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DOE OSTI2025

The HTAP_v3.2 emission mosaic: merging regional and global monthly emissions (2000–2020) to support air quality modelling and policies

This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the need of the global and regional atmospheric modelling community of having a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of a high scientific quality while also ensuring that the results remain relevant to policymakers. HTAP_v3.2, an ad-hoc global mosaic of anthropogenic inventories, is an update to the HTAP_v3 global mosaic inventory and has been developed by integrating official inventories over specific areas (North America, Europe, Asia including China, Japan and Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO 2 , NO x , CO, NMVOC, NH 3 , PM 10 , PM 2.5 , Black Carbon (BC), and Organic Carbon (OC), with a spatial resolution of 0.1 × 0.1° and time intervals of months and years covering the period 2000–2020 (https://doi.org/10.5281/zenodo.17086684, Crippa, 2025, https://edgar.jrc.ec.europa.eu/dataset_htap_v32, last access: 27 October 2025). The emissions are further disaggregated to 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3.2 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past two decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policy making, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

Guizzardi, Diego [European Commission, Ispra (Ital↗

DOE OSTI2025

Ab initio quantum many-body description of superconducting trends in the cuprates

Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order and estimate the pairing gap of several doped cuprate materials and structures within a purely electronic picture. We find that we can correctly capture two well-known trends: the pressure effect, where the pairing order and gap increase with intra-layer pressure, and the layer effect, where the pairing order and gap vary with the number of copper-oxygen layers. From these calculations, we observe that the strength of superexchange and the covalency at optimal doping are the best descriptors for these trends. Our microscopic analysis further identifies that strong short-range spin fluctuations and multi-orbital charge fluctuations drive the development of the pairing order. Our work illustrates the possibility of a material-specific ab initio understanding of unconventional high-temperature superconducting materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Deciphering decomposition pathways of high explosives with cryogenic X-ray Raman spectroscopy

We employed cryogenic X-ray Raman spectroscopy to investigate the early-stage decomposition of the high explosive molecule hexanitrohexaazaisowurtzitane (CL-20). By systematically varying the radiation dose under cryogenic conditions, we induced the decomposition of the molecule using ionizing radiation and observed the evolution of spectral features at the carbon, nitrogen, and oxygen K edges. Through extensive first-principles calculations, we identified key intermediates in the early stages of the decomposition process, resulting from C–C and C–N bond cleavage which leads to the opening of the internal cage structure. A detailed analysis of spectral trends and fingerprints provided evidence supporting N–NO 2 homolytic cleavage as the primary initial decomposition pathway. The combination of advanced core-level spectroscopy methods and state-of-the-art theoretical calculations enabled a comprehensive characterization of the molecular changes induced by controlled radiation dose exposures. In conclusion, our findings establish a benchmark for understanding the decomposition chemistry of high-explosive materials, offering important insights into their stability and reactivity under extreme conditions.

X-ray Raman spectroscopy↗

DOE OSTI2024

Long-term patterns of post-fire harvest diverge among ownerships in the Pacific West, U.S.A.

Abstract Post-fire harvest (PFH) is a forest management practice designed to salvage value from burned timber, mitigate safety hazards from dead trees, reduce long-term fuels, and prepare sites for replanting. Despite public controversy and extensive ecological research, little is known about how much PFH occurs on private and public lands in the U.S. Pacific West, or how practices changed with shifting forest policy and increasing area burned over the last three decades. We mapped PFH across 2.2M burned hectares in California, Oregon, and Washington between 1986-2017 and used time series intervention analysis to compare trends in area, rate (% of burned area harvested), and mean patch size between private (0.5M ha) and federal (1.6M ha) forest land and across a gradient of burn severity. Harvest rates varied by ownership (4.9% federal, 18.6% private, 8.0% overall), and practices evolved and diverged over the study period. PFH area and rate declined across all ownerships in the mid-1990s during a period of reduced fire activity. As area burned increased between the early 2000s and late 2010s, PFH area rebounded and surpassed late-1980s levels, while rates remained relatively low. On federal lands, PFH practices shifted in the early-to-mid 1990s towards lower rates (10.3% to 3.8%) and smaller patches (6.0 to 3.3 ha), following policy changes and increased litigation. PFH rates on federal lands decreased at all levels of burn severity, with the largest decreases (6.2% to 1.2%) in forests with low tree mortality (i.e. fire refugia). Conversely, private PFH rates and mean patch sizes more than doubled in forests burned at very low-to-moderate severity. Our results highlight how PFH practices have shifted with policy, socio-economic pressure, and increasing area burned over 31 years in the Pacific West. A similar area of post-fire harvest is now dispersed over larger fires, with practices diverging substantially between ownerships.

