New Jersey Institute Of Technology

NSF Awards · FY 2024 · 2024-10

This proposal takes advantage of an existing Short Wavelength InfraRed (SWIR) all-sky imager and is an example of advances in technology that has enabled measurements in the least studied spectral region spanning 800 – 1700 nm wavelength range. The investigators plan to utilize this instrument to map mesoscale spatial brightness structures to study auroral and airglow emissions. The project aims to investigate the role of Alfvénic waves in generating auroral arcs and to measure metastable Helium (He) and the associated dynamics. Alfvén waves are travelling ion oscillations and magnetic field tension in the plasma, which propagate along geomagnetic field lines, and transport energy. Electrons are accelerated during the Alfvénic wave propagation, which plays a dominant role in magnetosphere-ionosphere (MI) coupling through their interactions with ionospheric ions. To examine the role of the Alfvénic aurora relative to the electron aurora, midnight observations would be considered, since during this time Alfven waves are more dominant. Supplemental observations by the instruments currently operating at Poker Flat, which include meridian scanning photometers, all sky 630.0/557.7/482.1 imagers, and the Poker Flat incoherent scatter radar (PFISR) are also planned. The proposal seeks funds to address two science questions (SQ): (i) What is the role of Alfvén waves in exciting auroral arcs and forms as compared with monoenergetic particle influx producing auroral emission? and (ii) what is the exospheric density variability in the polar atmosphere over various time scales between minutes and days? To investigate the first SQ, the proposers plan to combine SWIR observations with other instruments as outlined in the first paragraph, while the second SQ will be explored through the observations of metastable He emissions at 1083 nm, which possibly acts as a tracer of exospheric density. Addressing Alfvén precipitation will contribute to (a) thermospheric responses that impact atmospheric drag calculations and (b) enhance magnetosphere-ionosphere interactions. Observations of exospheric metastable He 1083 nm brightness and its comparison with TIEGCM would provide new insights into exospheric dynamics and total atmospheric density variations relevant to Low Earth Orbit (LEO) atmospheric drag and how it responds to changes in geomagnetic activity and solar flux, hence benefitting space weather research. The proposal will involve several undergraduate/graduate students and will provide support to an early career researcher. This award has been made possible through co-funding from the GEO directorate and the EPSCoR Program. 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 NSF CSforAll Research Practice Partnership (RPP) program aims to provide all U.S. students with the opportunity to participate in computer science and computational thinking education in their schools at the preK-12 levels. STARS Computing Corps, a CISE Broadening Participation in Computing Alliance, in partnership with Trivium Consulting Group, will provide resources and technical support to potential Principle Investigators (PIs) in order to increase their interest and capacity for creating and submitting competitive CSforAll RPP strand grant proposals. By building connections and potential collaborations among researchers and practitioners in diverse regions with diverse backgrounds and experiences, the project has the potential to foster community and introduce new and future PIs to the broader research community. The 2024 CISE CSforAll Technical Support workshops aim to increase the breadth of projects supported through the NSF CSforAll program. The goal will be realized through multi-layered outreach to researchers and/or practitioners whose past experiences, interests, and expertise make them qualified candidates to carry out successful CSforAll proposals; registration and assessment of participant project ideas, teams, and expertise; provision of diverse online workshops and sessions (that can be engaged in synchronously or asynchronously) in critical elements of successful proposal development; and one-on-one technical support in key proposal development activities. 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

Solar eruptive events, such as flares and coronal mass ejections (CMEs), are major sources of space weather that can impact the near Earth environment adversely. They are known to cause malfunction on critical technology infrastructure such as satellite and power distribution networks. Therefore, understanding and forecasting solar eruptions is critical for national security and for the economy. It is well known that the structure and evolution of solar magnetic fields, especially in the corona (the Sun's upper atmosphere), are determining factors in storing energy and triggering harmful events. This project will develop a generative AI framework for quantifying risks of solar eruptions. The AI-generated products will provide valuable data on the Sun's magnetic field in the chromosphere (the Sun's lower atmosphere), in which observations are difficult and rarely obtained. This research will significantly advance understanding of the onset of solar eruptions and their predictions. All AI methods, data, and relevant scientific results will be distributed to a wide research community. The project will foster a collaboration between AI experts and solar physicists, integrating research and education through interdisciplinary learning and training activities. This project will develop advanced computing capabilities to characterize solar active regions (ARs) and apply machine learning tools to predict solar eruptions. With a new generative AI framework, named SolarDM, the following tasks will be carried out: (1) generate chromospheric vector magnetograms with an archive combining SDO/HMI photospheric magnetograms, H-alpha images and SOLIS chromospheric magnetograms; (2) create consistent high-resolution, high-cadence non-gapped time series photospheric vector magnetograms for Solar Cycle 23 based on previously AI-generated SOHO/MDI vector magnetograms; (3) with the synthetic data of high spatial-temporal resolution from both the chromosphere and photosphere, develop explainable AI (XAI) methods to make deterministic and probabilistic predictions of solar eruptions, and address two key science questions: (i) What are the essential roles of certain physical parameters, including dynamic and "true" 3-D magnetic parameters, in determining the AR productivity of eruptive events? (ii) Will these physical parameters improve the accuracy of forecasting flares and CMEs? 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

