University of Cincinnati Main Campus

NSF Awards · FY 2024 · 2024-09

This project aligns with the National Science Foundation’s mission for “the Professional Formation of Engineers, to create and support an innovative and inclusive engineering profession for the 21st Century”. The research project aims to address the recommendation of the National Academy of Sciences, Engineering, and Medicine (NASEM) to study the effects of mentorship on persistence and success in STEMM. This study will examine the correlation and impact between mentorship outcomes and persistence in engineering for First-Time In College (FTIC) undergraduate women. A non-dyad mentoring network involving the pairing of mentees with an assembled number of mentors will be established based on deep-level similarities in sociocultural identities. Mentees will be paired with near-peer mentors in the upper-level division, an academic advisor/coach, faculty, and an industry mentor. Training modules for mentors will be developed to enhance effective and inclusive mentorship. Resource guides on mentoring best practices, mentoring tools, and training will be provided to all mentors to prepare and support their mentoring activities. Both mentors and mentees will be prompted with discussion topics. For example, prompt questions could include, “Tell me about a time when you struggled in a course and what you did to pass?” Mentees are expected to engage in formalized group activities that facilitate academic awareness, provide college survival tips, and support talent development and student success skills. Also, they are expected to participate in journal entries, focus group studies, and regular one-on-one meetings with their four mentors at different stages of their academic pursuits. A set of instruments, including reflection prompts, interviews, an inclusive demographics questionnaire, a Sense of Belonging, and Academic Self-Efficacy Scales, will be administered to respective participants in this mentorship structure. This research project will be used to understand the impact of sociocultural contexts on mentoring structures, their processes, and their outcomes in the persistence of FTIC women in engineering. Specifically, to (1) determine the impact of traditional mentorship on FTIC women and their attrition rate in engineering at the University of South Florida; (2) investigate the effect of a structured mentorship model on FTIC women and their decision-making to continue pursuing engineering; (3) determine the effects of similar social and cultural perspectives of mentor and mentee relationships on the sense of belonging from their first year and beyond; (4) determine the impact of different social and cultural identities between mentors and mentees from their first year and beyond; and (5) examine and identify competency for sociocultural awareness relevancy to mentorship. Badura’s self-efficacy theory and Tinto’s theoretical framework will be employed to educate, motivate, prepare, and engage mentees. Statistical analysis and a coding software designed for qualitative and mixed-method assessment will be used to evaluate data, media, and text to decipher the outcomes between traditional and non-traditional mentoring structures and to examine the impact of similar sociocultural identities. The research will be conducted through a collaboration between engineering faculty at the University of South Florida and an engineering education faculty mentor at the University of Cincinnati. Results from this study will help expand the current knowledge base for creating and integrating a mentoring support system that intentionally targets engineering identity, persistence, and retention rates for students from populations underrecognized and underserved in STEM. The research outcome can potentially reveal, validate, and address the academic disparities in STEM education in the select population. 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 Description: This Major Research Instrumentation (MRI) award supports acquisition of an advanced direct write lithography (DWL) equipment. DWL is core to advancing semiconductor research in multiple key areas. Unlike traditional photolithography that requires expensive photomask fabrication first, DWL provides opportunities to write micro- to nano-scale features directly on photoresists on diverse set of planar and non-planar substrates that provides tremendous opportunities to accelerate research and education in these areas. This tool will have significant impacts at the regional (Southwest Ohio) and national levels. The tool will be located in a user-fees based, shared cleanroom microfabrication facility at University of Cincinnati that has diverse set of users from multiple colleges and departments, small and large businesses, other academic institutions, and research laboratories. University of Cincinnati is also leading semiconductor workforce development efforts called Ohio-Southwest Alliance on Semiconductors and Integrated Scalable-Manufacturing (OASiS) for Intel-Ohio fabrication plant. OASiS has 15 higher education partners, including community colleges and an HBCU, and offers Rapid Certification in Semiconductors to train Engineers and Technicians. The DWL tool will play a key role in these efforts that will train globally competitive workforce in STEM and semiconductors. The equipment will also be used for entrepreneurial activities in close collaboration with the Office of Innovation to promote start-ups in the region. Technical Description: The acquisition of the proposed DWL tool will enable fabrication of micro/nanostructures in 2D, 2.5D, 3D repeatably with capabilities of grayscale patterning, reconfigurable resolution, and backside alignment that will be core to high-quality research in the areas of Microelectronics, Semiconductors, Quantum