Texas A&M Engineering Experiment Station

NSF Awards · FY 2024 · 2024-10

This Faculty Early Career Development (CAREER) grant will fund research that enables the application of new architected metamaterials to vibration protection of large-capacity wind turbine blades, as well as the design of surface acoustic wave devices capable of high-precision sensing and signal processing, thereby promoting the progress of science and advancing the national prosperity. A major impediment to scaling up the output from wind energy generation is structural failure due to vibrations of turbine blades induced by unsteady wind loads and extreme weather conditions. Without effective means to guide or isolate vibrational energy in such structures, concerns about damage and safety enforce suboptimal designs and operation. To address this challenge, this project investigates a hitherto unexplored class of engineered materials, called moiré metastructures, that can guide and confine elastic wave energy in ways that are unattainable in conventional materials. An integrated set of education and outreach activities aims to positively impact engineering education and contribute to a diverse and globally competitive STEM workforce. These include metastructure design challenges in an undergraduate course, hands-on activities at workshops for K-12 students, international student exchange with a research group in France, and participation of individuals from underrepresented groups in research. This research aims to develop the foundations for the design of a class of bilayered architected plate metastructures, coupled by nonlinear between-layer springs, that allow independent engineering of dispersion and nonlinearity and, as a result, the possibility of uniquely nonlinear wave phenomena, such as solitons, frequency combs, wave bending, and unidirectional propagation. It achieves this aim by investigating how nontrivial topological properties of moiré metastructures, obtained by stacking two layers of hexagonal, square, and Kagome lattice-based plates, result in almost flat dispersion bands that can be exploited and combined with space-time modulation of stiffness to guide or confine wave energy. The approach relies on theoretical analysis of discrete spring-mass models, finite-element analysis using discontinuous Galerkin basis functions based on Bloch modes, design optimization and fabrication of coupling springs with the desired nonlinear stiffness, and experimental validation using laser-Doppler vibrometry and high-speed imaging. 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 · 2024-10

Reinforcement learning (RL) is a popular framework for learning optimal decision-making in complex environments, and many RL algorithms have been developed to improve decision-making of a single agent in normal environments. However, modern large-scale distributed learning applications usually involve multiple heterogeneous agents that interact with complex environments, making the optimal decision-making fundamentally more challenging to learn. For example, when navigating multiple drones in an open area, the drones need to properly cooperative with each other and take the environment uncertainty into account. As another example, in distributed wireless networks, the interaction of the agents (e.g., base stations or mobile phones) are subject to heterogeneous constraints on power and bandwidth, etc. This project aims to develop a resilient RL framework for managing heterogeneous multi-agent systems in complex environments, and systematically design efficient multi-agent RL algorithms with comprehensive convergence and complexity analysis. The project will produce RL algorithm packages that are fully accessible to the public. The research activities will also generate positive educational impacts on undergraduate and graduate students. The materials developed by this project will be integrated into courses on machine learning and optimization, and will benefit interdisciplinary students majoring in electrical and computer engineering, statistics and computer science. The project will actively involve underrepresented students and integrate research with education for undergraduate and graduate students in STEM. It will also produce introductory materials for K-12 students to be used in engineering summer research programs. The overarching goal of this project is to develop a resilient RL framework for managing multi-agent systems that involve heterogeneous agents in complex and structured environments, and systematically design scalable and computation-efficient RL algorithms with rigorous and comprehensive convergence and complexity analysis for managing such systems. The proposed research includes three major thrusts. First, to manage cooperative agents with heterogeneous constraints in various types of structured environments (e.g., homogeneity and uncertainty), the environment model structure will be leveraged to develop fully decentralized policy optimization algorithms with convergence and complexity analysis. Second, to manage competitive agents with heterogeneous constraints in uncertain environment, new tractable notions of constrained and robust equilibrium will be proposed. Their fundamental structures and properties will be studied, based on which fully-decentralized primal-dual type policy optimization algorithms and robust value-based algorithms with convergence guarantees will be developed. Lastly, to improve the generalizability of agents’ policies across heterogeneous environments, a new assistive RL framework that can substantially enhance the generalizability using few rounds of information exchange without data sharing will be developed. These RL algorithms will be applied to learn resilient and optimal control policies for interference management in wireless networks and energy control in power networks. 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 · 2024-10

The project aims to serve the national need to prepare high quality engineering teachers for the secondary classroom. Increasing retention and graduation rates in undergraduate engineering degree programs continues to be an issue. Researchers have identified several factors contributing to this issue. These include: i) the traditional teaching of concepts in mathematics, physics, engineering, and technology is isolated with students often not seeing the relationship between what is being taught and the world around them or their own interests, and ii) few teachers in K-12 education are prepared to teach engineering concepts. The objective of this project is to prepare engineering and engineering technology undergraduates to become effective educators who are adept at developing hands-on engineering activities for grade 6-12. The focus will be on developing strong technical skills combined with training in pedagogy, intensive classroom field experiences, clinical practice, and mentoring. Having trained engineers as practicing teachers in the secondary level classroom could potentially mitigate the high drop-out rate in engineering programs at the undergraduate level. Texas Experiment Station and Texas A&M University will partner with two rural school districts, Caldwell and Hearne Independent School Districts. Project goals include training prospective STEM teachers for the technologically advanced, multicultural, and diverse 21st-century classroom. The project intends to provide scholarships, over five years, for 20 prospective STEM teachers who are pursuing bachelor’s degrees in engineering, engineering technology, science or mathematics with secondary school teaching certification. The formation of both engineering and teaching identities and their impact on retention and persistence of prospective STEM teachers will be explored. Within the context of Identity Development Theory, researchers will investigate the extent to which co-curricular support that explicitly targets self-efficacy, such as engineering design projects and field experiences, influences the development of engineering and teaching identities in prospective STEM teachers. The intended goal is to develop prospective teachers who are multiculturally aware, pedagogically trained, and qualified STEM professionals with a strong emphasis on hands-on learning. As practicing teachers, they will be ideally suited to teach students representing the full spectrum of diverse talent for the next generation of the STEM workforce. This will create the potential for increasing the numbers of dynamic and innovative employees in STEM fields, allowing the United States to maintain the edge in technological and engineering advancements. The results of the project will be disseminated through the project's website, engineering duration conferences and journal articles. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (NOYCE). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. 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

