University of North Carolina at Chapel Hill

NSF Awards · FY 2024 · 2024-09

NON-TECHNICAL SUMMARY The growth of semiconductor materials from the vapor phase has, over the last several decades, permitted the development of advanced technologies including light-emitting diodes (LEDs) and lasers, among others. More recently, a new class of semiconductor material termed hybrid perovskites has emerged with a unique set of tunable properties, including the wavelengths of light that they absorb and wavelengths of light that they emit. Moreover, they exhibit unique electronic properties that can make them suitable for a variety of technologies, including solar cells, LEDs, lasers, etc. In this project, supported by the Solid State and Materials Chemistry program in the Division of Materials Research at the NSF, a new method to grow hybrid perovskite materials from the vapor phase will be developed. The method—termed metal organic chemical vapor (MOCVD)—has been widely used for other semiconductor materials but has yet to be developed for hybrid perovskites. A new, custom-built experimental apparatus will permit development of this MOCVD process. The system will allow direct control over each of the chemical constituents of the material to precisely modulate its chemical composition. The fundamental mechanisms of the growth process will be explored, and the ability to grow a variety of distinct hybrid perovskite materials will be tested. The results of this project are expected to elucidate fundamental principles underpinning the design of MOCVD processes for hybrid perovskite systems, enabling new technological devices to be explored in the long-term. TECHNICAL SUMMARY Metal organic chemical vapor deposition (MOCVD) is a widely used vapor-based method to grow solid-state materials, and it has become a particularly powerful technique for III-V semiconductors. Here, the vapor-phase synthesis of hybrid perovskite materials will be developed using a home-built MOCVD system that facilitates the precise flow of organo-lead, amine, and hydrogen halide precursors at a controlled pressure through a reactor with multiple temperature zones outfitted with in situ measurement capabilities that include optical extinction measurements, optical microscopy, and quartz crystal microbalance quantification of mass change. A broad set of amine and halide precursors will be utilized to probe the MOCVD synthesis of three-dimensional, two-dimensional (2D), and quasi-2D hybrid perovskite materials to prove the generality of the process, develop an understanding of the reaction chemistry, and synthesize complex heterostructures and superlattices. The proposal goals include: (1) to understand the vapor-phase and surface mediated reaction mechanisms that control the MOCVD deposition; (2) to synthesize specific hybrid perovskite compositions and phases including 3D materials, 2D layered materials, quasi-2D Ruddlesden-Popper phases and Dion-Jacobson phases; and (3) to develop microstructural control of grain size distribution and crystallographic orientation via the substrate and substrate functionalization. The research efforts will involve students in a project that bridges the interface between solid-state materials chemistry, physical chemistry, and chemical engineering, providing a breadth of experience that ranges from synthesis and mechanistic studies to instrument design/construction and solid-state characterization. Results will be disseminated through publications in high-impact journals as well as presentations at national conferences. In addition, the students and PI will be engaged in a broad set of outreach efforts, including demonstrations in elementary schools and an annual public science exposition. 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

In this project, the PIs use interviews and interviewer assisted surveys to expand our knowledge related to the housing of renters, homeowners, and unhoused people from an understudied urban population. Several research questions are investigated including how members of this population experience renting, homeownership, and houselessness and how such experiences shape their socioeconomic and health outcomes. Broader impacts of the research include the direct applicability of findings to housing decisions concerning this population, and the educational and training opportunities the project provides to graduate and undergraduate students. This project builds on an existing partnership to conduct a mixed-methods study of the housing experiences and socioeconomic and health outcomes of an understudied urban population. Methods used include a survey of 1,000 urban residents and longitudinal qualitative interviews of a subsample of 80 survey respondents. Four general research questions are examined. These concern how the housing experiences of this population vary, how members of this population interact with neighbors and landlords, the methods they use to deal with housing issues, and how their housing experiences affect their health and socioeconomic outcomes. 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 Large Enriched Germanium Experiment for Neutrinoless Double Beta Decay (LEGEND) uses an isotope of germanium, Ge-76, to search for a postulated rare decay process known as neutrinoless double beta decay (NLDBD). The observation of NLDBD would reveal the quantum nature of the neutrino, demonstrate matter creation, reveal that neutrinos and antineutrinos are indistinguishable, and offer a potential explanation of the mystery of why we see the predominance of matter over antimatter in the universe. This NSF grant supports the U.S. portion for operation of the LEGEND-200 experiment, currently collecting data deep underground at the Laboratori Nazionali del Gran Sasso in Italy. LEGEND-200 is an international effort, with participation of over 60 institutions in the U.S. and Europe. Over the course of its operation, it should achieve world leading discovery sensitivity for NLDBD. Potential benefits of this research include fundamentally changing our understanding of the nature and origin of matter, should the decay be observed. Additionally, the technology of large, low-background Ge radiation detectors will enable a new generation of highly-efficient, ultra-low-background gamma spectroscopy measurements. Among the fields that stand to benefit from this technology are: direct dark matter searches; nuclear structure; nuclear astrophysics; environmental monitoring; atmospheric, ocean, and groundwater environmental transport; methods of radioactive dating; reactor monitoring; bioassay for determining very low occupational exposures to radiation; and biological studies involving radiotracers at very low activities. Likewise, many of the same fields will benefit from LEGEND’s production of ultra radio-pure materials, with natural U and Th reduced to ultra-low levels. These technology advances will also likely have impacts on non-low-background applications such as nuclear medicine and Homeland Security. In operating and analyzing the data from LEGEND-200, students and postdoctoral fellows will be trained in underground-science-related disciplines, such as low-background techniques, detector technology, nuclear physics and neutrino physics. With the realization that neutrinos have small, non-zero masses there is intense interest in further elucidation of their intrinsic properties including understanding the neutrino mass generation mechanism and determining the absolute neutrino mass scale and spectrum. There is also the fundamentally important question – is lepton number conserved? Based on fundamental symmetries, there is nothing that would preclude each neutrino mass eigenstate being identical to its anti-particle, that is: a “Majorana” particle. Experimental evidence of NLDBD decay would demonstrate lepton number violation, definitively establish the Majorana nature of neutrinos, and provide information about the absolute neutrino mass. LEGEND-200 utilizes novel, large high-purity Germanium radiation detectors with an intrinsic energy resolution of 0.1% that are surrounded by low-Z shielding (water and argon). The instrumentation of the liquid argon provides an active veto through the detection of argon scintillation light. This proposal provides U.S. support for the operations of LEGEND-200 from 2024-2028. LEGEND-200 initiated first physics measurements in March of 2023 with 142 kg of installed detectors. The experiment plans to deploy up to 200 kg of detectors, with additional detectors slated to be installed in mid-2024 and 2025. LEGEND-200 will have world leading discovery potential and a half-life sensitivity of 1027 year for a 1 ton-year exposure. 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 The double-helix of DNA is a classic example of the geometric property known as chirality. While a chiral structure may look like its mirror image, it cannot be transformed into its mirror image by any combination of rotation or translation. Molecules with chiral structures have unique optical behaviors. Notably, they absorb left-handed or right-handed circularly polarized light differently, a phenomenon known as circular dichroism with applications such as 3D displays, drug discovery, and quantum optics. The focus of this project is to create a new class of chiral materials based on metal halide perovskites, hybrid semiconductors with both organic and inorganic components. Investigators will integrate chiral and achiral organic cations into two-dimensional hybrid perovskites, thereby engineering novel materials with tunable circular dichroism. Students involved in the project will be trained in interdisciplinary research at the interface of physical chemistry and materials science. Investigators will seek to broaden participation in STEM through outreach to K-12 students and using established mechanisms to recruit students from HBCUs. Technical Description This project is based on the recent demonstration of a new approach to achieve chiral two-dimensional hybrid metal halide perovskites (MHPs) via mixing chiral cation and non-chiral (achiral) cations. A chiral cation in can transfer chirality to the inorganic framework and that introducing an achiral cation would not diminish the circular dichroism. This project aims to develop chiral 2D hybrid perovskites with tunable circular dichroism by incorporating large, conjugated achiral organic cations via the mixed cations-based approach. To further expand the chiroptical properties of 2D hybrid perovskites, a new 'chirality transfer' concept would be combined with appropriate energy level alignments via a mixed cation approach. There objectives for this project are: (1) Synthesis and characterization of new chiral 2D hybrid MHPs that combine large, conjugated achiral cations with smaller chiral cations. (2) Investigating chiroptical properties of these newly synthesized hybrid perovskites, potentially leading to novel chiroptic behaviors, such as extending the response range and amplifying the response amplitude. (3) Understanding the mechanism of chirality transfer. The success of this project will offer new strategies to achieve new chiral hybrid perovskites, together with new mechanistic insights. The results will not only contribute to our understanding of chirality transfer, but also potentially open new avenues for applications such as circular polarized light detection and emission. 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

