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DOE OSTI2025

How Did Westward Volcaniclastic Deposits Accumulate in the Deep Sea Following the January 2022 Eruption of Hunga Volcano?

Most volcanic eruptions on Earth take place below the ocean surface and remain largely unobserved. Reconstruction of past submerged eruptions has thus primarily been based on the study of seafloor deposits. Rarely before the 15 January 2022 eruption of Hunga volcano (Kingdom of Tonga) have we been able to categorically link deep-sea deposits to a specific volcanic source. This eruption was the largest in the modern satellite era, producing a 58-km-tall plume, a 20-m high tsunami, and a pressure wave that propagated around the world. The eruption induced the fastest submarine density currents ever measured, which destroyed submarine telecommunication cables and traveled at least 85 km to the west to the neighboring Lau Basin. Here we report findings from a series of remotely operated vehicle dives conducted 4 months after the eruption along the Eastern Lau Spreading Center-Valu Fa Ridge. Hunga-sourced volcaniclastic deposits 7–150 cm in thickness were found at nine sites, and collected. Study of the internal structure, grain size, componentry, glass chemistry, and microfossil assemblages of the cores show that these deposits are the distal portions of at least two ~100-km-runout submarine density currents. We identify distinct physical characteristics of entrained microfossils that demonstrate the dynamics and pathways of the density currents. Microfossil evidence suggests that even the distal parts of the currents were erosive, remobilizing microfossil-concentrated sediments across the Lau Basin. Remobilization by volcaniclastic submarine density currents may thus play a greater role in carbon transport into deep sea basins than previously thought.

58 GEOSCIENCES↗

DOE OSTI2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

Li, Longlei [Cornell Univ., Ithaca, NY (United Sta↗

DOE OSTI2025

Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes

Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, in this study, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAI max ) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes. Contrary to the widely held assumption of coordination, our results revealed a negative correlation between ΔLAI max and ΔLOS. Notably, the trade-offs between ΔLAImax and ΔLOS were strongly explained by stand age. Younger DBFs, with lower baseline LAI max , predominantly located in eastern Asia, displayed an increase in LAI max with small changes in LOS. This acquisitive strategy facilitated younger DBFs to grow more photosynthetically efficient leaves with low leaf mass per area, enhancing their light use efficiency. Conversely, older DBFs with a higher baseline LAI max , primarily located in North America and Europe, extended their LOS by increasing leaf mass per area. This conservative strategy facilitated older DBFs to produce thicker, but less photosynthetically efficient leaves, resulting in decreased light use efficiency. Our findings offer new insights into the contrasting changes in leaf area and growing season length and highlight their divergent impacts on ecosystem functioning.

Wang, Fangyi [Sun Yat-Sen Univ., Zhuhai (China)]↗

DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗

DOE OSTI2026

The critical role of soil moisture in compound hazards

Soil moisture regulates the exchange of energy, water, and carbon across land–vegetation–atmosphere interfaces. Extremes in soil moisture can amplify natural hazards through interactions with diverse Earth system processes. Despite its mechanistic importance, soil moisture remains underrepresented in hazard research and predictive frameworks. Here, in this study, we review our current understanding of the role of soil moisture in the evolution and onset of diverse compound hazards by synthesizing the latest findings from observational and modelling studies. We highlight key soil moisture mechanisms, including atmospheric feedbacks that amplify drought–heatwave–wildfire events, precipitation couplings that promote clustered storms, and threshold responses that drive vegetation die-offs, trigger landslides, and induce flooding. Persistent challenges in observational data, model representation and operational implementation have limited the integration of soil moisture into hazard early-warning systems. Addressing these gaps through advances in observations, data assimilation, and physics-based and data-driven modelling will enhance hazard prediction and preparedness in a rapidly changing world.

Li, Chuxuan [University of California, Los Angeles↗

DOE OSTI2026

First-principles thermodynamic assessment of Sr-containing secondary phase formation in strontium-substituted lanthanum manganites for solid oxide cell applications

Sr-secondary phase formation is a potentially significant degradation mode with direct impact upon solid-oxide cell (SOC) commercial viability. A first-principles based thermodynamic study was performed for La 1−x Sr x MnO 3±δ (LSM) perovskites to assess their stability against formation of different Sr-secondary phases, including SrO, SrCrO 4 , SrSO 4 , SrCO 3 , and Sr(OH) 2 , for SOC applications. The Sr-secondary phase formation reaction free energies were determined via a thermodynamic model by combining ab initio lattice dynamics calculations for the solid phases and ab initio thermodynamic data for the gas phases. The current approach expands the previously reported thermodynamic modeling studies by integrating first-principles based point defect equilibria into the thermodynamic analysis. The modeling results obtained using this new approach indicate an increased tendency to form the SrO oxide upon decreasing the oxygen partial pressure. Additionally, the enhancing factors to form the Sr-related secondary phase from the associated SrO activity in LSM are further quantified by considering the equilibrium of SrO reacting with the contaminant gas species as a function of temperature and pressure.

defect thermodynamics modeling↗

DOE OSTI2025

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Roadmap on advanced and / real-time characterisation of solid state materials and devices for energy applications