Climate extremes in global breadbasket regions trigger ripple effects on global food security and American multinational food industries. While individual extreme events like droughts and heatwaves negatively affect crop yields, the most severe effects result from compound extreme events, posing significant challenges for climate adaptation. Quantifying these extreme climate shocks is of great interest to industries, insurance companies, and governments. This study aims to investigate the interconnected climate extremes of droughts and heatwaves, which can co-evolve over time (temporal compounding) or occur simultaneously in different breadbasket regions (spatial compounding). Although the risks of individual extremes have been studied, an integrated risk assessment of temporal and spatial compounding extremes on global breadbaskets and supply regions is lacking. Spatial compounding events can lead to significant economic impacts on American industries and strain interconnected supply chains. This GOALI project involves a university-industry partnership to investigate the risks of temporal and spatial compounding climate extremes on global breadbasket regions and PepsiCo's supply chain source regions. The project objective are to: (a) quantify the potential risk of drought, heatwaves, and compound drought and heatwave events on crop yields for breadbasket regions, (b) quantify the spatial compounding risk of extreme events simultaneously occurring over multiple (coupled) breadbasket regions, (c) investigate the potential impact of climate change on the evolution and spatial synchronization of drought, heatwave, and compound events for the breadbasket regions, and (d) develop seasonal prediction models for spatially compounding extreme events and associated risk on crop yields over the supply regions for PepsiCo. The research results will have a positive societal impact by reducing the risk of extreme events on global food security and enhancing the competitiveness of American multinational food industries globally. The collaborative framework will drive innovations and foster new talent, such as graduate students and postdocs, by creating a skilled workforce capable of addressing real-world challenges. Agriculture-related stakeholders, including business units, industries, and farmers who outsource agricultural products, can use the created tools to mitigate the risks of climate extremes, enhance environmental sustainability, promote social equity and economic stability for farmers, and strengthen global cooperation between American industries and international partners. 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 Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Research on Broadening Participation in STEM projects supports the development, implementation, and study of new theory-driven models and innovations related to the participation and success of underrepresented groups in STEM undergraduate education. It is expected that the award will further the faculty member's research capability, as well as improve the recruitment, retention, and success of underrepresented groups in STEM education and the workforce. This project at Prairie View A & M University, in collaboration with Texas A & M Engineering Experiment Station and University of Texas at El Paso, seeks to investigate the impact of a virtual reality (VR) environment on learning College Algebra among students enrolled at Prairie View A & M University, an Historically Black College and University. Researchers will develop 50 hours of constructivist-based, integrated STEM training content for college algebra, embedded as modules in Algeverse, the VR environment, which students will access using Oculus VR headsets. Three rounds of usability studies will gather feedback from HBCU instructors and students to iteratively improve the software. To assess Algeverse's effectiveness on learning performance, immersion, motivation, engagement, and user preferences, the team will conduct three studies comparing these outcomes between students using VR modules versus those using non-VR modules. Study 1 involves a single module, while Studies 2 and 3 are six-week longitudinal studies where students complete all modules either in a specified order or by personal preference, respectively. This project not only contributes to research on educational technology and math education at HBCUs, but has the potential to enhance STEM education, increase STEM degrees awarded to HBCU students, and boost the number of Black and Hispanic individuals in the STEM workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

A nontechnical description of the project: This project investigates experimental and computational approaches to the characterization of geometric and optical properties of the vias, establishing a new via metrology and inspection technology for advanced wafer-level packaging (WLP) that enables three-dimensional (3D) heterogeneous integration (HI). With the advent of high-performance electronic devices used in automotive, airplanes, computers, cellphones, televisions, or tablets, the semiconductor industry is looking for high-throughput, reliable, low-cost manufacturing, and inspection technologies of micro/nanoscale vias on circuitry and wafers to achieve advanced WLP. Semiconductor industry must have via metrology and inspection technology for the ongoing trend of miniaturization and integration of electronic devices. Vias on the electronic devices are copper-lined holes that enable a 3D electrical interconnection between the different layers of the circuitry. Vias on the silicon, flexible printed circuit board (PCB), or glass wafers are in the range of diameter 5~100 µm and depth 100~200 µm. Even a single via defect on the device can impact multi-layer chip stacking and prevent the vias from enabling electrical interconnection between the circuit layers. As the via size gets smaller and smaller, via parameters such as diameter, roundness, via-to-via distance, heat-affected zone, and via-edge roughness should be monitored and controlled. Additionally, even if the different layers are stacked somehow with defective vias, the performance of manufactured chips is not guaranteed, and there could be issues in signal integrity, reliability, thermal management, etc. Hence, via metrology and inspection are essential technologies in WLP. However, current via metrology is limited to assessing via geometry by conventional microscopy because via critical dimension (CD) decreases in the nanometer scale. The proposed transformative research will enable the inspection, metrology instrumentation, and in-situ analysis that not only benefits the electronics industry with emphasis on quality control but also enables the via manufacturing processes under tight control, quality improvement, and reduced scrap rates to enhance environmental sustainability. In addition to technological advancement, this project will educate future scientists and engineers in semiconductor engineering, raising awareness for semiconductor engineering workforce development at the university level, broadening a knowledge base, and promoting research and education engagement in academic communities. Advancements in semiconductor metrology, inspection and instrumentation technology will influence U.S. semiconductor technology and strengthen the U.S. industry and job market by upskilling the U.S. semiconductor engineering workforce. A technical description of the project: This research will reveal new knowledge and research paradigms in via metrology, design, light-matter interaction, and manufacturing by creating rapid modeling methodologies for the inspectability of various types of defects on vias to prevent catastrophic failures in complex WLP processes. Also, the experimental/computational modeling and calibration methods for the via property characterization will be introduced. This project (1) establishes a via-edge topography model to characterize its geometry (diameter, roundness, position, and via-edge roughness) and its scattering behavior by the proposed metrology system (grayfield imaging interferometric microscopy) and light-via computational models, (2) characterizes light-via interactions according to the corresponding changes in via edge topography, and (3) confirms the relationship between the via geometry and the corresponding optical property changes for via defectivity metrology and inspection. Through-focus scanning microscopy (TSOM) that stacks interferograms along the via depth direction will be to validate the computational approaches for enabling 3D via characterization. The successful completion of the proposed research will provide a novel approach to via defectivity metrology, design, models, and manufacturing. The outcomes of this project will provide the via CD defectivity database that the common types of via defects and their root causes can be identified. This result enables the semiconductor manufacturing process control, especially for WLP, in a new aspect, allowing the wafer handling processes to be under tight control and improving yield. The research team envisions a near future where HI chips, microelectronics devices, or systems significantly improved by emerging metrology, inspection, and instrumentation will leverage chip manufacturing process capabilities for high volume and high throughput production. Also, the outcomes of this research will have a great impact not only on semiconductors but also on materials chemistry, physics, and optics. 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 Interconnects are layers of metal conductors with nanoscale to microscale dimensions that connect different electronic devices in a computer chip. Copper is the standard for its abundancy, low cost and good electrical conductivity. However, when its dimensions are reduced below 10 nanometers, its resistivity increases dramatically. As a consequence, power consumption and heat generation increase dramatically. This scaling trend for copper interconnects has two causes. First, electrons in ultrathin copper nanowires can no longer move freely due to structure and property changes. Second, the surface of copper nanowires must be encapsulated with an insulating layer to make the structure more stable, but this further reduces charge transport. This project aims to develop a new way to synthesis copper nanowires and design effective encapsulation layers based on two-dimensional materials. These break the paradigm limiting current interconnect technology and enable next generation high-performance and energy-efficient computer chips. Research in this highly interdisciplinary project is integrated with education and workforce development. The project engages students at all levels, providing training in physics, materials science, and nanoelectronics. Investigators closely collaborate with industry, government, and education partners to cultivate future technology leaders and incubate technology transfer. Technical Description In modern microchip technologies, aggressive downscaling of the logic, memory, and interconnect components is crucial. Conventional interconnect technologies based on polycrystalline Cu face the following fundamental downscaling challenges: (i) the resistivity increases drastically as its linewidth is decreased due to electron scattering at the metal/insulator interfaces and grain boundaries; and (ii) the interfacial liners and barriers around Cu wires are essential to avoid the ionic diffusion across the metal/insulator interface, but these additional non-conductive structures further compromise the downscaling capability. This project aims to establish a multidisciplinary and closed-loop co-design framework to facilitate the investigation of the chemical and atomic structure at the metal/insulator interface and its electronic and ionic transport properties and to enable advanced interconnect applications that are scalable, high-performance, and reliable. Precise control of the metal surface orientation and its interface with the atomically thin 2D-material-based liner-barrier are established through multiscale simulation, novel metal deposition and heterostructure integration processes, and multi-modal material-device co-characterization. The 2D material layer encapsulated on the surface of Cu nanowires will facilitate the modulation Cu crystal surface orientation, and at the same time, serve as the ultrathin ion diffusion barrier to enhance the interconnect reliability. This project also offers potential pathways for integrating this new interconnect technologies with silicon integrated circuit chips, paving the way for upscaling such an emerging technology for industrial development and manufacturing. 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

