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At least 19 records

Cumulative Spatial Impact Layers™

Cumulative Spatial Impact Layers™ (CSIL) is a GIS-based tool that summarizes spatio-temporal datasets based on overlapping features and attributes. Applying a recursive quadtree method and multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets simultaneously by calculating data, record, or attribute density. The CSIL tool was designed based on the original approach (Bauer et al. 2015) and more information on the tool overall along with applications can be found in Romeo et al. 2019). Providing an efficient summary of disparate geospatial data, CSIL bridges the gap between understanding data and analysis. Bauer, J. R., Nelson, J., Romeo, L., Eynard, J., Sim, L., Halama, J., Rose, K., & Graham, J. (2015). A spatio-temporal approach to analyze broad risks and potential impacts associated with uncontrolled hydrocarbon release events in the offshore Gulf of Mexico (NETL-TRS-2-2015 EPAct Technical Report Series). U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV; p 60. Romeo, L., Nelson, J., Wingo, P., Bauer, J., Justman, D., & Rose, K. (2019). Cumulative spatial impact layers: A novel multivariate spatio-temporal analytical summarization tool. Transactions in GIS.

Multivariate Visualization↗

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗

Advanced User Interface Capabilities [Slides]

This tutorial will review the data plotting and geometry visualization capabilities in the Fulcrum user interface. This tutorial will help you become familiar with Fulcrum’s 2D plot, and 2D and 3D geometry visualization features. You will learn how to identify plottable data items, compose and export plot and plot data for SCALE plot formats (SDF, Ampx MG/CE, PLT, F71, PTP, SPF, ORIGEN Gamma data, etc.) and visualize, navigate, cut, hide, and export the geometry and spatial data (fission-, dose-map, etc.) overlays in 2D and 3D. No prior experience with SCALE is required. Attendees can follow along using 6.3.0-beta.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Quantifying the Influence of Defects on Selectivity of Electrodes Encapsulated by Nanoscopic Silicon Oxide Overlayers

Encapsulation of electrocatalysts and photocatalysts with semipermeable nanoscopic oxide overlayers that exhibit selective transport properties is an attractive approach to achieve high redox selectivity. However, defects within the overlayers-such as pinholes, cracks, or particle inclusions-may facilitate local high rates of parasitic reactions by creating pathways for facile transport of undesired reactants to exposed active sites. Scanning electrochemical microscopy (SECM) is an attractive method to determine the influence of defects on macroscopic performance metrics thanks to its ability to measure the relative rates of competing electrochemical reactions with high spatial resolution over the electrode. Here, we report the use of SECM to determine the influence of overlayer defects on the selectivity of silicon oxide (SiOx) encapsulated platinum thin-film electrocatalysts operated under conditions where two competing reactions-the hydrogen evolution and Fe(III) reduction reactions-can occur. After an SECM methodology is described to determine spatially resolved selectivity, representative selectivity maps are correlated with the location of defects that are characterized by optical, electron, and atomic force microscopies. This analysis reveals that certain types of defects in the oxide overlayer are responsible for ∼60-90% of the partial current density toward the undesired Fe(III) reduction reaction. By correcting for defect contributions to Fe(III) reduction rates, true Fe(III) permeability values for the SiOxoverlayers were determined to be over an order of magnitude lower than permeabilities determined from analyses that ignore the presence of defects. Lastly, different types of defects were studied revealing that defect morphology can have varying influence on both redox selectivity and calculated permeability. This work highlights the need for spatially resolved measurements to evaluate the performance of oxide-encapsulated catalysts and understand their performance limits.

