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DOE OSTI2022

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗

DOE OSTI2020

Accessing the Spectral Function in a Current-Carrying Device

The presence of an electrical transport current in a material is one of the simplest and most important realizations of nonequilibrium physics. The current density breaks the crystalline symmetry and can give rise to dramatic phenomena, such as sliding charge density waves, insulator-to-metal transitions, or gap openings in topologically protected states. Almost nothing is known about how a current influences the electron spectral function, which characterizes most of the solid’s electronic, optical, and chemical properties. In this work, we show that angle-resolved photoemission spectroscopy with a nanoscale light spot provides not only a wealth of information on local equilibrium properties, but also opens the possibility to access the local nonequilibrium spectral function in the presence of a transport current. Unifying spectroscopic and transport measurements in this way allows simultaneous noninvasive local measurements of the composition, structure, many-body effects, and carrier mobility in the presence of high current densities. In the particular case of our graphene-based device, we are able to correlate the presence of structural defects with locally reduced carrier lifetimes in the spectral function and a locally reduced mobility with a spatial resolution of 500 nm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Title Coherent X-ray Studies of Surface Growth and Patterning Processes

X-ray Photon Correlation Spectroscopy (XPCS) is being developed as a tool to study nanoscale dynamics of fluctuations during thin film growth and surface patterning. XPCS examines the evolution of the X-ray scattering speckle pattern in reciprocal space to reveal dynamics information not accessible through any other means. Unlike low-coherence conventional X-ray scattering, which incoherently averages over different regions of a sample, coherent X-ray scattering is sensitive to the detailed structure of a given sample at that moment in time. In thin film growth processes, heterodyning, which occurs due to coherent mixing of two scattered signals, can be used to investigate the relationship between surface growth velocity and defect propagation. And, for polycrystalline thin film growth, the spatial coherence of the X-ray beam can substitute for the missing spatial coherence of the growth process to track layered growth in detail, even in a growth regime in which there are no conventional growth oscillations of the X-ray intensity. This has opened the door for detailed dynamics studies of individual atomic layers during real-world growth and patterning, not just in perfect single-crystal growth cases, which greatly expands the applicability of in-situ X-ray scattering methods. Step-flow dynamics in mounded polycrystalline growth has been studied separately for two thin film organic semiconductors deposited by thermal deposition in a vacuum environment, C60 and diindenoperylene (DIP). Highly oriented polycrystalline thin films are readily obtained in both systems, where mounds are composed of crystalline monolayer-height steps and terraces in a so-called wedding cake morphology. The formation of mounds is understood to be due to significant Ehrlich-Schwoebel step edge barriers that inhibit molecules from hopping down from one layer to the one below. The mounds exhibit local step flow, which can be monitored using coherent X-ray scattering. This is made possible due to heterodyning between scattering from the average mounds and the moving steps, which becomes visible in XPCS analysis. The effect of desorption, i.e. re-evaporation of deposited molecules is found to be important for understanding these processes. The impact of this work is to enable testing of models of step dynamics, the shape of mounds, and merging of mounds to form continuous thin films. Highly ordered polycrystalline thin films deposited on inexpensive substrates have applications in thin film solar cells and other organic electronic devices. In a separate set of experiments, speckle analysis during self-organized ion beam nanopatterning reveals memory stretching back to the beginning of patterning in the early stages and enables measurement of the velocity of self-organized patterns across surfaces providing the possibility of stringent new tests of the theory of pattern formation and motion.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A five-year milestone: reflections on advances and limitations in GeoAI research

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Influence of Natural Gas Addition on Combustion Instabilities on a Lean-Premixed Hydrogen Flame

The influence of fuel composition on combustion instabilities in a high-pressure combustor operated with hydrogen and natural gas fuels is characterized. A micromix multi-stage injector with 19 elements is used to introduce hydrogen and natural gas premixed with heated air into an optically accessible combustor. As the equivalence ratio and flame temperature is varied along with change in hydrogen fraction in the fuel, self-excited longitudinal instabilities are observed in the combustor. The instability amplitude reduces considerably as hydrogen fraction is increased. This sensitivity differs at higher flame temperature, with large amplitude instabilities attenuating at lower hydrogen fractions for flames with more thermal power. Increasing hydrogen concentration is shown, through kinetic modeling, to decrease the bulk reaction timescale linearly with observed instability amplitude. Spatial distribution of heat release in the combustor evaluated from OH* chemiluminescence imaging reveals excitation of primarily the 2L mode in the combustion chamber at ~1100 Hz, at all equivalence ratios and fuel compositions. Increasing equivalence ratio and hydrogen addition decreases the length of the positively correlated region, which leads to instability dampening.

