Pennsylvania State Univ University Park

NSF Awards · FY 2024 · 2024-08

This project will integrate mathematical models and experiments to generate and test key hypotheses on the effects of disturbances on biodiversity. Environmental disturbances that cause mortality, such as fire, drought, and flood, are an important feature of any biological community. Through these changes, disturbances can alter the composition of a community and change how the community functions. Biological communities are increasingly facing novel disturbances, and humans routinely employ disturbances, such as pesticides and logging, to manage and harvest ecosystems. Uncovering the myriad effects of disturbances on communities is therefore important for our understanding and management of any ecosystem. However, a systematic understanding of disturbances has been challenging because they come in many forms and affect every type of community. Our project will employ two novel approaches to overcome this challenge. First, it will directly compare the effects of two broad types of disturbances: pulses, which are short and discrete events, and presses, which are long and sustained events. Second, the project will uncover the effects of disturbances on communities where organisms compete over limited resources as well as in communities where one organism consumes another. The project will use a community of bacteria and their predators which allows for highly controlled experiments, which will then inform field systems. It will also recruit a new generation of scientists into disturbance ecology through a course on student-led research projects, which has been shown to increase STEM retention rates in students across diverse backgrounds. A critical difference between pulse and press disturbances lies in how communities respond to them over time. To precisely compare the effects of pulse vs. press disturbances, one major challenge is to analyze how variations in dynamics arising from different frequencies and intensities of pulses result in different community outcomes, while controlling for the overall mortality rate over the duration of the disturbance. Consequently, ecologists have historically studied press and pulse disturbances separately. Our recent work, however, has developed novel methods to overcome this challenge. We further hypothesize that pulse and press disturbances can have qualitatively different effects on community dynamics. The project will test these methods and hypotheses by integrating mathematical models of disturbances with microcosm experiments. Using microbial systems, we will: (1) Determine the differential effects of pulse and press disturbances on competitive community dynamics in a single trophic layer using an experimental community of Pseudomonas fluorescens strains; (2) Develop mathematical models to elucidate the effects of pulse and press disturbances on communities with predator-prey interactions; (3) Experimentally analyze the differential effects of pulse and press disturbances on predator-prey dynamics using a protist predator Tetrahymena thermophila of P. fluorescens. Our work generalizes disturbance theory to consumer-resource interactions (e.g., predator-prey, host-pathogen, plant-herbivore), which are ubiquitous and fundamental processes across ecological systems yet often overlooked in the disturbance literature. 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 PIs will utilize a large sample of galaxies and active galactic nuclei (AGN) to study the long-term growth of supermassive black holes (SMBH). This research program will help astronomers better understand the connections between the SMBH and their host galaxies and extended environment. This program will also support an annual series of summer workshops, which help Pennsylvania high-school and middle-school teachers better educate their students about, black holes, cosmology, stars, and the scientific method. Teachers from underserved school districts will be recruited and provided with scholarship funds. Pencil-beam and wider field multiwavelength surveys are now making it possible to study, over almost the full span of cosmic history, the relations between long-term SMBH growth and host-galaxy properties/environment. The PI will investigate and interpret the links between SMBH growth, host-galaxy properties, and cosmic environment utilizing a sample of 1.3 million well-characterized galaxies/AGNs. They will address the following key questions: (1) What do relations between SMBH growth, host stellar mass, and redshift imply about SMBH/galaxy coevolution? (2) How does SMBH growth depend upon cosmic environment in rich, high-redshift structures? New data from, e.g., the Rubin LSST Deep-Drilling Fields and the VLT MOONS spectrograph will be critical in answering these questions. Large-scale structures, including clusters/protoclusters, will soon be mapped by intensive spectroscopic surveys. This will allow rigorous, systematic investigations of the dependence of SMBH accretion rate upon environment in rich, high-redshift structures where more-limited targeted studies indicate enhancements in SMBH growth. The Pis will also study the locations of AGNs within clusters/protoclusters for insights into feedback and quenching, and they will assess AGN obscuration levels to search for rapid, hidden phases of SMBH growth. 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

When two states face off in an international crisis, why does one state believe that its rival will capitulate, rather than escalate to war? Recent research suggests specific past actions – such as fighting in past crises or honoring alliance commitments – influence a state’s reputation as resolved to fight. Yet past studies have explored just one determinant of reputation in isolation from the others. This project will develop a comprehensive theoretical framework to explain which actions, or combinations of actions, matter most for building a reputation. It will also collect new data on cross-national historical events and cross-national survey data to establish which past foreign and domestic policy choices by states influence their likelihood of facing military challenges and the outcomes of international crises. This project advances U.S. national security in two ways. First, it will help to identify periods of heighted risk to U.S. interests by showing which specific domestic and foreign policy choices can create the impression of weak U.S. resolve. Second, it will explain how reputational damage from avoiding international conflict in one instance can be offset by other choices, such as alliance commitments or even domestic political actions. This means that the U.S. does not need to engage in every possible conflict in order to sustain a reputation as resolved to fight in future disputes. This project is the first to investigate how international reputations accumulate through many different past actions. Instead of simply studying whether past actions matter, it will provide a framework for assessing how much different actions matter relative to each other, and how they work in combination. The framework will bridge the gap between rationalist and psychological studies of reputation by using formal theory to predict how much actions that reveal certain psychological attributes will contribute to perceptions of resolve. The investigators will test expectations in a rigorous, multi-method manner. They will use U.S. and international survey experiments to provide causal estimates of the relative reputational impact of various actions. They will use quantitative coding of declassified intelligence documents, qualitative case studies, and interviews with U.S. officials to illuminate how elites utilize their rival’s diverse actions to estimate their rival’s resolve. Finally, they will collect novel cross-national data to understand which past actions influence reputations across history. 