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DOE OSTI2025

Insights Into Nature-Based Climate Solutions: Managing Forests for Climate Resilience and Carbon Stability

Successful implementation of forest management as a nature-based climate solution is dependent on the durability of management-induced changes in forest carbon storage and sequestration. As forests face unprecedented stability risks in the face of ongoing climate change, much remains unknown regarding how management will impact forest stability, or how interactions with climate might shift the response of forests to management across spatiotemporal scales. Here, we used a process-based model to simulate multidecadal projections of forest dynamics in response to changes in management and climate. Simulations were conducted across gradients in forest type, edaphic factors, and management intensity under two alternate radiative forcing scenarios (RCP4.5 and RCP8.5). This allowed for the quantification of forest stability shifts in response to climate change, and the role of management in modulating that response, where ecosystem stability is characterized as the resilience and temporal stability of net primary production, aboveground biomass, and soil carbon. Our results indicate that forest structure is primarily shaped by management, but the same management strategy often produced divergent structures over time, due to interactions with regional climate change. We found that management can be used to increase stability and minimize the release of stored carbon by reducing mortality, but also highlight the regional dependency of management-induced changes in resilience to climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Interaction between the emerging components of online shopping and in-person activities: insights from a behavioral survey

The rise of technological advancements has led to the commonplace practice of online shopping for retail, grocery, and food. However, little research has been conducted on the interplay of these components in burdened communities (BCs) that face issues of marginalization and limited access to digital resources. Here, this study aims to provide a comprehensive understanding of travel behavior changes by analyzing the interconnectedness of the emerging components of online shopping (retail, grocery, and food) and in-person activities in both BCs and non-BCs. A unique household-level database is created by linking the 2021 Puget Sound Household Travel Survey and the US Department of Transportation’s burdened community databases, and a conditional mixed process model is estimated to account for unobserved endogeneity. The findings suggest households living in BCs are less likely to order online retail goods and groceries compared to non-BC households. Additionally, the probability of making more restaurant trips decreases for households living in BCs. The study highlights the digital divide that exists in BCs and the differences in online and in-person shopping activities across socioeconomic levels. Policymakers may address these disparities to promote better access to goods and services for all. Besides, planners may need to improve the travel demand models by accounting for the emerging components of online shopping and the trip frequencies by purpose in BCs.

Digital Divide↗

DOE OSTI2025

Size and Structural Control of Mechanoluminescent ZnS:Mn 2+ Nanocrystals for Optogenetic Neuromodulation

Mechanoluminescent materials hold immense potential for various transformative applications, from medical imaging and diagnostics to health monitoring and wearable displays. Conventionally produced as bulk powders or microparticles, they face significant size limitations for advanced applications, particularly in biological systems and microscale devices. Here, this work presents an approach to ZnS:Mn 2+ nanocrystal synthesis that involves self-assembly and subsequent calcination. In addition to effective size control within the nanoscale, this approach promotes the formation of abundant stacking faults, significantly enhancing piezoelectric and mechanoluminescent properties by increasing trap density and reducing trap depth. Unlike mechanoluminescent materials produced using conventional methods, these nanocrystals demonstrate strong mechanoluminescence without requiring UV pre-excitation, and the light emission persists even after mechanical stress is removed. These advantageous properties make them promising candidates for optogenetic neuromodulation, as they can effectively trigger electrical signals in neurons upon ultrasound stimulation both with and without UV pre-excitation. The persistent mechanoluminescence prolongs the duration of neuronal electrical activity, providing an extended temporal window for neuromodulation compared to conventional mechanoluminescent materials. This study provides a scalable method for producing efficient mechanoluminescent nanoparticles and reveals the crucial role of particle size and defect structures in determining their mechanoluminescent behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

An Application of Molecular Recognition for the Efficient Removal of Cesium from Hanford Nuclear Waste by Modular Solvent Extraction

