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At least 1,153 records · Page 64

COMPARATIVE STUDY AMONG HYBRID GROUND SOURCE HEAT PUMP SYSTEM, COMPLETE GROUND SOURCE HEAT PUMP AND CONVENTIONAL HVAC SYSTEM

In this paper, a hotel with hybrid geothermal heat pump system (HyGSHP) in the Pensacola is selected and simulated by the transient simulation software package TRNSYS [1]. To verify the simulation results, the validations are conducted by using the monthly average entering water temperature, monthly facility consumption data, and etc. And three types of HVAC systems are compared based on the same building model and HVAC system capacity. The results are presented to show the advantages and disadvantages of HyGSHP compared with the other two systems in terms of energy consumptions, life cycle cost analysis.

15 GEOTHERMAL ENERGY↗

Data‐Driven Safety Risk Prediction of Lithium‐Ion Battery

Abstract Inevitable safety issues have pushed battery engineers to become more conservative in battery system design; however, battery‐involved accidents still frequently are reported in headlines. Identifying, understanding, and predicting safety risks have become priorities to further accelerate technology and industry development. However, diverse loading scenarios, significantly varied stress‐induced short circuit mechanisms, and highly coupled mechanical–electrochemical safety behaviors have remained grand challenges. Herein, the safety risk is termed as the probability of the mechanical triggering of an internal short circuit, to reflect the safety related behaviors of lithium‐ion batteries. Based on a mechanical model and experimental results, a sufficient dataset is generated consisting of strain states and their corresponding safety risks, covering both cylindrical and pouch cells, various states of charges, and loading conditions. Machine‐learning tools combined with the established finite element mechanical model are applied to predict the safety risks of the cells. The results achieve a high level of accuracy on the test data (the relative error of the average short circuit prediction deviation is less than 6.2%.). This work underpins the safety risk concept and highlights the promise of physics combined with data‐driven modeling methodology to predict the safety behaviors of energy storage systems.

Jia, Yikai↗

Unraveling spin entanglement using quantum gates with scanning tunneling microscopy-driven electron spin resonance

Quantum entanglement is a fundamental resource for quantum information processing, and its controlled generation and detection remain key challenges in scalable quantum architectures. Here, we numerically demonstrate the deterministic generation of entangled spin states in a solid-state platform by implementing quantum gates via electron spin resonance combined with scanning tunneling microscopy (ESR-STM). Using two titanium atoms on a MgO/Ag(100) substrate as a model, we construct a two-qubit system whose dynamics are coherently manipulated through tailored microwave pulse sequences. We generate Bell states by implementing a Hadamard gate followed by a controlled-NOT gate, and evaluate its fidelity and concurrence using the quantum-master equation-based code TimeESR. Our results demonstrate that ESR-STM can create entangled states with significant fidelity. This study paves the way for the realization of atom-based quantum circuits and highlights ESR-STM as a powerful tool for probing and engineering entangled states on surfaces.

Switzer, Eric D. [Donostia International Physics C↗

Performance Engineering Research Institute SciDAC-2 Enabling Technologies Institute (Final Report)

Enhancing the performance of SciDAC applications on petascale systems had high priority within DOE SC at the start of the second phase of the SciDAC program, SciDAC-2, as it continues to do so today. Achieving expected levels of performance on high-end computing (HEC) systems is growing ever more challenging due to enormous scale, increasing architectural complexity, and increasing application complexity. To address these challenges, the University of Southern California's Information Sciences Institute organized the Performance Engineering Research Institute (PERI). PERI implemented a unified, tripartite research plan encompassing: (1) performance modeling and prediction; (2) automatic performance tuning; and (3) performance engineering of high profile applications. Within PERI, USC's primary research activity was automatic tuning (autotuning) of scientific software. This activity was spurred by the strong user preference for automatic tools and was based on previous successful activities such as ATLAS, which automatically tuned components of the LAPACK linear algebra library, and other recent work on autotuning domain-specific libraries. Our other major component was application engagement, to which we devoted approximately 30% of our effort to work directly with SciDAC-2 applications. This report is a summary of the overall results of the USC PERI effort.