25 ENERGY STORAGE↗

Nanoscale Electronic Transparency of Wafer-Scale Hexagonal Boron Nitride

We report monolayer hexagonal boron nitride (hBN) has attracted interest as an ultrathin tunnel barrier or environmental protection layer. Recently, wafer-scale hBN growth on Cu(111) was developed for semiconductor chip applications. For basic research and technology, understanding how hBN perturbs underlying electronically active layers is critical. Encouragingly, hBN/Cu(111) has been shown to preserve the Cu(111) surface state (SS), but it was unknown how tunneling into this SS through hBN varies spatially. Here, we demonstrate that the Cu(111) SS under wafer-scale hBN is homogeneous in energy and spectral weight over nanometer length scales and across atomic terraces. In contrast, a new spectral feature-not seen on bare Cu(111)-varies with atomic registry and shares the spatial periodicity of the hBN/Cu(111) moiré. This work demonstrates that, for some 2D electron systems, an hBN overlayer can act as a protective yet remarkably transparent window on fragile low-energy electronic structure below.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

TORONE: Total Characterisation by Remote Observation of Nuclear Environments - 20591

This paper describes the autonomous robotic systems developed in the TORONE project (Total Characterisation by Remote Observation of Nuclear Environments) for combined characterisation by remote techniques in nuclear environments. There are four main components to the system. Firstly, robot operation which uses a Robot Operating System (ROS) allowing ease of communication between hardware and software alongside robot vision systems such as Lidar and/or photogrammetry. Secondly, radiation detection instrumentation for gamma and neutron sources which is also collimated for spatial resolution. Thirdly, photonic characterisation instrumentation for remote material identification by means of Raman spectroscopy and Laser induced breakdown spectroscopy (LIBS). Finally, a data management system is described which contains the TORONE characterisation data with the ability to overlay material characterisation information onto 3D maps of the nuclear environment. Preliminary results are described alongside discussion of some of the synergies of the combined approach. There are many applications of such an autonomous system in nuclear decommissioning, waste management and asset management. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A GIS-Based Suitability Analysis of a Phytomanagment Strategy for TCE in Groundwater and Soil Niagara Falls Storage Site, New York - 19385

The large number of complexly contaminated sites throughout the nation has propagated the application of phytoremedial techniques in a wide array of environmental remediation strategies. The use and breeding of vegetation for the in-situ treatment of environmental media (soil, groundwater, sediment) contaminated with organic or inorganic contaminants has shown to provide a low-cost alternative to conventional means of remediating contaminated sites (e.g., groundwater extraction, chemical oxidation or reduction, bioremediation, excavation, etc.). One impediment to effective phytoremediation is the selection of proper plant-species, which is complicated due to the many variables that are required for plant-species viability (i.e. range of soil pH, annual precipitation values, depth to groundwater table, etc.). This study employed the use of a Geographic Information Systems (GIS) to first define the spatial extent of contaminated media (soil and groundwater) at a project site and then support an effective means to optimize phytoremediative techniques to manage these impacts prior to final remediation. Spatial analyses and analytical calculations were coupled to determine an optimal plant species and spacing to maximize the effectiveness of a phytomanagement plan. The incorporation of fuzzy membership functions and fuzzy overlay provide an innovative method to help select the optimal plant-species for phytomanagement planning. Fuzzy membership functions were used to represent the plant growth requirements and provided an intuitive method to represent the uncertainty associated with plant-growth requirements. The fuzzy overlay function then employed a raster-based suitability analysis that together provided decision makers with a scientific solution to optimally design a phytomanagement strategy for site-related volatile organic compound (VOC) impacts. The Niagara Falls Storage Site (NFSS) VOC-impacted groundwater is contained in clayey glacial sediments, which commonly are technically impractical to remediate. Prior on-site studies show that native poplar (cottonwood variants) preferentially translocate or phytoextract the VOCs from the site soils and thus provide a proven mechanism to manage the multi-media impacts. Consequently, the Army Corps of Engineers Buffalo District evaluated phytoremediative approaches as a passive tool to manage the VOCs during the project life cycle prior to final remediation. The fuzzy membership functions and fuzzy overlay modeling of candidate species narrowed the selection to an optimal species and subsequent transpiration modeling estimated the plant spacing for the overall phytomanagement plan. The method employed for this study is easy applied to other phytoremediative and phytomanagement actions derived from hazardous waste decision making. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Quantum Spin Torque Driven Transmutation of an Antiferromagnetic Mott Insulator

