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DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Roadmap to reach global net-zero emissions for developing regions by 2085

As climate change intensifies, determining a developing region’s role in achieving net-zero emissions worldwide is crucial. However, regional efforts, considering historical emissions, remain underexplored. Here, we assess energy system changes, technology adoption, and investments needed for developing regions, including five major- and minor-emitting nations. Our analysis, using an integrated assessment model, shows a large gap in regional efforts toward global net-zero emissions, stemming from the necessary shift of energy systems to low-carbon resources. The use of new technologies, like electric vehicles, hydrogen, and carbon capture, varies by region, with the highest adoption required between 2020 and 2030. Financing this shift needs an average gross domestic product (GDP) investment rise of 0.464% in minor-emitting regions and up to 2.1% in major-emitting regions by 2085. Our results could guide policies and support setting quantifiable targets for developing nations. The findings are key to facilitating strategic technology use and finance mobilization to achieve a carbon-neutral future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Data for Aboveground rather than belowground productivity drives variability in Miscanthus x giganteus net primary productivity

This dataset contains the data used for the publication “Aboveground rather than belowground productivity drives variability in Miscanthus x giganteus net primary productivity”. This dataset contains Miscanthus x giganteus biomass, carbon, and nitrogen tissue data for aboveground and belowground plant parts collected in 2021 for three different sites in Iowa with three different nitrogen application rates. Data at the Iowa sites were collected via biometric hand harvesting, belowground excavations, and soil coring both in-clump and beside-clump. Data were collected at two collection timepoints to calculate the contributions of belowground parts to Miscanthus x giganteus net primary productivity. This dataset also includes Miscanthus x giganteus and Switchgrass soil coring and excavation data collected in 2012 at the University of Illinois Urbana Champaign Energy Farm.

Belowground Biomass↗

DOE OSTI2024

Enhancing Lifetime and Reducing Costs for Fish Diversion Netting Structures (Final Report)

In this effort Pacific Northwest National Laboratory (PNNL) developed and demonstrated an anti-fouling coating that is effective on fish diversion structures. The coating was based on a previously developed superhydrophobic lubricant infused composite (SLIC) coating but modified for application to flexible substrates. We showed that the new reformulated coating – FlexiSLIC – can be applied to flexible fibrous structures to significantly reduce biofouling of netting and anchor lines, to enable longer operational lifetimes, reduced costs, and more reliable operations. The coating developed in this effort was tested on relevant fish diversion structural netting and rope materials (e.g. nylon, Dyneema, polypropylene) to reduce biofouling. Engagement with industry in the development and demonstration further supported progress toward commercialization of the materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Leadership and Community Engagement in Chile: Deploying Net-Zero Technologies and Solutions

The report presents the 2023 Net Zero World Chile program results from activities of the the four program workstreams: 1) energy-system wide modeling LEAP modeling and analysis results including accelerated Net Zero scenarios with aggressive energy efficiency improvements, fuel switching, and electrification across demand sector; 2) district energy chapter provides thermal district energy systems modeling results of the Recoleta-Independencia pilot project and comparative analysis of district energy systems' potential versus competing technologies in Chile; 3) just transition action steps for the creation of a sister city relationship between Tocopilla and a US counterpart city; and 4) power decarbonization capacity building activities focused on the selection and adoption of grid-enhancing technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Interactive effects of precipitation, temperature, and grazing pressure on the net ecosystem carbon balance of grazing lands across the continental United States