Electronics, Photonics, BioMEMs, Vacuum Electronics, and Energy Harvesting devices. In the areas of Microelectronics and Semiconductors, the tool will enable integration of high-bandwidth non-volatile memories, aging and integrity monitoring sensors on Complementary Metal Oxide Semiconductor dies that will revolutionize Artificial Intelligence and Trusted Microelectronics. The tool will also allow us to develop Transition Metal Dichalcogenides based Field Effect Transistors (FETs) and phototransistors. In BioMEMs field, the tool will allow us to study the growth of neurons in various constricted structures, manipulation of their growth and regeneration leading to a better understanding of cortical circuits. In Vacuum Electronics, it will lead to the development of new Vacuum FETs for high-power RF applications. In the areas of Quantum Science, it will enable the development of new resonators, meta-materials, and study of spin-transport. Finally, in the area of energy harvesting, the tool will enable development of nanomaterials-based thermoelectric devices. Overall, this tool will enable multidisciplinary research and education in keys areas of the next generation of semiconductors and devices. 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 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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

A function is considered to be smooth or differentiable if at every point it is has a derivative, or in other words, a well-defined rate of change. Many familiar functions are smooth, and smoothness properties are convenient and prevalent in scientific applications. However, non-smooth functions also frequently arise in mathematics and its applications, such as optimization. This project concerns differentiability phenomena in non-smooth environments. Specifically, it seeks to understand when non-smooth objects possess hidden smoothness structures. While non-smooth objects are more difficult to understand, they are often equipped with additional structure that is not initially visible. For instance, Lipschitz functions (i.e., those functions which expand distances by at most a multiplicative factor) are differentiable at most points of their domain. The project investigates these and related phenomena, it seeks to describe when a partially defined function can be extended to a smooth function, and explores when a function can be approximated by a smooth function. The project will promote research collaboration and will generate research training opportunities for both graduate and undergraduate students. The project centers on two broad topics of research. First, the PI seeks a deeper understanding of the Whitney extension and Lusin approximation questions for mappings between Carnot groups. A significant complication, not present in the Euclidean case, is that the maps to be constructed must satisfy nonlinear constraints reflecting the underlying geometry of these non-Euclidean environments. A second line of study investigates the differentiability properties of Lipschitz functions in Euclidean spaces, Carnot groups, and metric or Banach spaces. A fundamental theorem due to Rademacher states that every Lipschitz function defined in a Euclidean domain is differentiable almost everywhere. However, in many situations one in fact finds differentiability points inside measure zero sets. This observation led to the modern study of sets of universal differentiability. The project seeks to test the limits of Rademacher’s theorem through an improved understanding of universal differentiability sets, via the use of maximal directional derivatives and other methods. 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

Transitioning from high school to university presents various challenges for first-year engineering students, particularly as they adapt from a high school mindset to the rigors of higher education. These challenges are not just academic; they include social issues, the need for independent study, cognitive adjustments, and demanding coursework. Research has shown that certain social factors play critical roles in helping students navigate this transition successfully. These social factors include engineering identity, sense of belonging, and self-efficacy, all of which are essential for students to feel integrated and competent in their new academic environments. The COVID-19 pandemic has further complicated the educational landscape, prompting the creation of diverse learning environments to cater to the varied needs of students. These environments range from fully online formats, asynchronous and synchronous, to hybrid models, combining online and in-person instruction, such as HyFlex formats. The HyFlex model, in particular, offers a blend of hybrid and flexible learning, allowing synchronous participation both online and in person. Despite the proliferation of these innovative learning models, there is little research on how the HyFlex format impacts student learning and engagement in engineering education compared to traditional in-person methods. In response to this gap in research, a quasi-experimental study is proposed to assess the efficacy of the HyFlex learning environment within the context of drone education at a rural public university. The project will involve a new course titled "iDrone 101," which will be available in both HyFlex and traditional in-person formats for first-year engineering students at the University of Idaho. This course aims to provide students with foundational knowledge in automatic control, sensors, ground robots, and drones. Moreover, it will seek to integrate students' cultural experiences and values into problem-solving exercises that address real-world issues, such as wildfire monitoring, river restoration, and animal migration. The overarching goal of this project is to explore