97 MATHEMATICS AND COMPUTING↗

Universal Solvent Viscosity Reduction via Hydrogen Bonding Disruptors

Liquid Ion Solutions LLC (DBA RoCo Global) in partnership with Carnegie Mellon University and Carbon Capture Scientific LLC, has performed lab-scale development and evaluation of novel additives that lower the viscosity of water-lean amine solvents for post-combustion carbon dioxide capture. This project focuses on developing additives that minimize the formation of long-range electrostatic and hydrogen bonding (HB) networks, decreasing the solvent viscosity, improving diffusion, and improving the process economics. The project objectives included: 1) performing computer simulation to understand the molecular interactions of the additive molecules in water-lean CO 2 capture solvents, 2) design and synthesis of HB disruptors additives, 3) performance testing with additive molecules on model amine solvents, and 4) demonstration of the effectiveness of the optimized additives in the presence of synthetic flue gas. To meet the abovementioned objectives, the project team utilized a holistic approach that combines molecular simulation, experimental testing, and economic analysis studies. The project team developed ab initio molecular model and then perform computer simulation to develop relationship between hydrogen bonding, viscosity, and performed quantitative analysis of additive on the viscosity of the solvent. The team completed computational comparative study on a range of organic functional groups such as ethers, esters, cyclic carbonates, alkanes, and ammonium salts for their effect on viscosity gaining key insights into molecular interactions and the impact of various functional groups and molecular shapes on viscosity. Assisted with molecular simulation insights, the project team conducted additive synthesis and testing, including a proof-of-concept study, additive screening, optimization, and synthetic flue gas testing. The experimental proof-of-concept study proved that the hydrogen bonding acceptors result in significant decrease of viscosities. Detailed additive screening (exploring various functionalities and molecular structures) has been performed. Several promising additives showed excellent reduction in viscosity (30-41%) at 5% additive loading, and over 50% viscosity reduction at 10% additive loading for the model solvents. The team also performed complex screening studies on additive loadings and mixing effect among additives using the design of experiments. Based on multiple screening experiments, one additive-solvent candidate was down-selected for synthetic flue gas testing. A 100-hour continuous absorption/desorption study was conducted under simulated flue gas using a lab-scale continuous capture and separation system. No degradation (for both solvent and additive) was observed based on the GC results of the solvent samples collected from the continuous study. The team conducted preliminary engineering analyses and cost-benefit analyses to quantify the potential economic benefits of the additive approach for solvent viscosity reduction. Based on the experimental data, CO 2 capture cost savings from the capital and operating cost savings are estimated at $\$$4.7/tonne and $\$$0.3/tonne CO 2 captured, respectively. Considering the additive cost, the net benefit is estimated to be between $\$$4.32~$\$$4.86/tonne CO 2 captured.

20 FOSSIL-FUELED POWER PLANTS↗

Transformer Health Monitoring Using Dissolved Gas Analysis

As integral components of any power plant, transformers supply the generated electricity to the grid. However, a transformer’s cellulose-based paper insulation and the mineral oil in which it is immersed break down over time under standard operating conditions—or more rapidly due to potential faults within the system. As the transformer’s mineral oil breaks down, gases are released that can be measured and monitored. This technical brief exhibits a collection of diagnostic and prognostic techniques that utilities can adopt in lieu of labor-intensive periodic preventive maintenance routines. Furthermore, prognostic models have been incorporated using the latest version of the Institute of Electrical and Electronics Engineers (IEEE) standard (IEEE, 2019) for dissolved gas analysis (DGA), thus expanding it to include estimation of the time to maintenance. Overall, four different methodologies are explained, each of which aids in determining a transformer’s state of health. These methodologies include the Chendong model, the IEEE thermal life consumption model (IEEE, 2012), a diagnostic model for DGA, and a prognostic model for DGA that uses an autoregressive integrated moving average (ARIMA) model. An additional improvement for estimating missing system parameters by using monitoring data (i.e., a tool for parameter estimation utilizing Powell’s method) is presented, enabling the IEEE thermal life consumption model to benefit not only the collaborating power plant, but also the power industry at large.

24 POWER TRANSMISSION AND DISTRIBUTION↗

An Improved Model For Determining Salinity Recharge Time for Deep Borehole Disposal - 20405