In this work, experimental results leading to flowsheet design are presented showing how a calixarene-crown ether based solvent-extraction process can meet the challenge of cesium removal from nuclear tank wastes stored at the US Department of Energy Hanford site. Cleanup of legacy Cold War nuclear waste stored in underground tanks represents one of the greatest environmental challenges facing the US Department of Energy in terms of risk, cost, and effectiveness of applicable science and technology. Planning for the cleanup at the Hanford Site calls for the removal of the radioactive fission product 137Cs from its alkaline salt waste, including the use of modular processes that can be deployed near the tank farms. To meet the resulting need for extremely high selectivity, the Next-Generation Caustic-Side Solvent Extraction (NG-CSSX) process employing a calix[4]arene-crown ether in modified kerosene has been adapted to remove sub-millimolar cesium in competition with molar sodium and potassium in a high-nitrate alkaline matrix. Potassium loading in the solvent was determined in extraction, scrubbing, and stripping, leading to an empirical model closely approximating cesium distribution ratios for a variety of Hanford waste types. Process chemistry has been developed based on this molecular-recognition approach, focusing on the competitive effect of potassium loading and the mitigating process modifications needed, including extending the scrub section. The result is a modular flowsheet design that can achieve cesium decontamination factors well in excess of 15,000 even for the worst-case Hanford waste.

Williams, Neil [ORNL] (ORCID:000000023159226X)↗

DOE OSTI2025

Advancing Quantum Many-Body GW Calculations on Exascale Supercomputing Platforms

Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum many-body GW methods are state-of-the-art for treating electronic excited states and couplings but often hindered due to the costly numerical complexity. Here, we present innovative implementations of advanced GW methods within the BerkeleyGW package, enabling large-scale simulations on Frontier and Aurora exascale platforms. Our approach demonstrates exceptional versatility for complex heterogeneous systems with up to 17,574 atoms, along with achieving true performance portability across GPU architectures. We demonstrate excellent strong and weak scaling to thousands of nodes, reaching double-precision core-kernel performance of 1.069 ExaFLOP/s on Frontier (9,408 nodes) and 707.52 PetaFLOP/s on Aurora (9,600 nodes), corresponding to 59.45% and 48.79% of peak, respectively. Our work demonstrates a breakthrough in utilizing exascale computing for quantum materials simulations, delivering unprecedented predictive capabilities for rational designs of future quantum technologies.

Zhang, Benran [University of Southern California, ↗

DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗

DOE OSTI2025

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing

Energy efficiency of training and inferencing with large neural network models is a critical challenge facing the future of sustainable large-scale machine learning workloads. This paper introduces an alternative strategy, called phantom parallelism, to minimize the net energy consumption of traditional tensor (model) parallelism, the most energy-inefficient component of large neural network training. The approach is presented in the context of feed-forward network architectures as a preliminary, but comprehensive, proof-of-principle study of the proposed methodology. We derive new forward and backward propagation operators for phantom parallelism, implement them as custom autograd operations within an end-to-end phantom parallel training pipeline and compare its parallel performance and energy-efficiency against those of conventional tensor parallel training pipelines. Formal analyses that predict lower bandwidth and FLOP counts are presented with supporting empirical results on up to 256 GPUs that corroborate these gains. Experiments are shown to deliver ∼50% reduction in the energy consumed to train FFNs using the proposed phantom parallel approach when compared with conventional tensor parallel methods. Additionally, the proposed approach is shown to train smaller phantom models to the same model loss on smaller GPU counts as larger tensor parallel models on larger GPU counts offering the possibility for even greater energy savings.

Seal, Sudip [ORNL] (ORCID:0000000332330656)↗

DOE OSTI2025

XPS post-mortem analysis of plasma-facing units extracted from WEST after the C3 (2018) and C4 (2019) campaigns

Four monoblocks coming from one ITER-like plasma-facing unit from the Q3B sector of the lower divertor, named as monoblock (MB)3, MB9, MB20, and MB30, were exposed to the deuterium and helium plasma mixture during the C3 (2018) and C4 (2019) campaigns of the Tungsten Environment in Steady-state Tokamak (WEST), followed by a detailed ex-situ X-ray photoelectron spectroscopy investigation. The surface and in-depth chemistry of the tungsten monoblocks indicated the formation of a re-deposited mixture in the deposition-dominated area of the divertor, thicker than 218 and 172 nm for MB3 and MB9, respectively. The redeposition layer was dominated by a mixture of boron carbides accompanied by tungsten carbides in MB3, while in MB9, the redeposition layer was dominated by tungsten borides. The remaining two monoblocks, MB20 and MB30, were collected from the erosion region and showed similar chemical behavior with a blended mixture of oxidized and metallic tungsten followed by boron carbides within a 50 nm depth range. Boron fixation in the layers is an expected consequence of the boronizations used during the operation, but the chemical status of redeposited elements was characterized for the first time in this work.