how the HyFlex model influences student perceptions and behaviors concerning emerging technologies when compared to a conventional in-person classroom setting. The research objectives include (a) increasing student engagement with emerging technologies through the "iDrone 101" courses, (b) fostering positive attitudes towards engineering, reflecting strengthened engineering identity, enhanced sense of belonging, and increased self-efficacy, and (c) evaluating the specific impacts of the HyFlex learning environment relative to the traditional in-person format. The mixed-methods research from quantitative and qualitative methods including pre-post surveys and semi-structured interviews will evaluate the influence of HyFlex and in-person learning on students' perceptions and behaviors, regarding rapidly advancing technologies, particularly autonomous unmanned vehicles, including unmanned aerial vehicles (e.g., drones), unmanned ground vehicles (e.g., ground robots), and unmanned surface vehicles (e.g., drone boats). This project is designed to be easily scalable and implementable. Therefore, the HyFlex and in-person learning modules developed for the iDrone 101 course curricula from this project will be available to a broader audience across different states through the project's website. 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 project aims to advance understanding of some key problems in the field of Calculus of Variations, specifically the Aviles-Giga conjecture, and more broadly, how restrictions on gradients of functions imply rigidity, stability, and compactness properties. The Aviles-Giga conjecture is a central open problem in the Calculus of Variations, modeling phenomena such as thin film blistering and micromagnetics. The conjecture seeks to provide a mathematical justification for a scaling law observed in physics, leading to more accurate modeling of certain physical phenomena. Part of the conjecture involves sharp regularity estimates for a well-studied class of equations known as Eikonal equations, which arise in liquid crystal models and optics. These estimates are valuable for numerically solving such equations and are of broad mathematical interest. The Aviles-Giga theory is closely connected to the theory of scalar conservation laws, and its methods are being applied to understand a class of solutions of scalar conservation laws that arise in probability, specifically the large deviation conjecture. The project also aims to propagate its outcomes through seminars, lectures, graduate student recruitment, and the research produced. The project consider problems in Calculus of Variations. The first problem is the Aviles-Giga conjecture, where the main open problem is showing that the energy concentrates, as it is not even known if the measure representing the limiting energy is singular. Achieving this goal would lead to a complete understanding of the regularizing properties of the Eikonal equation on the Besov scale. The second problem deals with quantitative rigidity for non-elliptic differential inclusions and builds on a previous result for rotation matrices and an optimal generalization to connected 1D elliptic curves in the space of two-by-two matrices. One of the purpose of this work is a more general regularity/rigidity theory for non-elliptic curves. The third project studies compensated compactness and conservation laws in higher dimensions. Reformulating regularity and uniqueness questions of PDEs as differential inclusions has led to the solution of a number of outstanding conjectures. This part of the research focuses on further developing methods initiated by the principal investigator and collaborators to study the differential inclusion problem related to regularity and uniqueness questions for conservation laws in higher dimensions. The final project on gamma-convergence for the Bellettini-Bertini-Mariani-Novaga functional considers a proposed gamma-limit related to certain conjectures in large deviation theory. The project focuses on a special case of this conjecture. 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

Tutoring programs that are jointly supported by schools and universities can offer benefits to both parties. Schools benefit by receiving additional instructional support for their students. Universities benefit by providing meaningful, one-on-one teaching experiences for their students who are studying to become teachers. Tutoring programs, however, are only helpful to the extent they respond to the needs and interests of the students and schools they serve. This project will establish a partnership between a large, urban university and a small, rural high school to collaboratively create a tutoring program to support the mathematics learning of students with learning disabilities. Mathematics tutoring can be especially helpful for students with learning disabilities, who likely need support for strategically organizing mathematical information and who may also experience math anxiety. Building on promising work in urban school settings, the project will co-create a model of mathematics support that is informed by the rural community intended to receive the support. Using a framework adapted from collaborative, participatory research, the university team will work with teachers, students, and other school personnel in a rural high school—along with leaders from local businesses and community organizations—to examine the most productive models of tutoring for students with learning disabilities. The design of this project draws on research from special education and, respectively, mathematics education documenting the cognitive and affective needs of students with learning disabilities in math. Across the yearlong project, meetings will be held to assess the needs and strengths of the project partners, plan and evaluate pilot tutoring programs, and establish a long-term partnership to support both high school students in math and college students preparing for future careers as teachers. Transcripts and field notes collected throughout the work will be analyzed and synthesized in an ongoing project report. An advisory board, comprising school and community members, will provide ongoing evaluation of the work and its outcomes through review of the project report and independent review of transcripts and field notes. The final product will be an established tutoring program as well as a set of recommendations for mathematics tutoring for students with learning disabilities in remote, rural settings. This project is supported by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  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