In a previous publication (K. P. Travis, D. Burley and F. G. F. Gibb, WM2017 Conference, Phoenix Arizona paper no. 17480) we introduced a numerical model to estimate the time it would take for a pressure perturbation to subside following its creation from the construction of a deep geological borehole in water-saturated rock. The model was based on the notion of a point source of momentum - a solution of the time-dependent pressure-diffusion equation. Using the model, we estimated that it would take on the order of 10 k years for physical equilibrium to become re-established following the sinking of a 5 km borehole in granite. The significance of such a model is that it places an upper bound on the lifetime required of a borehole sealing system - a necessary input to a borehole post-closure safety case assessment. No engineered sealing system has ever been devised which is capable of retaining its sealing properties for 10 half-lives of long-lived radioisotopes in spent fuel or high-level waste. One of the key advantages of disposing of nuclear waste via Deep Borehole Disposal (DBD) is the natural sealing provided by density stratified groundwater. Upon sinking of a borehole, and subsequent filling with fresh water, brine or drilling mud, this natural barrier may be temporarily damaged. Over time, fresh brine from the far-field will flow towards or away from the hole (driven by a pressure gradient) and will eventually re-establish the original salinity gradient. It follows that the engineered seals need last only as long as the time required for this salinity gradient to reset itself. One of the limitations of our previous model was the use of a static boundary condition on the borehole wall. The model used a boundary pressure which varied quadratically with depth (arising from differences between the pressure of a column of fresh water in the borehole and that of a column of brine in the host rock). However as brine replaces fresh water in the borehole (driven by a pressure gradient), the boundary function must change with time. We now introduce an improved model which takes this time dependent boundary condition into account. The model also takes into account the time taken for a mixture of brine and fresh water to re-establish chemical equilibrium through the process of diffusion. The paper contains the mathematical details of our new iterative model as well as results showing the time taken to reach steady state, and concentration profiles for the components of the brine-filled borehole as a function of time together with a discussion on the implications of the results for developing a post-closure safety assessment for DBD. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

FENIX: An Open-Source Multiphysics Integrated Framework Enabling Collaborative Development of Plasma Facing Component Modeling Capabilities

Advanced modeling and simulation tools have a crucial role to play in accelerating fusion energy deployment as a sustainable power source. Multiphysics, high-fidelity computational tools can help understand, model, and quantify the complex interactions between materials performance, plasma and neutron exposure, and engineering processes. As a result, they accelerate the design, safety analysis, and performance evaluation of fusion systems. This webinar introduces the Fusion ENergy Integrated multiphys-X (FENIX) framework, an open-source multiphysics tool for plasma facing component modeling. FENIX leverages the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which has been developed by the United States Department of Energy Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. FENIX couples various MOOSE capabilities such as heat transfer, thermomechanics, thermal hydraulics, electromagnetics, and plasma kinetics with the MOOSE-based applications Cardinal (neutronics) and TMAP8 (tritium transport). During the webinar, we will present FENIX and discuss how its modularity, openness, software quality assurance processes, and licensing approach supports effective collaborations, including public-private partnerships.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

CYRANO3 the EDF fuel code performance: Global overview and recent developments on MOX fuel

CYRANO3 is the thermal mechanical industrial code developed and used by EDF to simulate the nuclear fuel rod performance in pile, and during the disposal storage period as well. This code has already been used successfully for the last ten years by the EDF engineering teams in justifying the overall reloading options covering various fuels: UO{sub 2}, UO{sub 2} + gadolinium, MOX, UO{sub 2} plus additives, and various cladding as well proposed by the nuclear fuel suppliers. Its particularity is related to a construction around a standard one-dimensional thermal and mechanical finite elements kernel with an appropriate architecture to plug in any new functionalities or models. Since the beginning it is developed as a module able integrating within a core calculation chain. Other constrain is to always guaranty a running time consistent with the calculation of a core within a reasonable time. The objective is to provide the EDF designers with a convivial and qualified code, with robust algorithms and able to quickly evolve with computer performances. Algorithms are able to account for adiabatic transients and are robust enough to avoid any undesirable overflows. The permanent challenge is indeed to provide with the more descriptive simulations not degrading to much the running time. Developments are obviously performed within the strict Quality Insurance rules. The code is then associated to a large documentation base, including model specification, realization, validation and test procedures. Maintenance is managed through a system of anomaly and improvement forms. Computers are still evolving quickly. Therefore, after an architecture adaptation, the CYRANO3 code is now integrated in the PLEIADES platform as a PWR industrial application. The platform is an exchange space allowing sharing functionalities, models and properties. In this paper, we will give a global overview of the capabilities of CYRANO3 code to simulate the thermomechanical behavior of fuel rods, and we will focus on recent developments realized on MOX fuel. In CYRANO3 code, heterogeneous models (neutronic model, micromechanical behavior law and fission gas model) are developed for MOX fuels and integrated in order to account for differential evolutions between phases under irradiation. The methodology and these models are presented in this paper. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Family of neural wiring receptors in bilaterians defined by phylogenetic, biochemical, and structural evidence