Networks play a major role in many disciplines, both as the primary medium of interest, for instance, the flow of (mis)-information in social networks, or unobserved relationships describing developmental trajectories in cells in individuals, or as a fundamental ingredient in representing high dimensional data in sophisticated multi-step machine learning pipelines. While tremendous advancements have been made in the formulation and application of network-driven techniques on data, the main aim of this project is to provide a theoretical understanding of such models and the accuracy of ensuing scientific conclusions. The project will focus on two major sub-domains, (1) understanding multilayer network data, e.g., network-valued data on a single individual over multiple time points or multi-population data points across different tasks as well as understanding the time evolution of such systems and (2) developing mathematical techniques to understand properties of a major class of techniques used to analyze high dimensional data, namely Gaussian graphical models. This project also provides research training opportunities for graduate students. The project is focused on two major areas of statistical methodology related to functional data analysis for complex systems: (I) Optimal transport for multilayer networks and trajectory inference for complex systems and (II) Continuum scaling limits in Graphical models. In the first domain, the PIs will develop statistically principled techniques for network summarization, clustering, and extraction of principle directions of variation building on Gaussian process optimal transport techniques from functional data analysis, and specifically the representation of Procrustes metrics on covariance operators via the Wasserstein distance between corresponding Gaussian processes. Related to this first domain, motivated by single-cell RNA-seq and network neuroscience, the project will develop mathematical techniques to understand optimal transport-based methods for registration (time synchronization) and supervised learning tasks, including network clustering, after quotienting out the underlying developmental trajectory. Next, driven by areas such as gene-expression data from cancer genomics, the main goal for the second theme is the study of high-dimensional data with underlying dependency structures modulated by a latent network connecting the features. Mathematical techniques that will be developed include (a) Thresholding pipelines from covariance and correlation matrices and local weak convergence of associated objects to limit infinite structures and corresponding implications for thresholding schemes; (b) Hierarchical Representation learning for complex systems and their connection to convergence to continuum scaling limits via connections between linkage clustering and thresholding; (c) Structured alternatives, penalized estimation and limiting distributions of random adjacency matrices including localization phenomena for eigenvectors and their use in hub-detection. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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 Major Research Instrumentation Project (MRI) supports the acquisition of a Waters High-Resolution Ion Mobility Time-of-Flight Mass Spectrometer which will be housed at the University of North Carolina Department of Environmental Sciences and Engineering Mass Spectrometry Facility. This cutting-edge instrument will replace aging instrumentation and enhance a wide range of ongoing atmospheric chemistry and other research projects. This instrument will provide new opportunities to train and education for graduate and postdoctoral researchers from North Carolina Agricultural and Technical State University NC&AT University (the nation’s largest HBCU) and from UNC-Wilmington, a largely undergraduate institution. Outreach efforts include K-12 visits via the Research Experiences for Teachers programs, as well as visits from collaborators from Africa, the U.S., and other international universities. Research projects to be enhanced by this instrument acquisition include: (1) mechanisms of atmospheric gas-phase and multiphase chemistry and discovery of secondary organic aerosols formation pathways from biogenic and anthropogenic emissions; (2) atmospheric oxidative aerosol aging leading to volatilization and functionalization; (3) investigation of feedback loops connecting biogenic and anthropogenic emissions and the effects of the changing environment on atmospheric chemistry; (4) atmospheric gas, aqueous and multiphase oxidation chemistry of African biomass burning emissions leading to brown carbon and organic aerosol; and (5) the emerging area of atmospheric gas- and multiphase chemical processes involving per- and polyfluorinated substances. A wide range of educational and outreach opportunities will be enabled including K-12 outreach through visits by participants of Research Experiences for Teachers programs as well as collaborations with researchers from both domestic and international institutions. 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Dr. Aleksandr Zhukhovitskiy of the University of North Carolina at Chapel Hill and Dr. Ian Tonks of the University of Minnesota-Twin Cities will develop catalytic methods to edit the molecular architectures of various plastics such as polyesters and polyurethanes. Architecture—e.g., the extent and type of branching—of a polymer underpins its thermomechanical properties and, consequently, applications. For instance, linear architecture of high-density polyethylene (HDPE) leads to stiff materials that could be used as milk jugs; meanwhile, highly branched linear low-density polyethylene (LLDPE) is more flexible and extensible, which supports applications like plastic bags. Accessing a spectrum of architectures for a given polymer remains a challenge. The proposed research will address this challenge by developing catalysts and new mechanisms that can rearrange the bond between atoms in the polymer skeleton, thereby turning branched chains into linear ones, and vice versa. This chemistry will allow scientists and engineers to design new types of plastics with variable and changeable properties, such as force-responsive materials that change properties upon stretching or compressing, or materials with improved degradation/recyclability properties. This project will provide interdisciplinary research training to students and help to prepare a skilled workforce for academia and industry. As a part of this work, polymer-focused educational programs will be developed that integrate concepts of sustainability and circularity. This proposal will develop branched-to-linear transformations of polymer backbones via catalyzed sigmatropic rearrangements. Transition metal- and organo-catalyzed [3,3]-sigmatropic rearrangements will be developed to isomerize a broad range of vinyl sidechain-containing polymer classes between branched and linear architectures. The specific ratio of the branched-to-linear conversion will be dictated by the percent conversion and the thermodynamics of a given system. These rearrangements will result in transformations of the thermal properties of polymers, namely lowering their glass transition temperatures and increasing their crystallinity. The stereospecific nature of concerted [3,3]-rearrangements will be utilized to enable tacticity transfer from starting polymers to rearranged polymers. Additionally, mechanical force will be utilized to alter the thermodynamic landscape of the rearrangement reaction coordinates, creating a thermodynamic bias toward linear isomers. Ultimately, this work will leverage a detailed understanding of catalyzed [3,3]-rearrangements of polymer backbones to enable broad architectural and property editing of soft 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 · 2024-09