Marin, Alexandru [Pennsylvania State University]↗

DOE OSTI2025

Updating the Building Science Advisor (BSA): A Tool to Assist in the Design of Durable Building Envelopes

Predicting the moisture durability of building envelope components remains challenging due to multiple influencing factors, including material selection, assembly positioning, local climate conditions, air tightness, interior environment, and construction quality. Building codes increasingly emphasize energy efficiency through enhanced insulation and tighter envelopes but offer limited guidance on moisture durability considerations. Consequently, builders face uncertainty, particularly as new materials and assemblies enter the market.The Building Science Advisor (BSA) is a free, web-based expert system developed to address these challenges by providing actionable insights into the moisture durability and energy efficiency of both new and retrofit wall designs. Recently updated, we are now providing version 3.0 of the tool. BSA features significant user interface improvements, enhancing navigation and user interaction through a refreshed, intuitive design. Additionally, the tool incorporates a newly developed database containing pre-simulated wall assembly cases, significantly reducing response times and improving the accuracy of moisture durability assessments. Furthermore, the updated BSA includes moisture content as a performance criterion, providing users with a more comprehensive understanding of moisture-related durability risks. These enhancements enable rapid, reliable assessments tailored to specific climate zones and local building practices. BSA continues to offer targeted guidance on wall retrofit scenarios and delivers access to an expanded library of location-specific building science resources.This paper describes these key updates, highlighting the enhanced features, expanded capabilities, and overall improvements to user experience and educational content. The paper includes a demonstration that illustrates how the revised BSA effectively supports practitioners in designing durable, energy-efficient building envelope assemblies.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗

DOE OSTI2024

Effect of Si impurities on microstructure and tensile properties of a cast Al-Mg-Fe alloy

Al-Mg alloys are attractive for structural castings owing to their superior strength and ductility in the as-cast state. However, given the tight tolerance for impurities, Al-Mg alloys produced from secondary sources still face multiple challenges. Here, we report the effect of increased Si impurity (0.05–1.6 wt%), which is commonly found in secondary Al sources, on microstructure and tensile properties of a cast Al-4.3Mg-1.6Fe (wt%) alloy, commercially referred to as Castaduct-42 alloy. Microstructural characterization revealed that Si addition increased the volume fraction, size, and aspect ratio of primary Al13Fe4 intermetallic particles as well as the volume fraction of other binary and ternary eutectic phases. Tensile testing results demonstrated that increasing Si impurities from 0.05 to 1.6 wt% reduced ductility from 14.3 ± 1.4 % to 2.2 ± 1.0 %. A particle cracking damage accumulation model coupled with failure analysis indicated primary Al13Fe4 intermetallic particles to be the major contributing factor to the deterioration in ductility with increasing Si content. Additionally, the stabilizing effect of Si on primary Al 13 Fe 4 was inconsistent with the CALPHAD calculation results based on existing CALPHAD databases which predict a slightly decreasing primary Al13Fe4 phase fraction with increasing Si concentration. This work provides new insights into the phase stability and mechanical behavior of the lesser studied Al-Mg-Fe-Si alloy system that will contribute to the development of sustainable cast Al-Mg based alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Lab-Scale Cable-Driven Parallel Robot Prototype for Automated Prefabricated Component Manipulation