Ground optical telescopes are a powerful tool for observations of the Sun, including its atmospheric layers such as photosphere and chromosphere. The Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO) is the second largest solar telescope in the world, providing high-resolution data about the state of the Sun's atmosphere as well as spectroscopic and polarimetric measurements of the photosphere and chromosphere. These observations are invaluable for studying disturbances on the Sun's surface such as solar flares. The project will develop the Wide-band High-resolution Imaging Spectro-Polarimetric Explorer (WHISPER) for GST. The addition of WHISPER will increase the sensitivity and fidelity of high-resolution solar measurements for GST, providing the community with a more accurate quantitative inference of the conditions in the solar atmosphere than what is possible with any other facility instrument in use at any observatory today. WHISPER will employ an image reconstruction scheme developed for slit spectrographs to undo the effect of the residual seeing, optical aberrations and modulation transfer, allowing us to achieve the special resolution of 50 km on the Sun, as well as to restore the original image contrast and signal amplitudes. Moreover, WHISPER will capture a carefully chosen very wide spectral range that comprises a plethora of spectral lines for diagnosing the photosphere and chromosphere, thus drastically increasing the signal-to-noise ratio when inferring parameters of solar atmosphere. This will substantially increase the ability to study solar processes including the formation of sunspots and superstrong solar magnetic fields. BBSO will continue operating GST as a community facility, with the WHISPER data to be used as the basic dataset in much Ph.D. theses research in the U.S. and worldwide. BBSO will continue to provide undergraduate students with hands-on experience in instrument development, high-resolution observations, and astronomical data analysis. Six undergraduate students will be involved in the research at BBSO. This effort will directly impact and improve the education and training of the next generation of solar physicists and instrument engineers, as well as contribute to the revitalization and growth of the solar physics and space science communities. 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 This project seeks to develop an improved understanding of the basic causes of flow properties of dense suspensions, which are materials containing large amounts of solid particles immersed in a liquid. Examples include cements and ceramics in the building industry, mud in nature, and chocolate in the processing phase, where sugar and chocolate particles are suspended in cocoa butter. Understanding the flow properties improves our ability to control flows in industrial processes as well as predict those related to natural disasters such as mudslides. For dense suspensions, the resistance to flow often undergoes “discontinuous shear thickening” (DST) in which the viscosity increases abruptly due to an increase in the flow rate, as when the mixing rate is increased for cement. Recent research has shown that DST is partially explained by flow driving particles into contact. This breaks the film of liquid between particle surfaces, and then they exert friction and cannot easily slide past one another. This is much like the fact that friction of tires on a dry road surfaces can stop sliding, while a film of oil between the surface and the tire does not. What is not known, and is the key subject of this research, is how the contacts between particles generate networks within the material, or “contact networks.” A contact network, in this case, refers to the connected set of pathways through the particles, which in two dimensions may look like a spider web or a road map. One benefit of understanding what controls network formation is the ability to increase or decrease flow resistance as desired in applications. The project’s primary goal is to characterize the contact networks created by suspensions undergoing DST under various flows and different amounts of solid and liquid. In addition, the project will consider the influence of mixing different size particles. To achieve these objectives, the project will use the tools developed within the project team for computational simulation of the flow. The new aspect of the research is its focus on the networks formed at each time instance. This will use two mathematical methods, one known as k-core analysis (KCA), which uses the contacts between particles, and one called persistent homology (PH), which is based on the strength of the forces between particles that are carried by the contacts. The project will enhance our understanding of how the network structure formed allows the flowing material to influence the flow itself. Small regions of connected particles within the flowing suspension appear to behave as if they were rigid solids, and one goal of the project is to find ways to relate the contact networks to this solid behavior. The project will further seek to use PH and KCA for data reduction, allowing for developing models of the flow behavior that can be tested in applications. The broader impacts of the project include relevance to both fundamental non-equilibrium statistical physics and materials applications from traditional cement to new developments in advanced ceramics and additive manufacturing. The project’s approach has great promise to provide an understanding of the essential features of the networks developed by these amorphous materials, considering, for example, how local rigidity that is exposed by KCA is related to rigidity percolation and the closeness to jamming, with clear metrics from PH correlated to these behaviors. Such understanding will point the way to efficient mechanical or chemical modifications to improve material design and performance. The project includes a strong multi-level educational component, involving undergraduates, PhD candidates, and PIs working jointly in an interdisciplinary environment involving two minority-serving institutions. TECHNICAL SUMMARY This project focuses on developing fundamental new directions in dynamic material science through a study of the network topology in flowing dense suspensions. The specific aim is to develop and place on a robust mathematical foundation the physical relationship of these properties to the underlying networks of contacts and forces between particles. The suspensions will be described via simulations which have the potential to generate vast amounts of data due to large particle numbers in time-dependent ensemble calculations. These data will be analyzed using tools based on persistent homology (PH) and k-core analysis (KCA). These methods allow for enormous data reduction and also for significant enhancement of physical understanding based on the elucidation of essential structural measures by the two network theoretical approaches. From a mathematical perspective, a coupled study of PH and KCA, one based upon physical cluster classification by connectivity and one based on well-defined metrics of the overall network structure, will deepen our understanding of both methods. This award is jointly supported by the Division of Materials Research and the Division of Mathematical Sciences. STATEMENT OF MERIT REVIEW This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This NSF project aims to create new techniques for fault detection in inverter-dominated power systems. The most typical faults are unintentional short circuits caused by, for example, lightning and tree contact, and can cause substantial damage if not quickly dealt with. Detecting and extinguishing short-circuit faults are thus critical aspects of reliable power system operation. In power systems with synchronous machine-based generation, large, unbalanced fault currents provide clear information about the existence and location of faults. Inverters prevent such currents even during faults. As a result, conventional fault detection schemes can fail in grids that are rich in inverter-based resources like wind and solar generators. One way to make it easier to distinguish normal operation from a fault is for the inverter to add a perturbation, or auxiliary signal, when there is suspicion of a fault. This project will design new, auxiliary signal-based fault detection schemes for inverter-dominated power systems. The intellectual merits of the project include characterization of when and what auxiliary signals are necessary, how many inverters in a grid must inject them, and minimal infrastructure investments necessary to guarantee fault detection. The broader impacts of the project include enhanced reliability of modern power systems, which will further facilitate the integration of inverter-based resources like renewables and energy storage; and curriculum development at the graduate and undergraduate levels, and energy-oriented programming for K-12 students. This project will develop the use of auxiliary signals in inverter-dominated power systems. Typical choices of auxiliary signal include negative sequence current, as in IEEE Standard 2800, and harmonics. The auxiliary signals will be optimized so as to minimize disruption while guaranteeing detection of all possible faults. The existing theory will be extended in scenarios not covered by existing tools, for example, networks with multiple inverters and relays. The mathematical formalization of this problem will constitute a streamlined, optimization-based procedure for designing new detection schemes, which is relevant today as grids and grid codes continue to rapidly evolve. All new detection schemes will be validated in electromagnetic-transient simulation and in controller hardware-in-the-loop testing. 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 Abstract: Bio-inspired vision represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current state-of-the-art high-frame rate cameras. Realizing such imaging technologies using semiconductor hardware will have significant implications in various application domains, from manufacturing automation to self-driving cars. This project seeks to develop infrared vision systems that emulate the function of the biological retina by enabling the fusion of sensing and processing into one sensor element. In addition, the utilization of lead selenide semiconductors will expand the vision sensor capability toward the mid-wavelength infrared spectral region that is invisible to human eyes and mainstream semiconductor technologies. Advances from this research project will also be widely disseminated through academic courses, undergraduate research opportunities, and K–12 outreach activities, which will reach a broad student population in Newark, NJ. Technical Abstract: The goal of this research is to realize mid-infrared retinomorphic sensor devices and to demonstrate in-sensor image convolution processing via electrical programming of sensor array networks. Specifically, this research will investigate field-effect gated reconfigurable photodiodes for enabling in-sensor processing based on lead selenide, a sensor material known to be the primary choice for low-cost, uncooled mid-wavelength detector technology. Moreover, a new cross-layer-optimized retinomorphic processing engine capable of single-cycle, in-sensor processing that will enhance the image processing speed, with a low-overhead, dual-mode, reconfigurable design that will significantly reduce power consumption, will be investigated. Based on these approaches, the proposed research will generate fundamental understandings of the device physics, circuit designs, and system architecture optimizations of the mid-infrared retinomorphic vision system. 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 near-Earth (Geospace) environment is mostly controlled by the geomagnetic field that protects life on the planet from natural phenomena of electromagnetic nature, such as major geomagnetic storms caused by solar flares and coronal mass ejections. It is increasingly clear that the Geospace environment’s physics and dynamics are crucial for the functioning of the planet within the solar system, and understanding the Sun’s influence on technological systems deployed on the ground and in space becomes more and more important. Indeed, space weather impacts a wide range of technologies, including spacecraft operations and orbits, GPS/GNSS systems, HF radio communications, and power grids and pipelines. The Earth’s polar regions are specific areas where geomagnetic field lines are open and directly interact with the interplanetary magnetic field (that is extended magnetic fields of the Sun). During strong geomagnetic disturbances, the geomagnetic “polar caps” mirror these disturbances in the ionospheric level (as “aurorae on a screen”) and may increase their size, sometimes dramatically. Monitoring the Earth polar regions, ionospheric currents that flow over these regions, and polar cap boundaries dynamics are critically important for space weather studies and forecasts. Hemispherically simultaneous observations of Geospace phenomena are critical in understanding how the solar wind energy and momentum are transferred to the coupled magnetosphere-ionosphere system. The holistic approach to the Geospace research in the Antarctic is to integrate clustered instrumentation at multiple manned and unmanned locations and have a simultaneous look at the solar wind interactions within the global Geospace system. This award will support studies of interrelated geospace phenomena observed at southern high latitudes through the coordinated and collaborative effort while deploying and maintaining geospace instrumentation at the U.S. Antarctic stations South Pole, McMurdo, and Palmer. The suite of geospace instrumentation at these stations has a sustained track-record of robust operation and overwhelming support of the research community. These are ground-based fluxgate and search-coils magnetometers, ELF and VLF receivers, imaging and broadband riometers, sky-looking optical systems, GPS scintillation-rated and GNSS receivers, as well as several other instruments. Measurements collected from these instruments will be synergistically combined for the studies of synoptic variabilities of the magnetospheric open-closed boundary and associated cusp structures, understanding natural low-frequency electromagnetic emissions and their relationship to the ionospheric and magnetospheric conditions. These topics are only a partial listing of the research effort that can be performed with the data obtained from the Antarctic geospace instruments, especially via the already established and planned collaborations with other geospace projects operating across Antarctica and at magnetically conjugate regions in the Arctic. The project will train and educate young scientists, graduate, and undergraduate 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.