Plants grow in soil that is teeming with microbes. Because plants provide habitat and food, in the form of secreted sugars and amino acids, many soil bacteria evolved to colonize the plant root and shoot systems. Some plant-microbe interactions can lead to plant disease if the microbes can damage plant cells and tissue. However, other plant-microbe interactions can be neutral or even beneficial to the plant. The sum of the microbes that colonize and interact with plants is called the plant microbiome. This project addresses three important questions in plant microbiome research. First, how does common beneficial bacterium called Sphingomonas swim to and attach to the plant and how does this early colonization step evade activation of the plant immune system? Second, how do beneficial bacteria turn off the plant immune system? And third, how do beneficial microbes change their gene expression when they encounter a plant. This proposal includes training opportunities for undergraduate students and interfaces with the UNC Morehead Science Center to educate the public about the useful bacteria that may someday replace environmentally unfriendly diseases to combat plant disease and increase yields. This research will lead to understanding of how commensal microbes influence plant performance. This research uses collections of sequenced microbes that provide plant growth advantages or suppress or enhance immune responses; many are amenable to mechanistic studies during host interactions. This research will lead to the definition of single isolates or reduced complexity consortia of sequenced microbes that influence plant performance. These can be robustly deployed in re-colonization synthetic community microcosm experiments to define and iteratively test models of the principles that drive community formation and resiliency. The researchers use collections of sequenced microbes that provide plant growth advantages or suppress or enhance immune responses; many taxa are amenable to mechanistic studies during interactions with the host. The work moves beyond description of plant-associated microbial communities to generate and test mechanistic hypotheses. The aims are to: address the hypothesis that a core plant microbiota taxon uses flagellar switching during plant colonization and that this flagellar switching is monitored by the host immune system; dissect the function of two host transcription factors in immune system-microbiota détente; develop and deploy single-cell RNAseq and spatial transcriptomics for host and microbe in the context of defined synthetic bacterial communities to understand how host-commensal interactions shape microbe-microbe interactions on the plant. The research will contribute to predictive interventions that will modulate endogenous plant immune system function, increase plant health and productivity and facilitate carbon sequestration through the rational utilization of probiotic microbes and mixtures of microbes tuned to function in particular soils and local environments. 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 practice of charging different prices to different consumers, i.e., price discrimination, is commonly used by firms in a variety of industries with the intention of increasing profits. Yet, little is known about the impact on consumers of modern sophisticated discriminatory pricing practices that leverage increasingly detailed information. This gap in understanding must be overcome to develop rules that balance positive aspects of these practices like efficiency against redistributive harm and privacy concerns. The researchers study these issues in the context of two different strategies used in the airline industry: personalized pricing via targeted discounts and auctions for upgraded services and amenities. The empirical analysis relies on unique data obtained from a North American airline to gain deeper insights into the practice of price discrimination by evaluating current and potential price-discrimination strategies. The results of the analysis provide insight into the effectiveness of different strategies and methods on profit and consumer welfare, while also offering guidance to decision makers on how to limit any negative impacts on consumers. The research contributes to knowledge of the welfare impacts of modern discriminatory pricing practices, as well as the challenges associated with implementation. The detailed proprietary data used in the empirical analysis provides a unique opportunity to study two different practices used in the airline industry: personalized pricing via targeted discounts and upgrade auctions. The results of the analysis regarding firms’ ability to leverage increasingly detailed data and implement sophisticated mechanisms to price discriminate is directly of interest to economists and important for development of rules to limit harm to consumers. The contribution of the research is in the field of econometrics and industrial organization, and specifically advances econometric methods for personalized pricing and integration of auctions into a dynamic-pricing environment. These advancements provide a framework for studying these topics in other industries, and guidance for important discussions around data privacy and price discrimination. 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