Grazing lands cover approximately one-third of the contiguous United States, support much of the nation's beef production, and are an important component of the U.S. terrestrial carbon budget. In this study, we quantified net ecosystem carbon balance (NECB), the net status of grazing lands as a carbon sink (C-sink) or source (C-source) and a key determinant of soil health and productivity. Our primary objective was to synthesize multiple years of annual NECB across heterogeneous grazing lands across the continental U.S and evaluate annual NECB against physical drivers (mean annual precipitation (MAP), mean annual temperature (MAT), vegetation, and moisture condition) and management practices (grazing pressure index (GPI) and fertilization history). We hypothesized that (1) NECB is higher in mesic and fertilized grasslands; (2) NECB increases with MAP and MAT but decreases with GPI; and (3) interactive effects exist among MAP, MAT, and GPI. Using carbon fluxes measured by eddy covariance towers and methane emissions including both enteric methane and manure derived from stocking rates across seven USDA Long-term Agroecosystem Research Network sites, we found: (1) grazing lands were a C-sink or carbon neutral at most sites; (2) vegetation type, moisture conditions, or fertilization had no significant effect on NECB; (3) NECB increased with MAP and MAT, but decreased with a higher GPI; and (4) MAT had a significant positive effect on NECB when MAP exceeded 750 mm (greater water availability). The effect of GPI on NECB was significantly negative when MAP was below 1000 mm, significantly negative when MAT < 12 °C and significantly positive when MAT > 16 °C. Thus, most grazing lands in our study acted as C-sinks unless water deficit, low temperature, or heavy grazing were interactively present. Understanding how climate and management influence NECB of grazing lands is key to maintaining resilient agroecosystems that secure beef production and sustain rural prosperity.

LTAR↗

DOE OSTI2025

Transportation in net-zero emissions futures: Insights from the EMF-37 model intercomparison study

Transportation is currently the largest source of U.S. anthropogenic CO 2 emissions, at about a third of the total. Achieving net-zero emissions by mid-century will require substantial reductions in transportation emissions across passenger and freight travel. Here we leverage a model intercomparison study to explore the role of transportation in scenarios achieving net-zero economy-wide CO 2 emissions by 2050. We find the transport sector is poised to play the most significant role in reducing demand-side emissions, mostly driven by technology substitution, as modeling results suggest a limited role for mode shifting and for reduced use of personal car travel in the U.S. Among various technology solutions, models show agreement that passenger on-road vehicles will largely transition to electric vehicles (EVs), while solutions to decarbonize heavier travel modes are more diverse and include greater use of liquid biofuels and hydrogen. Research should continue to investigate the evolution of on-road electrification, the role of biofuels and hydrogen across heavier travel modes, and the role of mode shifting and travel behavior change to support personal transportation decarbonization at national and regional scales to temper the rapid growth in clean fuel and electricity demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Upconversion of non-recycled MSW paper fractions into biochar via slow pyrolysis and life cycle analysis: Pathways to net negative GHG emission

This study presents an integrated and sustainable approach to valorizing non-recycled municipal solid waste (MSW), a heterogeneous and underutilized waste stream destined for landfilling, by converting it into valuable biochar resources. Specifically, we investigated the upcycling of nonrecycled paper waste based on compositional analysis into four major fractions: high cellulose, high lignin, high contamination, and high ash content papers. These fractions were then homogenized and subjected to slow pyrolysis. The high cellulose fraction (36.1 %) was the most abundant, and contained 66.7 % cellulose, while the high lignin fraction showed the highest lignin (12.1 %) and carbon content (44 %), resulting in highest energy value of 17.4 MJ kg −1 . Biochar yields ranged from 25.6 % to 35.6 %, with the high ash fraction producing the highest yield and alkalinity (pH ≈ 11.2) due to its higher mineral content. Elemental analysis revealed enhanced carbon content up to 76.9 % and reduced oxygen and hydrogen, confirming effective carbonization. The high lignin-derived biochar showed the highest aromatic carbon content (82.8 %) and greater structural stability, while contaminated and ash-rich fractions exhibited dense, low-porosity surfaces due to the presence of contaminants and minerals. Spectroscopic analysis revealed degradation of carbohydrates, disappearance of cellulose peaks and formation of aromatic and mineral derived phases. The scaled life cycle process yielded a global warming potential (GWP) of 119.3 kg CO 2 -eq per ton of dry paper waste, offset by soil carbon sequestration of − 556.41 kg CO 2 -eq, resulting in a net impact of − 427.36 kg CO 2 -eq. This represents a net carbon removal exceeding by ~186 % the emissions associated with landfilling paper waste with electricity generation.