NSF Awards · FY 2024 · 2024-08

Artificial Intelligence (AI) has reached groundbreaking milestones in recent years. Its usage has spanned critical application domains, such as computer vision, audio perception, and natural language processing. However, these breakthroughs come with substantial security challenges. The machine learning (ML) models serving as the computational cores of AI systems are inherently vulnerable to attacks. By exploiting vulnerabilities in AI systems, adversaries can make the models produce incorrect predictions, leading to serious consequences such as misinterpreting traffic signs for autonomous vehicles or generating incorrect responses in speech recognition systems. Current AI-related educational efforts are limited on teaching the security perspective of ML. To bridge this gap, this project aims to develop comprehensive educational modules to prepare students and future engineers to address these ML security vulnerabilities and achieve trustworthy AI. By creating a practice-in-the-loop learning experience, students can obtain hands-on experiences with the security vulnerabilities of ML models and corresponding solutions. This project will develop a comprehensive educational program that focuses on three key perspectives of AI security. First, this project will create a practice-in-the-loop learning experience for students to understand the security of ML in computer vision, such as image recognition and object detection. Educational modules will be developed to cover various ML models for vision sensing and their security vulnerabilities and solutions. Second, this project will extend the interactive learning experience for students to understand the security problems of ML in voice assistant systems, such as speech recognition and speaker identification. The educational modules will be developed to introduce ML models for audio data processing and security vulnerabilities in voice assistant AI systems. Third, this project will develop software-based labs and training projects to enhance students’ understanding. The outcomes of this project, such as teaching slides, software labs, and training projects, will enable various undergraduate student training and outreach activities. They will also be disseminated online and through academic publications, ensuring diverse communities can readily access and employ the educational resources. 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