Modern day incarceration is a product of historical patterns of geography and complex social structures often spanning decades or even centuries. This doctoral dissertation research examines how deep historical contexts contributed to the development of a modern-day prison system. The project investigates patterns of social groups and migration through a case study spanning multiple centuries including an analysis of the legal frameworks, economic processes, and place-based cultural structures. Prison complexes are often located in rural areas formerly used for agricultural land uses and embedded in regions with complex cultural histories. This project investigates how the development of localized prison complexes involves a disruption of existing cultural traditions seeking to understand diverse implications for future socio-economic change. This project uses a combination of archival resources and interviews to understand how present-day prisons are embedded in regions with complex historical contexts. The project utilizes a case study approach to investigate: (1) how minoritized groups were embedded in historical legal frameworks related to incarceration, (2) the role of private prisons in geographies of incarceration, and (3) how natural, built, and human systems are modified through the development of prison infrastructure. Archival data sources are coded in using MAXQDA software to identify code patterns that correspond to core research questions. To analyze the legal dataset a chronological timeline of law and legal decisions, including policy and relevant case law, is derived from archival and interview data sources. Legal developments relevant to the site are tracked over time. Legal texts and interview data are coded using a priori and post various semantic terms to identify consistent and inconsistent patterns across disparate data sources. Research findings are shared with local communities and decision-makers, and analytical tools developed will be incorporated into university curricula. 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 lies at the intersection of differential geometry and nonlinear partial differential equations. The differential equations under consideration have geometric motivation and content, and model natural problems such as the reconstruction of the shape of a geometric object based on knowledge of how that object curves in its ambient environment, or the motion of a geometric object which deforms in a manner determined by its extrinsic curvature. Other equations of interest describe the structure of geometric objects which locally minimize a suitable notion of area subject to predetermined constraints. Such objects arise classically in mathematical models for soap films. Prescribed curvature equations and the equations describing time-dependent curvature flow also find applications in mathematical physics, e.g., to the geometry of spacetime and to the deformation theory of elastic bodies. The project will generate research opportunities for graduate students and will facilitate the mentoring of graduate students and postdocs through interactive research seminars. In addition, the principal investigator will prepare publicly accessible educational materials through the writing of survey articles on the subject. Two important types of nonlinear geometric partial differential equations feature heavily in this project: Lagrangian mean curvature equations (and the associated flows), and the Hamiltonian stationary equation (along with other fourth order equations of a similar type). Lagrangian mean curvature equations feature in the existence theory for special Lagrangian submanifolds of Calabi-Yau manifolds, a central issue in mirror symmetry. The Hamiltonian stationary equation identifies critical points for the volume functional on Lagrangian submanifolds under Hamiltonian variations. A priori estimates are crucial for solving certain fully nonlinear equations and for determining fundamental properties of their solutions. Building on prior work in the complex Euclidean setting, the PI will investigate regularity and well-posedness of the variable Lagrangian phase function. In the Lagrangian context, variational problems for the volume functional lead to nonlinear equations of fourth order. A relevant challenge is to identify submanifolds that are minimal within a specific Hamiltonian isotopy class. In contrast with general minimal surfaces, the underlying constraints in this setting permit the existence of compact minimal submanifolds. From an analytic viewpoint, the maximum principle is no longer applicable. This project will develop new strategies for the existence theory for Hamiltonian stationary submanifolds. 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

In daily life, people experience new events that resemble the past. Processing new information requires the updating of older memories to keep thoughts current. For example, when discussing a shared TV show, a friend may recount details of an older episode and describe how those details are relevant to a new episode. Importantly, the new details must be considered in the context of the older details without overwriting them to later be remembered. This discrimination of new from old events may be supported by brain areas that complete and separate observed patterns. An established view is that pattern completion and pattern separation processes occur primarily in the CA3 and dentate gyrus subfields of the hippocampus. However, mounting evidence suggest that these areas of the hippocampus interact with larger brain regions in the neocortex to keep memories up to date. Understanding how these interactions allow people to keep event details straight may be critical for preserving memory function and reducing interference among memories. The present project aims to use cutting-edge neuroimaging technology to describe the contributions of hippocampal subfields and cortical regions while people discriminate current events from similar memories. The ability to identify how the present is distinct from the past depends on whether the reinstatement of existing memories supports their comparison with similar experiences. Such mnemonic discrimination may reflect a hippocampal-dependent mechanism called pattern separation. Neural investigations have established contributions of hippocampal subfields to pattern separation enabling mnemonic discrimination. These investigations have emphasized the dentate gyrus (DG), area CA3, and other connected areas. Prior studies have examined separated DG and CA3 brain activity and whole-brain functional connectivity associated with mnemonic discrimination, but no study has been able to adequately assess both using a singular 3T imaging protocol. This gap severely limits conclusions about how hippocampal subfields interact with cortical regions to discriminate new events from memories of similar events. The present project aims to overcome this limitation by introducing an ultra-high resolution functional magnetic resonance sequence that achieves whole-brain coverage, with DG separated from CA3, while minimizing signal dropout in key areas of the prefrontal cortex and anterior temporal lobes. This technology can be used to identify brain activity, representational similarity, and joint whole-brain and hippocampal functional connectivity supporting mnemonic discrimination of everyday objects. Successful completion of this project may provide the proof required to motivate a series of experiments examining the neural mechanisms that enable mnemonic discrimination of both everyday objects and naturalistic ongoing 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 · 2024-08