With support from the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Anna D. Gudmundsdottir of the Department of Chemistry at the University of Cincinnati will analyze how crystals respond to external stimuli. Her research team will investigate the photodynamic and mechanical properties of crystalline benzoyl peroxide, alkyl- and aryl-azide derivatives. Upon exposure to light, the crystalline compounds dissociate vividly, as they burst, coil, fracture and change color, while resulting in the production of either CO2 or N2 gas, along with photoproducts. Because the crystals respond vigorously to light, they have potential use in various applications, such smart materials for sensors, actuators and photopatterning. Professor Gudmundsdottir and her research team will organize outreach and training activities that will benefit diverse group of high school students participating in the ACS Project SEED Program, and undergraduates pursuing research in chemistry and materials science. The mechanism of the photoreactivity of the benzol peroxide and organic azido derivatives will be elucidated in solution and the solid state, by performing laser flash photolysis, matrix isolation, Electron Spin Resonance spectroscopy, and product studies. The reaction mechanisms and the characterization of excited states and reactive intermediates will be further supported by theoretical calculations. In addition, Raman confocal microscopy will be used to determine the chemical compositions of crystal surfaces before and after irradiation, to determine accurately the surface reactivity, and how it affects the photodynamic behavior. The proposed research will provide intensive training opportunity for graduate students in the interdisciplinary of photochemistry and material sciences. 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

Particle physics explores the shortest experimentally-accessible distances in nature, addressing fundamental questions about forces and matter. Progress in particle physics is driven by interpretations of results from particle-physics experiments. These results rely on expensive, computer-based simulations of particle collisions, which are performed using simulations utilizing random number sequences (Monte-Carlo (MC) event generators). These simulations are at the core of particle-physics discoveries, such as the observation of the Higgs boson at the Large Hadron Collider (LHC) in 2012 and are a key part of cyberinfrastructure for particle physics. This project addresses a common issue in MC event generators – consistently, accurately, and efficiently determining uncertainties from model parameters. Because so much simulated data is required in particle physics, the outcome of this project will enable the production of sufficient simulation samples necessary for the timely analysis of upcoming data from the LHC. Robust MC uncertainties in the very near future are also critical for understanding the scientific impact of upcoming large-scale neutrino and nuclear physics projects. Event generators simulate particle collisions in three steps: a high-energy collision, evolution of the collision to lower-energies, and hadronization into observable particles. Each step depends upon a large number of model parameters that are unknown a priori and are fit to data with uncertainties. To test the robustness of the simulation predictions, the dependence of the results on parameter variations must be understood. Because these models are probabilistic, it is not possible to determine the effect of parameter changes using methods like automatic differentiation. In practice, multiple runs of the same model are performed, but with different parameter settings. This approach is resource intensive, requiring both time and storage. The goal of this project is to instead develop an innovative approach, where the effect of changing these parameters can be captured with event weights and incorporate this approach into Pythia, the most widely used event generator in the particle physics community. The outcomes of the project are relevant for a large community of users in particle and nuclear physics, since event generators are used by every major particle and nuclear physics collaboration. Special attention is given to ensure that the uncertainty framework is implemented in a sustainable way. As part of the project, comprehensive documentation and examples provided via Jupyter notebooks are also being developed for use in outreach activities. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier program in the Division of Physics in the Directorate of Mathematical and Physical Sciences. 