The evolution of complex nervous systems was accompanied by the expansion of numerous protein families, including cell-adhesion molecules, surface receptors, and their ligands. These proteins mediate axonal guidance, synapse targeting, and other neuronal wiring-related functions. Recently, 32 interacting cell surface proteins belonging to two newly defined families of the Ig superfamily (IgSF) in fruit flies were discovered to label different subsets of neurons in the brain and ventral nerve cord. They have been shown to be involved in synaptic targeting and morphogenesis, retrograde signaling, and neuronal survival. Here, we show that these proteins, Dprs and DIPs, are members of a widely distributed family of two- and three-Ig domain molecules with neuronal wiring functions, which we refer to as Wirins. Beginning from a single ancestral Wirin gene in the last common ancestor of Bilateria, numerous gene duplications produced the heterophilic Dprs and DIPs in protostomes, along with two other subfamilies that diversified independently across protostome phyla. In deuterostomes, the ancestral Wirin evolved into the IgLON subfamily of neuronal receptors. We show that IgLONs interact with each other and that their complexes can be broken by mutations designed using homology models based on Dpr and DIP structures. The nematode orthologs ZIG-8 and RIG-5 also form heterophilic and homophilic complexes, and crystal structures reveal numerous apparently ancestral features shared with Dpr-DIP complexes. The evolutionary, biochemical, and structural relationships we demonstrate here provide insights into neural development and the rise of the metazoan nervous system.

59 BASIC BIOLOGICAL SCIENCES↗

Generic Natural Systems Evaluation - Thermodynamic Database Development and Data Management

Thermodynamic data are essential for understanding and evaluating geochemical processes, as by speciation-solubility calculations, reaction-path modeling, or reactive transport simulation. These data are required to evaluate both equilibrium states and the kinetic approach to such states (via the affinity term or its equivalent in commonly used rate laws). These types of calculations and the data needed to carry them out are a central feature of geochemistry in many applications, including water-rock interactions in natural systems at low and high temperatures. Such calculations are also made in engineering studies, for example studies of interactions involving man-made materials such as metal alloys and concrete. They are used in a fairly broad spectrum of repository studies where interactions take place among water, rock, and man-made materials (e.g., usage on YMP and WIPP). Waste form degradation, engineered barrier system performance, and near-field and far-field transport typically incorporate some level of thermodynamic modeling, requiring the relevant supporting data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Production Facility System Reliability Analysis Report

This document describes the reliability, maintainability, and availability (RMA) modeling of the Los Alamos National Laboratory (LANL) design for the Closed Loop Helium Cooling System (CLHCS) planned for the NorthStar accelerator-based 99 Mo production facility. The current analysis incorporates a conceptual helium recovery system, beam diagnostics, and prototype control system into the reliability analysis. The results from the 1000 hr blower test are addressed.

Accelerator Design, Technology, and Operations↗

Sodium Pumping via Condensation within a Non-Wetting Porous Structure

The sodium thermal electrochemical converter (Na-TEC) is a heat engine that generates electricity via the isothermal expansion of sodium ions through a beta”-alumina solid-electrolyte. The transfer of liquid sodium from a low-pressure condenser to a high-pressure evaporator is necessary to complete the thermodynamic cycle for this conversion process. A unique sodium capillary pump for the Na-TEC is proposed, whereby low-pressure sodium vapor is condensed within a non-wetting (i.e. contact angle > 90o) stainless steel porous structure. Due to the curvature of the non-wetting liquid-vapor interface, the liquid adjacent to the interface is at a higher pressure than the vapor. This is in contrast to traditional wicks, where the liquid adjacent to the interface has a lower pressure. Thus, the curvature created at this non-wetting condensation interface supplies a directed force upon the liquid sodium that effectively “pushes” it towards a high pressure region, whereas a traditional capillary wick “pulls” the liquid. Porous structures made of stainless steel 316 are commercially available, and this material is non-wetting to sodium at the relevant temperature range of the Na-TEC condenser (500K – 673K). An experimental set-up is used to measure several interfacial and transport properties of the condensed sodium within this porous structure. These properties include the temperature dependent Laplace pressure generated at the phase change interface and the permeability of the porous structure. Liquid sodium is in contact with the porous structure and is progressively pressurized with argon gas until the sodium “breaks through” and the liquid begins to flow. This break-through pressure is determined when electrical contact is established between the flowing liquid metal and a set of electrodes cemented along the flow path. The velocity of the flowing sodium can also be measured with these electrodes, and the permeability is then determined by fitting velocity data to the classical Darcy law. These interfacial properties are then applied to a conjugate heat transfer model, which treats the coupled momentum and thermal transport processes within the non-wetting porous structure as sodium vapor is condensed. Due to the low mass flowrates in the Na-TEC (< 0.1 mg/s), higher order friction terms are neglected and the classical Darcy’s law is used for non-isothermal, compressible sodium vapor flow. At lower temperatures, the sodium vapor density becomes sufficiently small and there is a transition from classic Poiseuille flow (Kn < 0.1) to Knudsen flow (Kn > 10). Furthermore, transport due to binary diffusion is considered for situations where there is a substantial presence of non-condensable argon inside the sodium vapor plenum. Initial modeling results showing expected mass flowrates, temperature profiles, and liquid-vapor interface locations are presented for various physical conditions. Finally, the main design features of an experimental set-up used to demonstrate this capillary pumping is described in detail, including the method used to measure the low sodium mass flowrates. Initial experimental results demonstrating sodium pumping for a range of temperatures (800K – 1100K) will be presented and compared against the model predictions. A pump curve used to gauge the performance of this pumping mechanism is also created.