This project builds on observations in the Neuse Estuary (North Carolina) that have been collected previously and expands upon them through 3-d modeling with a widely used model called ROMS (short for Regional Ocean Modeling System). Unique features of the Neuse Estuary include a close to 90 degree bend and a micro-tidal environment, with wind forcing thus thought to be dominant over tides. Wind effects on estuaries are understudied because usually tides are more prevalent and cause most of the mixing. In this case, the proposed model experiments will investigate how wind forcing at different angles relative to the two connecting estuary legs as well as the duration and intervals of the wind events affect circulation and salinity distributions in a model set-up that resembles the Neuse Estuary. The results from this work will inform communities around the Neuse Estuary how to better interpret water quality time series, knowing more about the circulation features that affect residence and flushing times of the estuary. The results will also help enhance prediction and management of water quality in the Neuse Estuary. More specifically, the project will investigate how the circulation and salinity distribution in estuaries with curved sections respond to wind events using simulations on idealized and real estuary domains. Of primary interest is how interaction between estuary legs parallel and perpendicular to the wind, and lateral circulation and mixing in the connecting curved region, impact the strengths of horizontal and vertical salinity gradients and the distance that high salinity water extends upstream. Also of interest is how wind event duration and interval between wind events relative to timescales associated with wind mixing, wind-driven currents, and the baroclinic response, control the circulation and salinity distribution. To address these questions, simulations on idealized estuary domains using ROMS will be conducted. Three sets of simulations will be performed in which parameters are varied systematically: 1) constant wind at varying angles to a straight estuarine channel; 2) constant wind on an estuary with perpendicular legs connected by a curved region, varying wind speed, direction, freshwater inflow, and radius of curvature; and 3) finite length wind events, varying event duration and separation. Finally, simulations on a realistic Neuse Estuary domain will be conducted to examine how theory developed from idealized simulations can be applied to a real wind-dominated estuary. This work will advance understanding of wind effects on estuary dynamics by extending it to more general estuarine geometries that have bends and more realistic situations in which wind varies with time. 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

Neutrinos, the universe's lightest particles, are notoriously hard to detect but nonetheless important. Their unknown properties, including their masses, their interactions with other as yet unobserved particles, and whether they are their own antiparticles could explain why the universe contains so little antimatter. Experiments to observe a process called neutrinoless double-beta decay, in which two neutrons inside an atomic nucleus change into protons while emitting no neutrinos (in contrast to the usual two) can help us learn; the decay can occur only if neutrinos are their own antiparticles, and its rate reflects the properties both of neutrinos themselves and of undiscovered particles they might interact with. What we learn will be limited, however, without a better understanding of what happens inside the nucleus that hosts the decay. This understanding, at a quantitative level with a good estimate of uncertainty, is the FRHTP's goal. The project connects theorists across the country to achieve it, and at the same time provides postdocs with intensive training in several areas at the interface of nuclear and particle physics. It also supplies experiments and information to K-12 students across the country, helping them understand what it's like to work on a cutting-edge scientific problem. The effect of the nucleus on the decay can be summarized in a "matrix element" between the initial and final states of a decay operator. Both determining the operator and reliably computing its matrix element are challenges. The operator depends on the particle physics causing the decay, and on the behavior of quarks and gluons inside nucleons. The operator's matrix element depends on the structure of the nucleus, which is made up of those nucleons. Thus, an accurate and precise calculation of the matrix element (a restatement of the Hub's goal) requires coordinated work in particle phenomenology, quantum chromodynamics, and nuclear-structure theory, as well as the use of effective field theory to bridge the energy scales associated with new particles, nucleons, and nuclei. It also requires statistical expertise to combine uncertainties at each of the scales and from several computational methods into an overall error estimate. The Hub uses lattice quantum chromodynamics, three different ab initio nuclear-structure methods, chiral effective field theory, and Bayesian model mixing to produce matrix elements for isotopes used in experiments that are vastly more accurate than anything presented earlier. Bayesian statistical techniques result in a reliable uncertainty estimate, something that has never been achieved before. 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

Networks have been used for many years to model and study a variety of phenomena, including social interactions, co-authorship of scholarly work, and financial interactions. More recently, networks themselves have become objects of study. This research project will develop new statistical approaches for comparing and aligning networks, and will include applications of these methods to problems in computational neuroscience, systems biology, and urban planning. This research will advance the state of network analysis by providing new statistical methods as well as theoretical support for these methods. The broader impacts of the project include applications, collaborations with disciplinary scientists, educational outreach from high school to the graduate level, and community outreach. This research project focuses on the statistical analysis of network data, including the design of new methods, the development of rigorous theoretical support for these methods, and the application of these methods in several relevant scientific domains. The project has four specific objectives. First, investigate optimal transport-based distances for Markov embeddings of networks. Second, develop new methods for network alignment and comparison based on these distances, and apply these or new methods to the problems of model fitting, classification, and node feature prediction. Third, establish rigorous theoretical results concerning the properties of optimal transport distances on networks, investigate relationships between distances and different embedding procedures, and provide theoretical support for the associated methods. Fourth, apply the methods to address problems in computational neuroscience, systems biology, and urban planning. This research will bring together ideas from Markov chains and optimal transport in the setting of network analysis, and the applications of this research will involve the development of efficient, scalable algorithms for analyzing network data. 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 award will support scientific research on the orbital dynamics and gravitational wave emission of black hole binary systems. Over one hundred observations by LIGO and Virgo of merging stellar mass black holes and neutron stars in the last nine years have opened up the era of gravitational wave astronomy. These events reveal new astrophysics, including confirming the site of heavy-element formation and discovering a new class of intermediate-mass black holes, and provide novel ways to test relativistic gravity theory. The breakthrough also motivates the development of the LISA space-based detector and raises prospects of eventual gravitational-wave observations of extreme-mass-ratio inspirals (EMRIs). EMRIs involve a stellar-mass black hole (or neutron star or white dwarf) spiraling into supermassive black holes, known to exist in the center of virtually all large galaxies. Immersed in dense star clusters, these supermassive black holes will frequently capture compact stars into highly eccentric and inclined orbits. As these compact stars orbit, they radiate gravitational waves, causing the orbit to decay and the compact object to eventually be swallowed by the supermassive black hole (or tidally disrupted in the case of white dwarfs). The gravitational radiation will be observable with LISA. The theoretical work funded by this award will provide improved predictions of the expected signals from highly eccentric or inclined-precessing merging black hole binaries. Along the way, the work also contributes to the development of young scientists trained in high-performance computing and advanced mathematical methods, many of whom go on to strengthen the U.S. technical human resource base. The U.S. is a leader in gravitational-wave astronomy and these theoretical activities support future extensions of such observations. Future detection of gravitational waves from EMRIs will provide unique strong-field tests of relativistic gravity theory and probe the nature of black holes, while also uncovering the astrophysical properties of the dense central regions of galaxies and their cosmic history. To pursue this effort, new techniques in black hole perturbation theory and gravitational self-force methods will be developed, along with writing associated advanced computer codes. Symbolic mathematical calculations of black hole perturbation theory and the gravitational self-force will be made of Kerr (spinning) EMRIs to high order in the post-Newtonian (PN) expansion, for systems with both eccentric orbits and inclined, precessing orbits. The high post-Newtonian (PN) order perturbation and self-force findings support and reinforce broader efforts to advance arbitrary-mass-ratio PN theory and may provide calibrations of effective-one-body and surrogate models of merging binaries. These efforts to model Kerr EMRIs draw upon the lengthy experience of the PI's group over more than a decade in studying Schwarzschild EMRIs. Generated gravitational wave flux and self-force data will be incorporated in adiabatic and post-adiabatic inspiral calculations to provide accurate waveforms for much of an EMRI's evolution. A major part of this work will be conducted in collaboration with former students and colleagues at University College Dublin. Results and some computer codes will be made available in online repositories. 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