Solar flares are the most energetic phenomena in the solar system, during which the flare Above-the-Loop-Top (ALT) regions are the key to understanding energy release in solar flares. This project aims to obtain a more comprehensive picture of the energy release in flare ALT regions by systematically investigating the energy transfer associated with spatially resolved supra arcade downflows (SADs). The improved knowledge of the energy transfer in solar flares is critical to understanding the basic science needed to meet the goals of the National Space Weather Strategy and Action Plan, which aims to develop tools to forecast space weather and mitigate its impacts. The project involves state-of-art MHD models, innovative particle simulations, and an image synthesis approach, to compare with observations in multiple wavelengths and viewing perspectives (including microwave imaging spectroscopy in fans that have been rarely studied in the community). This project, led by several early-career women PIs, will involve undergraduate students and support a graduate student. There are outreach activities and developing open-source codes planned for this project. Over the decades, the developments of 2D standard solar flare models have significantly improved the comprehension of solar eruptions. However, recent observations have indicated the importance of SADs, which are frequently observed from a face-on viewing perspective, to energy transfer in flare ALT regions. The project will thoroughly explore the role of SADs in energy transfer in the ALT regions by combining observations and simulations. The science questions to be addressed are: (1) How do SADs engage in energy transfer in ALT regions? (2) What are the statistical features of plasma conditions around SADs? And (3) What are observational signatures related to energy transfer processes of SADs in ALT regions? First, they will perform a statistical study on the thermal characteristics and the physical relationship between these characteristics and the dynamics of SADs. Second, they will perform 3D magnetohydrodynamics (MHD) simulations to investigate the energy release in flares. They will synthesize the emissions incorporating different viewing perspectives and multi-wavelengths to compare with observations, including radio emissions in fans that are rarely discussed in the literature. Third, they will carry out particle simulations with time-resolved dynamics provided by the MHD framework to explore the impact of SADs on energetic particles. Combining the three steps above, they will comprehensively determine the role of SADs on energy transfer in flares and use the findings to improve the traditional scenario of energy release in flares. 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

Several marine mammal populations are at risk of extinction because limited information on their behaviors precludes successful protective regulations. What scientists often lack to inform drafting of effective protective legislation is near real-time data on the behaviors and migration routes of critically endangered marine mammal populations. Current tagging strategies either do not stay attached for long enough or are made permanent through puncture methods that can be harmful to the organism being studied. To this end, this project will use a bioinspired attachment device, modeled after remora fishes that attach to any substrate under water, to design and test a tagging device that will be able to attach for long periods of time without causing harm to the attachment surface. Through the last decade, the principal investigator's research team has worked on understanding the fundamental mechanisms ruling the long-term, nondestructive adhesion of remoras. As one result of those efforts, a low-fidelity bioinspired prototype of the disc portion of the remora attachment mechanism has been prototyped. To be able to meet end-user needs of a non-invasive, long-term tag capable of adhering at any speed or depth for a determined amount of time, this project lays out a clear objective for research that can be accomplished through cross-sector partnerships uniting multiple universities, several industry partners with experience in this field, and multiple federal agencies, including NOAA Fisheries, the Alaskan Eskimo Whaling Commission, North Atlantic Right Whale Consortium, and the US Navy. The overarching goal of this project is to radically improve the current state of endangered marine organism monitoring and conservation by dramatically increasing the reliability of biotelemetry tag attachment. The initial demonstrator model will advance sealing interface technology and include pressure modulation systems to ensure attachment to depths of 1800 m and at any animal swim speed. This will be the first animal tracking sensor that can stay attached for extended periods of time without harming the animal and can also be programmed to release at a user-specified time, greatly improving the ability to monitor endangered cetacean migratory behavior. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project will analyze data at multiple wavelengths to analyze the movement, acceleration, heating, and magnetic properties of particles near solar flares. The investigators will use data obtained from radio and X-ray telescopes that are sensitive to the magnetic and plasma structure above solar flares to develop sophisticated modeling tools for further analysis. The computational models and datasets will be provided with open access to the community and train other scientists to test models of solar flare eruptions and aid forecasting of flares and coronal mass ejections, which may be disruptive to society if aimed toward Earth. The data visualization and modeling tools will also be incorporated in graduate courses and with undergraduate research projects. The team will analyze four-dimensional imaging spectra obtained at 1-18 GHz with the Expanded Owens Valley Solar Array (EOVSA) that record information in two spatial dimensions, time, and frequency. They will also use the Spectrometer/Telescope for Imaging X-rays (STIX) instrument on Solar Orbital, which records data over the energy range of 4-150 keV. Together these datasets will provide constraints for the modeling tools of highly energetic particles near solar flares. The project will measure the coronal magnetic field in solar flares along with the thermal and nonthermal electron distributions with high spatial and temporal resolution to quantify the dynamics of the coronal magnetic field and nonthermal particles in solar flares. They will then use these results to obtain new fundamental knowledge on universal physical processes occurring in solar flares such as magnetic reconnection, particle acceleration and transport, and plasma heating. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project is concerned with the design and the analysis of computational methodologies aimed at solving applied problems in materials science and engineering involving various physical observables (elastic and electromagnetic fields, acoustic fields in the frequency and the time domain) within and around complex structures (photonic or electronic devices, singular geometries with corners, edges or cracks, manmade structures built from metals or modern composite materials). The approaches also address frameworks containing complex materials—including composite elastic media, dielectrics, perfect and lossy conductors, as well as clouds of scatterers that can be described by media with dispersion and frequency-dependent absorption. Motivating applications for the solvers to be developed in this project include the radar clutter produced by chaff, photonic crystals and metamaterials, and communications. These are of fundamental significance in a wide class of areas concerning photonics (meta-materials, nanophotonics, meta-surfaces), antenna design (communications, remote sensing), electromagnetic interference and compatibility, and geophysical exploration. High-quality software implementation of the algorithms to be developed as part of this project will be released to the applied scientific community. This project will have a significant educational component, as both graduate and undergraduate students will be trained in scientific computing and mathematical modeling, and thus they will acquire the skills required to have a successful career in academia or industry. The computational methodologies underlying the proposed work are based on a class of density interpolations developed in recent years by the investigator and collaborators that is applicable to both Galerkin and Nystrom discretizations of all types of integral formulations. These methods combine the versatility of the Method of Fundamental Solutions with the robustness of integral formulations, are compatible with all kinds of meshes and quadratures, and can be seamlessly integrated with existing acceleration strategies such as Fast Multipole Methods. In practice, these types of solvers have demonstrated numerics that are fast and accurate for simulation of wave propagation problems in complex media, and thus they advance the state of the art in high-accuracy solution of partial differential equations. Owing to its ease of implementation and portability to solving a range of partial differential equations, the density interpolation method technology provides a vehicle to make integral equation solvers accessible to computational scientists from diverse backgrounds. 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.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY Many light-responsive systems have been produced via natural evolution, including opsins, and phytochromes, while chemists have added to the list of light-responsive molecules for new tools. Scientists have embraced the use of these light-responsive tools to construct new materials, and we’ve just scratched the surface of the potential in this important field, particularly in the direction of making efficient red-to-near-infrared (red-to-NIR) light-responsive groups. The promise of red-to-NIR light resides in deeper penetration depth, less scattering and absorption by the sample, and less photodamage. Though approaches that use two-photon and lanthanide nanoparticles exhibit promising progress in allowing red-to-NIR absorption, developing organic-based materials that work under low power LED light is still challenging and such materials would expand the toolboxes and facilitate addressing a number of urgent questions. The organic-based platforms will benefit the fundamental understanding of chemical structure-to-property relationships, as well as impact many intriguing emerging applications, such as precision drug delivery, neuron modulation, light-triggered reactions, and gene therapy activation. Because of the much lower photon energy in the red-to-NIR region compared with UV and blue light, efficient red-to-NIR responsive is still challenging. This proposal aims to develop novel boron-dipyrromethene (BODIPY) based photo-uncaging groups that build upon weak covalent N-O bond. In particular, the weak dissociation energy of N-O permits the cleavage after absorbing low energy red-to-NIR photon, which is ideal for biological and biomedical applications. By varying and modifying the chemical structures, we intend to increase photo-uncaging efficiency by rigidifying the structure and for the first time facilitate dual cargo release from BODIPY. After conjugating with a cancer targeting unit, biomedical applications of these novel photo-uncaging materials will be demonstrated in vitro and in vivo in light-triggered drug delivery. Overall, the capability of efficient photo-uncaging in the red-to-NIR window will support more advanced experimental designs, and the convergence of basic research with applications will contribute to expanding knowledge while benefiting undergraduate researchers for broad impact.