Suspended particulate material is a complex mixture of living and detrital organic and inorganic material. The composition and concentration of suspended particulate material vary greatly throughout the ocean and can be considerably influenced by local processes. The microbial and biogeochemical processes occurring within this material greatly influence the flow of carbon and other nutrients through the marine environment. The scale of the ocean makes these particulate processes globally relevant and, at the same time, challenging to fully characterize. Equipping robotic vehicles with a sensor system capable of rapidly distinguishing environmental variations in marine particle size, shape, and composition will enable navigation based on the properties of the ambient particle field. More specifically, it will enable the targeted collection of samples by autonomous robotic vehicles to those zones of the ocean where particle processes are most relevant and dynamic. This research will develop an optical sensing instrumentation suite and integrate it with existing robotic sampling tools for both remotely operated vehicle Jason and the autonomous underwater vehicle Clio and optimize their use to characterize hydrothermal plume particle processes in an engineering sea trial in the vent fields of the Juan de Fuca Ridge. This research will develop an optical sensing system to directly characterize marine particles and enable adaptive robotic collection of biogeochemical and biological samples from autonomous vehicles and remotely operated platforms. The optical sensing components of this system will characterize marine particles based on multiple parameters indicative of size, shape, and composition. Optical sensing will be based on a tightly integrated sensor suite consisting of camera-based particle imaging, using both wide-field stereoscopic and microscopic cameras, fluorometry, and optical transmission sensors. These sensors will be controlled by a single board computer capable of running real-time classification algorithms that can be used to control adaptive particle and fluid sampling systems. The close integration of the sensing elements is intended to both achieve a smaller overall payload size and allow for maximum control of sensing parameters including timing, sequencing, and frequency. The intent is to maximize the use of open-source software and hardware so that the resulting design can be shared within the broader community to allow for modification, adaptation, and experimentation. The goal is to improve the oceanographic community's ability to target novel biogeochemical environments using robotic oceanographic vehicles so that we can more efficiently study geochemically important environments in an otherwise very large ocean. This optical sensing system will be designed to enhance the observational capabilities of both large and small underwater vehicles and platforms. The system will consist of a stereo camera pair, a flow-cell/microscope camera, a commercial chlorophyll-a fluorometer, a commercial backscatter sensor, an electronic stack in a custom pressure housing, and an adaptive sampling subsystem. The operation of the system will be tested both in the laboratory and in field on remotely operated vehicle Jason and autonomous underwater vehicle Clio in the hydrothermal plumes of the Juan de Fuca Ridge during an engineering sea trial cruise in 2026. 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 award will contribute to the advancement of national prosperity and manufacturing capabilities by supporting study of state-of-the-art computational approaches for accurately characterizing the mechanical behavior of materials critical to sectors such as defense, construction, and energy. This research will develop generalizable analytical methods for material characterizations with unprecedented accuracy in significantly reduced time, taking into account experimental errors and inhomogeneous materials and significantly accelerating the discovery of new materials. The interdisciplinary nature of this research will create new channels of communication between academics and practitioners, train a doctoral student in interdisciplinary research through multilateral collaboration with national laboratories and create educational materials for both operations research and materials science. This research will establish a global programming epsilon-optimal spatial branching technique based on a novel class of efficient convex underestimators with proven asymptotic convergence. An innovative decomposition-based scheme will be introduced that achieves data decoupling through a regularization procedure, enabling separability into tractable sub-optimization problems allowing convergence to an epsilon-optimal robust solution. Finally, the research will introduce a new nested spatial branching scheme that solves a class of constraint-based multi-objective problems through a reformulation scheme, casting the problem as an equivalent bilevel optimization problem. This research fills an important gap in the optimization literature by introducing scalable global programming techniques that can handle challenging non-convex structures. It will also advance our understanding of how to effectively use data for the accurate characterization of material mechanics and for predicting their behavior, even when faced with extrinsic uncertainties and intrinsic material variabilities. The performance assessments of the optimization approaches will be informed by data obtained through collaborations with national laboratories. 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