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 Faculty Early Career Development Program (CAREER) grant supports research that will create, evaluate, and disseminate new methods for incorporating decision support through artificial intelligence during early building design. The project addresses challenges in structuring and managing the data used to train predictive models that augment design, reveal relationships between key early design decisions and the predicted performance of the building, and communicate suggestions for improving the design in a way that is intelligible and actionable for professionals in architecture and engineering. Parametric modeling is increasingly used in architectural engineering to consider the performance benefits of design options through early simulation. Benefits of exploring different building geometries, configurations, and systems include reduced energy use and embodied carbon as well as increased daylight availability. However, common methods for systematically searching through design options have historically required specialized modeling knowledge, long simulation times, and automated optimization algorithms that are difficult to control. Such workflows can be tedious to formulate and often fail to deliver clear, actionable directions during a natural design process. In response, this research will create and interrogate a system that learns the anticipated behavior of building forms, predicts these values for a new form, and provides suggestions for significant drivers and how to improve an initial design. The research will advance interactive parametric design while reducing repeated simulation, a strategy which could promote national welfare by generating high-performance, sustainable future buildings. Guided by an industry advisory board, the integrated research and education component of the project involves educating pipeline and undergraduate design students as well as mid-career architectural engineers in the use of computational tools in design space exploration. The research plan involves curating building data, using it to train flexible machine learning models that are widely applicable in design, and then exploring and testing new methods for interacting with such models during the design process. The first step is to create and consolidate digital libraries of building forms and corresponding performance models at early design resolution and then segment them into typology-specific “design spaces." The libraries will come from existing datasets, models of fully designed buildings, and self-generated simulation data. Next, the design models will be reduced to representative features that are universal for each design space segment. The data will then be used to identify performance patterns and train surrogate models that predict key performance metrics for new forms based on the underlying data structures, which can be updated as more data is generated. Finally, several methods will be synthesized into a new flexible design procedure to read in potential geometries, visualize expected performance, and provide improvement suggestions while combining sensitivity analysis, dimensionality reduction, and interactive optimization. Design suggestions will be based on simplified design latent spaces and user preferences, enabling true, interactive human-computer collaboration. The methods will be validated by comparing prediction results to full simulations and databases of existing buildings, as well as an initial design study of the interface assessing designer behavior while engaging with design suggestion. Interactive computational tools will be integrated into coursework to develop student intuition about building behavior and help them think creatively and synthetically during technical design tasks. Students will participate in programs to educate practicing engineers in computational thinking, parametric design, and optimization, which are critical to their engagement with an industry that increasingly manages design information and makes decisions using code. 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

NON-TECHNICAL: Everyday products like gaskets and tires are made from rubber mixed with solid particles. The characteristics of these materials provide robust shapes when pressed and pulled under operating conditions. However, these same characteristics lead to waste disposal problems at the end of their useful life. Although re-processable analogs that could be recyclable have been reported, a significant challenge is maintaining the robust shape fidelity of traditional materials. This proposal seeks to overcome this challenge through controlling how the particles in the recyclable materials are arranged. It is hypothesized that the chemistry of the particles can be used to manipulate how they are organized within the re-processable rubber and that string-like, rigid assemblies could act as struts to limit undesired shape changes in the material during operation, but also that these could then be re-processed into new products at higher temperature. The fundamental insights developed from the proposed work could enable a transformational change in the waste management of rubber-based products to enable re-use and recycling that reduces crude oil required for their manufacture. The concepts associated with the work will be included within a recycling class that is aimed to arm future scientists and engineers with the knowledge required to design plastic products with recyclability in mind. TECHNICAL: The morphology of nanocomposites is controlled by a balance of intermolecular forces between components and the entropy associated with chain conformations available in the nanocomposite relative to the neat melt. Polymer grafting to nanoparticles provides an elegant approach to modulate these characteristics to enable structural control in thermoplastic nanocomposites, but there is limited knowledge on how covalent adaptive networks impact the ability to control structure with grafted nanoparticles. The proposed research will establish the fundamental science needed for nanocomposite design principles of covalently adaptive network elastomers through the physiochemical characteristics (molecular mass, composition) of the matrix and brushes grafted on the filler to translate design characteristics of the vitrimer and the nanoparticle brush to the morphology and mechanical performance of the nanocomposite. This structural control at multiple length scales provides an opportunity to overcome the limitation of creep in vitrimer materials through the filler topology developed from assembly that limits the large-scale reorganization of the network that leads to substantial permanent deformation. In these cases, controlled aggregation into string-like fractals is hypothesized to increase the activation energy for flow. Additionally, the ability to control these structures through the particle functionalization also offer opportunities to better understand fatigue mechanisms in thermoset composites. These fundamental studies will offer insight into how structure impact creep and fatigue in vitrimer nanocomposites and potential design rules for fillers to be used with vitrimers. . This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Rioux of the Pennsylvania State University will investigate the role of emergence and cooperativity in the synthesis of anisotropic colloidal nanostructures. Utilizing the synthesis of Ag nanocubes (NCs) by the polyol method as a prototypical example of a complex nanostructure synthesis, Professor Rioux and their group will demonstrate the impact of polymer structure and halide acid identity on the emergence of shape control, which requires cooperativity between solid Ag halide dissolution and the reduction of Ag ions to form Ag NC structures. The team will demonstrate the ubiquitous nature of emergent phenomena and cooperative behavior of reducing agents and halide ions utilizing the synthesis of small Au nanorods (NRs). In this colloidal synthesis, cooperative behavior between constituents dictates nucleation and growth events, in a manner similar to that proposed for Ag NC synthesis. The research aims to advance the understanding of the complex