NSF Awards · FY 2024 · 2024-08

The Earth’s radiation belts are a dynamic and complex plasma environment. Large amplitude waves can nonlinearly accelerate particles to energies high enough to pose a radiation hazard in the near-Earth space environment. These large amplitude waves are common but only occur in small regions for a short period of time. Therefore, it is unknown if such a drastic but localized acceleration can have an impact on a global scale. By modeling the nonlinear wave-particle interaction in a global scale radiation belt model, this research will conclusively show the importance of these large-scale waves on the whole space environment. The research will promote the development of two early-career researchers. Additionally, undergraduate students at a minority-serving institution will be trained as an integral part of this project. The physics of wave-particle interactions in the Earth’s radiation belts is well understood in the linear and quasilinear regimes, but large amplitude waves create a complex nonlinear problem. Significant theoretical and computational work has been done to understand how nonlinear wave-particle interactions can efficiently energize or pitch angle scatter high-energy electrons. As successful as local studies of nonlinear wave-particle interactions have been in explaining the micro-scale physics of a particle in a large amplitude wave, it has yet to be demonstrated that these nonlinear effects lead to global, macro-scale changes in the radiation belts. In this study, we will use theory, modeling, and data analysis to answer the fundamental science question: Do nonlinear wave-particle interactions affect the radiation belts on a global scale? This will be done by calculating advection and diffusion coefficients from nonlinear wave-particle interactions that can be directly included in the K2 radiation belt model. K2 is a global scale radiation belt model based on the stochastic differential equation (SDE) framework and accurately captures wave-particle interactions at an individual particle level. By simulating real events with K2, the sensitivity of the whole radiation belt system to localized large amplitude waves can be quantified. 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.

NIH Research Projects · FY 2026 · 2024-08

Alzheimer's disease (AD) is a neurodegenerative disorder that affects a patient’s memory, language, and executive function. AD affects one in nine (>10%) in the US aged 65 years and older. The specific pathogenesis and the underlying neurophysiology of AD have not been fully understood. The white matter (WM) regions of the brain, making up approximately 50% of the total brain volume, provide the communication pathways necessary for information exchange between gray matter (GM) cortical regions, and many brain disorders have been associated with WM deficiencies. Recent developments in resting-state functional magnetic resonance imaging (rfMRI) have enabled the study of the human brain’s functional connectivity and functional networks. Studies have found that there is detectable functional connectivity within WM regions and between WM and GM regions. However, these findings were based on healthy adults, and WM-rfMRI of typical aging and AD are not known. In this study, we will systematically characterize the signal properties of WM-rfMRI of typical aging, mild cognitive impairment (MCI) and AD progression. We will characterize the functional connections between the WM and GM regions and the underlying WM structural tracts. We will also investigate how WM-rfMRI is associated with phenotypic traits in MCI and AD. We will use five large neuroimaging datasets with a total of more than 5,200 subjects for the aims of this study. The rationale for this study is based on our preliminary studies that investigated WM-rfMRI of healthy adults from the Human Connectome Project dataset. We found that WM-rfMRI was associated with GM-rfMRI functional networks and that there was a significant overlap between the WM--rfMRI and the underlying WM structural tracts. In this study, we will build on our previous studies to examine WM-rfMRI properties of typical aging, MCI, and AD. Aim1: To characterize WM-rfMRI of typical aging. Aim 2: To characterize WM-rfMRI of MCI and AD; Aim 3: To investigate WM-rfMRI–phenotype associations and the predictability of phenotypes in MCI and AD. Aim 4: To develop and disseminate a WM-rfMRI in Aging, MCI, and AD toolbox. We hypothesize that the WM- rfMRI properties will have significant test-retest reliability, will be significantly different among typical aging, MCI and AD and can reliably predict clinical scores of MCI and AD. We will apply advanced analytic and machine learning approaches on WM-rfMRI for the aims of this study. To the best of our knowledge, this study will be one of the first to perform a comprehensive analysis of WM-rfMRI in typical aging, MCI, and AD. This study will contribute towards uncovering potential WM-rfMRI markers of MCI and AD, and will facilitate the use of WM- rfMRI in future aging studies. This study will also provide a strong foundation to study brain function and dysfunction as an integrated system of both WM and GM. The long-term goal of this project is to better understand the effect of WM structure and function on cognition, and to apply rfMRI for more reliable diagnosis, prognosis and treatment of AD.