Small particles are ubiquitous in nature and many times they can sit on the surface of liquids, such as water, or between two liquids, such as when oil and water meet. These particles then impact how molecules move between the air and the water, or the oil and the water, and thus influence many natural processes. This collaborative project will help answer important questions about how sheet-like particles organize on the surface of liquids, and how the organization alters the movement of molecules from a gas to a liquid, or a liquid to a liquid. This knowledge is important for creating materials for sustainable foods, pharmaceuticals, and coatings, and for helping to design and build better particles. The proposed work involves two research groups, one in chemical engineering at Colorado School of Mines, and one in materials science and engineering and chemistry at Texas A&M. Undergraduate and graduate students from both institutions will share knowledge across the different disciplines while they perform research. They will gain the foundational skills required to be leading scientists in the STEM workforce. The goal of this project is to understand the relationship between the structure of 2D particle films at fluid-fluid interfaces and the mass transport across the films. Preliminary work indicates a complex and unknown relationship between particle area concentration and permeability, and microscopy data reveal that 2D particles form heterogenous films with structure that depends on area coverage and particle oxidation. The central hypothesis is that permeability across 2D particle films will be governed either by film heterogeneity or tortuosity depending on particle area concentration. The researchers will probe this hypothesis by combining theoretical transport models with experiments visualizing graphene oxide (GO) film structure and experiments quantifying interphase mass transport. This collaborative work leverages expertise in fabricating particles and organizing nanosheets at interfaces, as well as development of an array of techniques for visualizing 2D particle film structure at fluid-fluid interfaces with microscopy. The PIs will support the development of undergraduate and graduate student researchers and will jointly develop and implement a half-day workshop on particles at interfaces to be held in association with the ACS Colloid & Surface Science Symposium. Graduate students will be trained in laboratory skills, critical thinking, data analysis, and dissemination of research results, and they will participate in joint meetings between lab groups to facilitate the exchange of knowledge and expertise. 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

Non-technical Summary: Supported through the Solid State and Materials Chemistry Program within the Division of Materials Research, the principal investigators and their research groups investigate the scientific foundation required for developing strong fibers and nanofibers, which are predominantly made of carbon and can surpass the load bearing capacity of existing fibers. The fibers are prepared via extreme heating of polymers in a process referred to as pyrolysis or carbonization. The researchers study the emergence of the most likely strength-compromising defects and devise methodologies to mitigate the adverse effect of defects on fiber strength. The project consists of a series of experiments investigating the fibers at the very small length scales, the nanoscale, coupled with computational modeling that is enhanced with artificial intelligence. The findings of this research may transform how super-strong fibers are made via pyrolysis. These carbon nanofibers could be used as reinforcements in composites or other assemblies such as yarns in order to develop structural components in applications in which weight is a premium such as aeronautics and space missions. The project also has a strong educational component with a workshop to train the next generation of engineers on this topic. Technical Summary: The emergence of nanomaterials such as carbon nanotubes (CNTs) raised hopes for materials with mechanical strength far exceeding that of the industry standard, carbon fibers (CFs). However, this hope in nanotechnology has remained largely unfulfilled in the past two decades, due to limitations in controlling defects and limited understanding of their impact on strength. Therefore, the focus of this project, supported through the Solid State and Materials Chemistry Program in the Division of Materials Research, is to unravel the effect of defects on the strength of partially graphitic carbon nanofibers (CNF) made via pyrolysis. The CNFs consist of amorphous carbon and stacked graphitic-like nanoribbons, i.e., turbostratic (TB) domains. With this project, the PIs investigate a nearly 50-year-old scientific dilemma: strength predictions for carbonized fibers deviate greatly from experiments; while theory predict strength of over 30 GPa, experiments peak at ~10 GPa; moreover, unlike theoretical predictions, experiments show that the strength does not monotonically increase with graphitic content. To investigate this mismatch, the project is subdivided into three aims targeting one of three features: nanoscale voids, cleaved crosslinks, and stress concentration at boundaries of misaligned TB domains. Aim 1 evaluates the effect of nanovoids that form due to competitions between material loss and consolidation. Continuum models with the aid of Machine Learning (ML) approaches are used to resolve stress fields around the. In Aim 2, the PIs study correlations between strength and atomic crosslinks between TB domains, and ion bombardment is used to assess the reversibility of atomic crosslinks. In Aim 3, they investigate the impact of residual stresses and failure along misoriented TB domains via a novel Raman-based approach. The distribution of residual stresses is measured via Raman-based method, and correlation between TB alignments, residual stress distributions, and mechanics model predictions is used to evaluate the effect of domain alignment on strength. 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

Flash drought events, characterized by rapid intensification over a short period, lead to significant socio-economic impacts. These episodes are triggered by complex interactions among multiple factors, including climate and land processes. Recent notable flash droughts have resulted in considerable interest among researchers, state and federal agencies, and various stakeholders. This study aims to engage community members—such as farmers, industry representatives, and county extension agents—who have firsthand experience with the direct impact of flash droughts on agricultural crop yields. Collaboration among researchers, academics, and community leaders is central to this study, which seeks to develop science-based solutions to improve resilience and forecasting, thereby reducing the socio-economic losses faced by farmers, ranchers, and stakeholders due to flash droughts. The outcomes will advance flash drought monitoring and prediction tools, enhancing crop resilience to rapid climate fluctuations. By engaging a wider network of producers, the project aims to deploy effective risk reduction strategies and enhance crop resilience, especially for marginal farmers. These efforts benefit agriculture and food industries by managing the potential impact of flash droughts and fostering regional economic growth. The project advances the monitoring and forecasting of flash droughts and provides an opportunity to mitigate their impacts by developing a robust flash drought indicator for better monitoring and prediction across various agricultural crops. It quantifies the drivers that trigger flash droughts using a cascade modeling framework to improve seasonal to sub-seasonal predictions. Additionally, it fosters equitable community partnerships to quantify flash drought risks and create actionable solutions to enhance the resilience of the agriculture sector. The results are used to create agricultural extension materials, educational content, and training resources for farmers, aiming to deepen scientific understanding of flash drought risks within the agricultural sector. 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