In today's world, most data arrive at compute locations as packets from the network through a Network Interface Card (NIC). Traditional NICs are simple devices attached to a server, used to receive packets from the network and place them in the server's memory. Packets then wait for processing time by the general-purpose processor, or CPU, at the server. Recently, a new generation of NICs, known as SmartNICs, has emerged. SmartNICs allow programmers to offload certain tasks, such as network and security tasks, from the server's CPU to the SmartNIC. They also enable programmers to write customized applications running on the SmartNIC’s domain-specific processors at speeds that may be orders of magnitude faster than those running on the server's CPUs. These capabilities improve data processing performance, enhance security, and reduce the processing load on the server's CPUs. While large cloud providers are now using SmartNICs, campus networks and small- and medium-sized enterprises have yet to fully benefit from their advantages. An important barrier preventing the adoption of SmartNICs is the lack of engaging training materials for cyberinfrastructure contributors and professionals. This project aims to bridge that gap by developing hands-on virtual labs for instruction that are hosted on web-based platforms for easy and broad access. By providing accessible and practical training, the project will lower the barrier to innovation and promote progress on areas such as scientific applications requiring massive data transfers, machine learning relying on high processing speeds, and cybersecurity applications requiring massive traffic inspection. The project has two overarching goals. The first project goal, contributing to the project’s intellectual merit, is to advance the state of the art in SmartNIC training within the research community in order to promote and facilitate the broader adoption of SmartNICs among cyberinfrastructure professionals, contributors, and network owners. The project will develop training material in the form of virtual labs and companion material, including guided experiments and interactive electronic booklets, on technologies related to SmartNICs. The virtual labs will be used for workshops and self-paced training. These labs will enable cyberinfrastructure contributors (including developers and researchers) to learn how to implement offloaded applications on various SmartNICs. The virtual labs will also permit cyberinfrastructure professionals (including system administrators, research support staff, and facilitators) to learn how to deploy those applications, how to manage SmartNICs, and how to provide effective support. The virtual labs will be deployed on the NSF-funded FABRIC platform (NSF award #1935966) and on the Academic Cloud at the University of South Carolina, which will serve as training platforms. The virtual labs will cover open-source technologies that are compatible with commercial SmartNICs. The second project goal, contributing to its broader impact, is to incorporate virtual labs into educational curricula and instructional resources. The project will target associate, bachelor, and graduate programs. Two-year community colleges will use the virtual labs to train students on SmartNICs administration and operation, including the deployment of pre-developed applications. Four-year bachelor and graduate-level programs will use the virtual labs to provide in-depth training on SmartNIC programming, starting from the foundational principles to the development of advanced applications that accelerate data processing and analytics. Training activities for the two project goals include organizing workshops with cyberinfrastructure communities, including national and regional Research and Education Networks; professional development events with the NSF-funded Minority-Serving Cyberinfrastructure Consortium, a collaborative consortium that provides professional development and training opportunities to minority serving institutions; and train-the-trainer tutorials with centers supporting college instructors and students. Best practices and technical specifications produced by this project are incorporated into NSF's ACCESS Knowledge Base, to disseminate them to the broader community of researchers. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Computer and Network Systems within the Directorate for Computer and Information Science and 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 · 2024-08