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 internet provides vast resources to aid undergraduate engineering students in their studies. In addition to the course-supplied materials, which often go underutilized, students tend to use three major types of external resources: (1) solution sets (whether provided by the publisher, instructor, or homework support service); (2) supplemental videos; and now (3) generative artificial intelligence (AI) tools. AI tools are quickly growing in popularity and possess unparalleled capabilities compared to what has been seen in the past from educational products. Still, they come with significant shortcomings that demand new skills and approaches to be optimally employed. For example, although modern chatbots exhibit an impressive ability to solve a wide range of problems across standardized tests like the LSAT and SAT and converse about advanced topics at just about any level, they exhibit undesirable behavior that can have negative impacts on the learning process. In particular, ChatGPT and similar tools have been described as having the capacity to lie; the phenomenon of a large language model “lying” by producing plausible but incorrect information is often called a hallucination. Considering the often authoritative and human-like text that chatbots produce, students must be able to identify accurate and useful information independently within the outputs. Although a considerable amount of work has been done to understand what students are using generative AI tools for, we have much less information on how they interact with them – in other words, how students prompt tools like ChatGPT – and how they evaluate the outputs the tools provide. This study will focus on how undergraduate engineering students utilize external resources and analyze the strategies employed to achieve the maximum benefit of generative AI tools. The study will help educators better teach students how to interact with generative AI tools to improve their learning. Additionally, this study will serve a training opportunity for the PI to develop educational research tools under the mentorship of the Co-PI. With the advent of generative AI, there is considerable excitement about the progress toward the grand challenge of personalized learning for all students. However, there is still a gap in the literature regarding how students engage with ChatGPT and similar tools to supplement their learning, especially from the perspective of metacognition. Thus, we will address the following research questions: RQ1a) How do engineering students use external resources for problem-solving assistance in their coursework? RQ1b) Why do engineering students engage with specific external resources over others, such as ChatGPT, with respect to the resource’s perceived ease of use and usefulness? RQ2) What metacognitive strategies do students employ when collaboratively problem-solving with external resources, especially generative AI? Additionally, this study will serve a training opportunity for the PI to develop educational research tools under the mentorship of the Co-PI. The project will be an explanatory sequential mixed methods design consisting of a quantitative phase followed by a qualitative phase. The first phase, a survey grounded in the principles of metacognition and the Technology Acceptance Model will be administered at the University of Cincinnati to understand student adoption and continued use of external resources to complete coursework. Students will then be sampled in a stratified fashion to contextualize the survey findings with think-aloud interviews concerning their practices when engaging with these outside resources, which addresses the third research question. In the think-aloud interviews, students will be tasked with a novel design challenge that they will be instructed to solve using ChatGPT as a co-designer. By directly investigating how students interact with generative AI tools while problem-solving, the project will provide insights to educators on designing technologically supported learning experiences that cater to diverse student groups. 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 goal of this project is to meet the rapidly expanding need for AI jobs by establishing an experiential learning pathway in the emerging Trustworthy Artificial Intelligence (TAI) technology for diverse learners into careers in Artificial Intelligence (ExLAI). ExLAI aims to provide a diverse pool of individuals enrolled in associate degree community college programs in STEM with experiential learning opportunities that deepen knowledge and skills in Artificial Intelligence (AI) fields. There are two major pathways through ExLAI program: (1) Direct Career Pathway: Upon completing the eight-week training, community college students are equipped to secure internships and embark on a rewarding full-time career within the field of AI; and (2) UC Immersion Pathway: Students gain entry into the esteemed UC Computer Science (CS) department, undergoing comprehensive AI training and participating in mandatory Cooperative Education (Co-op) experiences. Through this, the ExLAI program will enroll 180 ExLAI scholars over three years, with each cohort trained for eight weeks, 20 hours per week. ExLAI partners with four STEM-focused community colleges as well as five companies that will provide industrial experiential mentoring to the scholars. ExLAI’s goal will be achieved by fulfilling the following objectives: (1) Create a robust experimental AI learning pathway program for ExLAI scholars. The ExLAI Program will enroll 30 community college associate degree ExLAI scholars per cohort and two cohorts each year. As a pathway program, it will begin as an eight-week summer program for 60 community college associate degree students each year to develop their confidence as future computer scientists and engineers in TAI. Our recruiting efforts will focus on underrepresented minority (URM), female, and low-income students. The program will then expand to provide targeted, trustworthy AI education, developing in-depth TAI research skills. Each scholar will work with a mentor and conduct research on the assigned project for at