NSF Awards · FY 2024 · 2024-08

This collaborative project aims to understand the heating mechanisms of the solar flare atmosphere. Solar flares are dramatic releases of energy which briefly increase X-ray emission from the Sun. They are believed to occur by rapidly converting magnetic energy, stored in the Sun's outer atmosphere, to heat, radiation, and super-sonic flows. It is a persistent puzzle that this conversion takes place on spatial scales so much smaller than the vast volume of energy being released. The team will conduct high-cadence, high-resolution, imaging and spectroscopic flare observations combining the Goode Solar Telescope (GST)’s unprecedented capabilities observing the flaring lower-atmosphere, and the upcoming high-cadence hard X-ray burst observations in Earth orbit, including a new very fast detector built in Montana and recently flown to the International Space Station. This combination will provide vital, new clues to the processes working on small scales to release large amounts of stored magnetic energy. The improved understanding of energy conversion will lead to a clearer understanding of solar flares and improvements in our ability to forecast flares and their effects on Earth. Graduate and undergraduate students will be supervised to conduct the research. The team will engage K-12 for education and public outreach. To elucidate the mechanisms of heating the flare atmosphere, high-cadence, high-resolution, imaging and spectroscopic flare observations with the high-cadence (10 ms) hard X-ray burst observations will be used. The science questions are 1) what are the temporal scales of flare elementary bursts observed in multiple wavelengths? 2) what are the spatial scales of flare elementary bursts, in particular, where do hard X-ray radiation elementary bursts originate? And 3) what are the dynamical consequences of energy release by elementary bursts? They will investigate the temporal, spatial, and magnetic structures of elementary bursts, which are the basic units of flare energy release. The team will identify the temporal structures of elementary bursts (below 1 s) from both observations, and identify the spatial structures, locations, and magnetic environment of these bursts (of below 1 Mm). They will categorize the temporal and spectral behavior of flare elementary bursts with respect to the evolution stage of the flare and the magnetic environment hosting these bursts. They will also model the flare emissions observed in multiple wavelengths by multiple instruments and estimate the heating rates and their distribution in elementary bursts. Characterization of these properties will help improve flare modeling taking into account these scales and the implied viable heating mechanisms. 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

Software developers spend much of their time reading and understanding (“comprehending”) code, because it is a prerequisite for adding new features, correcting defects, improving existing functionality, and performing other changes to software systems. Prior research has proposed many syntactic metrics that claim to measure code complexity, but the correlation between these metrics and measurements of code comprehension effort from humans is weak, at best. Developers still have limited support for assessing and reducing the complexity of their code to decrease code comprehension effort. This project will investigate new semantic metrics of code complexity derived from existing automated reasoning tools (“verification tools”). These new metrics will be semantic (i.e., based on the program’s meaning) rather than syntactic (i.e., based on the program’s textual representation). If these new, semantics-based metrics correlate better than prior syntactic metrics with human comprehension effort, they will help guide software developers to write code that is easier to understand and modify, thereby improving software quality and reducing software development costs. The key insight that inspires this project is that the output of verification tools contains useful information about a program’s semantic complexity. Verification tools try to prove that a program does or does not have a particular property, such as “cannot dereference a null pointer” or “eventually halts." Because these kinds of semantic program properties are undecidable, verification tools are always approximate: they conservatively answer “I don’t know” when they cannot construct a proof. Such “I don’t know” answers from verification tools may be a useful source of information about a program’s semantic complexity, and a preliminary study found a correlation between such “I don’t know” answers and human comprehension effort. Intuitively, the fewer facts that a suite of verification tools can prove about a program, the more complex that program probably is. This project will investigate three research directions based on this insight: (1) validate the hypothesis that the success or failure of verification (i.e., verifiability) is correlated with code complexity and human-based code comprehension effort (i.e., comprehensibility), and establish whether a causal relationship exists between verifiability and comprehensibility or whether they have some mutual cause; (2) use the semantic code information encoded in the output of verifiers to improve the performance of existing predictive models of comprehensibility that currently rely on syntactic features only; and (3) “verifier-guided” code refactoring to reduce complexity and comprehensibility in an automated way, using information about the parts of programs that verifiers struggle with as a guide for where and how to apply refactorings. 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

Networks, also referred to as graphs, consist of nodes (vertices) connected by edges (links). Many types of information can be represented as networks. For example, in social networks, vertices can represent people and edges can represent friendships, while in biological networks the vertices can represent proteins and the edges can represent interactions between proteins. A basic problem in network analysis is to partition the vertices of a graph into non-overlapping sets so that each set represents a cohesive group. This problem, referred to as “community detection” or “graph clustering”, has widespread application in many domains, including biology, engineering, and the social sciences. In this project, new community detection methods will be developed that can run on large networks in the order of millions and billions of vertices and will be implemented in highly efficient software that can be used in high performance computing platforms. An educational component is included with advanced training for both undergraduate and graduate students. Community detection, otherwise known as graph clustering, is the problem of partitioning the vertices of a graph into disjoint sets, so that each set has desirable properties, such as being well-connected (i.e., not having a small edge cut), having high internal density, and being relatively separated from other clusters. Common approaches for graph clustering include optimizing under the modularity criterion or the Constant Potts Model. Because these are NP-hard optimization problems, heuristic searches are used that can be very computationally intensive on large datasets. Furthermore, recent research has revealed limitations to currently popular methods, including the tendency to produce very poorly connected clusters, i.e., clusters with small edge cuts. The Connectivity Modifier software was developed to address this problem: it modifies a given clustering by iteratively finding and removing small edge cuts from clusters and then reclustering, until all clusters are well-connected. This project will develop new performant implementations of the Connectivity Modifier, and expand the set of clustering methods that can be used within the framework. The project will also develop a modular suite of clustering tools that address other problems in community detection, such as finding center-periphery clusters and overlapping clusters, that will enable developers to explore algorithmic approaches to clustering on large networks and also enable exploratory data analysis for applications researchers. The current implementations of these codes have not been implemented for very large networks nor for high performance computing (HPC) platforms. This project will develop parallel codes for these methods that can be deployed on a wide range of compute platforms. The research in this project will be integrated into graduate level courses and undergraduate and graduate students will participate in advanced research under the mentorship of the project investigators. The expected benefits include new software capable of analyzing networks with billions of vertices and that can be deployed in a wide range of hardware, enabling researchers to make discovery in a wide range of application areas, including systems biology, scientometrics, and social network analysis. 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