The CD300 protein family of immune receptors is encoded by a family of genes present in all mammalian species, including humans. CD300 proteins have been implicated in multiple important roles in human health, including the regulation of cancers, inflammatory diseases, and viral infections. By directly binding pathogens and initiating an immune response, CD300’s play a crucial role in the immune response of humans and other animals. However, the number of CD300 genes varies dramatically between species; for example, rodents, dogs, and armadillos encode more CD300 genes than humans. Little is known about the function of these receptors across different species. Results from this project will shed light on the evolutionary history and functional diversification of the CD300 gene family, providing valuable insights into how these receptors contribute to immune function and disease susceptibility in all mammals including humans. This knowledge has the potential to enhance our understanding of rules that govern the emergence of novel immune function while simultaneously informing the development of new therapeutic strategies. Additionally, the interdisciplinary efforts from this project will yield new K-12 educational modules that align with state standards, will be widely available to public school teachers, and will feature gamified elements to enhance STEM education. This award was co-funded by SBS/DEB. The human genome encodes seven structurally similar CD300 genes in a single cluster on chromosome 17 which are presumed to have arisen through tandem gene duplication events. Select CD300 proteins have been shown to bind specific phospholipids and are implicated in pathogen recognition and immune defense. However, mammals and other vertebrate species also encode clusters of CD300 homologs with variable gene content across species. Little is known about the functional diversification and evolutionary dynamics of CD300 genes across these lineages. As a consequence, it remains unknown whether the emergence of novel CD300 genes is associated with the development of novel functions. This project will use a multi-omic approach to fill this knowledge gap by mapping the molecular and functional diversification of CD300 orthologs and paralogs, and experimentally testing ancestral CD300 functions. By using a comparative approach to study CD300 genes, the proteins they encode, and the lipids they bind, this research will provide insights into the mechanisms that drive the generation of immunogenetic diversity across vertebrates, while also creating a critical receptor-ligand framework for novel therapeutic development. Additionally, this project will contribute to broader impacts by integrating research findings into STEM education curricula, particularly benefiting underserved middle and high schools, including the Eastern Band of Cherokee Indian community, and fostering scientific literacy and engagement. 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 REU Site equips undergraduates with knowledge and approaches to help them study the experiences of youth and communities. Through an immersive training experience, participants will learn multidisciplinary and integrative approaches advancing science in human development and adaptation across the lifespan. This program provides an educational opportunity for undergraduates in two primary training areas: (1) how multidisciplinary science can advance scientific understanding of factors that shape development and adaptation, and (2) novel strategies for scientific dissemination and translation. This program supports an 8-week learning experience for 8 undergraduates annually, providing a research training opportunity that is typically not available at participants’ home institutions. The primary objective is to expose participants to research methodology in a multidisciplinary and collaborative learning context. Participants will learn to conduct independent research in partnership with youth and communities and as a result develop a comprehensive skillset that prepares them to pursue research careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With the support of the Chemical Synthesis Program in the Division of Chemistry, Professor Simon Meek at the University of North Carolina at Chapel Hill is studying the development of sustainable coupling methods for the preparation of complex molecules. Demand for chemical processes that economically and selectively prepare organic compounds is critical to sustainable manufacturing and future breakthroughs in drug discovery. In this project, Professor Meek is tackling these challenges by developing complexity-building protocols that efficiently and selectively assemble multiple chemical components in a single operation. These protocols leverage readily available unsaturated hydrocarbons and common chemical groups with reactions driven by catalysts (additives that make reactions more efficient and/or faster) made from nickel. Through designing new catalysts, Professor Meek and his team are unlocking the conversion of unsaturated hydrocarbons into useful intermediates, and their transformation into high-value compounds. Professor Meek's group is actively involved in outreach initiatives that extend the scope of his research, delving into crucial subjects pertinent to both undergraduate and graduate education. These endeavors highlight the transformative influence of sustainability, organic chemistry, and catalysis on everyday life. By amplifying the involvement of undergraduate students from varied backgrounds and integrating comprehensive educational elements into courses across different levels, Professor Meek aims to enrich STEM education for all students and cultivate nurturing environments conducive to empowering the scientists of tomorrow. In this project, funded by the Chemical Synthesis Program, Dr. Meek and his team of graduate and undergraduate students at the University of North Carolina at Chapel Hill, develop new sustainable nickel-catalyzed coupling methods for the stereoselective synthesis of complex organic molecules. These transformations unlock efficient, site- and stereo-controlled multicomponent reactions of simple carbonyl-type electrophiles and readily available unsaturated hydrocarbons leading to the formation of two new bonds and up to three new stereocenters. Molecular assembly is catalyzed by phosphine-nickel complexes that display high reactivity and selectivity, characteristics essential to efficient and economical manufacture of important functionalized acyclic and cyclic molecular scaffolds. Fundamentally, these reactions leverage the diverse chemical reactivity of organonickel intermediates with an array of nucleophilic coupling partners. Overall, this synthetic tactic is enabling the formation of tertiary and quaternary carbon stereocenters, in addition to amine, alcohol, and heterocycle functionality. Moreover, this research opens new avenues for chiral ligand and catalyst design with abundant transition metals that is of importance to both industry and academia. 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 width of tree rings are more sensitive to dry extremes than wet extremes in rainfall. Therefore long-term records from tree rings on past rainfall do not robustly record wet extremes. This bias in the baseline data of the past rainfall variability is problematic for water management and projecting the impact of future rainfall extremes. The goals of this project are to use dendrometers (instruments that record tree growth in real time) to measure the response of tree growth to rainfall at four sites that are part of the AmeriFlux network, sites that have weather stations that measure detailed records of rainfall through time. The investigators will use what they learned from the dendrometer study on how trees respond to wet extremes to refine reconstructions of rainfall and wet extremes from the past from an existing archive of tree ring cores, and use models to study the uncertainty in tree ring reconstructions of rainfall extremes. The project will include training of a postdoc, graduate student and undergraduate students, and public outreach events on climate change through collaboration with a nonprofit cinema's "Science on Screen" series. Tree-ring based hydroclimate records of wet extremes are not as robust, which is problematic for water management and projecting the impacts of future hydroclimate extremes. The goals of this project are to use dendrometers to measure how tree growth responds to changes in the frequency, intensity and timing of precipitation in trees at sites in the AmeriFlux network in Arizona, New Mexico, Colorado and Indiana; reconstruct wet extremes of precipitation from previously collected tree ring width data in the International Tree Ring Database (ITRDB) in a variety of climate zones in the USA; and use forward proxy modeling to evaluate uncertainties in tree-ring hydroclimate reconstructions and estimate the impact of future hydroclimate extremes on forest health. The Broader Impacts consist of training a postdoc, graduate student, and undergraduate students; and climate communication in partnership with a nonprofit cinema’s “Science on Screen” series. 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