08 HYDROGEN↗

DOE OSTI2026

Molecular concentration field design using closed-form steady-state solutions

Control over spatial concentration fields represents a fundamental challenge in designing synthetic biological systems and programmable soft materials. While nature creates morphogen gradients that orchestrate complex developmental processes, synthetic approaches have largely relied on empirical optimization and computationally intensive simulations. Here, we present an analytical framework for steady-state concentration fields generated by finite-sized localized sources in diffusion–degradation systems and derive closed-form solutions for one-, two-, and three-dimensional geometries. By expressing these solutions in dimensionless form, we show that gradient steepness and spatial structure are organized by the Thiele modulus, which captures the competition between diffusion and degradation length scales. The analysis reveals distinct design regimes: in degradation-dominated systems, gradient shape is governed by exponential decay and becomes dimension-independent, whereas in diffusion-dominated systems, gradient magnitude and extent follow dimension-dependent power-law scaling. Building on these results, we introduce a quantitative design strategy that uses threshold-based criteria to program concentration ranges by tuning physically accessible parameters, most directly the production rate, while holding transport and degradation properties fixed. Comparisons with numerical solutions and reported experimental systems demonstrate consistency with the predicted scaling behavior. Together, this work provides a generalizable and physically transparent framework for designing steady-state concentration fields in synthetic biological and soft matter systems, enabling predictive control of gradient-mediated organization without reliance on extensive numerical optimization.

Kim, Dong Woo [Johns Hopkins University, Baltimore↗

DOE OSTI2025

Measuring the growth of Alfvén wave parametric decay instability using counter-propagating waves: Theory and simulations

The parametric decay instability (PDI) of Alfvén waves—where a pump Alfvén wave decays into a backward-propagating child Alfvén wave and a forward ion acoustic wave—is a fundamental nonlinear wave-wave interaction and holds significant implications for space and laboratory plasmas. However, to date there has been no direct experimental measurement of PDI. Here, we propose a novel and experimentally viable scheme to quantify the growth of Alfvén wave PDI on a linear device using a large pump Alfvén wave and a small counter-propagating seed Alfvén wave, with the seed-wave frequency tuned to match the backward Alfvén wave generated by standard PDI. Using hybrid simulations, we show that energy transfer from the pump to the seed reduces the latter's spatial damping. By comparing seed-wave amplitudes with and without the pump wave, this damping reduction can be used as a direct and reliable proxy for PDI growth. The method is validated in our simulations across a range of plasma and wave parameters and agrees well with theoretical predictions. Notably, the scheme exhibits no threshold for PDI excitation and is, in principle, readily implementable under current laboratory conditions. Finally, this scheme is a critical step toward solving the challenge of experimentally accessing Alfvén wave PDI and provides an elegant method that may be used to validate fundamental theories of parametric instabilities in controlled laboratory settings.