Biological signaling pathways, required for proper cellular decisions, paradoxically are both common to all animals, but also diverse in their action, depending on the needs of each animal and/or tissue. For example, the Bone Morphogenetic Protein (BMP) signaling pathway can be found in flies, worms, fish, and humans, and is used in processes ranging from stem cells to inflammation. Given the extensive reuse of this pathway, it is unclear how can it meet the needs, or “performance objectives” (POs), of such a wide range of tissues. For example, in some tissues, BMP signaling must have a fast (and thus, noisy) response, while in others, a noise-free response. To address this question, researchers from Texas A&M, Notre Dame, and Purdue will probe the relationship between the concentration of signaling proteins and which POs are emphasized. The goal is to determine how signaling pathways common to all animals can be both widely used and flexible to the needs of every tissue, which may point to novel engineering principles that can be leveraged for human systems. Each year, undergraduates from each university will take part in an exchange program that will include research and course modules on engineering principles in signaling dynamics. The overall hypothesis of the project is that, while the BMP pathway is highly conserved in topology and protein sequence, differing concentrations of proteins in the BMP pathway (Smad1, Smad4, and phosphatase) allow the pathway to achieve differing emphases among three competing POs: response speed, noise filtering, and linear sensitivity. As such, the four research groups will study the BMP pathway in three organisms and four tissues: the Drosophila embryo, Drosophila wing disc, zebrafish embryo, and human induced pluripotent stem cells (hIPSCs). Advanced confocal microscopy techniques – such as optogenetic control of the pathway, raster image correlation spectroscopy (RICS), and fluorescence recovery after photobleaching (FRAP) – will be used to obtain time courses of concentrations of fluorescently-tagged BMP pathway components, which in turn will be used to quantify the three POs. Precise perturbations to the pathway will be achieved through optogenetics. The outcome of the project is expected to be a cross-species mathematical model of the BMP pathway predictive of each of the four biological systems, as well as the relationship between Smad protein concentration and POs. Predictions will be tested by altering protein concentrations to change which POs are met by the pathway. This project is supported by the Systems and Synthetic Biology Cluster of the Division of Molecular and Cellular Biosciences. 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

Optical hybrid materials (OHM) are emerging systems combining organic (carbon-based) and inorganic (non-carbon-based) components at the nanoscale. These materials can potentially lead to applications in efficient light-emitting diodes, advanced medical imaging, and enhanced solar cells for cleaner energy. However, the manufacturing of OHM remains challenging due to the undesired scattering from the poor design of the matrix-nanofiller interfaces. This NSF project will use a combinatorial printing technique to understand how compositional doping influences OHM hybrid structures and develop a spatially resolved optical analysis system to identify the key factors affecting these materials under extreme material gradients. The successful execution of this research will enable new manufacturing capacities of high-performance optical materials for advanced lenses, lasers, and optoelectronics. The developed system will offer an ideal model for manufacturing and characterizing soft optical hybrid systems that are challenging for the existing fabrication techniques. In addition, the collaboration of two Hispanic-serving institutions (Texas Tech and Texas A&M University) could increase the participation rate of underrepresented groups. The team will actively recruit undergraduate researchers for the project and provide K-12 students with opportunities for hands-on research experience in the multidisciplinary fields of manufacturing science, chemical engineering, and advanced materials. While optical hybrid materials (OHM) hold great promise due to unique optical structures combining the benefits of soft polymers and functional nanofillers, a lack of understanding of polymer-nanofiller interactions at the interface level and subsequent difficulty controlling undesired scattering pose considerable challenges. As a result, it is crucial to develop knowledge connecting nanoscale OHM compositions with their detailed optical characteristics (refractive index, birefringence, etc.). This grant supports fundamental research to understand the effect of dopant compositions and compositional gradients on OHM. The team will leverage the combinatorial printing of structure-programmable nanofillers to understand defects and doping of combinatorial optical materials and their hybrid structures. In addition, a spatially resolved optical characterization system will be developed to identify the key factors controlling the interface-related phenomena and properties of the OHM under extreme material gradients. In this process, the project will quantify the effect of graded optical dopants in polymers and examine the refractive index gradients, birefringence gradients, and photoluminescent gradients (both compositionally and optically). If successful, this method will yield rich knowledge in advanced optical manufacturing and potentially challenge the conventional energy-intensive melting-based or clean-room thin-film deposition approaches for OHM and related 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 · 2024-08

The nuclear envelope (NE) is a physical barrier between the cytoplasm and the nucleus that is essential for the survival and function of eukaryotic cells. The NE has a complex geometry, consisting of two lipid membranes fused at hundreds of donut-shaped pores and maintained at a stable distance from each other. How the NE’s complex geometry enables its critical functions is not understood. Prior work suggests that double-layered membrane geometries have unexpected mechanical properties that are not found in manufactured materials. This award supports studies to develop new fundamental insight into the mechanical properties of the NE, with two broad goals: 1) discover the link between NE structure and NE mechanical properties, and 2) identify mechanical principles for the design of a new generation of biologically inspired complex materials with unique functions. Findings from this project will be used to develop physics-based games for a virtual mechanics and biomechanics lab (VMBL) for teaching students about the interplay between topology and mechanics in 2D materials. The project will train students from underrepresented groups and promote their success in research and teaching. The overarching goal of this experimental and computational project is to explain how passive forces, active forces, and geometry impact NE mechanics. The researchers will experimentally quantify spatial fluctuations in the NE under perturbations of passive load-bearing proteins, active force-generating cytoskeletal proteins, and ATP depletion. Monte Carlo simulations on a double membrane system with donut-shaped pores will be performed to interpret these experimental observations and quantify NE mechanics. Experimental data will provide snapshots of membrane geometry which will be interpreted with the computational model to develop insights into the underlying mechanics and forces. Overall, the study will unravel the interplay between geometry, topology, and mechanics in soft 2D materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The National Center for Therapeutics Manufacturing (NCTM), a center within the Texas A&M Engineering Experiment Station (TEES)’s ExLENT project “Beginnings: Diversifying Talent in Advanced Biologics Manufacturing – mRNA Vaccines and Gene Therapies (DTABM)” will address the national skills gap in advanced biologics manufacturing. This project will deliver hands-on training in a simulated cGMP, pilot-scale facility, to build a highly skilled, technical workforce for future jobs in biomanufacturing. NCTM’s training programs are thoroughly vetted by biologics manufacturing companies and has enabled graduates to obtain jobs with companies in the biopharmaceutical industry. At the end of the 3-year performance period of the DTABM initiative, 90 skilled trainees will be ready for employment in the emerging biomanufacturing industry, specifically skilled in manufacturing processes for the production of recombinant proteins, viral vectors for gene therapy, and mRNA-based vaccines and therapeutics. Preparing trained operators and technicians to meet workforce needs for biopharmaceutical manufacturing aligns with the goals of the Manufacturing USA Institutes, specifically the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) and the Advanced Regenerative Manufacturing Institute (ARMI). Finally, the DTABM initiative is committed to promoting diversity, equity, inclusion, and accessibility within the advanced biomanufacturing industry. NCTM will partner with MSI colleges and universities to increase accessibility to underserved populations and boost the employability of these students, thereby contributing to a more diverse workforce. NCTM offers a customizable, online, hands-on, and instructor-led training covering all aspects of biomanufacturing, including upstream and downstream processing for biologics production, analytics, Current Good Manufacturing Practices (cGMP), quality systems, and regulatory compliance. All training is designed, developed, and delivered by industry professionals and academic subject matter experts and is unique in the following ways. First, all training courses are delivered in a small cohort model, where class size is capped at 10-12 participants to ensure each trainee has extensive hands-on time with each piece of equipment. Second, NCTM performs training in a simulated cGMP facility, allowing for a comprehensive training regime that includes important aspects of manufacturing, including gowning, personnel and material flow through controlled airlocks, functional adjacencies, and other concepts that are typically overlooked in many training courses. Third, the equipment utilized during NCTM training is industry-standard, pilot- or manufacturing-scale equipment, which gives participants a unique opportunity to train on skids relevant to their current or future careers in biomanufacturing. This project will provide pre-hire industrial training to students who will fill the workforce pipeline in Texas, Louisiana, North Carolina, and Maryland, as NCTM has long-standing partnerships with the MSIs in each of these states. 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