In the past fifteen years, there has been explosive growth in the number of applications seeking to leverage Machine Learning (ML) models. Before an ML model can be deployed, the model must be “trained” by repeatedly processing examples. Each example helps the model to make progressively more accurate predictions, "learning" how to solve the problem at hand. Unfortunately, this training step requires a large amount of expensive, highly-specialized hardware and can take several hours to complete. Given limited hardware resources, it is not obvious how to allocate (share) these resources across a stream of ML training jobs. The goal of this project is to develop new resource allocation policies that allow us to produce highly-accurate ML models, quickly, and with limited resources. The main challenge in this work is that ML training jobs present a number of unique characteristics compared to other computing workloads. ML training jobs are highly parallelizable, meaning a single training job might run across multiple servers. Each job also has a large number of configuration options that affect how fast the model learns a particular problem. This project uses mathematical modeling to develop new allocation policies specifically designed for ML training jobs. This research will be accompanied by the development of new courses and mentorship programs designed to recruit students into research on the modeling of computer systems. Machine Learning (ML) models are increasingly being deployed across a wide variety of applications. The growth in ML models has been accompanied by the development of specialized hardware accelerators that help reduce the training time for ML models. However, there has not been a similar degree of specialization in the scheduling algorithms used to train ML models on clusters of specialized hardware. The question of how to best schedule ML training jobs is non-trivial. ML training jobs present many unique characteristics compared to other computing workloads. First, ML training jobs vary in their degree of parallelizability (ability to scale out across servers), with some jobs being highly elastic and others being inelastic in their scalability. It is not clear how to allocate (share) hardware resources among jobs with such different characteristics. To make things more complicated, each training job is parameterized by a set of tuning parameters called hyperparameters. The parallelizability of an ML training job varies over time as the job hyperparameters change. Hence, systems which either rely on static resource reservations or existing heuristic scheduling policies are poorly suited for ML training jobs. Finally, the inherent work associated with an ML job is not a fixed quantity. Instead, ML jobs are generally trained until the model meets a desired level of accuracy. Hence, the existing scheduling theory that favors “short” jobs does not apply in this case. This proposal develops a new theoretic framework for modeling the dynamics of ML jobs running in a shared cluster. This framework will be used to develop scheduling and allocation policies that are specialized to ML training jobs, and to prove performance guarantees on these policies. 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

Statistical uncertainty plays a significant role in a diverse range of models for complicated dynamic phenomena, leading to wild, stochastic behavior. Such probabilistic effects are caused, for instance, by unpredictable market shifts in the global economy, or turbulent or chaotic weather patterns at the evolving front of a massive forest fire. The investigator will develop a mathematical understanding for the equations arising in these applications, while also studying the stabilizing and regularizing effects of stochastic noise, for which there is often experimental or numerical evidence. This project will generate opportunities to mentor graduate and undergraduate students by providing both professional advice and mathematical knowledge related to the project. Dynamical random behavior under various complex influences is often described by nonlinear stochastic partial differential equations. Such equations cannot be solved through the superposition of simple formulae and are therefore not yet well-understood mathematically. The project will draw on tools from functional analysis and probability to resolve the well-posedness of nonlinear stochastic partial differential equations arising in competitive large population dynamics and in stochastically forced interface evolutions. The effects of stochasticity will be further analyzed by studying the long-time behavior of solutions, probabilistic averaging and regularizing phenomena, and stochastic selection principles for models with a small level of background noise. The material influence of stochasticity indicates that the statistical fluctuations in experimental data cannot be completely ignored, thereby justifying the technical study of those stochastic partial differential equations involved in this project. 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 · 2024-01

Peptides, short chains of amino acids, mediate up to 40% of known protein-protein interactions and play a key role in protein trafficking and cellular signaling. However, peptide-protein interactions present a challenge for conventional computational modeling, due to slow dynamics and high peptide flexibility. It remains difficult to predict binding structures of highly flexible peptides to target proteins. The goal of this project is to accurately predict peptide binding structures through the development and validation of a public workflow termed “PepBinding”, which integrates peptide docking, accelerated molecular simulations and machine learning. PepBinding will be able to fully account for the peptide and protein flexibility, and thus greatly improve the accuracy of peptide binding structure predictions. It will provide a generally applicable approach for the world-wide Critical Assessment of PRediction of Interactions (CAPRI) community to predict peptide-protein binding structures. In addition, the PI will combine research with evidence-based education and outreach programs of PepBinding for exceptional training of graduate, undergraduate and high school students as the next-generation computational biologists, especially underrepresented minorities and STEM science and technology students. A peptide Gaussian accelerated molecular dynamics (Pep-GaMD) enhanced sampling method has been developed, which selectively boosts the peptide essential potential energy and has been shown to tremendously accelerate peptide motions by orders of magnitude. Tens to hundreds of nanosecond Pep-GaMD simulations are able to sufficiently sample peptide conformations in the bound state. The project aims to (1) develop and benchmark PepBinding for accurate predictions of peptide binding structures by combining Pep-GaMD with peptide docking and machine learning, (2) assess PepBinding performance through blind tests in community challenges and validate new predictions in collaborative biochemical experiments, and (3) implement Pep-GaMD in widely used simulation packages and disseminate PepBinding through a public website. Successful development of PepBinding is expected to greatly drive research frontiers in peptide docking, molecular dynamics, enhanced sampling, and modeling of biomolecular interactions. The results of the project can be found at https://miaolab.ku.edu. 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.

Fonds de recherche du Québec – Société et culture · FY 2023-2024 · 2023-04

Volet: Bourses postdoctorales; Domaine: Enjeux fondamentaux et finalités de la vie humaine; Objet: Épistémologie et méthodologie; Objet: Éthique individuelle et des collectivités; Application: Structures et relations sociales; Application: Solidarité sociale; Mots-clés: COMPREHENSION, CONNAISSANCE, COMPREHENSION D'AUTRUI, RESPECT, TEMOIGNAGE, VALEUR EPISTEMIQUE