heliosphere↗

DOE OSTI2022

WIRE-21 Sensor Irradiation Experiment Ready for HFIR Insertion

The ability to deploy new nuclear fuels for current or future reactor concepts requires a wealth of data regarding fuel performance during normal operation, anticipated operational occurrences, and design-basis accidents. Most of these data have historically been collected during experiments in materials test reactors, ideally with online instrumentation to collect as much data as possible. However, advanced instrumentation could also allow for in situ monitoring of fuel operating conditions during commercial reactor operation to maximize fuel utilization, reduce unnecessary conservativism in design margins, and improve operator understanding of limiting peaking factors. The latter approach would complicate fuel handling, particularly during refueling, unless the instrumentation could be placed inside the fuel rods and transmitted wirelessly to a receiver located outside the fuel’s primary pressure boundary. To this end, Westinghouse Electric Company (WEC) developed wireless sensors based on inductive coupling that can transmit information regarding fuel centerline temperatures and rod internal pressures wirelessly from within a fuel rod to a nearby instrument thimble. After testing these sensors in lower-power university research reactors, the next step is to perform high neutron fluence testing to characterize the performance of these wireless sensors under conditions that are more representative of the intended application—in this case, light-water reactors (LWRs). The removable Be (RB) positions of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) provide the neutron flux, experiment volume, and access to instrument leads required to achieve these sensor testing goals. This report summarizes the design, analysis, and assembly of the Wireless Instrumented RB Experiment 2021 (WIRE-21). This is the most highly instrumented irradiation experiment ever performed in HFIR. The experiment will use seven different sensing techniques to measure temperature, pressure, neutron flux, and neutron fluence during reactor operation. In addition to WEC’s wireless temperature and pressure sensors, WIRE-21 includes an array of thermocouples, self-powered neutron detectors, spatially distributed fiber optic temperature sensors, passive SiC temperature monitors, and flux wires. The design of WIRE-21 and the cabling that was installed in HFIR also provide the infrastructure to enable accelerated, economical testing of advanced sensor technologies while leveraging the extremely high neutron flux that is available in HFIR. The containment for WIRE-21 is similar to previous RB irradiation vehicles but includes a few modifications, most notably the use of integrated compression seals to pass a larger number of sensor leads through the experiment’s pressure boundary. In addition to the sensor leads, inert gas lines are passed into the experiment to enable active temperature control and the ability to pneumatically actuate a bellows-driven pressure sensor. WIRE-21 is targeting component temperatures (300–350°C) and neutron fluence levels (~10 22 n/cm 2 ) that would be expected in the plenum region of LWR fuels, except for the active sensing region of the wireless temperature sensor, which is targeting LWR fuel centerline temperatures (~800–1,100°C). WIRE-21 was successfully assembled, passed all nondestructive examination, and was delivered to HFIR for insertion during upcoming cycle 498 (April 2022).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Accessing terapascal pressures on two-stage light-gas guns for high-energy-density research

We present pressure amplifiers that have been developed to enable the study of material behavior at terapascal (TPa) pressures using two-stage light-gas gun (2SLGG) facilities. When impacted by a hyper-velocity projectile, Mach reflections within the pressure amplifier eventually merge to form a Mach stem, generating a planar output shock that greatly exceeds the initial drive. Validation experiments demonstrated that peak pressures of 0.6 and 1.073 TPa were achieved in quartz samples for two different amplifier designs. These pressures represent an approximately fivefold increase over the sample pressures typically attainable at 2SLGG facilities. The temporal and spatial uniformity of the pressure drive is suitable for high-precision equation-of-state measurements of materials relative to a shock standard. This work greatly extends the capabilities of 2SLGG facilities and opens new avenues of study in high-energy-density physics using these drivers.

Gorman, Martin G. [First Light Fusion, Oxford (Uni↗

DOE OSTI2023

Data from: "Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions"

This archive contains data files that were used to draw conclusions in “Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions”, by Moyes et al., 2015. All field research was completed in common garden plots set up as part of the Alpine Treeline Warming Experiment (ATWE) on Niwot Ridge, Colorado, USA.There are two main data file formats in this archive: comma-separated values (.csv), and Microsoft Excel (.xls and .xlsx). .xlsx files can be read using Microsoft Excel and Google Sheets, and .csv files can be read using any simple text editor program, such as TextEdit (Mac) and Notepad (Windows). This .pdf data user’s guide can be read using Adobe Acrobat Reader, or any other compatible software. Seedling photographs and their corresponding leaf area-processed images are available in .jpg/.JPG image format, and can be opened using Preview (Mac) and Photos (Windows). To provide additional spatial context, two types of geospatial files are also published in this data package: ESRI shapefiles (.shp) and .kml files. Shapefiles are compatible with any GIS software able to read the file type (such as QGIS or ESRI’s ArcGIS suite), and .kml files can be opened with Google Earth or Google Maps. Figures 3 and 4 in the publication contain data from Moyes et al. 2013. This publication is cited in the References section in this archive, and data files can be accessed via the Alpine Treeline Warming Experiment project portal on ESS-DIVE. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Climate change is altering plant species distributions globally, and warming is expected to promote uphill shifts in mountain trees. However, at many cold-edge range limits, such as alpine treelines in the western United States, tree establishment may be colimited by low temperature and low moisture, making recruitment patterns with warming difficult to predict.- We measured response functions linking carbon (C) assimilation and temperature- and moisture-related microclimatic factors for limber pine (Pinus flexilis) seedlings growing in a heating × watering experiment within and above the alpine treeline. We then extrapolated these response functions using observed microclimate conditions to estimate the net effects of warming and associated soil drying on C assimilation across an entire growing season.- Moisture and temperature limitations were each estimated to reduce potential growing season C gain from a theoretical upper limit by 15–30% (c. 50% combined). Warming above current treeline conditions provided relatively little benefit to modeled net assimilation, whereas assimilation was sensitive to either wetter or drier conditions.- Summer precipitation may be at least as important as temperature in constraining C gain by establishing subalpine trees at and above current alpine treelines as seasonally dry subalpine and alpine ecosystems continue to warm.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗

DOE OSTI2025

WELLS Interactive Application

The Wellbore Exploration and Location Logistic System (WELLS) Interactive Application is an interactive tool to enable easy exploration and visualization of the living national wellbore database (WELLS Database (https://edx.netl.doe.gov/dataset/wells_database)). The tool and underlying database were created and are maintained by the National Energy Technology Laboratory (NETL), providing visualization of the more than six million public wellbore records from more than 65 authoritative state, federal, and tribal resources. The WELLS Interactive Application serves up wellbore data from oil, gas, underground injection, research, geothermal, geotechnical, groundwater, and other types of wells in a single, standardized, unified system. In addition to the surface location of these wells, the underlying database combines select key attributes for features such as well age, depth, and operating status. The system also provides users with references back to the original sources used in this unified platform. The underlying data can be accessed through the WELLS Database: https://edx.netl.doe.gov/dataset/wells_database Additional Information: The WELLS Interactive Application (formerly titled CO2-Locate) enables visualization and access to the public wellbore records through an intuitive web-based mapping tool. The WELLS Interactive Application was designed to help users visualize, query, analyze, and download wellbore records. Public wellbore points are included as a layer in the Map page, called Public Wells. Additionally, a multivariate hexagon grid summarizing well density from proprietary well data, called Well Density, is included to identify data gaps between the public and proprietary well data. Filtering functionalities in the tool allow these two layers to be spatially filtered by state, county, or basin as well as by status, type, true vertical depth, and spud year. The WELLS Interactive Application also contains a Near Me tool can be used to search and explore wellbore data within a user-defined distance of a specified location on the map, which can also be downloaded. The Query tool allows users to query the selected or filtered wells in the Public Wells layer and export the data. For additional information on these tool functionalities, see the help documentation on the About page of the tool. Notes for Consideration: The Well Density layer provided in this application is derived from proprietary wellbore data, the records of which do not always contain values for key features (status, type, true vertical depth, or spud year). Therefore, data might not be available when layers are queried for all filter combinations. Additionally, visualizing layers and applying filters may take additional time to load (i.e., draw on the map) due to the large size of the data.

ccs↗

DOE OSTI2024

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

An On-Demand Electric Transit Case Study of New Rochelle, New York

This work explores the extent to which an on-demand mobility service utilizing lightweight electric vehicles (EVs) provides community and sustainability benefits in New Rochelle, New York. Travel and survey data from September 2019 through 2023 are used to describe the system and estimate impacts on travelers. The system was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service is utilized primarily for short trips (86% under 2 miles), and the small, right-sized EVs have carbon dioxide emissions associated with charging the fleet that are roughly one-quarter of the fleet emissions of conventional hybrid vans and nearly 50 times less than a fleet of diesel buses. Mapping current socio-spatial dynamics of travel demand can inform equity performance, as well as assist future planning and service area development and possible extensions to similar smaller, lower-density environments that are nearby and connected to major metropolitan areas. The findings in this case study suggest on-demand electric transit may be a significant and growing space for advancing clean and highly valued public mobility services. Sustainable public transport interventions that consider right-sized, electric, on-demand vehicles can help achieve improved accessibility and reduce energy use and greenhouse gas emissions. This work was presented at the Transportation Research Board (TRB) 2025 Annual Meeting on January 7, 2025.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2025

Retrospective on decadal progress of the NOAA/NPS ocean noise reference station network