Zuspan, Aaron (ORCID:0000000315833710)↗

DOE OSTI2024

Numerical Evaluation of Effective Thermal Conductivity of PCM with Metal Foam Incorporating Buoyancy Effects for Thermal Energy Storage

The thermal energy storage (TES) system has the capability to efficiently preserve thermal energy directly derived from the energy source, minimizing any conversion losses. Especially latent heat storage offers distinct advantages, including a substantial increase in energy storage density and minimization of temperature fluctuations within the plants. However, the phase change material (PCM) employed in latent heat storage has low thermal conductivity. Consequently, various studies are being conducted to enhance heat transfer. One approach to enhance heat transfer involves utilizing metal foam to maximize the heat transfer area. However, modeling metal foam with its intricate structure is a challenging task in numerical analysis. For this reason, ongoing research focuses on simplifying the modeling of metal foam. Nevertheless, fully encompassing all the characteristics of actual metal foam proves to be a challenging task for the simplified analytical model. The objective of this paper is to interpret the simple lattice metal foam analysis model from the perspective of behavior induced by buoyancy. When comparing the analysis results of solid PCM and liquid PCM with the same thermal conductivity under changes in porosity and gravity direction, we conducted an analysis to discern the trends in effective thermal conductivity that are overestimated due to convection. In the analysis, a constant heat flux of 10 kW and a constant surface boundary condition of 350 K were applied, and a sensitivity study regarding the mesh was conducted. The results indicate that, from the perspective of gravity in the simple lattice model, the solid analysis yields an effective thermal conductivity 29-47% higher compared to the liquid analysis. Additionally, as porosity increases, there is an observed increase of 24-33% in effective thermal conductivity.

25 ENERGY STORAGE↗

DOE OSTI2026

Individual Motorist Data - Ohio EV Ownership Trends

The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Supply Chain for Photovoltaics in the United States: 2024 in Review

Solar photovoltaic (PV) and battery energy storage system (BESS) technologies are two immediately available options for meeting U.S. electricity demands, which are increasing due to expansion of loads from end uses including data centers, buildings, vehicles, and factories. Globally, PV and BESS supply chains are dominated by products manufactured in China and elsewhere by Chinese companies. However, U.S. PV and BESS manufacturing have recently grown, with some capacity in each step of the PV supply chain, albeit not enough to currently meet demand with domestic manufacturing alone. This study analyzes U.S. PV supply chains and costs in 2024, for the crystalline silicon and cadmium telluride module supply chains. The full report additionally addresses the PV balance of system, inverter and BESS supply chains. The study concludes with an analysis of technology installation trends, government support for domestic manufacturing, manufacturing jobs, and the domestic content of PV systems installed in the United States in 2024.

14 SOLAR ENERGY↗

DOE OSTI2025

Autonomous Nanoparticle Synthesis Guided by In Situ Multiscale Structural Characterization