The basic model of spin-transfer torque (STT) in antiferromagnetic spintronics considers the exchange of angular momentum between quantum spins of flowing electrons and noncollinear-to-them localized spins treated as classical vectors. These vectors are assumed to realize Néel order in equilibrium, ↑ ↓ ∙∙∙ ↑ ↓, and their STT-driven dynamics is described by the Landau-Lifshitz-Gilbert (LLG) equation. However, many experimentally employed materials (such as archetypal NiO) are strongly electron-correlated antiferromagnetic Mott insulators (AFMIs) whose localized spins form a ground state quite different from the unentangled Néel state | ↑ ↓ ∙∙∙ ↑ ↓$\rangle$. The true ground state is entangled by quantum spin fluctuations, leading to the expectation value of all localized spins being zero, so that LLG dynamics of classical vectors of fixed length rotating due to STT cannot even be initiated. Instead, a fully quantum treatment of both conduction electrons and localized spins is necessary to capture the exchange of spin angular momentum between them, denoted as quantum STT. We use a recently developed time-dependent density matrix renormalization group approach to quantum STT to predict how injection of a spin-polarized current pulse into a normal metal layer coupled to an AFMI overlayer via exchange interaction and possibly small interlayer hopping—mimicking, e.g., topological-insulator/NiO bilayer employed experimentally—will induce a nonzero expectation value of AFMI localized spins. This new nonequilibrium phase is a spatially inhomogeneous ferromagnet with a zigzag profile of localized spins. The total spin absorbed by AFMI increases with electron-electron repulsion in AFMIs, as well as when the two layers do not exchange any charge.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation

Autonomous electric vehicles (AEVs) provide unique opportunities to cope with the uncertainties of distributed energy generation in distribution networks. But the effects are limited by both inherent radial topology and the behaviors of decentralized AEVs. As such, we investigate the potential benefits of dynamic distribution network reconfiguration (DDNR), taking into account AEVs' spatial-temporal availability and their charging demand. We propose a mixed integer programming model to optimally coordinate the charging/discharging of AEVs with DDNR, while satisfying AEVs' original travel plan. Numerical studies based on a test system overlaying the IEEE 33-node test feeder and Sioux Falls transportation network show that DDNR and AEV complement each other, which improves the operation of the distribution system. We also conduct sensitivity analyses on inputs including renewable fluctuation and AEVs penetration level.

33 ADVANCED PROPULSION SYSTEMS↗

Lithium Spatial Distribution and Split-Off Electronic Bands at Nanoscale V 2 O 5 /LiPON Interfaces

A combination of depth-resolved cathodoluminescence spectroscopy (DRCLS) and X-ray photoemission depth profiling (XPS) measured the pronounced changes in both the electronic density of states and lithium composition near the nanoscale LixV2O5/LiPON interface. DRCLS studies of electrochemically lithiated bare V2O5 and the sputterdeposited V2O5 plus LiPON overlayer electrochemically lithiated in stages both showed that in the bulk the luminescence intensity of the “split-off” hybridized bonding density of states was anticorrelated with XPSmeasured Li content, decreasing as the Li content increased. However, the LiPON overlayer was found to modify the band structure of the underlying LixV2O5 (LVO) to a depth of at least 30 nm beneath the V2O5 interface. DRCLS spectra near the electrochemically lithiated LiPON/ LVO interface showed a significant intensity of the split-off band, implying a low Li content. However, XPS depth profiling revealed a pronounced negative gradient of Li extending from a maximum Li content at the intimate LiPON boundary to its lowest content of ∼30 nm into the V2O5 in the same region, indicating a strong interaction between band structure and Li electrochemical potential near this heterojunction. These results provide evidence for substantial effects on the local band structure near an electrolyte/cathode interface and insights into the electrochemical interface behavior of solid-state batteries in general.