This award investigates expanded and improved use of stochastic simulation models for optimal decision making under uncertainty. Simulation optimization (SO) can guide decisions that effectively hedge against risk, thus greater adoption will have many practical benefits across problems of importance to society in, for example, healthcare, transportation, and finance. The award addresses the lack of well-developed cyberinfrastructure for SO, which has hindered progress in the design and testing of efficient and reliable software for solving SO problems known as solvers. Significant steps will be taken to enhance the "SimOpt" testbed of SO problems and solvers to make it more powerful, widely applicable, aligned with emerging data-driven applications, and integral to the research community. Wider use of SimOpt through online content and tutorial workshops will foster more rigorous and reproducible experimentation in SO for researchers and practitioners in different fields and yield high-performing solvers for practical use. The improved library will also provide carefully curated resources for simulation educators to incorporate into their teaching efforts at all levels. Research completed for this project will help SimOpt achieve its full potential by improving the existing code base and increasing interoperability, expanding the kinds of experiments and analyses that can be carried out, and extending the role data plays in driving the library's models and problems to open up new frontiers in methodology and algorithm design. The next generation of SimOpt will accelerate advances in SO, including solver development and testing, more extensive experiments comparing new solvers to the state of the art, and hyper-parameter tuning to improve solver performance. The work will create a new data-centered capability in SimOpt that enables more comprehensive study of trace-driven simulation and an empirical risk minimization capability that bridges to closely related areas in machine learning. These data-centered initiatives will enable researchers from diverse fields to better identify and tackle critical open problems in calibration, empirical risk minimization, and distributionally robust optimization. The resulting cyberinfrastructure will enable significant developments in SO solver capabilities, leading to enhanced use of these powerful engines in applications and intellectual bridges to adjacent research communities. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Operations Engineering program in the Division of Civil, Mechanical and Manufacturing Innovation within the NSF Directorate for Engineering. 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