Selective hydrogenation of acetylene to ethylene is an important industrial purification process that removes trace amount of acetylene from ethylene, thus protecting downstream chemical processes that convert ethylene to polymers and other important chemicals. This EArly-concept Grant for Exploratory Research (EAGER) project will investigate the structural and electronic properties of a novel catalyst design for selective acetylene conversion to ethylene. The catalyst design has already shown improved performance with respect to conventional catalyst designs. However, additional research is needed to better understand the mechanisms by which the catalyst works, thus opening the door to improved performance and extension of the catalyst design to additional materials combinations and other reactions. The project also provides resources for the investigator to train undergraduate and high-school students in aspects of catalysis research. The project focuses on improved design of a novel catalyst structure consisting of metal (M) single-atoms catalysts (M-SACs) supported on carbon nanotubes (CNTs). In particular, the design consists of Pd single-atoms deposited on 8-member polynitrogen strands anchored on the CNTs (i.e., Pd1-N8/CNT). The catalyst design provides a high density of active sites for hydrogen splitting, consisting of proximate N and Pd single sites that work in concert to both adsorb acetylene and react it with H-atoms to ethylene. Although the investigator’s preliminary work confirms that a Pd1-N8/CNT catalyst system is significantly more selective than CNTs without the polynitrogen structure, additional details regarding the differences in properties are needed to advance the concept. Thus, the project will employ a suite of characterization methods including HR-TEM, XPS, XAFS, TPD and CO-DRIFTs techniques, to probe the structural and electronic properties of the Pd1-N8/CNT catalyst versus the baseline (i.e. non-SAC) Pd-N/CNT catalyst. The resulting data will open the door to optimization studies and extension of the design to additional materials and hydrogenation reactions. 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

More than ever, technological advances in industries such as aerospace, microchips, telecommunications, and renewable energy rely on advanced numerical solvers for wave propagation. The aim of this project is the development of efficient and accurate algorithms for acoustic and electromagnetic wave propagation in complex domains containing, for example, inlets, cavities, or a multilayer structure. These geometrical features continue to pose challenges for numerical computation. The numerical methods developed in this project will have application to radar, communications, remote sensing, stealth technology, satellites, and many others. Fundamental theoretical and computational issues as well as realistic complex geometries such as those occurring in aircraft and submarines will be addressed in this project. The obtained algorithms will facilitate the use of powerful computers when simulating industrial high-frequency wave problems. The numerical solvers obtained through this research will be made readily available to scientists in aerospace and other industries, which will contribute to enhancing the U.S leadership in this field. Several aspects in this project will benefit the education of both undergraduate and graduate students. Graduate students will gain expertise in both scientific computing and mathematical analysis. This will reinforce their preparation to face future challenges in science and technology. The aim of this project is the development of efficient and accurate algorithms for acoustic and electromagnetic wave propagation in complex domains. One of the main goals of this project resides in the design of robust algorithms based on high-frequency integral equations, microlocal and numerical analysis, asymptotic methods, and finite element techniques. The investigator plans to derive rigorous asymptotic expansions for incidences more general than plane waves in order to support the high-frequency integral equation multiple scattering iterative procedure. The investigator will introduce Ray-stabilized Galerkin boundary element methods, based on a new theoretical development on ray tracing, to significantly reduce the computational cost at each iteration and limit the exponentially increasing cost of multiple scattering iterations to a fixed number. Using the theoretical findings in conjunction with the stationary phase lemma, frequency-independent quadratures for approximating the multiple scattering amplitude will also be designed. These new methods will be beneficial for industrial applications involving multi-component radar and antenna design. In addition, this project includes development of new non-overlapping domain decomposition methods with considerably enhanced convergence characteristics. The main idea resides in a novel treatment of the continuity conditions in the neighborhood of the so called cross-points. Analysis of the convergence and stability will be included in parallel to numerical simulations in the two and three dimensional cases using high performance computing. 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 new skin grafting method that enhances healing for chronic wounds like those from burns, skin cancer, and diabetes. This project introduces an advanced technique aimed at maximizing skin area expansion during split-thickness skin graft surgery while minimizing mechanical strain within grafts. The commercial potential of this innovation lies in its ability to reduce the amount of healthy skin needed for grafting procedures, thereby minimizing patient trauma and improving recovery. Additionally, by minimizing strain generated within grafts, this solution reduces the likelihood of cell activation, thereby decreasing the risk of postoperative complications such as secondary skin contracture. Beyond benefiting individual patients, this technology could set new standards in surgical practices, leading to more efficient healthcare delivery and lower long-term healthcare costs. 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 precise mathematical models and experimental techniques that unravel the complexities of skin graft mechanics. This research has led to the creation of innovative meshing patterns for skin grafts that enhance graft expansion and minimize internal strain, minimizing donor site trauma and improving healing outcomes. These advancements are based on solid mechanobiological principles and represent a significant improvement over traditional skin grafting techniques, which often result in skin waste and even graft failure. 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.