Autonomous synthesis platforms promise rapid exploration of vast parameter spaces; yet, integrating in situ structural characterization in closed-loop synthesis optimization remains challenging. We demonstrate a realization of such a closed-loop platform coupled with a droplet-flow microreactor, in situ X-ray scattering methods (SAXS/WAXS), and Gaussian process optimization to synthesize citrate-reduced Au nanoparticles with targeted characteristics. The system efficiently explored ∼19,000 synthesis recipes through 365 experiments, achieving precise control over size (4–60 nm) and polydispersity (σ < 0.11) across large citrate/gold ratios, exceeding traditional synthesis boundaries (1–10). Beyond confirming classical Turkevich–Frens trends, partial-dependence analysis revealed strong nonlinear coupling among precursor, citrate, and pH effects. Combining quantitative SAXS/WAXS analysis with electron microscopy characterization, we uncovered that crystallite size (d c ) and particle size (d) follow d c = 0.18d + β, where synthesis chemistry controls the intercept β while maintaining a universal slope. This parallel-band structure enables independent tuning of crystallite domain size at fixed particle diameter through a combination of chloride, gold precursor, citrate, and pH contributions (cross-validated Spearman ρ = 0.7 ± 0.1). High-resolution electron microscopy shows multiple lattice-fringe orientations within single particles, directly confirming polycrystalline domains and the ability to tune d c at the fixed d. The platform’s validation includes indistinguishable static versus flowing measurements, stable droplet transport at 100 °C, and <5% run-to-run variation, establishing a robust framework for mapping and controlling multiscale nanoparticle structure across expansive chemical spaces. In conclusion, the developed closed-loop platform can be applied to a borad range of nanosyntheis processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2026

From Oxo to Oxyl to Biradical: Systematic Multireference Calculations of Methane Activation at MOF Nodes

Methane C–H activation at transition-metal sites often involves electronic structures that challenge conventional single-reference electronic structure descriptions. Although Kohn–Sham density functional theory (DFT) is widely used to study catalytic trends, its reliability for reactions involving strongly correlated species remains uncertain. Here we present a systematic multireference investigation of methane activation at metal–organic framework (MOF) node catalysts across the 3d transition-metal series. We introduce an automated workflow for active space selection to enable consistent application of multireference methods, including multiconfiguration pair-density functional theory and n-electron valence state perturbation theory, to these catalytic systems. These calculations show substantial static correlation in the C–H activation reaction step and predict activation barriers that differ from DFT by 30–70 kJ mol–1, with DFT often qualitatively disagreeing in barrier height trends across transition metals. Analysis of multireference wave functions shows that reactivity is governed by the electronic structure of the M–O moiety along a continuum from metal–oxo to oxyl radical and O biradical character. Increased oxygen-centered spin density and weakened M–O bonding are identified as descriptors of catalytic activity which correlate with lower activation barriers.

Wardzala, Jacob↗

DOE OSTI2024

Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming

Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, in this study, we analyzed η values using 3,013 plots and 26,337 species-specific measurements across eight sites on the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems, climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Furthermore, we observed a threefold strengthening of the warming effect on η over the past 27 y. Soil moisture was found to modulate the sensitivity of η to soil temperature in alpine meadows and alpine steppes, but not in alpine wetlands. Our results contribute to a better understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems and climate feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Trade can buffer climate-induced risks and volatilities in crop supply

Climate change is intensifying the frequency and severity of extreme events, posing challenges to food security. Corn, a staple crop for billions, is particularly vulnerable to heat stress, a primary driver of yield variability. While many studies have examined climate impact on average corn yields, little attention has been given to the climate impact on production volatility. This study investigates the future volatility and risks associated with global corn supply under climate change, evaluating the potential benefits of two key adaptation strategies: irrigation and market integration. A statistical model is employed to estimate corn yield response to heat stress and utilize NEX-GDDP-CMIP6 climate data to project future production volatility and risks of substantial yield losses. Three metrics are introduced to quantify these risks: Sigma (σ), the standard deviation of year-on-year yield change, which reflects overall yield volatility; Rho (ρ), the risk of substantial loss, defined as the probability of yield falling below a critical threshold; and Beta (β), a relative risk coefficient that captures the volatility of a region's corn production compared to the globally integrated market. The analysis reveals a concerning trend of increasing year-on-year yield volatility (σ) across most regions and climate models. This volatility increase is significant for key corn-producing regions like Brazil and the United States. While irrigated corn production exhibits a smaller rise in volatility, suggesting irrigation as a potential buffer against climate change impacts, it is not a sustainable option as it can cause groundwater depletion. On the other hand, global market integration reduces overall volatility and market risks significantly with less sustainability concerns. Furthermore, these findings highlight the importance of a multidimensional approach to adaptation in the food sector. While irrigation can benefit individual farmers, promoting global market integration offers a broader solution for fostering resilience and sustainability across the entire food system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Trends in spatial correlations, two-phase coexistence, and criticality in a class of type-2 Schloegl models for autocatalysis