reaction networks associated with colloidal nanostructure formation. The research will provide the information needed to synthesize nanostructures with specific properties to enable precise function. A postdoctoral scholar and undergraduate students will be trained in colloidal synthesis with a specific emphasis on mechanistic evaluation of anisotropic growth. Trainees will present their results via publications and presentations to the scientific community. The postdoctoral scholar will participate in a pilot program with the Materials Characterization Laboratory at Penn. State to develop rigor and reproducibility standards in nanochemistry. The size and shape of nanostructures impact their specific function, but apriori control of such properties is challenging due to a lack of understanding the governing physico-chemical interactions within complex reaction networks leading to shape control. Ag NCs or other shapes will be synthesized utilizing different lactam polymers and hydrohalic acids (HX, X = Cl, Br, and I). Changes in the lactam ring size and alkyl amide polymers will be utilized to examine the influence of structure and polymer-solvent interactions on the rate of Ag ion autocatalytic reduction. Kinetic-thermodynamic coupling between AgX dissolution, where the identity of X controls solubility, and autocatalytic Ag ion reduction by lactam polymer ends is required for shape control. Deviation from balanced dissolution-reduction leads to low selectivity to cubes. Slow dissolution of AgX or lactam polymer structure promoting fast Ag+ reduction leads to rapid Ag+ reduction leading to the formation of Ag structures with large aspect ratios. They will employ a new synthetic approach, called ‘perturbative synthesis’ to probe the mechanism of nanostructure growth through the introduction of new reagent(s) at varying extents of reaction. This approach will enable the ranking of the kinetics of nucleation to growth events at different reaction conditions. Professor Rioux and their team's research aims to develop a complete mechanistic understanding of Ag NC and Au NRs syntheses to propose a unified mechanism inclusive of emergent and cooperative features. 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 doctoral dissertation project assesses how and to what extent chronic stress impacts skeletal growth in the brain floor. To achieve this goal, the study uses tissue samples derived from a previous experimental animal model. Understanding the effects of stress on the skull base is important because this region sits directly beneath the developing brain. If the growth of the brain floor is interrupted the resulting skull has an abnormal shape that can impact the developing brain. The study provides valuable health information and tests whether the brain floor area is protected from stressors. By using samples derived from an earlier study, the researchers reduce to zero the number of experimental animals involved in the study. The study supports the training of students with diverse ages and background and contributes to a workshop and museum exhibit. The study uses tissues derived from an experimental murine model in which some mice experienced stressful changes at different ages. Histological and immunohistochemical analyzes are used to measure the effects of stress on the bones, vasculature, and cartilage of the braincase floor (i.e., cranial base). Histomorphometric methods assess cellular morphology and cellular composition, whereas immunohistochemical assays quantify the presence of cytokines on the induction or inhibition of bone formation, and the prevalence of a red blood cell marker on the development of blood vessels in the braincase floor. 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 supports attendance at the 9th Physics At eXtreme (PAX) workshop, focusing entirely on next-generation gravitational-wave science opportunities and challenges. PAX is a discussion-driven workshop and includes experts from a wide range of fields that will benefit from next-generation detectors: astronomers, astrophysicists, general relativity theorists, cosmologists, nuclear physicists, numerical relativists, and data analysts. PAX provides the optimal venue to identify actionable items and initiate collaborations to tackle the work needed to maximize the scientific output of the next-generation detectors. Next-generation detectors will be built in the second half of the 2030s when the current generation of students will lead the field. A major goal of PAX is to directly and actively involve early career scientists in shaping the future of the field. The 9th PAX workshop will include an early career scientist lunch where young and experienced researchers can discuss how to make academia more accessible and equitable. 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

Non-technical abstract: A central theme in contemporary condensed matter physics is the design of ‘topological quantum materials’ whose unusual physical properties are best described by an elegant interplay between core concepts from modern physics (quantum mechanics, special relativity) and mathematics (symmetry, topology). In this project, the principal investigator (PI) focuses on designing, synthesizing, and studying a new family of topological quantum materials (‘hybrid topological semimetal heterostructures’) whose topological character is not immutably determined by the crystal composition and structure, but rather can be tuned using an external knob (electrical voltage). This provides a powerful framework for developing a rigorous understanding of emergent quantum phenomena that are predicted to arise in these quantum materials. The hybrid topological semimetals developed in this project also lend themselves to the fashioning of device components that are relevant for future quantum technologies. The PI uses state-of-the-art materials synthesis (‘molecular beam epitaxy’), accompanied by advanced nanopatterning and measurement methodologies, to carry out this research. The project trains students in sophisticated experimental techniques so that they can effectively contribute at the frontiers of quantum science and technology in academia and industry. The PI is committed to developing a research program that combines scientific excellence with an inclusive, supportive, nurturing climate. The PI and graduate students are also developing outreach activities aimed at engaging and educating general audiences about contemporary quantum materials through hands on activities such as a ‘quantum materials escape room’ during Penn State’s Family weekends and during visits to local elementary schools. Technical abstract: Dirac and Weyl semimetals form an important part of the contemporary landscape of topological quantum materials because the presence of Weyl nodes leads to remarkable physical consequences, such as surface Fermi arcs, the chiral anomaly, the anomalous Hall effect, and the axial magnetoelectric effect. Most experimental studies of these topological semimetals focus on materials whose topological band structure is fixed by the crystal lattice structure and composition. This project centers on developing and studying epitaxially engineered hybrid topological semimetal heterostructures wherein the underlying symmetries (time reversal, inversion, gauge) and topology can be electrically tuned in a single material via interfacial proximity effects. Molecular beam epitaxy is used to synthesize heterostructures that interface the canonical Dirac semimetal Cd3As2 with a ferromagnetic III-V semiconductor (such as [In,Mn]As) and with a conventional superconductor (such as Nb), thus creating a hybrid quantum material system wherein electrostatic gating tunes the breaking of time reversal symmetry and gauge symmetry. A comprehensive suite of spin-sensitive probes (anomalous Hall effect, quantum transport, spin-dependent tunneling, polarized neutron reflectometry) and phase coherent measurements (Josephson effect, mesoscopic transport) are marshaled in a search for signatures of topological phase transitions as well as emergent topological states such as skyrmions and monopole superconductivity. 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