NON-TECHNICAL SUMMARY Tiny groupings of solute atoms at the nanometer scale in materials are referred to as "solute clusters," and they have the potential to dramatically improve the strength of metal alloys. This process has been recognized only in the last two decades and is not yet fully understood, especially when compared to more conventional strengthening methods, such as precipitate strengthening, which involves growing hard particles in alloys to make them stronger. In this work, Mg alloys are selected as the model system. The goal of this research is to understand when and why the nanoscale clustering of solute atoms can work better than other strengthening mechanisms. It is anticipated that certain types of solute clusters, based on their chemical compositions and spatial arrangements, can be particularly potent at blocking the movement of dislocations, which are line defects in alloys that accommodate plastic deformation. Via a combination of advanced experimental characterization and computer simulation techniques, the research will be carried out in the following steps: first, to understand the structure and spatial dispersion of solute clusters; next, to elucidate how these solute clusters interact with dislocations at the nanometer scale; and finally, to quantify how much stronger these solute clusters can make the metal macroscopically. This research could lead to a new theory to quantitatively predict metal strength based on the presence of these solute clusters. The significance of this research lies in its potential to advance our knowledge of alloy strengthening, which could result in the development of stronger and lighter metallic materials for everything from cars to planes. It also includes educational outreach, such as engaging students of various age groups and the public with materials science and creating new learning opportunities in the field. TECHNICAL SUMMARY Recognizing solute cluster strengthening as a novel strengthening mechanism, the goal of the project is to address the knowledge gap in quantitatively modeling and understanding the interactions between solute clusters and dislocations at the atomistic level and the subsequent macroscopic yield strength enhancement. Using Mg as the model system, the project's objective is to elucidate the fundamental mechanisms underpinning solute cluster strengthening through a multiscale approach and to develop a quantitative predictive model of the associated strengthening stress. One key scientific question to be tackled is why solute clustering is more effective than traditional precipitate strengthening under certain conditions. The hypothesis is that specific solute cluster configurations and chemistries offer superior strengthening effects compared to both a superposition of individual solute atoms in a randomized solid solution and precipitates containing an equivalent number of solute atoms. To validate this hypothesis, the following research activities are proposed: atomic-scale prediction and identification of solute cluster chemistry and structure; nanometer-scale modeling and characterization of dislocation-cluster interactions; and continuum-scale prediction and measurement of the critical resolved shear stress enhancement due to solute clusters across different slip systems. By integrating computational simulations with experimental validation, the proposed research seeks to develop a transformative multiscale model that integrates the intricate atomic-level details of cluster-dislocation interactions for quantitative modeling of dislocation slip mechanics and prediction of flow stress. Moreover, this research will offer insights into the specific scenarios wherein solute cluster strengthening outperforms conventional precipitate hardening. The anticipated outcome is a new physical model that complements existing alloy strengthening theories, advancing the field of materials science and the development of alloys with enhanced mechanical performance. The educational component of the project will promote engagement with K-12 and underrepresented student groups, facilitating their participation in science and engineering through special events and student exchange programs. Additionally, the development of a new summer school course on multiscale modeling of structural materials will further reinforce the nation's scientific and engineering workforce competitiveness. 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 broader impact of this I-Corps project is the development of a technology system for smart infrastructure enabled autonomy. The promise of autonomous vehicles (AVs) has not come true despite the tremendous economic and societal benefits of AVs, potentially avoiding 40,000+ fatalities annually. The complexity, unreliability, and cost of additional on-board sensors required for autonomous driving have been major roadblocks preventing significant market deployment and adoption. As a result, the only viable market for AVs has been ride sharing and hauling services. The poor performance of robo-taxis has increased safety concerns over these technologies. For instance, such AVs have blocked road and emergency vehicles. They have also been involved in hundreds of crashes, including fatal ones. A significant portion of the underlying technological challenges can be resolved by leveraging smart infrastructure, leveraging recent dramatic growth in connectivity – 4G-LTE/5G and edge computers. This project will help overcome the challenges associated with complex driving scenarios, such as interaction with emergency vehicles, detecting vulnerable road users, merging onto highways, picking up and dropping off customers, etc. 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. This solution is based on the development of an integrated hardware/software platform that leverages sensors on infrastructure to infer traffic conditions and create a common situational awareness for all entities on the road. The platform sends situational awareness information wirelessly to vehicles and other consumers for real-time use, in-turn enabling multiple benefits, such as lower cost and faster deployment of autonomous vehicles, improved vulnerable road user safety, traffic optimization, and road maintenance. For these applications to be effective, situational awareness needs to be generated in real-time and be reliable across a range of sensing and communication faults and environmental conditions (adverse conditions). The core algorithms and software implementations developed during research based on resilient data fusion, enable automatic detection, mitigation, and graceful recovery from adverse conditions. 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 broader impact of this I-Corps project is based on the development of a novel artificial intelligence-enhanced chatbot system to provide affordable engagement and enhanced accessibility to students. By automating routine tasks, the system can free up time to focus on instructional design and teaching, alleviating the workload on instructors and teaching assistants. This innovation represents an affordable alternative for institutions (including community colleges and other non-traditional teaching institutions) that cannot afford additional teaching assistance for instructors, allowing them to focus on more complex and creative educational tasks and student engagement. The benefit of this approach is its potential to enhance teaching effectiveness, increase engagement in the learning process for large classes, and improve individualized learning, while preserving and reflecting the personalized approach of the instructor. 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 the development of a Retrieval Augmented Generative artificial intelligence (RAG) “always available” chatbot system called ChaTA (short for Chat Teaching Assistant). The technology is an instructor-customizable interface between students and instructors, based on a novel approach to RAG prompting where a Large Language Model (LLM) is combined with an instructor supplied database of information (their notes, presentations and videos, and answers). The LLM will interpret the student questions, classify them based on instructor policy, convert them into a query of the database, and evaluate if the results answer the original question. The chatbot learns from the student ratings of the acceptability of the answers and is moderated by the instructor (to identify "hallucinations" or erroneous or nonresponsive answers) as it learns to reflect the instructor’s point of view. Unlike current approaches where a chatbot is used as a fully automated teaching assistant, the current approach is designed to help instructors by providing summary reports of student questions and acceptable answers, helping the instructor fine-tune their teaching. 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 artificial intelligence (AI) is making fast progress and impacting society, the cost of running AI systems is also becoming prohibitively high. To be sustainable, next-generation AI needs to be much more efficient in power consumption and speed. In-memory computing is a highly promising solution to this challenge; however, its many noise mechanisms require effective error correction schemes to make its computing reliable. This project will explore new error-correcting codes for in-memory computing in next-generation AI systems. The codes can be integrated with AI systems that directly use analog values for computing, which will help AI achieve much higher efficiency. The codes will focus on the correction of significant errors in computing that are most likely to affect the performance of AI, thus help AI systems achieve an optimal tradeoff between efficiency and reliability. By making in-memory computing more reliable, the project can help AI systems overcome the "von Neumann bottleneck" and become more scalable. The project will also develop on-line course materials related to the research topic, and organize workshops to bring together researchers and practitioners in the field. This project proposes Quantized-Analog Error-Correcting Code (QA-ECC), a new type of code for reliable in-memory computing. It focuses on the dominant operation in deep neural networks---the vector-matrix multiplication---and accommodates various practical constraints of analog AI systems. The project will conduct a comprehensive study of QA-ECCs, including their theoretical foundations, practical constructions, and efficient integration with AI systems. It will explore a new paradigm for error correction codes, where redundancy is initially added to the input data for computing, while error correction is performed on the result of computing, making it different from conventional error-correcting codes used in data storage and communications. It will consider multiple resolutions for analog data, and focus on the correction of the most significant errors in computing, making it practical for in-memory computing. The project will develop new theoretical foundations for the new error-correcting codes, including maximum code rates, analytical tools for measuring their error-correction capabilities, and the impact of various parameter settings on the codes' performance. The project will develops new techniques for building error correction codes for analog computing, new algorithms for code searching and error correction, and new methodologies for integrating the codes closely with various aspects of AI systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