This paper presents the design and evaluation of a lab-scale cable-driven parallel robot (CDPR) developed as a flexible platform for automated installation of prefabricated components onto exterior building envelopes. Traditional manual installation methods for prefabricated components, which depend on scaffolding, cranes, cherry pickers, and verbal coordination, are not only labor-intensive and error-prone but also face significant limitations in dense urban environments due to site access constraints. To address these challenges, we developed a lab-scale CDPR platform capable of autonomously transporting building envelope components from a designated pickup zone to their target installation location, minimizing the need for human intervention. This study describes the system’s mechanical design, actuation architecture, real-time feedback system, and control strategy of the CDPR, and evaluates its performance in a laboratory environment. The robot’s actuation system uses torque control for end-effector manipulation. The robot’s real-time pose feedback comes from a construction-grade total station and a wireless inertial measurement unit (IMU), which together support precise end-effector control. Experimental results demonstrate the successful integration of the hardware, sensing, state estimation, and control subsystems. Preliminary tests showed that our lab-scale prototype can position the end effector with an error of less than 3 mm, which is a level of precision not previously achieved by existing CDPRs in construction applications. The key findings are twofold: (1) torque-only control is necessary but not sufficient for minimizing final pose error, and (2) incorporating real-time pose feedback can achieve the desired placement accuracy.

Liu, Yifang [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2025

3D modeling of n = 1 RMP driven heat fluxes on the SPARC tokamak PFCs using HEAT

3D heat flux calculations at the lower outer divertor plate of SPARC using the HEAT code show that 3D fields generated from error field correction coils can lead to enhanced peak heat fluxes up to 15 times larger compared to the axisymmetric case. Previously employed to simulate axisymmetric heat flux on 3D plasma facing components, the HEAT code can now predict 3D heat flux generated by non-axisymmetric plasmas. This is achieved via a new HEAT module which leverages the 3D field line tracing capabilities of MAFOT starting from an M3D-C1 (MHD resistive code) perturbed equilibria. The resulting heat flux is assigned using the magnetic footprint and the heat flux layer model, an extension of the 2D heat flux model also known as the Eich, to 3D non-axisymmetric plasmas. For SPARC, the new capabilities of HEAT are used to calculate the 3D heat loads resulting from n = 1 perturbation fields (with n indicating the toroidal periodicity) applied through a toroidal array of six picture frame coils with different amplitude. The comparison with the unperturbed case shows significant changes in shape and intensity of the heat flux profile. The results show that the application of n = 1 3D field leads to a localized enhancement of the heat flux peak, influenced by the wetted area impacted by the magnetic footprint, and the appearance of a secondary heat flux peak, whose intensity depends on amplitude of the applied 3D field and toroidal location.

3D heat flux↗

DOE OSTI2024

Forecast of Wildfire Potential Across California USA Using a Transformer

Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.

Limber, Russell [ORNL]↗

DOE OSTI2026

Hydrogen Analysis by Gas Chromatography–Mass Spectrometry

The detection of hydrogen in complex gas mixtures is essential for many applications. Conventional approaches such as gas chromatography (GC) with thermal conductivity detection (TCD) and residual gas analyzers (RGAs) face significant limitations: TCD exhibits poor response when helium is used as a carrier gas, whereas RGAs lack chromatographic separation, preventing reliable quantification of hydrogen because of interference from other species. Traditional GC methods rely on dual-column configurations with packed or molecular-sieve porous layer open tubular (PLOT) columns to separate hydrogen from O2, N2, CO, CO2, CH4, and other hydrocarbons, increasing system complexity and limiting compatibility with mass spectrometry (MS) detection. In this work, we developed and validated a robust GC–MS method capable of directly detecting and quantifying hydrogen using electron ionization (EI) without dopants, reagent gases, or ion–molecule reaction schemes. By integrating a modified EI source and a cryogenically cooled single capillary column configuration, we achieved baseline separation of hydrogen from all major permanent gases and hydrocarbons in a refinery gas mixture using helium as the carrier gas. The method demonstrated excellent linearity, high sensitivity, and exceptional reproducibility. Adjustable sample-loop volumes and split ratios enabled optimization of peak shape and signal-to-noise performance for trace-level and percent-level hydrogen concentrations. Beyond hydrogen quantitation, the method provides simultaneous compositional profiling of other gases in a mixture in a single run, making it valuable for a wide range of tasks.