Additive manufacturing, or 3D printing, has great potential for making customized patient-specific implants and biodevices in complex shapes. Currently available raw materials used for printing polymer devices for these purposes are mostly not strong enough or do not possess antimicrobial characteristics, which make the implants and biodevices susceptible to microbial infection. Such biodevice-related infections burden the healthcare system and threaten patients’ lives. This Engineering Research Initiation (ERI) award supports fundamental research to provide the knowledge needed to make strong antimicrobial materials for 3D printing. A composite in filament form will be made from polymers in the PAEK (polyaryletherketone) family, which is known to be strong, and silver nanoparticles, which are known to have a broad spectrum of antimicrobial activities against bacteria, fungi, and viruses and low toxicity to living cells. The new materials will allow the fabrication of customized implants and biodevices that are strong and inherently antimicrobial. This research will contribute to knowledge in the areas of advanced manufacturing, biomedical engineering, materials science, and microbiology. The multi-disciplinary research will be incorporated in course content. The extrusion of antimicrobial filament of the PAEK family with incorporated silver nanoparticles presents two challenges: agglomeration of the nanoparticles within the polymeric matrix, which significantly reduces the antimicrobial efficacy, and variation of the filament diameter, which produces defective parts when extruded during additive manufacturing. This research investigates the hypothesis that adding a dispersion agent will prevent the agglomeration of the silver nanoparticles and improve the extrusion process. The research team will conduct experiments to determine the relations between silver nanoparticle and dispersion agent contents and antimicrobial efficacy, dispersion uniformity within the polymeric matrix, cytotoxicity, and fabricated part mechanical properties. An algorithm will be developed to optimize the extrusion process parameters for polymeric nanocomposites and will be validated using the experimental results. Characterization of 3D printed test specimens, including antimicrobial efficacy, cytotoxicity, thermal, morphological, and mechanical properties, will be performed to establish the relationship between process parameters and properties of the new antimicrobial PAEK nanocomposites. 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

Tissue stiffness is known to impact cell growth, death, and function. However, less is known about the effect of the fluid-like properties of tissues on cell fate decisions. This award supports fundamental research to understand how cells sense the fluid-like properties of their environment. The goal is to reveal information about how tissue mechanical properties guide cells to grow, die, or exhibit specialized functions. This information will help to understand how organs and embryos grow, how tissues repair from injury, and how to make artificial organs for replacement therapies. Therefore, the work may provide biomedical and healthcare benefits to society. Research efforts will be integrated with education by hosting undergraduate students in research experiences, developing hands-on activities about tissue mechanics for an undergraduate-level laboratory course, and developing an undergraduate research module for first-year seminar courses. While the effect of tissue elasticity on cell function is well recognized, there is a lack of knowledge regarding the regulation of mechanotransduction pathways by matrix viscoelasticity. To fill this knowledge gap, this project will elucidate how the viscous properties of the extracellular matrix influence cell response to growth factors and will delineate mechanoresponsive pathways regulating how matrix viscoelasticity is sensed by cells to guide cell fate decisions. To achieve this, the research team will develop and characterize viscoelastic materials with tunable elastic and viscous properties. This platform will be used to determine the impact of viscous dissipation on cell proliferation, apoptosis, and differentiation in response to growth factors. Studies will also establish how matrix viscous dissipation regulates cell-matrix adhesion and kinase-mediated signaling pathways. Furthermore, the role of mechanoresponsive signaling molecules in regulation of growth factor receptor signaling and degradation will be examined. By elucidating mechanotransduction mechanisms downstream of matrix viscoelasticity, this work will advance fundamental understanding of how cells respond to the combined effects of growth factors and matrix physical properties. 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 proposal will help to answer one of the most important questions in the geosciences: Why plate tectonics operates on Earth. By considering non-Earth-like compositions the project will also constrain the likelihood of plate tectonics developing on other planets, an important goal in astrophysics. Ultimately, this work will help to place the Earth within the broader context of rocky planets in the galaxy. This project will also provide important educational and research opportunities to graduate students at Penn State University and Washington University in St. Louis. Undergraduate students from astronomy and planetary science will study the development of plate tectonics, giving them interdisciplinary training between astronomy and geoscience that is critical for future leaders studying planets beyond our solar system. Plate tectonics requires the formation of narrow zones of weakened rock that act as plate boundaries, separating stable plate interiors. It is within these plate boundaries that most deformation associated with plate movement occurs. However, the physical mechanism(s) allowing localized weak zones to form are not well understood, nor why this behavior is only seen on Earth. One promising mechanism to explain localized deformation, based on multiple lines of evidence, is deformation-induced reduction of the mineral grain sizes and the activation of grain-size sensitive deformation mechanisms. A planet where the mantle is dominantly composed of one mineral may not be able to experience enough phase mixing during deformation for significant grain size reduction. The goal of this proposal is to test whether certain planet compositions, among those inferred for rocky planets both within our solar system and beyond, would preclude the operation of plate tectonics due to inhibited phase mixing, using a combination of laboratory experiments and theoretical models. Motivated by the potential importance of mineral phase mixing in shear localization, and the compositional variety expected for extra solar planets based on compositions of rock forming elements in stars, this project hypothesizes that planet composition, which dictates the mineral makeup of the mantle, exerts a key control on whether plate tectonics can operate. Specifically, mantles that approach 50-50 mixtures of the two dominant mineral phases may be most favorable for plate tectonics, while plate tectonics may be precluded for planets whose mantles are near monominerallic. This hypothesis will be tested with a project integrating new rock deformation experiments with new numerical models of mantle convection to determine which mantle compositions are most favorable for plate tectonics and which, if any, preclude plate tectonics. Experiments will deform relevant materials to high strains to assess the factors that control the rates of phase mixing. Specifically, these experiments will 1) determine how the relative proportions and strength contrast between the dominant mineral phases of mantle rock affects the efficiency of phase mixing and grain size reduction. Numerical models of phase mixing, benchmarked against these experiments, will be used to develop parameterizations of this process to include in mantle convection models. The mantle convection models will then be used to 2) determine how the conditions needed for plate tectonics to develop are modified when the physics of phase mixing is considered; and 3) determine how the volume fractions of, and strength contrast between, primary and secondary mineral phases, affects the conditions for plate tectonics. Finally, the modeling and experimental work will be integrated to 4) assess the likelihood of plate tectonics across the range of proposed mantle compositions for exoplanets. This work will provide new insight into the mineralogical factors that control whether plate tectonics can develop on rocky planets, helping to both explain its operation on Earth and give new constraints on its feasibility on exoplanets. In addition, this project will provide the most complete description of mineral phase mixing, and its dependence on rock composition, to date and be the first to incorporate the physics of mineral phase mixing into mantle convection models of plate tectonics on Earth and other planets. 