium Planning Committee (GSSPC) to organize this symposium at the ACS Fall 2024 National Meeting in Denver, Colorado. The GSSPC is responsible for all aspects of the symposium, including selecting a topic, inviting speakers, securing funds, and recruiting and mentoring the next GSSPC. The symposium's theme, "Breaking the Mold: Building Communication to Promote Green and Sustainable Practices," revolves around examining and discussing innovative approaches to implementing green and sustainable practices in both academia and industry. The primary objectives of the symposium are: (1) to raise awareness about the relevance and need for green chemistry in academic and industrial settings, (2) to establish a collaborative platform for the exchange of best practices among the attendees, and (3) to promote the notion that green and sustainable practices are achievable without compromising quality, cost, and efficiency. To maximize engagement and knowledge dissemination, the symposium will consist of two events: 1) Lectures by invited speakers (i.e., individuals from academia, scientists who work at government agencies, and active members working in industry), and 2) Green Connect: A networking event held to facilitate engagement among speakers, sponsors, and attendees. This gathering will create an environment conducive to forging meaningful connections that could transcend the symposium. Invited speakers have been thoughtfully chosen by the planning committee as distinguished lecturers to ensure a comprehensive and holistic perspective of the dynamic and evolving landscapes of green and sustainable chemistry. The symposium aims to address the pressing need for environmental protection and sustainability by fostering essential dialogue between the attendees and representatives from academic institutions, industries, and non-profit organizations. Moreover, the symposium intends to promote equity and diversity, inclusivity, and accessibility in STEM. The selection of speakers reflects a deliberate effort to represent a diverse scientific and cultural community to provide attendees with role models whose experiences and challenges resonate with a diverse audience. Additionally, the GSSPC presents a valuable opportunity for the professional development of both the current and future Graduate Student Symposium Planning committees. 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

This conference proposal provides support for scholarships to engage students in the growing domain of computational social science at the International Conference on the Web and Social Media (ICWSM). ICWSM has long served as a critical outlet for research that pairs novel technical contributions with substantive social science research. This work has been central to both the computational and social science communities, such as groundbreaking papers showing biases in the Twitter API and widely-used tools for sentiment analysis. To this end, the conference will contribute to the enhancement and improvement of scientific and engineering activities by continuing to provide an outlet for such work, and a venue to continue to engage in deep, interdisciplinary discussions about the critical role that social media and the web play in American society, both in the past and in the future. The aim of this grant is to help ICWSM contribute to the enhancement and improvement of educational activities by 1) opening access further to students who may otherwise not be able to attend and whose voices might thus otherwise be removed from discussions, and 2) to provide a window into this interdisciplinary area of research and practice for students who are early in their research careers and who might be interested in pursuing a career in academia in the field of computational social science. 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 2023 · 2023-12

This project aims to serve the national interest by developing a tool to measure undergraduate STEM students’ perceptions about the climate and culture of STEM departments. Students’ sense of belonging is key in retaining and graduating STEM undergraduates, especially STEM students from populations marginalized in STEM. The project plans to adapt questions from an existing tool, as well as develop new questions about STEM department climate and culture based on student interviews and focus groups with STEM department stakeholders. After initial development in three institutions, the project team intends to implement the tool in STEM departments in multiple institutions across the country. Additionally, the project plans to develop a handbook to guide departments in using this tool. This project should advance understanding of STEM student experiences in STEM departments and support efforts to retain students in STEM. This project at Florida International University (FIU) aims to develop a research-based assessment tool to measure undergraduate STEM students’ sense of belonging to their department, as well as their perceptions about departmental climate and culture. FIU is a Hispanic-Serving Institution (HSI) and will collaborate with California State University, Fullerton and California State University, San Marcos, which are also HSIs. The project plans to adapt an existing assessment tool that examines sense of belonging and to develop new items to probe departmental culture and climate through input gathered from student interviews and focus groups with a diverse population of faculty, staff, advisors, and other departmental stakeholders. In addition to traditional closed-ended Likert-type questions and open-ended written assessment probes, the project plans to develop items based on visual narratives to probe students’ perceptions of departmental climate and culture. The new assessment tool will be piloted with a sample of ~1,400 undergraduate students in biology, chemistry, and physics departments across the three collaborating institutions to gather evidence of validity. Once initially validated, the assessment tool will then be implemented across at least 14 additional STEM departments, with data from the study returned to departments for reflection and discussion. Project evaluation will be guided by a 5-member advisory board composed of scholars from across different institutions and multiple relevant disciplines. Dissemination efforts include conference presentations, peer-reviewed publications, and development of a departmental handbook to guide use of the tool. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This project is also supported by the NSF IUSE:HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM. 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.