25 ENERGY STORAGE↗

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗

Center for Frontiers of Subsurface Energy Security (Final Report)

For the past two decades a slowly growing consensus has been that rising temperature is being caused by increases in carbon dioxide (CO 2 ) in the Earth’s atmosphere. What is less consensual is what to do about it. Solutions range from reducing emissions to various ways to capture CO 2 (from effluents of power plants, as plants, etc.) and store it away from the atmosphere. The problem is of such magnitude that it is likely that we will need many methods. Also some methods would cause draconian harm to global economies. This work is about the storage part of the reducing of CO 2 in the atmosphere. There have been many methods proposed for storing CO 2 : in vegetation, at the bottom of oceans, in land farms, and in subsurface formations. The latter is particularly prominent given the large estimated volume in porous formation in the Earth, and the 50-year old experience in injecting CO 2 for enhanced oil recovery. This report is about the science behind subsurface storage, called here geological carbon storage or GCS. Effective GCS requires that billions of metric tons of CO 2 be injected into storage reservoirs annually. GCS feasibility requires balancing the opposing impacts of large emplacement (injection) rates without compromising caprock seal integrity during injection. Once injected, the challenge is to assure the injected CO 2 remains permanently stored. Under most conditions, CO 2 is in a supercritical state, or scCO 2 . scCO 2 is immiscible with the subsurface brine and has a factor of ten smaller viscosity, leading to the development of two-phase fluid flow and scCO 2 bypassing. After the injection of large volumes of scCO 2 , the pore pressure, state of stress, chemical, thermal, and biological steady-state or equilibrium conditions in the subsurface are disrupted, which may lead to unpredictable behavior because the responses to these changes are often non-linear. Current challenges for efficient and reliable scCO 2 storage are: (1) sustaining large injection rates; (2) using available pore space efficiently, and (3) controlling undesired or unexpected behavior, such as scCO 2 leakage. Various aspects of these challenges are addressed by collaborative projects within Center for Frontiers in Subsurface Energy Security (CFSES). Challenge 1 – Sustaining large storage rates. To offset the annual CO 2 emissions of in the United States, ~7 billion metric tons of CO 2 must be injected into the storage reservoirs annually. Injected CO 2 forms a plume that starts spreading, displacing originally resident brine, though inefficiently so. Because of its low viscosity, and buoyant forces, a CO 2 plume rises to the reservoir rock-caprock interface. Injecting large volumes of scCO 2 at sufficiently high rates into subsurface storage formations leads to increases in pore pressures, potential expansion of the reservoir rock, and may result in fracturing of the reservoir rock and/or caprock. CFSES research uses existing and new experimental and modeling approaches to identify chemical-physical controls on the permeability and pore pressure dynamics, anticipating geomechanical (fracturing) events, predicting multi-phase flow patterns and trapping of CO 2 . Challenge 2 – Using pore space with unprecedented efficiency. Since large volumes of scCO 2 are to be stored, the volume available in storage reservoirs must be used efficiently. Storage efficiency is defined as a fraction of pore space occupied by carbon dioxide. Current estimates are that less than 5 percent of available pore volume is available for storage because scCO 2 , having a smaller density and viscosity than reservoir brine, tends to form viscous fingers, and bypass available pore space. This flow regime results in a CO 2 plume spreading over large areas in subsurface. The research goal in Challenge 2 is to advance the fundamental understanding of two-phase flow through porous media, including molecular-to meter- scale experimental and modeling approaches. This work leads to novel strategies to ensure that injected CO 2 occupies more than 5% of the pore space, ideally up to 50%. Challenge 3 – Controlling undesired or unexpected behavior. Following the injection of scCO 2 , the subsurface reservoir re-equilibrates to different physical and chemical states. This re-equilibration in space and time could lead to unexpected and/or undesired behavior: fracturing of reservoir rock and/or caprock, development of preferential flow paths, CO 2 leakage (to the surface or overlaying fresh water reservoirs), induced seismicity, and activation of faults. One of the main research goals of CFSES was to advance the fundamental understanding of how individual chemical and physical processes are coupled. We particularly focused on the chemical-mechanical coupling, and flow-mechanical coupling. We collected experimental and modeling data to determine the temporal and spatial scales of the coupled processes, which may lead to pore collapse caused by creep, activation of fractures and faults, chemically-induced fracture propagation, and self-focusing of scCO 2 flow.