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 supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Neutron star mergers are one of the most powerful events in the Universe. These events shake the very fabric of spacetime generating so-called gravitational waves that can be detected at distances of hundreds of millions of light years by observatories such as NSF's Laser Interferometric Gravitational-wave Observatory (LIGO). In the extreme conditions found in these events, some of the material inside the neutron stars is transformed into rare elements. Indeed, it is thought that most rare-earth elements, which power all our modern technology, are produced in neutron star mergers. The gravitational wave and light signals from neutron star mergers contain a wealth of information about the physics of matter at extreme densities and the conditions in which these elements are formed. However, their interpretation is complicated by the fact that the motion of plasma in neutron star mergers is very chaotic. This project aims to develop new techniques, inspired by the methodology developed in weather forecasts, to predict the motion of matter in neutron star mergers and its resulting astronomical signals. This project is dedicated to studying the multimessenger emission and the nucleosynthesis yield from neutron star mergers. The team will perform the first "zoom-in" simulations of general-relativistic magnetohydrodynamic turbulence in the hot, dense, nuclear matter formed in mergers. These simulations will be used to develop turbulence models and train machine learning algorithms bridging the scale separation between the neutrino-viscous scale (centimeters) and the disk size (hundreds of kilometers). Global simulations using these new models will be employed to develop uncertainty-quantified models of the gravitational wave and electromagnetic signal from merging neutron stars to interpret past and future observations. 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 supports the Neotoma Paleoecology Database. Neotoma is one of the most widely used and trusted international data resources for fossil data, growing rapidly in the volume and variety of its data holdings, functionality of its software services, and the size and scope of its user community. This award will allow Neotoma to grow and enhance systems to support higher rates of data additions, more streamlined data curation, and better support solutions for new communities seeking to use Neotoma data. This project provides access to publicly funded data and supports researchers, educators, and the public by providing a high-quality, expert-curated open data resource for paleoecological and paleoenvironmental data. Specific activities for this project include better support for rapid upload of hundreds to thousands of datasets from participating research teams through enhancements to the Data Bulk Uploader System (DataBUS), with newly added ORCID user authentication and support for the popular Linked Paleodata (LiPD) format. Embargo Manager will support early data contributions and better data management practice, in alignment with NSF Division of Earth Sciences (EAR) Data and Sample Policy. The Hierarchical Vocabulary and Taxonomy Manager (HVTM) will improve data quality and interoperability by enabling efficient viewing and curation of controlled vocabularies. Neotoma will freely upload supported data types, with priority for NSF-EAR PI data, and will help on-board major geoscience paleodata communities. Neotoma PIs will develop and provide multiple training support activities for scientists, with focused workshops for early career researchers (ECRs) and scientists from underserved regions, multi-lingual support for workshops and online resources, publicly posted training videos, and model workflows for data handling. Neotoma developers will reduce barriers to access and support artificial intelligence and machine-learning applications by deepening Neotoma’s metadata provisioning to Science-on-Schema and DataCite. Lastly, Neotoma stewards will create custom-tailored training and leadership opportunities for ECRs by designing workshops, videos, and code vignettes to address ECR-identified challenges. 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 Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Future engineers will need to tackle energy and climate issues in a wide range of sectors as nations work towards decarbonization. College students show their interest in addressing climate issues, but most undergraduates, even engineering students, have never engaged with real-life power-producing systems. Therefore, the purpose of this project is to investigate the longitudinal learning and development of undergraduate engineering students as they work in long term research experiences on six different research areas centered on decarbonization. Each of the individual projects answers fundamental issues associated with net-zero fuels and seamlessly integrates in the context of combined heat and power (CHP) decarbonization. In addition, undergraduate students will be both researchers and participants in educational research and be studied over five years as they engage in long-term situated work environments. The Broader Impacts of this proposal relate to both decarbonization technologies and the development of an energy-literate future engineering workforce along three major themes: broadening participation; curricular reform; and integration of research, industry, and campus operations. In this project, rigorous research focuses on both the technical engineering decarbonization research thrusts as well as the education components. The two convergent research questions this project addresses are: How do we overcome key challenges in the implementation of renewable natural gas (RNG) in district CHP systems to achieve potentially negative carbon emissions? How do engineering undergraduate students co-develop energy literacy and domain-specific engineering identity in highly situated learning environments over time? This project uniquely leverages university infrastructure for undergraduate students to co-develop energy literacy and domain-specific engineering identities that will aid in their retention into the future workforce. Undergraduate students will conduct long-term, embedded, fundamental research related to CHP decarbonization in six areas: (1) capture and filtration of RNG from agricultural sources; (2) capture and filtration of RNG from water treatment; (3) impact of RNG composition on flame stability in gas turbines; (4) impact of RNG on turbine efficiency; (5) impact of RNG on turbine cooling; and (6) trigeneration for CHP efficiency improvements. The educational research plan proposes longitudinal phenomenological multiple-case study methods, employing multiple quantitative and qualitative methods throughout the five years of the grant. This novel integrated approach addresses both the technology as well as the human capital sides of decarbonization. The Intellectual Merit of the decarbonization research lies in the integrated approach of using life cycle analysis (LCA) to drive component-specific research directions, maximizing the impact on decarbonization. Each of the individual projects answer fundamental issues associated with net-zero fuels and seamlessly integrate in the context of CHP decarbonization. The Intellectual Merit of the educational research is a transformation of overarching theories of academic literacy and engineering identity development to be domain-specific with attention to highly situated learning environments, yielding impactful advances in theory and understanding of the formation of energy-literate engineers. By turning attention toward the deeply situated learning processes of these scholars, the field will gain a transformative understanding of the disciplinary aspects of both energy literacy and of domain-specific identity development. This novel approach to using university infrastructure as an educational environment may be extended to other universities. 