The "Emerging Memory Infrastructure" workshop is dedicated to exploring novel materials and technologies poised to transcend the limitations of traditional computing. Traditional von Neumann architectures, which segregate processing and memory, often incur significant energy and time costs due to necessary data transfers during computational tasks. These inefficiencies are magnified by the growing volume and complexity of data-centric applications. In contrast, in-memory computing, leveraging cutting-edge memory materials and devices, heralds a significant shift. Integrating analog and digital processing directly within the memory's storage and control circuits paves the way for innovations in materials, device architecture, and circuit design. The upcoming workshop is set to gather 40-60 senior researchers and 12 graduate students specializing in the design and fabrication of novel and emerging memory materials, device, and circuit design architecture. Given the wide geographic spread across the US of the research community focused on this topic, this workshop will serve as a vital platform for educational and professional in-person engagement. It aims to bring together a geographically and demographically diverse group of students, each with unique personal experiences and professional goals. These students will have the opportunity to connect with peers and a broader network of mentors within their field of study. Present-day memory technologies, including DRAM and Flash memory, hinge on charge storage and can retain data without power. However, the quest to scale these technologies below 10 nanometers has surfaced formidable challenges that undermine performance, reliability, and endurance. The industry's ambition is to forge a path toward non-volatile memory systems that excel in speed, durability, and energy efficiency and are scalable to 2 nanometers or below. However, such a solution is only possible by using a new class of materials and devices emerging since 2010. In the past decade, four emerging non-volatile memory technologies have shown considerable promise; Filamentary Memories (RRAM), Phase Change Memories (PCM), Magnetic Memories (MRAM), and Ferroelectric memories (FeRAM). Despite promising developments in these emerging memories, bridging the knowledge gaps has been a protracted endeavor. Distinct challenges thwart the progression: the nuanced requirements of in-memory processing vis-à-vis conventional storage solutions; the prevalence of inconsistent and unvalidated research findings attributed to less-than-ideal fabrication conditions; and the restricted availability of state-of-the-art fabrication equipment are typically confined to selected industrial and national research facilities. This confluence of obstacles underscores the critical need to convene the relevant research community, including the pivotal participation of graduate students, to engage in discourse, confront these challenges, and ideate on solutions. 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

Millimeter-wave channelizers with ultra-wideband, dynamic tuning capability, ultra-fast channelization and interferer resilience are essential for many applications including cognitive radios, software-defined radios for wireless and satellite communications, electronic warfare, radar and navigation systems and instrumentation. It is extremely challenging to achieve this through conventional electronic channelization approaches due to their performance limitations, or through traditional photonics benchtop techniques due to their cost and size limitations. Silicon photonics (SiP), on the other hand, has the potential to provide mm-wave channelizers with simultaneous ultra-wide bandwidth, dynamic tuning, ultra-fast channelization, interferer resilience, and small footprint. This project will utilize photonics components along with integrated electronics to enable mm-wave silicon photonics and fulfill the above challenging requirements. In addition to the technical impacts, the proposed project also promotes outreach activities to increase participation of students in science and engineering, including annual one-week summer camps for teachers and PK-12 students. The research and educational results of this project will be disseminated to academic, industrial, and government sectors. The goal of this project is to develop a novel chip-scale SiP mm-wave channelizer with ultra-wide band, dynamic tuning, ultra-fast channelization, and interferer-tolerance capabilities implemented using hybrid SiP and nanometer CMOS chips. Using electronic integrated circuits (ICs) allows for channelizer optical frequency comb (OFC) generation with flat spectral lines, in-band interference rejection, and compensation of severe SiP fabrication process variations. The proposed research objectives are: (1) definition of a SiP mm-wave channelizer architecture based on integrated dual-OFC heterodyning/demultiplexing and image rejection along with performance analysis, (2) development of a novel SiP unit and its components including both received signal and local oscillator OFCs, filters, demultiplexers and hybrid couplers, and algorithms/hardware for their automatic turning, (3) implementation of a novel nanometer CMOS unit including the dual-OFC generator, image rejection electronic circuitry, and automatic tuning hardware, and (4) hybrid integration and performance tests of the SiP and CMOS chips. The emergence of SiP technology enables the realization of SiP channelizers to achieve mm-wave signal channelization with small size and low power consumption. However, this realization has two main challenges: (a) Generating OFCs with a large number of spectral lines and flat spectrum using SiP process is demanding. (b) The initial responses of photonic components are distorted due to the fabrication process variation of SiP technology, and therefore an automatic calibration methodology of these initial responses is required. In this project, nano-scale electronics will be integrated with SiP components to perform opto-electronic frequency comb generation with flat spectral lines, in-band interferer rejection, and the automatic calibration of SiP components. 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

Antibiotics provide a textbook example of biologically active chemicals that can impact human, animal, and ecosystem health. In soils, many bacterial strains carry genes that encode for antibiotic resistance (AR). The use of treated wastewater for irrigation has emerged as an important source of antibiotics in soils. In agriculture fields irrigated with treated wastewater, previous studies have found that antibiotics are frequently detected in soils. In addition, some studies have reported that irrigation intensity and the occurrence of AR markers in irrigation water are correlated to the abundance of those markers in soils. However, a fundamental understanding of the fate, transport, and reactivity of antibiotics in agricultural soils during irrigation with treated/polluted wastewater has remained elusive. The overarching goal of this CAREER project is to advance the fundamental understanding of the reactive transport processes that occur during the infiltration of antibiotic-polluted water and their impact on the levels of AR bacteria in agricultural soils. To advance this goal, the Principal Investigator proposes to test the hypothesis that changes in the abundance and persistence of AR bacteria in agricultural soils are directly linked to the physicochemical interactions between soils and antibiotics during the infiltration of treated/polluted irrigation wastewater. The successful completion of this project will benefit society through the generation of new fundamental knowledge on how soils function as natural filters that accumulate and/or degrade antibiotic pollutants and control their availability to soil micro-organisms. Additional benefits to society will be achieved through student education and training including the mentoring of two graduate students and one undergraduate student at Texas A&M University. Antibiotic pollutants are a group of chemicals that infiltrate through soils during irrigation when treated wastewater is used as alternative to address water scarcity in agriculture. Despite the importance of soils in assimilating antibiotic pollution, little is known about how the flow of antibiotic-polluted water impacts the spread and persistence of antibiotic resistance (AR) in soils during irrigation. This CAREER project will address these critical knowledge gaps through the integration of field investigations, bench scale lab experiments, and process-based modeling. The specific objectives of the research are to (1) identify the factors that control the persistence of antibiotics and the amplification of AR markers in soils irrigated with treated wastewater, (2) identify and quantify the soil-antibiotic interactions that induce persistence and horizontal transfer of AR genes among soil bacteria, and (3) elucidate key reactive processes controlling the amplification of AR in soils that receive a mix of antibiotics and AR bacteria in polluted water. The successful completion of this project has the potential for transformative impact through the generation of new data and fundamental knowledge about the AR attenuation capacity of soil ecosystems under the continuous infiltration of antibiotic polluted wastewater. To implement the educational and mentoring goals of this CAREER project, the Principal Investigator proposes (PI) to leverage existing programs and resources at Texas A&M University to (i) engage students in the design, implementation, and deployment of a human-like dynamic conversation interface (educational chatbot), and (ii) use the educational chatbot to promote interactive communication between virtual STEM scholars and K-12 student audiences. The PI plans to build upon these educational activities to design and implement new pedagogical frameworks that foster equal participation and a sense of belonging among all students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.