14 SOLAR ENERGY↗

Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics

This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.

Data Assimilation↗

Electronic properties of bimetallic metal–organic frameworks (MOFs): Tailoring the density of electronic states through MOF modularity

The development of porous well-defined hybrid materials (e.g., metal-organic frameworks or MOFs) will add a new dimension to a wide number of applications ranging from supercapacitors and electrodes to 'smart' membranes and thermoelectrics. From this perspective, the understanding and tailoring of the electronic properties of MOFs are key fundamental challenges that could unlock the full potential of these materials. In this work, we focused on the fundamental insights responsible for the electronic properties of three distinct classes of bimetallic systems, M x-y M' y -MOFs, M x M' y - MOFs, and M x (ligand-M' y )-MOFs, in which the second metal (M') incorporation occurs through (i) metal (M) replacement in the framework nodes (type I), (ii) metal node extension (type II), and (iii) metal coordination to the organic ligand (type III), respectively. We employed microwave conductivity, X-ray photoelectron spectroscopy, diffuse reflectance spectroscopy, powder X-ray diffraction, inductively coupled plasma atomic emission spectroscopy, pressed-pellet conductivity, and theoretical modeling to shed light on the key factors responsible for the tunability of MOF electronic structures. Experimental prescreening of MOFs was performed based on changes in the density of electronic states near the Fermi edge, which was used as a starting point for further selection of suitable MOFs. As a result, we demonstrated that the tailoring of MOF electronic properties could be performed as a function of metal node engineering, framework topology, and/or the presence of unsaturated metal sites while preserving framework porosity and structural integrity. Finally, these studies unveil the possible pathways for transforming the electronic properties of MOFs from insulating to semiconducting, as well as provide a blueprint for the development of hybrid porous materials with desirable electronic structures.

36 MATERIALS SCIENCE↗

Realization of a Hole-Doped Mott Insulator on a Triangular Silicon Lattice

The physics of doped Mott insulators is at the heart of some of the most exotic physical phenomena in materials research including insulator-metal transitions, colossal magnetoresistance, and high-temperature superconductivity in layered perovskite compounds. Advances in this field would greatly benefit from the availability of new material systems with a similar richness of physical phenomena but with fewer chemical and structural complications in comparison to oxides. In this paper, using scanning tunneling microscopy and spectroscopy, we show that such a system can be realized on a silicon platform. The adsorption of one-third monolayer of Sn atoms on a Si(111) surface produces a triangular surface lattice with half filled dangling bond orbitals. Modulation hole doping of these dangling bonds unveils clear hallmarks of Mott physics, such as spectral weight transfer and the formation of quasiparticle states at the Fermi level, well-defined Fermi contour segments, and a sharp singularity in the density of states. These observations are remarkably similar to those made in complex oxide materials, including high-temperature superconductors, but highly extraordinary within the realm of conventional $sp$-bonded semiconductor materials. Finally, it suggests that exotic quantum matter phases can be realized and engineered on silicon-based materials platforms.

36 MATERIALS SCIENCE↗

Development of Community, Capabilities and Understanding Through Unmanned Aircraft-Based Atmospheric Research: The LAPSE-RATE Campaign

Members of the international community developing unmanned aircraft for atmospheric research conducted a highly-successful coordinated flight week in Colorado during summer 2018 to collect measurements, develop capabilities, and enhance community in the field. Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) has been developed and have convened annual meetings and 'flight weeks'. The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), involved a one-week deployment to Colorado's San Luis Valley. Between 14-20 July 2018 over 100 students, scientists, engineers, pilots and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated 'community day' which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

54 ENVIRONMENTAL SCIENCES↗