09 BIOMASS FUELS↗

DOE OSTI2024

Net lithium deposition and dominant self-sputtering in lithium tokamak experiment-β with a liquid lithium wall

We observed enhanced net lithium deposition and lithium erosion, possibly dominated by physical sputtering of lithium by lithium-ion bombardment, on the outer plasma-facing surface in the Lithium Tokamak eXperiment-β (LTX-β) during liquid lithium wall operations. Silicon crystal samples with micro-trenches (30 μm × 30 μm × 2–7 μm deep) were exposed to hydrogen plasmas in LTX-β for solid and liquid lithium wall operations. Post-mortem analysis using X-ray photoelectron spectroscopy combined with argon ion sputtering measured net lithium deposition of 8.2 or 21 nm on the silicon crystal surface exposed for ~ 50 repeated shots of ~ 50-ms hydrogen plasma discharges during the liquid lithium wall operations at a vessel temperature of 475 K. Energy dispersive X-ray spectroscopy measured oxygen concentration patterns on the micro-trench floors, which were due to oxidized lithium deposition. Using the inhomogeneous oxygen concentration pattern caused by an ion-shadowing effect associated with the micro-trench’s geometric structure, we determined a polar incident ion direction of 68.4 ± 1.6° referenced to the surface normal direction. This observation was well-explained by the hypothesis that self-sputtering of Li was a dominant lithium erosion source in addition to lithium sputtering by hydrogen bombardment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Energy Emissions Accounting Methods Can Determine Whether Direct Air Capture with Storage Achieves Net Removal

The voluntary carbon market within the United States has expanded rapidly in recent years and enabled private companies and other organizations to provide revenue streams to carbon dioxide removal (CDR) technologies. For a CDR technology to participate in the voluntary carbon market (VCM), the emissions associated with constructing and operating the technology must be less than the CO 2 captured from the atmosphere. Assessing the extent to which this is true for direct air capture with storage (DACS), a relatively energy-intensive CDR technology, strongly depends on the accounting method used to assess the emissions intensity of purchased energy. We simulate the hourly weather-dependent operation of sorbent- and solvent-based DACS in California, Louisiana, Texas, and Wyoming, representing a wide range of local weather and electric and natural gas grid compositions. In all cases, the single most important emissions accounting decision is the method used to estimate the emissions intensity of purchased grid electricity, which varies the calculated net removal by −1049% to +108%. All other factors influencing net removal introduce a variation of at most ±14%. No electricity emissions accounting method is universally conservative across all scenarios, and none is objectively more accurate. High-spatiotemporal-resolution, high-quality, publicly available data sets and models for electricity emissions accounting do not currently exist and are urgently needed to enable standardization of emissions accounting methods to more accurately determine the true emissions impacts of DACS and other energy-intensive facilities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗

DOE OSTI2025

Realizing string-net condensation: Fibonacci anyon braiding for universal gates and sampling chromatic polynomials

Abstract The remarkable complexity of a topologically ordered many-body quantum system is encoded in the characteristics of its anyons. Quintessential predictions emanating from this complexity employ the Fibonacci string net condensate (Fib SNC) and its anyons: sampling Fib-SNC would estimate chromatic polynomials while exchanging its anyons would implement universal quantum computation. However, physical realizations remained elusive. We introduce a scalable dynamical string net preparation (DSNP) that constructs Fib SNC and its anyons on reconfigurable graphs suitable for near-term superconducting processors. Coupling the DSNP approach with composite error-mitigation on deep circuits, we create, measure, and braids Fibonacci anyons; charge measurements show 94% accuracy, and exchanging the anyons yields the expected golden ratioϕwith 98% average accuracy. We then sample the Fib SNC to estimate chromatic polynomial atϕ + 2 for several graphs. Our results establish the proof of principle for using Fib-SNC and its anyons for fault-tolerant universal quantum computation and aim at a classically hard problem.

Science & Technology - Other Topics↗

DOE OSTI2025

A multi model ensemble reveals net climate benefits from regenerative practices in US Midwest croplands

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification frameworks of carbon markets, but model-specific differences limit their applicability across diverse pedo-climatic conditions and agronomic practices. Multi-model ensemble (MME) provides an opportunity to better estimate changes in soil organic carbon (SOC) and nitrous oxide (N 2 O) emissions from agronomic practices at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4-km 2 to assess the aggregate ability of different regenerative practices to sequester SOC and N 2 O emissions compared to their counterfactual dynamic baselines. MME was validated against long-term trials and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 ± 0.12 Mg ha -1 yr -1 , corresponding to a net regional SOC gain of 16.4 Tg C yr -1 compared to business-as-usual baselines. These benefits are halved when each management is practiced individually, and the SOC gains are only fully realized with low initial carbon stock. By including N 2 O emissions, we can assess the overall climate mitigation potential, specifically, the extent to which carbon sequestration can offset direct N 2 O emissions. The magnitude of this potential varies depending on management practices and geographic location with net climate benefits on average ranging from 0 to 3 Mg CO 2 -eq ha -1 yr -1 . High-resolution MME results allow for robust estimates of climate mitigation, reducing barriers to carbon market participation and supporting regenerative agriculture initiatives at scale.