The National Oceanic and Atmospheric Administration (NOAA), in partnership with the U.S. National Park Service (NPS), established the Ocean Noise Reference Station Network (NRS) in 2014 as a foundational component of NOAA’s Ocean Noise Strategy. This long-term effort aims to characterize baseline ocean ambient sound conditions across diverse marine environments and to inform management of noise impacts on protected species and habitats within U.S. waters. The NRS is now composed of 13 autonomous passive acoustic monitoring stations strategically positioned across the U.S. Exclusive Economic Zone (EEZ), extending from Arctic regions to tropical waters in depths ranging from 33 to 4,790 m. These locations include several National Marine Sanctuaries and National Parks, such as the recently designated Chumash Heritage National Marine Sanctuary off the coast of California. Each station is equipped to continuously sample low-frequency underwater sound at five kHz, enabling the detection of anthropogenic, geophysical, and biological acoustic signals. To date the network has sampled over 72 years of calibrated acoustic data. The spatial breadth and consistent methodology of the NRS allow for comparative acoustic assessments across diverse marine ecosystems. In addition to applied research functions, the NRS has served as a platform for education and training, offering opportunities for students to develop skills for marine science and data analysis. Looking forward, the NRS project team is focused on network expansion, improved data delivery, and broader integration with collaborative scientific initiatives. NRS recordings are being archived in partnership with NOAA’s National Centers for Environmental Information to enhance accessibility and long-term utility. Efforts are underway to develop standardized metadata and summary products to accompany raw audio files, making the data more usable for a wide range of stakeholders in the ocean science community. The NRS is evolving into a fully integrated national framework for ocean sound monitoring that supports scientific inquiry, management decision-making, national security interests, and public engagement with ocean acoustic environments.

Long-term monitoring↗

DOE OSTI2021

PS3: The Pheno-Synthesis software suite for integration and analysis of multi-scale, multi-platform phenological data

Phenology is the study of recurring plant and animal life-cycle stages which can be observed across spatial and temporal scales that span orders of magnitude (e.g., organisms to landscapes). The variety of scales at which phenological processes operate is reflected in the range of methods for collecting phenologically relevant data, and the programs focused on these collections. Consideration of the scale at which phenological observations are made, and the platform used for observation, is critical for the interpretation of phenological data and the application of these data to both research questions and land management objectives. However, there is currently little capacity to facilitate access, integration and analysis of cross-scale, multi-platform phenological data. This paper reports on a new suite of software and analysis tools – the “Pheno-Synthesis Software Suite,” or PS3 – to facilitate integration and analysis of phenological and ancillary data, enabling investigation and interpretation of phenological processes at scales ranging from organisms to landscapes and from days to decades. We use PS3 to investigate phenological processes in a semi-aride, mixed shrub-grass ecosystem, and find that the apparent importance of seasonal precipitation to vegetation activity (i.e., “greenness”) is affected by the scale and platform of observation. We end by describing potential applications of PS3 to phenological modeling and forecasting, understanding patterns and drivers of phenological activity in real-world ecosystems, and supporting agricultural and natural resource management and decision-making.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Semiconductor-compatible topological digital alloys

Recently, GeSn alloys have attracted much interest for direct-gap infrared photonics and as potential topological materials which are compatible with the semiconductor industry. However, for photonics, the high-Sn content required leads to low detectivity, associated with poor material quality, and the (>35 %) Sn required for topological properties have been out of reach experimentally. Here, we demonstrate that by patterning the Sn distribution within Ge, the electronic properties have a far greater tunability than is possible with the random alloy. For the GeSn δ-digital alloy (DA) formed by confining Sn atoms in atomic layer(s) along the [111] direction of Ge, we show that ∼ 10 % Sn can lead to a triple-point semimetal. These findings are understood in terms of Sn ordering causing spatial separation of Sn and Ge band edges, leading to band inversion. This mechanism can also lead to a weak topological insulator, Weyl semimetal, and enables tunable direct bandgaps down to 2 meV, covering the entire infrared range. This DA induced topological properties are also identified in compound semiconductors, such as InAs 1-x Sb x , showing the general applicability of the DA design for realizing topological properties on conventional semiconductor platforms. Furthermore, our findings not only point to a new class of currently unexplored topological systems accessible by epitaxy, but also establish the promise of low-Sn GeSn DAs for application as infrared laser diodes and photodetectors in Si photonic integrated circuits and infrared image sensors.

3D topological semimetal↗