A class of type-2 Schloegl models is considered for particles on a square lattice with variable-range cooperativity. These models involve: (i) spontaneous particle annihilation at rate p; (ii) autocatalytic particle creation at unoccupied sites (i, j) with n ⩾ 2 particles within a specified neighborhood, Ω 𝑁 (i, j), of sites at rate $k_n$ = $\frac{^{(^n_2)}}{_{(^N_2)}}$ = $\frac{n{(n-1)}}{_{N(N-1)}}$; and (iii) possible spontaneous particle creation at unoccupied sites at “small” rate ɛ ⩾ 0. In some cases, Ω 𝑁 just includes all symmetry-equivalent sites at a single specific distance 𝑑 (in units of lattice constants) from the unoccupied site, e.g., 𝑑 = 1 (nearest-neighbor sites) where 𝑁 = 4, or 𝑑 = √5 (or √13 or…) where 𝑁 = 8. In other cases, Ω 𝑁 includes sites multiple distances from the unoccupied site, e.g., 𝑑 = {1,√2}, where 𝑁 = 8. Kinetic Monte Carlo (KMC) simulation reveals that these models exhibit a nonequilibrium discontinuous phase transition between high- and low-density states below a critical point, ɛ < ɛ c , with generic two-phase coexistence (2PC) at least for smaller 𝑁. With some exceptions, there is an approach toward mean-field behavior with increasing 𝑁 (so the regime of generic 2PC shrinks, and ɛ c approaches the mean-field value of 1/27). Additional insight into trends is provided by analysis of the exact master equations for the models via hierarchical truncation. These truncations utilize suitably tailored pair approximations which reflect the dominant nonequilibrium spatial correlations. These correlations in turn are shown to reflect the details of the autocatalytic particle creation process. For spatially heterogeneous states, the truncations produce coupled sets of lattice differential equations (LDE) which can describe orientation-dependent propagation of an interface between high- and low-density steady states for ɛ < ɛ c . Pair approximation values of 𝑝 = 𝑝 eq where the interface is stationary, and its orientation-dependence, are in semiquantitative agreement with KMC results. In conclusion, this comparison accounts for propagation failure in the LDE which complicates interpretation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth’s subsurface, acoustic imaging and nondestructive testing (NDT) in material science, and ultrasound computed tomography (USCT) in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning (ML)-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific ML techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

42 ENGINEERING↗

DOE OSTI2025

Deep Design Data Portal (D3P) v0.01

The Deep Design Data Portal (D3P) tool was developed to demonstrate how readily accessible data sources, such as building energy model reports for design and baseline energy performance data for projects, can provide the data required for reporting to an industry initiative (AIA 2030 commitment), as well as more detailed data that makes the industry dataset more valuable to all stakeholders, enabling project level analysis and analysis of BEM industry trends. D3P provides an easier and less time-consuming way for firms to auto-extract data from this data source, compared to the current reporting workflows of the firms. The BEM reports are the first of several data sources that D3P could integrate. D3P also provides the ability for firms to review, compare, and evaluate the performance of their projects to not only their portfolio, but also to the larger anonymized industry dataset created each time a project is added to D3P. The intent of D3P is to become part of a data-sharing ecosystem to assist creating large anonymized industry datasets that are accessible to industry.

Regnier, Cynthia [Lawrence Berkeley National Labor↗

DOE OSTI2026

Annual Supply Chain for Photovoltaics (ASC-PV) in the United States: 2024 in Review

This report analyzes U.S. PV and BESS supply chains and costs in 2024, for PV module and battery technologies, structural and electrical balance of system (BOS) components, as well as PV recycling. The report concludes with an analysis of technology installation trends, government support for domestic manufacturing, manufacturing jobs, and the domestic content of PV systems installed in the United States in 2024.