Lobodin, Vlad [ORNL]↗

DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗

DOE OSTI2025

Real-time monitoring of trace noble gases using laser-induced breakdown spectroscopy—An investigation of the impact of bulk gas on plasma properties and sensitivity

The impact of Ar and He bulk gases on laser-induced breakdown spectroscopy (LIBS) real-time monitoring of trace Xe and Kr was assessed. LIBS is being developed as a monitoring tool for measuring noble gas transport in molten salt systems, in which traditional sensors may face challenges associated with radiation, corrosive materials, and/or mixed phases. The plasma temperature and electron densities of LIBS plasmas were measured in both static and various flowing Ar and He streams (0–5 L min −1 ). The use of an Ar bulk gas resulted in higher plasma temperature, greater electron densities by an order of magnitude, and extended plasma lifetime compared with when He bulk gas was used. Gas flow rate was found to have little impact on plasma temperature; however, its effect on electron density was significant, indicating the need to consider flow rate–specific models. Matrix effects on emission peaks were reported for both bulk gases. Due to these matrix effects, multivariate models were developed for Xe and Kr ranging from 0 to 700 ppm in both bulk gases. Although the predictive behavior was similar (root mean square error of prediction ranging from 11.1 to 20.6 ppm), the limits of detection were superior in He (Xe: 22.9 ppm, Kr: 30.4 ppm). Furthermore, these models were employed in demonstrative real-time tests (>1 h), which showed strong predictive precision (relative standard deviation <5 %) regardless of the bulk gas. Ultimately, this study provides a guide for the considerations required when developing gaseous LIBS models for real-time monitoring.

Gas flow effects↗

DOE OSTI2026

Voltage Service Limits Smart Contract Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Generator

Modern electrical grids face growing stability risks from customer-owned generators, especially at points of common couplings (PCCs). Disruptive behavior from power-electronic sources can cause protective relays to isolate problematic generators, making measurement integrity critical. This article presents a distributed ledger technology (DLT) approach that uses smart contracts to evaluate PCC voltage measurements and trigger backup breaker operations. The approach is framed as a verifiable, multi-organization attestation and audit layer, not as a real-time control security mechanism. In the proposed architecture, voltage measurements from a hardware protective relay are anchored on a DLT through the Cyber Grid Guard (CGG) system for attestation by both the grid utility and customer-owned generator. A Voltage Service Limits (VSLs) smart contract evaluates the on-chain measurements against allowable phase-voltage limits derived from the ANSI C84.1 standard. The framework is validated in a hardware-relay-in-the-loop test bed under sustained-undervoltage, sustained-overvoltage, and transient line-to-line fault scenarios. The results show that the VSL smart contract can process these measurements and issue backup breaker actions consistent with the defined service-limit criteria, demonstrating the DLT potential as a verifiable audit layer at the PCC that complements primary protection.

cyber security↗

DOE OSTI2026

Insights into elevated temperature tensile deformation mechanisms and kink banding in additively manufactured tungsten

Additive manufacturing (AM) potentially enables fabrication and repair of plasma facing components (PFCs) of tungsten. However, deformation mechanisms in AM-fabricated tungsten under service-relevant thermomechanical conditions remain unknown majorly due to challenges in achieving crack-free W. This study reveals the deformation mechanisms operative under tension at 800 °C and 1200 °C in the electron beam melting powder bed fusion (EBM-PBF) fabricated W. Textured columnar grains with mixed <001 >/<111 >|| build-direction were observed. Strong anisotropy in tensile properties persisted across test temperatures, and all specimens exhibited extensive deformation-induced banding phenomena. Grain boundary character analysis of the deformed specimens indicated that these deformation bands were kink bands with tilt grain boundaries prominently present at the band-matrix interface. Interestingly, the material within the kink bands rotated toward higher resolved shear stress, providing insights into the origins of kink band formation in body-centered cubic refractory metals. Intragranular misorientation axis analysis revealed that plasticity was dominated by {110} <111 > slip at 800 °C, whereas additional (213)[11⁢̄1] and (112)[11⁢̄1] slip systems activated at 1200 °C. A dense low-angle boundary networks observed near fractured surface indicated the onset of dynamic recrystallization. Results reveal plasticity governing mechanisms in AM-fabricated W under power plant-relevant conditions, and provide insights into kink band attributes in refractory metals.

Mayes, Riley [ORNL] (ORCID:000900089307010X)↗