NIH Research Projects · FY 2026 · 2024-04

Project Summary/Abstract This proposal describes a research and training program to advance my academic career in biofilm treatment and facilitate my transition towards independence. Over the past years, I have acquired a broad scientific background and extensive research experience in multiple fields, including synthesis of nanomaterials, chemistry, substrate-based nanocrystals, nanomedicine, in vitro and in vivo imaging. My long-term career goal is to develop nanomaterials with different shapes, sizes, and compositions for biofilm-associated diseases. This proposal was therefore designed to strengthen and diversify my nanomaterial synthesis and characterization skills, complementing them with training in infection diseases and therapies. During my postdoctoral training period, I have developed a unique structure termed Wulff in cage nanoparticles (WICN) that integrate the competencies of both cage and core structures to allow their use as contrast agents for photoacoustic imaging, computed tomography and photothermal therapy (PTT). Using these structures, I have shown that their PTT properties are critically affected by shape, size and the plasmonic properties of nanoparticles. Therefore, systematically studying the therapeutics applications of these unique morphologies, particularly towards infection diseases, is an imperative step towards improving nanotherapeutics. To that end, here I propose to exploit new photothermal nanoparticles (PTNP) with nanoshell, nanocage or nanoframe morphologies and different compositions (Ag, Au, Pt, and Pd) which enhance photothermal behavior and result in effective and rapid dental caries, wound and skin infection treatments (Aim 1, K99). This photothermal effect of nanoparticles enables both precise spatial control and whole tissue irradiation, while being a rapid treatment. The developed morphologies will be used to examine the anti-biofilm efficacy and biocompatibility of the PTNP in vitro. In this aim, PTNP will be assessed for their antibacterial properties to reduce oral and wound infections while accelerating the photoablation rates (Aim 2, K99-R00), I will select the most effective formulation to follow the in vivo research in (Aim 3, R00). The knowledge acquired in Aims 1 and 2 will be applied to enhance the photothermal ablation of biofilms in vivo as a flexible, fast and low cost treatment method. We will test PTNP in an animal model using rodent models of dental caries and excisional wound model to investigate the effect of light and heat generation on biofilms in vivo. We will confirm that photothermal treatment and the anti-biofilm effect of developed structures could be a substitute to the use of broad-spectrum antibiotics to heal wound infection, prevent dental caries and kill the bacteria while irradiating with NIR light. To guide me in this undertaking, I have assembled a multidisciplinary mentoring team. At University of Pennsylvania, Dr. Cormode (Primary Mentor, a leading scientist in Nanomedicine) from the Radiology department , Dr. Koo (Co-mentor, biofilm-associated oral diseases expert) from the Department of Orthodontics and Dr. Grice (Co-mentor, biofilm-associated skin diseases expert) from the Department of Dermatology will continue mentoring me on in vitro and in vivo experiments. They will support my research activities and also guide my transition to independence. These departments will provide resources and support to conduct laboratory research, and foster my career development to achieve my goals.

NIH Research Projects · FY 2026 · 2023-04

Project Summary/Abstract Developing a skilled biomedical workforce is critical for future economic and societal success. The New Jersey Institute of Technology (NJIT) is a public university with a focus on STEM education. As an R1 research-active university, NJIT’s vision is to provide undergraduate students with research training and mentorship to produce the future generation of scientists and engineers. This training proposal aims to combine the strengths of the NJIT Biomedical Engineering, Biological Sciences and Chemistry and Environmental Science Departments to support seven undergraduate students per year to participate in a three year biomedical research immersion program. NJIT draws students from over 100 countries and conducts extensive outreach to women and underrepresented populations in STEM disciplines, ranking in the top 20 of campus ethnic diversity. Embracing the diversity of students at NJIT, this program will emphasize the training of underrepresented groups, with the goal of increasing participation in research and the transitioning to graduate programs. This program will augment NJIT’s existing culture of inclusivity, engaging students from all racial backgrounds, national origins, gender preferences and abilities. Program Training Objectives: To accomplish our mission, the program intends to: 1) increase the number of fellowship-supported UG students committed to finishing a Bachelor’s Thesis 2) increase the number of underrepresented groups in research through engagement in self-directed learning-based research experiences, 3) retain trainees for the 3 year training period and graduate within the predicted time to graduation 4) foster science and engineering identity through asset-based individualized research experiences, 5) increase the number of students who choose to pursue a research-focused higher degree program in a biomedical field, and 6) enhance the visibility and competitiveness of NJIT trainees in the biomedical field. Our overall training approach will be based on the Staged Self-Directed Learning model as a framework for a structured training program to assist students to grow into self-directed learners. The program comprises didactic training, career development activities, individualized mentorship, and two semesters of research for credit culminating in an undergraduate thesis and scientific dissemination. As an urban technological university serving one of the most ethnically diverse populations in the country, we hope to utilize this training program to prepare a competitive cohort of undergraduate students who, as future scientists and engineers, would help diversify the field of biomedical research.

NIH Research Projects · FY 2025 · 2022-09

ABSTRACT Extensive efforts are being dedicated to design β-sheet nanofibrils by amyloid-inspired peptides as they exhibit mechanical properties that are desirable for various biomedical applications. These efforts require tools that are accurate at predicting the propensity of a peptide to form fibrils from its amino acid sequence. Moreover, they must consider that deposits of amyloid fibrils in different tissues and organs are emblematic of diseases like Alzheimer’s and Parkinson’s. Accordingly, engineered non-toxic amyloids are expected to show a low degree of homology compared to diseases-causing amyloids. Existing bioinformatic tools, which are informed by disease- causing amyloid, are often not suitable to describe this class of peptides. This project combines all-atom molecular dynamics (MD) simulations, machine learning, and experiments, to develop and validate an approach that will be accessible, accurate, and efficient at predicting fibril formation for any peptide sequence. This project expands on recent studies showing that the combination of faster computers and more accurate force fields are now allowing all-atom molecular dynamics to simulate the spontaneous formation of amyloid fibrils from unbiased initial conditions. These studies have been used to identify intermediate states on pathway to fibril formation as well as describe the mechanisms allowing peptides to lock onto the fibril tip with atomic precision accounting for its growth. In addition, for a limited set of designed peptides, all-atom simulations showed more accurate propensities to form fibrils than bioinformatic tools highlighting its predictive potential. However, simulations remain computationally intensive requiring several weeks to be completed. Thus, they cannot be used for a high throughput investigation of sequences required in efforts to design peptides for biomedical applications. This project addresses this knowledge gap and expands the scope of all-atom simulations to peptides that form complex fibrils that resemble more closely the ones from disease-causing amyloids. Moreover, undergraduate students are involved in all aspects of this project including managing, setting up, and running MD simulations. The three aims of the project are: Aim 1 of this project develops machine learning algorithms to predict in a few seconds if a peptide will self- assemble into amyloid fibrils in MD simulations. These predictions will be validated and tested experimentally to establish the scope of application of different MD force fields. Aim 2 of this project performs a high throughput analysis of the sequence space to determine peptides that form fibrils and discover rules in the amino acid sequence that encode for these structures. Aim 3 of this project expands the use of unbiased all-atom simulations to study peptides that form complex fibrils characterized by parallel β-sheets connected to each other via β-arcs. The molecular mechanisms and pathways accounting for these fibrils will be investigated and will be used to provide insight into disease causing amyloids.