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 draws on recent community efforts in data synthesis, the development of open-source software for climate field reconstructions, and advances in deep learning to quantitatively assess shifts in maize production in North America over the past 1,200 years by merging paleoclimate, weather generation, and crop modelling. Such shifts were influenced by changes in atmospheric carbon dioxide, water stress, crop genetics and management, yet studies on the degree of control of these variables on maize production are circumscribed to the last 100 years. Notably, the time period 800 to 2000 years ago brackets prolonged periods of droughts, termed megadroughts due to their duration. These megadroughts are unrivaled in the instrumental record, but similar events could emerge under climate change scenarios. Understanding the impacts of such potential droughts is crucial for addressing future food production challenges. Progress on quantitative crop modeling using paleoclimate scenarios pertinent to future climate impacts has been difficult, in part, due to computationally inefficient downscaling techniques. This project, however, leverages recent data synthesis and modeling efforts, the development of toolboxes for climate field reconstructions, as well as advances in machine-learning based downscaling methods, to provide quantitative, sub-seasonally resolved, and high-resolution output relevant for quantitative regional agriculture modeling. The potential Broader Impacts include using the research framework to reconstruct hydrology, water resources and ecological conditions from the past. The project has the potential to inform agricultural changes in a future warmer world and it supports substantial activities for sharing methodologies with STEM-pipeline communities, from high-school through early-career scholars, by leveraging a variety of existing educational programs, including new workshops and hackathons on machine/deep learning technologies in paleoclimatology. The plan for open access of data is more comprehensive than most, incorporating data products, methods and code, as well as commitment to open-access publication. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global environmental change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries. The teams will develop transdisciplinary and convergent research approaches on cultural heritage and environmental change, foster collaboration among the research community across several regions, and contribute to knowledge advances at the global level. The project intersects participatory action research, science co-construction, and transdisciplinarity. The RETRACE project will enable communities to identify and strengthen unique resilience levers, enhancing understanding and response to environmental risks through the integration of traditional knowledge and scientific insights. The approach will bridge the gap between theoretical resilience science and the actual experiences of community resilience, by synthesizing local narratives with scientific research, making resilience strategies more understandable and accessible to both communities and policymakers. A significant output of the project is the development of a spatial decision-support system, combining qualitative and quantitative data to aid decision-making. The project outcomes will offer a model for other vulnerable communities, providing a framework to understand and strengthen resilience in various settings. 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

Vehicle-to-everything (V2X) communication is one of the key pillars upon which connected and automated vehicles rest. In V2X, vehicles communicate directly with other road users and infrastructure, allowing perception of road conditions and driving environment beyond their own sensors, in turn supporting safer and more efficient transportation. However, V2X can also raise security issues in which malicious actors compromise a vehicle to send false or fabricated information that could mislead driving decisions and actions made by both autonomous vehicles and human drivers. This project’s goal is to better understand the risks V2X attacks may pose to drivers' situation awareness -- i.e., their perception of elements in the situation, their comprehension of what is happening, and their ability to respond to road hazards or autonomy failures -- and to develop warnings, explanations, and other interactions with drivers to increase the situation awareness. In particular, the project will focus on cases where the V2X attack sends information that is inconsistent with the physical environment, using those discrepancies to enhance drivers' vigilance for not only situation-specific cues but also the presence of possible attacks. Through this work, the project team will increase understanding and safety in autonomous vehicles that still require drivers to interact and intervene. This work consists of three research thrusts. First, the project will develop usable and trustworthy V2X warning interfaces to enhance drivers' situation awareness of their physical vicinity during automated driving. The project team will leverage a suite of approaches in human-centered design, using comprehensive evaluations that include online studies and driving simulator-based experiments. Second, to mitigate information integrity attacks on V2X communication, this project will systematically generate and assess driver-centered counterfactual explanations that integrate information from the physical environment and cyberspace to identify coherence or discrepancy between the two spaces, across a wide range of driving contexts and dynamic situations. The third thrust seeks to establish shared situation awareness between drivers and automated driving systems through human-in-the-loop reinforcement learning that leverages how drivers respond to and reason about the counterfactual explanations to identify and correct false V2X messages. The research activities will be integrated with educational activities through interdisciplinary curriculum development, hands-on training for graduate students, research experience for undergraduates, and K-12 outreach programs designed to inspire and train the future workforce in the areas of usable security and privacy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Age at death estimates are critical for the study of demography and health in past populations and for correct identifications in forensic cases. However, the accuracy and reliability of age estimation methods based on the human skeleton have limitations resulting from biases in the age, ancestry and sex composition of the collections originally used to develop them. This project advances research on an alternative skeletal age-estimation method based on the analysis of chemical changes that occur in human DNA as we age and can be preserved in the skeleton. To date, applications of such epigenetic methods