least ten hours per week; (2) To increase the capacity for the education of TAI professionals through experimental learning. The ExLAI Program will work closely with UC’s Office of Career Services (OCS), Co-op, and NSF ExLENT program to connect diverse industry employment sectors and enhance placement services provided to ExLAI scholars; (3) Expand UC’s CS/TAI research, development capabilities, and infrastructure. UC provides a unique research, development, and education environment focused on TAI issues that collectively foster interactions with a wide range of stakeholders; and (4) Strengthen partnerships among UC, community colleges, industry employment sectors. The collaboration between the ExLAI program and the Co-op program/OCS is positioned to enhance internship/full-time job opportunities for participants in the industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The University of Cincinnati (UC) ADVANCE Partnership Project brings the Women in Engineering and Proactive Network (WEPAN) and the ten institutions comprising the Ohio Louis Stokes Alliances for Minority Participation (LSAMP) Alliance into a partnership to identify challenges and successes of recruitment, retention, and advancement of faculty in engineering, then to develop, pilot, and evaluate corresponding systemic interventions in academic departments. The research literature on collective self-esteem indicates that group members maintain or enhance how they view the social groups to which they belong by positively differentiating the dominant in-group from a relevant comparison out-group on both evaluative dimensions and reward allocations. Phase 1 of the project will explore the lived experiences of four different participant groups using a qualitative community based participatory action research methodology and semi structured interviews based on the pillars of collective self-esteem and intersectionality. Phase 2 of the project will develop, pilot, and evaluate interventions based on the data collected in Phase 1. In Phase 1, the UC ADVANCE Partnership Project will employ Group-Level Assessment to foster collaboration in each participant group in to understand challenges and strengths in their communities and organizations with the intent of developing action plans, in a 7-step process involving trust-building, anonymous prompt responses, up-voting, reflection, understanding, identifying themes, and developing action plans. Semi-structured interviews will also inform this phase of the project. Findings from Phase 1 will inform the development or adaptation in Phase 2 of Action Steps/Plans to help increase equity in the recruitment, retention, and promotion of faculty, to be deployed at UC and partner institutions. In addition to traditional dissemination of results, Phase 2 will include the development, filming, and dissemination of Impact Stories to bring awareness of everyday lived experiences engineering faculty members. This partnership will be evaluated formatively and summatively by an external evaluator and Project Oversight Advisory Committee. 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

The ability of bacteria to swim in fluid media is critical for their survival and proliferation in diverse environments and plays a crucial role in many important natural, environmental and bioengineering processes. As one of the most common types of bacteria, peritrichous bacteria such as Escherichia coli swim by rotating a single helical bundle, which is formed from multiple flagellar filaments growing all over the bacterial body. This proposal addresses a fundamental question in the fluid dynamics of the swimming of peritrichous bacteria, i.e., how do the multiple flagellar filaments of a bacterium synchronize and rotate collectively to provide a coherent thrust, enabling the swimming of the bacterium? Toward solving this long-standing problem, the project will integrate experiments on macroscopic model flagella with experiments on microscopic living bacteria and state-of-the-art numerical simulations. Through a systematic and iterative approach, the project aims to resolve the underlying fluid-mechanics principles governing the complex dynamics of bacterial flagellar bundles and uncover their effects on bacterial swimming. The project will provide good opportunities for recruiting undergraduate students from a minority-serving college in frontier research and for designing scientific demonstrations on bacterial swimming for outreach activities. The goal of this project is to understand the synchronization and collective dynamics of multiple flagella in a bacterial bundle. Particularly, the project aims to reveal how multiple flagella synchronize to form a functioning bundle – an indispensable process for the swimming and chemotaxis of a large class of bacteria – and illustrate the collective dynamics of flagella in the bundle. More specifically, the project will construct the most accurate scale experiments to date with previously unexplored features, which will provide a benchmark to develop an immersed-boundary numerical model for simulating flagellar dynamics at different scales. The predictions of both the scale experiments and numerical simulations will be finally compared with microscopic experiments on real bacteria. More broadly, the work will shed light onto the origin of hydrodynamic synchronization and facilitate the development of engineering techniques for tailoring the synchronized dynamics of micron-sized objects. Beyond the specific scientific and engineering questions, the project will expand the limited toolbox to tackle challenging issues associated with low-Reynolds-number fluid-structure interactions. A versatile experimental platform and a quantitative numerical model will be delivered to the research 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.