54 ENVIRONMENTAL SCIENCES↗

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗

Visualization, understanding, and mitigation of process-induced-membrane irregularities in gas diffusion electrode-based polymer electrolyte membrane fuel cells

Polymer electrolyte membrane fuel cells (PEMFC) show substantial promise for their application in electric vehicles. For large-scale manufacturing of PEMFCs, roll-to-roll coated gas-diffusion-electrodes (GDE) offer certain advantages over other production pathways. Procedures including hot pressing and coating an ionomer overlayer may be necessary for this manufacturing pathway to enable a suitable catalyst layer/membrane interface. The same procedures may potentially introduce membrane irregularities, especially when thin membranes are used. Limited understanding exists regarding if and to what extent such irregularities impact PEMFC performance and lifetime, and therefore be considered defects. In this study, NREL's customized fuel cell hardware that enables quasi in-situ infrared (IR) thermography studies was utilized to visualize spatial hydrogen crossover and identify membrane irregularities that originated from the GDE-based MEA fabrication process. The structure of these membrane irregularities was investigated by scanning electron microscopy (SEM) and its impact on initial H2/air performance was determined. Accelerated stress testing (AST) revealed that these irregularities develop into failure point locations. These results were validated across many MEAs with identified process-induced membrane irregularities. By selecting specific gas diffusion media properties and by fine tuning the MEA hot pressing parameters, the formation of such membrane irregularities was mitigated.

08 HYDROGEN↗

Atomic-Scale Characterization and Electronic Properties of Gold-Capped Niobium Films for Superconducting Qubits

Niobium thin films are central to superconducting qubits, but their complex native oxides contribute significantly to microwave losses. A promising mitigation strategy is to suppress oxide formation using engineered thin-film encapsulation layers. Recent experiments have shown that ∼10 nm metallic overlayers on Nb capacitor films can improve the energy relaxation time T1 of transmon qubits. Here, we present a comparative study of Au-capped Nb films fabricated in situ by molecular beam epitaxy and ex situ by sequential deposition benchmarked against bare Nb films. Using complementary structural, chemical, and spectroscopic techniques, we correlate the interface quality with superconducting electronic properties of the Au surface. Low-temperature scanning tunneling spectroscopy (STS) provides spatially resolved quasiparticle density-of-states maps. While both Au-capped Nb samples exhibit large areas with uniform, fully gapped density of states, clear differences emerge between the two interfaces. Compared to in situ Nb-Au, ex situ Nb-Au exhibits a reduced induced superconducting gap, broadened coherence peak, and localized in-gap states, consistent with a residual NbxOy layer at the interface. Supported by complementary structural and chemical analyses, these findings demonstrate that STS directly links nanoscale superconducting properties to interface preparation, highlighting the importance of controlled in situ encapsulation for minimizing dissipation and improving quantum coherence.