have been primarily limited to blood and cheek DNA sources. This study advances epigenetic methods that use DNA extractions obtained from bone. Workshops to learn the theory and application of this new method are open to students, faculty and other researchers. Engagement and outreach opportunities are offered - introducing participants to epigenetic methods as applied to studies of human variation, and aging and health in the past. A high-quality short film about the anthropology of aging and the epigenetic clock is made freely accessible to other researchers and the public. This study develops a new method for biological age estimation from the human skeleton, using a genome-wide age-associated DNA methylation approach tailored to damaged/degraded DNA. The project aims to: (1) develop and validate a cost-efficient, robust-to-degradation, genome-scale method for methylation typing, (2) develop a high-quality predictive model of age from methylation signals in bone, and (3) characterize the potential impact of lifestyle factors on any discrepancies between chronological and biological (DNA methylation) ages. Parts 1 and 2 of this study are based on an integrative genomic-osteological analysis of 100 individuals with documented age, sex, and postmortem exposure to various taphonomic conditions from the University of Tennessee, Knoxville, Donated Skeletal Collection. The project integrates epigenomic, osteological, and lifestyle data, broadening the applications of this method. The study analyzes the relationship between biological and chronological age in the context of osteological signs of aging and stress, differentiating the biological and developmental roots of osteological traits used to estimate age in past populations and individuals. This project is jointly supported by the NSF Biological Anthropology program and the National Institute of Justice, Office of Investigative and Forensic Sciences This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Given the constant threat of software vulnerabilities and malicious attacks, the computer security community is always working on improving defense mechanisms for real-world use. At the same time, they also need to make sure these defenses can stand up to determined attackers who are ready to exploit any weaknesses. This project aims to evaluate these practical defense mechanisms, spotting and fixing potential issues before attackers can cause major problems. The results will push for more automated defense improvement, affecting many research areas. The project addresses high-profile memory errors. The educational part of the project focuses on teaching students how to design, evaluate, and improve memory protection techniques, starting from K-12 and beyond, to spark interest in computer science and security. To achieve these goals, the project will investigate a set of systematic approaches to evaluate practical defense mechanisms to understand their strengths and help enhance their robustness. The project will identify configuration variables that determine the strength of defenses, and measure the feasibility for attackers to manipulate these bytes to undermine protections. Second, defense-debloating techniques will identify radical optimizations that remove essential security checks and bring old threats back into hardened programs. Third, the project will focus on detecting and preventing resource-exhaustion attacks introduced by out-of-band scrutiny that requires extra software or hardware resources. Finally, a new technique, squeezing analysis, will be used to achieve strong and fast protection. These analyses and enhancement will be applied to diverse defenses such as control-flow integrity (CFI) and reassembly. 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

Creativity is a fundamentally interesting and important aspect of cognition. It is already among the most valuable attributes of the STEM workforce, especially in engineering which requires generating and evaluating novel solutions to complex problems. It is projected to become even more vital in the innovation economy of the near future. Nonetheless, creativity is not generally integrated into engineering coursework nor is the explicit assessment of it a common part of student evaluation. Indeed, it has proven to be difficult to define and measure. More effectively integrating creativity into undergraduate engineering education requires knowing which dimensions of creativity contribute most to students’ future STEM success. In this project, researchers will parse creative cognition among engineering undergraduate students along core dimensions, including visual vs. verbal creativity and generative vs. evaluative creativity, and, in a longitudinal study, examine the relationships between creativity and engineering success during and after college. The substantial time, effort, and cost required for humans to score creativity limits the integration of such measurement into STEM classrooms and other educational and professional contexts. The researchers will use emerging computational approaches to transform the measurement of creativity, seeking to open new avenues for researchers to capture creativity in STEM on a large scale and in efficient and valid ways. Improved means of assessing creativity at such a scale may be especially important in light of the rise of hybrid creativity in which humans evaluate AI-generated ideas. Finally, traditional STEM assessment has been shown to lead to attrition among underrepresented minority and female students in STEM. Encouragingly, creativity is generally associated with less disparity by race and Socio-Economic Status (SES) than are traditional academic assessments, and generally does not show gender disparity. This work will lay the foundation for the eventual development of assessments that allow STEM educators to identify promising and creative students who might otherwise not persist in STEM, with downstream implications for broadening STEM graduate training and STEM workforce inclusion. The project has three primary objectives. In Aim 1 the research team will investigate how core dimensions of creative cognition (visual, verbal, generative, evaluative) relate to future STEM success in a 3-year longitudinal study. Using two cohorts of engineering students, one tracked during college and one tracked post-college, they will test verbal and visual measures of generative and evaluative creativity in both of these cohorts, including students’ evaluation of AI-generated ideas. Generative creativity will be scored by human raters. Evaluative creativity will be scored relative to human expert-rated criteria. These scores will be used to predict both college and post-college STEM success at a 3-year longitudinal timepoint. In Aim 2, researchers will test computational creativity scoring models and determine which best predicts human creativity ratings as well as which of those computational models best predicts future college and post-college STEM success, parsing the relative contributions of verbal and visual creativity. The human and computational creativity scoring approaches will also be examined to see whether they show less disparity for under-represented minority students than do traditional academic assessments. In Aim 3, the researchers will test computational creativity scoring approaches to predict college STEM success, using a large-scale data set of college applicants (n > 500,000) as a testbed. The dataset was compiled across 24 diverse U.S. universities (including public, private, and minority-serving institutions). The study will identify metrics that best predict human ratings of applicants’ creativity and use these metrics to predict STEM success (e.g., STEM Grade Point Average, STEM degree attainment). They will look to see whether disparity on race and SES is reduced relative to SAT scores. This project is supported by the EDU Core Research (ECR) program. ECR supports fundamental research that generates foundational knowledge that advances the research literatures in STEM learning and learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This Level 3 Engaged Student Learning project aims to serve the national interest by creating environments in engineering design courses, which are pivotal for persistence in engineering. By implementing evidence-based teaching practices in engineering design courses from first year (cornerstone) to final senior design projects (capstone), the project aims to improve retention of engineers. While more individuals from these communities are enrolling in engineering programs, students often face unwelcoming academic environments. As such, this project seeks to address a critical need in the engineering community: developing and validating scalable teaming models that foster success in undergraduate engineering communities for all students. In order to achieve the project goals, three aims are proposed: 1) identify the impact of INTEGRAL training materials on students’ persistence in engineering and their ability to develop collaborative teams across varying university settings; 2) foster instructors’ abilities to facilitate teaming through a validated train-the-trainer program, and; 3) increase sustainability by identifying what factors impede or enhance effective implementation in different university settings. The methods used in this project include both short-term and long-term components in collaboration with external evaluations. The mixed-methods approach is intended to lead to richly contextualized and generalizable data sets, transforming understanding of learning environments in STEM. The implementation and evaluation of the project's teaming educational practices seeks to impact 29,000 students and 140 faculty members across 12 campuses from two public universities. The systems focus (student, faculty, campus, and university lenses) are likely to lead to scientific advancements in how educators prepare, train, and implement teaming practices across different educational levels and institutions. The NSF IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This 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

Since the advent of internet social media, people have been able to communicate openly with others in their communities of choice. In some cases, such as with new users, their communications may not follow community rules and norms are and therefore may be inappropriate, as in cyberbullying. When users engage in harmful behavior, they may be virtually admonished or punished online, for example with account suspension. This research takes an alternative community support approach to educating users, including especially teenagers, to interactions online that avoid harm such as bullying. The approach builds on research in the field of sociology that shows supportive and community-based education can help people interact and collaborate effectively online, learning how rules and norms apply. The project leverages sociological science to enhance and support online interaction in online groups and communities. By understanding and supporting users’ and community perspectives, the project deepens the understanding of user interaction online, bridges real-world sociological research with cyberspace scholarship, and contributes to the well-being of individuals and the sustainability of communities. The project investigates users' experiences with their interactions online, and their access to existing community-based education resources. It also explores design interventions for guiding users' community cooperation and enhancement. Empirical methods, including interviews and surveys, are used to generate empirical and conceptual knowledge about how users perceive and reflect on their online behaviors, and to identify effective resources and strategies that work for effective support and teaching social integration of users in their communities. Participatory design methods are employed to explore how community-based education can be integrated into online systems for communities that desire additional mechanisms beyond those currently deployed. 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 project aims to conduct comprehensive statistical and computational analyses, with the overarching objective of advancing innovative nonparametric data analysis techniques. The methodologies and theories developed are anticipated to push the boundaries of modern nonparametric statistical inference and find applicability in other statistical domains such as nonparametric latent variable models, time series analysis, and sequential nonparametric multiple testing. This project will enhance the interconnections among statistics, machine learning, and computation and provide training opportunities for postdoctoral fellows, graduate students, and undergraduates. More specifically, the project covers key problems in nonparametric hypothesis testing, intending to establish a robust framework for goodness-of-fit testing for distributions on non-Euclidean domains with unknown normalization constants. The research also delves into nonparametric variational inference, aiming to create a particle-based algorithmic framework with discrete-time guarantees. Furthermore, the project focuses on nonparametric functional regression, with an emphasis on designing minimax optimal estimators using infinite-dimensional Stein's identities. The study also examines the trade-offs between statistics and computation in all the aforementioned methods. The common thread weaving through these endeavors is the synergy between various versions of Stein's identities and reproducing kernels, contributing substantially to the advancement of models, methods, and theories in contemporary nonparametric statistics. 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

Understanding human behaviors in wildlife tourism settings is essential to ensuring the well-being of wildlife, protected areas, and individuals whose livelihood depends on them. Despite tour guides’ significant influence as ambassadors, educators, and role models for conservation behaviors, their potential for minimizing the negative impacts of tourism is rarely acknowledged, leveraged, or studied. Social science research that aims to improve wildlife tourism management and decision-making remains urgently needed. To address this need, the current research is designed to generate knowledge about how the interfacing moral engagement of guides and tourists influences risks caused by their behaviors in the human-wildlife contact zone. Given the biodiversity conservation challenges faced across the planet, the current research is well-poised to provide generalizable insights into the underpinnings of decision-making and management of tourist behaviors during wildlife tourism activities in protected areas. Linking theory on ethical decision-making process of wildlife tour guides and wildlife tourists, and their associated behaviors during tourism activities, this research is guided by the following overarching question: how does moral engagement influence the decision-making and management of risk among tour guides and tourists on wildlife tourism game drives? More specific research questions and hypotheses assess: 1) the pre-contact zone conservation values and expectations that tourists and guides hold; 2) the expected and actual situational characteristics of game drives; 3) the interpersonal dynamics between tourists and guides during tourism activities; 4) the mediator variables that influence the valence of moral engagement of tourists and guides during tour activities; and 5) the prevalence of unsustainable behavior decisions during tour activities. The rigorous mixed methods analysis plan is both qualitative and quantitative in nature and tests a dual-process model of moral engagement and its influence on decision-making and management of wildlife tourism. The resulting understanding of complex interpersonal dynamics in wildlife tourism settings will be of value to research undertaken in tourism-dependent protected areas of high biodiversity value around the globe. 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.