A strong societal and political drive is motivating the development and optimization of novel energy conversion and storage systems for decarbonization. The successful implementation of solid state devices such as fuel cells and secondary batteries depends, however, on achieving ambitious targets in terms of performance, reliability and cost competitiveness. Research and technology are addressing these needs through a holistic approach including exploration of new materials and nanoarchitectures, as well as system engineering. These significant efforts require the support of appropriate characterization tools capable of assessing nanometer-scale phenomena such as concentration profiles of ionic and electronic charges, local chemical compositions and their evolution over time across interfaces. This roadmap provides an overview of selected advanced characterization techniques for energy materials and devices. Specific focus is put on in situ/operando methods for probing electrochemical phenomena in real-time under realistic working conditions. Experts in the field provide an extensive review of the current state of the art in 2025 and the current and future challenges for the characterization of local chemistry and kinetics in the bulk of the material, in nanoarchitectures (e.g. thin films) and at the interfaces (e.g. grain boundaries, phase contacts, solid/liquid and solid/gas interfaces) . The aim is to provide a detailed guide to the techniques, describing opportunities and bottlenecks for their practical deployment and examples of successful applications.

25 ENERGY STORAGE↗

DOE OSTI2026

Engineered Production of Hydroxycinnamoyl Tyramine Conjugates Limits the Growth of the Pathogen Pseudomonas syringae in Arabidopsis

Hydroxycinnamoyl tyramine conjugates are phenolamides produced by plants in response to pathogen attack and biotic stresses. Their proposed mechanisms of action include cytotoxicity towards pathogens, cell wall reinforcement to restrict pathogen proliferation, and signaling activity to trigger general stress responses. Here, we engineered the production of the tyramine conjugates p-coumaroyltyramine (CT) and feruloyltyramine (FT) in Arabidopsis to gain insight into their mode of action. Co-expression of feedback-insensitive 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase and tyrosine decarboxylase increased tyramine content. Additional expression of tyramine hydroxycinnamoyltransferase led to de-novo production of CT and FT, which were found as soluble and cell-wall-bound forms. FT was associated with lignin in stems. The growth of pathogenic Pseudomonas syringae was reduced in rosettes of the Arabidopsis CT- and FT-producing lines compared to wild type. These lines also exhibited increased transpirational water loss in excised rosettes. Transcriptomic analysis of transgenic lines grown under normal conditions revealed alterations in the expression of genes associated with the biological circadian clock. These changes led to a reduction in flavonoids and an early flowering phenotype. Important changes in the expression of genes related to abiotic stress such as drought, cold, heat, and hypoxia potentially contribute to reduced growth of P. syringae in engineered Arabidopsis.

Phenolamides↗

DOE OSTI2024

Thermodynamic Modeling of Point Defects in Triple Conducting Perovskite Ba 0.95 La 0.05 FeO 3- $_δ$ with Incorporation of the Hydride Defect Formation Reaction for Solid Oxide Cells

Distinct from the proton defect, the hydride defect species may be present in certain perovskite materials in reducing environments such as in fuel electrodes of solid oxide cells (SOCs) or on the reducing side of ceramic membranes. A generalized defect thermodynamic model was developed for the triple-conducting perovskites (La,Ba)Fe 1-x M x O 3-δ (M = Y and Zr) to allow inclusion of the hydride defect formation reaction in addition to the other three main defect reactions, namely, the oxygen vacancy formation, hydration, and charge disproportionation reactions. This comprehensive defect model also allows the incorporation of polynomial functional forms of oxygen nonstoichiometry δ to describe the defect reaction energies and entropies and to enable refinements of the defect reaction equilibrium constants in the defect thermodynamic analysis. As a first step, the developed model is applied to the Ba 0.95 La 0.05 FeO 3-δ material as an illustrative system to obtain its Brouwer diagrams with both the proton and hydride defects in relevant SOC conditions, particularly for more reducing environments. In conclusion, the results provide direct guidance on the influence of electronic and ionic defect concentrations upon thermodynamic properties and ultimately on the performance of Ba 0.95 La 0.05 FeO 3-δ and potentially other (La,Ba)Fe 1-x M x O 3-δ perovskite materials involved in SOC applications.

25 ENERGY STORAGE↗

DOE OSTI2025

Tailoring Nitinol for elastocaloric application

This study focuses on tailoring commercial Nitinol, the most commonly used elastocaloric material, for near-room-temperature cooling applications. Short heat treatments near 500 °C were used to fine-tune the material’s transition temperature, resulting in austenite finish temperatures ranging from 6.0 to 25.5°C and altered superelastic and elastocaloric properties. Plateau stresses decreased while temperature changes rose from 21.5°C up to 27.9°C at 6% strain. Significant variability in the Nitinol response when testing below its austenite finish temperature was observed. In conclusion, the effect of mechanical cycling on transition temperatures was also evaluated, demonstrating an increase for all the samples.

Efficiency↗

DOE OSTI2026

AmeriFlux US-NYa NYSM - Voorheesville

This is the AmeriFlux version of the carbon flux data for the site US-NYa NYSM - Voorheesville. Site Description - Located in an orchard/vineyard setting. It is an open area surrounded by orchards and some larger trees. Obstructions within 100m include some orchards/trees. The soil type is chenango gravelly silt loam, loamy substratum. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.