14 SOLAR ENERGY↗

DOE OSTI2026

Genesis Mission-Enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE)

Argonne National Laboratory is supporting the U.S. Department of Transportation’s (USDOT’s) Bureau of Transportation Statistics (BTS) with collaborative research on development and application of privacy preserving AI frameworks that leverage unmatched AI expertise and secure computing resources made available through the U.S. Genesis Mission1 . This research advances U.S. energy security goals by supporting a safe offshore energy industry with secure, domain-specific AI tools to analyze confidential industry datasets collected by BTS to rapidly improve identification of hazards, precursors, and systemic safety risks in high-risk operational environments. The staged, security-first approach begins with development and testing of Argonne’s Genesis Mission-enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE) framework within Argonne’s accredited secure computing enclave (ABLE) leveraging Argonne’s AI scientific assistant substrate (AISAC). Methods to build synthetic datasets were developed together with BTS for use in preparing synthetic datasets that can be used to validate data containment, governance, and security controls in the ABLE environment. Future research directions would focus on applying the Genesis-SAFE framework to CIPSEA-protected datasets entirely within ABLE to support confidentiality-preserving analysis of safety risks, trends, and contributing factors.

Kim, Hyekyung [Argonne National Laboratory (ANL), ↗

DOE OSTI2026

U.S. Hydropower Development Pipeline Data, 2026

The U.S. Hydropower Development Pipeline dataset provides a comprehensive, regularly updated view of proposed and potential hydropower projects across the United States. This resource compiles information from federal agencies and other public sources to track non-powered dams considered for electrification, proposed hydropower facilities at stream reaches with no existing dams, conduit exemptions, and emerging pumped storage hydropower proposals. The dataset includes project characteristics such as location, development status, technology type, ownership category, and other attributes that support analysis of future hydropower trends. It is designed to help researchers, planners, policymakers, and stakeholders assess national‑scale development patterns, understand the evolving hydropower landscape, and explore opportunities and challenges associated with new hydropower deployment. The dataset is updated annually to reflect changes in project status, new proposals entering the pipeline, and projects that are cancelled, completed, or otherwise removed from active consideration. Note: Capacity additions to existing hydropower plants are not included in this database due to reliance on a proprietary data source.

Johnson, Megan [ORNL] (ORCID:0000000290141741)↗

DOE OSTI2025

EVs@Scale Next-Gen Profiles - Fleet Utilization 2024

As part of the U.S. Department of Energy’s EVs@Scale initiative, the Next-Gen Profiles (NGP) project provides a comprehensive, data-driven analysis of electric vehicle (EV) and electric vehicle supply equipment (EVSE) operations across real-world fleet deployments. This paper presents findings from the NGP’s Fleet Utilization study, which investigates operational behavior and asset usage across seventeen EV fleets and two EVSE fleets, encompassing a wide range of vehicle types and use cases. Data collected from diverse sources—varying in format and temporal resolution—are first reformatted into a unified structure. From this harmonized dataset, a suite of rigorously defined performance metrics is calculated at an hourly cadence, enabling consistent cross-comparison of charging, routing, and other key operational behaviors. Amid rapidly increasing EV adoption and growing demands for energy-efficient fleet operations, the analysis reveals clear utilization trends—including diurnal and weekly activity cycles, differences in short versus long charging session dependencies, and route-specific energy usage patterns. These findings highlight the need for tailored infrastructure strategies and the deployment of advanced energy management systems, such as Distributed Energy Resource Management Systems (DERMS) and Site Energy Management Systems (SEMS), which can optimize charging schedules and mitigate peak loads. By leveraging anonymized, harmonized datasets and standardized metrics, this study offers critical insights into fleet behavior and performance, providing a foundation to improve operational efficiency, reduce costs, and enable the scalable deployment of electrified transportation.

Wells, Landon↗