Makita, Junki [MIT; Temple U.] (ORCID:000000026889↗

Spatially Enhanced Electrostatic Doping in Graphene Realized via Heterointerfacial Precipitated Metals

Forming heavily-doped regions in 2D materials, like graphene, is a steppingstone to the design of emergent devices and heterostructures. Here, a selective-area approach is presented to tune the work-function and carrier density in monolayer graphene by spatially synthesizing sub-monolayer gallium beneath the 2D-solid. The localized metallic gallium is formed via precipitation from an underlying diamond-like carbon (DLC) film that is spatially implanted with gallium-ions. By controlling the interfacial precipitation process with annealing temperature, spatially precise ambipolar tuning of the graphene work-function is achieved, and the tunning effect preserved upon cooling to ambient conditions. Consequently, charge carrier densities from ≈1.8 × 10 10 cm -2 (hole-doped) to ≈7 × 10 13 cm -2 (electron-doped) are realized, confirmed by in situ and ex situ measurements. The theoretical studies corroborated the role of gallium at the heterointerface on charge transfer and electrostatic doping of the graphene overlayer. Specifically, sub-monolayer gallium facilitates heavy n-doping in graphene. Extending this doping strategy to other implantable elements in DLC provides a new means of exploring the physics and chemistry of highly-doped 2D materials.

36 MATERIALS SCIENCE↗

Spin Frustration and Unconventional Surface Spin Canting State in Van der Waals Ferromagnet/Antiferromagnet Heterostructures

Atomically flat surfaces of van der Waals (vdW) materials pave an avenue for addressing a long-standing fundamental issue of how a compensated antiferromagnet (AFM) surface frustrates a ferromagnetic (FM) overlayer in FM/AFM heterostructures. We investigate Fe5GeTe2/NiPS3 vdW heterostructures by characterizing AFM and FM spins separately. We find that in-plane zig-zag AFM NiPS3 develops three equivalent AFM domains, which are robust against external magnetic field and magnetic coupling with Fe5GeTe2. Moreover, evidence is provided of in-plane-AFM-induced perpendicular magnetic anisotropy (PMA) in adjacent Fe5GeTe2, and an unconventional out-of-plane surface spin canting state with the Fe5GeTe2 spins spatially turn from out-of-plane direction near the interface to in-plane direction away from the interface in Fe5GeTe2/NiPS3. The out-of-plane surface spin canting is a unique property of spin frustration in vdW magnetic heterostructures.

Wang, Tianye↗

Biogeographic classification of streams using fish community– and trait–environment relationships

Abstract Aim Present a hybrid biogeographic and environmentally constrained clustering approach to classify ~853,000 stream reaches in the eastern United States. Examine the frequency of stream typologies in the landscape relative to anthropogenic stressors to identify potential stream conservation needs. Location Eastern United States. Methods Fish communities at 956 least‐disturbed sampling reaches were characterized using taxonomic and functional composition of native species. Environmental variables summarized within stream reaches included stream discharge, channel gradient, hydrologic regime, summer water temperature and boundaries of freshwater ecoregions. Multivariate regression trees were used to relate environmental variables to fish communities while simultaneously developing taxa‐ and trait‐specific biogeographic classifications. We then overlaid stream classifications with indices of anthropogenic disturbances to evaluate rarity and risk of complete loss of natural representation of stream types. Results Taxonomically based classes represented a combination of ecoregional boundaries and environmental gradients, whereas temperature gradients were most important in differentiating functional composition. An optimal taxonomy‐based classification contained 13 classes and explained 26.4% of community variation, and the optimal trait‐based classification contained 6 classes, explaining 25.2% of community variation. Overlaying class maps with an anthropogenic disturbance index revealed substantial variation in the severity of degradation among classes relative to their abundance in the landscape, especially rare classes (i.e., large rivers and cold headwater systems). Main conclusions We demonstrate the value of integrating local, regional and historical biogeographic factors with taxonomic and functional characteristics of freshwater biota to classify ecosystems based on mechanistic biota–environment relationships. This approach is important to addressing the challenge of developing stream classifications that reconcile the environmental drivers of community trait composition with the constraints of biogeographic regionality at large spatial scales. Furthermore, the spatially explicit and high‐resolution maps disseminated here can uniquely inform management decisions regarding anthropogenic threats to the biodiverse freshwaters of the eastern United States.

Troia, Matthew J.↗