carbon credits↗

DOE OSTI2026

Hydrogen-based ore-to-part manufacturing of near-net-shape stainless steel

Decarbonizing iron and steelmaking, combined with global disruptions to raw material supply chains, necessitates novel approaches to iron and steel production. In this work, we demonstrate a direct ore-to-part manufacturing route using a mixture of ore-derived oxide powders of Fe 2 O 3 , Cr 2 O 3 , NiO, and MoO 3 as feedstock for additive manufacturing, combined with sintering under H 2 to produce a near-net-shape austenitic stainless-steel. Complete reduction of all constituent oxides, including MoO 3 and Cr 2 O 3 , is achieved in-situ at 1300 °C, resulting in dense, crack-free bulk alloy. The fabricated part retains geometric fidelity while undergoing substantial volumetric shrinkage inherent to redox and sintering. Thermodynamic calculations elucidate the co-reduction mechanisms and alloying pathways that enable complete metallization. This work is the first demonstration of net-shaping metal parts directly from ore derived oxides, and this ore-to-part approach can minimize the emissions and lead time for manufacturing associated with downstream processing such as rolling, forging, and machining.

Yang, Mingzhang [Univ. of Waterloo, ON (Canada); F↗

DOE OSTI2025

Precision Measurement of Net-Proton-Number Fluctuations in Au + Au Collisions at RHIC

We report precision measurements on cumulants (𝐶 𝑛 ) and factorial cumulants (𝜅 𝑛 ) of (net) proton number distributions up to fourth order in Au + Au collisions over center-of-mass energies $\sqrt{s_{NN}}$ = 7.7–27 GeV from phase II of the Beam Energy Scan program at RHIC. (Anti)protons are selected at midrapidity (|𝑦| < 0.5) within a transverse momentum range of 0.4 < 𝑝 𝑇 < 2.0 GeV/𝑐. Relative to various noncritical-point model calculations and peripheral collision 70%–80% data, the net proton 𝐶 4 /𝐶 2 measurement in 0%–5% collisions shows a minimum around 19.6 GeV for significance of deviation at ∼2–5⁢𝜎. A minimum in 𝐶 4 /𝐶 2 with respect to a noncritical baseline is expected to be a characteristic feature of the signature associated with a critical point in the QCD phase diagram. In addition, deviations from noncritical baselines around the same collision energy region are also seen in proton factorial cumulant ratios, especially in 𝜅 2 /𝜅 1 and 𝜅 3 /𝜅 1 . As a result, dynamical model calculations including a critical point are called for in order to understand these precision measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

String membrane nets from higher-form gauging: An alternate route to 𝑝-string condensation

We present a new perspective on the $p$-string condensation procedure for constructing 3+1D fracton phases by implementing this process via the gauging of higher-form symmetries. Specifically, we show that gauging a 1-form symmetry in 3+1D that is generated by Abelian anyons in isotropic stacks of 2+1D topological orders naturally results in a 3+1D $p$-string condensed phase, providing a controlled non-perturbative construction that realizes fracton orders. This approach clarifies the symmetry principles underlying $p$-string condensation and generalizes the familiar connection between anyon condensation and one-form gauging in two spatial dimensions. We demonstrate this correspondence explicitly in both field theories and lattice models: in field theory, we derive the foliated field theory description of the $\mathbb{Z}_N$ X-Cube model by gauging a higher-form symmetry in stacks of 2+1D $\mathbb{Z}_N$ gauge theories; on the lattice, we show how gauging a diagonal 1-form symmetry in isotropic stacks of $G$-graded string-net models leads to string-membrane-nets hosting restricted mobility excitations. This perspective naturally generalizes to spatial dimensions $d \geq 2$ and provides a step towards building an algebraic theory of $p$-string condensation.

Anyons↗