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DOE OSTI2022

High Throughput Coefficient Thermal Expansion Testing Utilizing Digital Image Correlation

Dr. Fitzgerald, a postdoc at Sandia National Laboratories, works in a materials of mechanics group characterizing material properties of ductile materials. Her presentation focuses specifically on increasing throughput of coefficient of thermal expansion (CTE) measurements with the use of optical strain measurements, called digital image correlation (DIC). Currently, the coefficient of thermal expansion is found through a time intensive process called dilatometry. There are multiple types of dilatometers. One type, a double push rod mechanical dilatometer, uses and LVDT to measure the expansion of a specimen in one direction. It uses a reference material with known properties to determine the CTE of the specimen in question. Testing about 500 samples using the double push rod mechanical dilatometer would take about 2 years if testing Monday through Friday, because the reference material needs to be at a constant temperature and heating must done slowly to ensure no thermal gradients across the rod. A second type, scissors type dilatometer, pinches a sample using a “scissor-like” appendage that also uses a LVDT to measure thermal expansion as the sample is heated. Finally, laser dilatometry, was created to provide a non-contact means to measure thermal expansion. This process greatly reduces the time required to setup a measurement but is still only able to measure one sample at a time. The time required to test 500 samples gets reduced to 3.5 weeks. Additionally, to measure expansion in different directions, multiple lasers must be used. Dr. Fitzgerald solved this conundrum by using an optical measurement technique called digital image correlation to create strain maps in multiple orientations as well as measuring multiple samples at once. Using this technique, Dr. Fitzgerald can test 500 samples, conservatively, in 2 days.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Computationally Efficient Control Co-Design Optimization Framework with Mixed-Fidelity Fluid and Structure Analysis: OpenTurbineCoDe (OTCD) - ARPA-E ATLANTIS Phase I Project (Final Technical Report)

The goal of the Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) Aerodynamic Turbines Lighter and Afloat with Nautical Technologies and Integrated Servo-control (ATLANTIS) Program is to develop new technologies for floating offshore wind turbines, or wind farms, using the discipline of control co-design (CCD.) In this context, our goal is to develop a computationally efficient optimization framework for design of floating offshore wind turbines. Our specific aim is to utilize high-fidelity structural, aerodynamic, aero-structural tools, and to derive control-oriented reduced- or low-order models directly from the high-fidelity tools. We proposed a mixed-fidelity modeling approach which means that we are also using low- and mid-fidelity tools when necessary. This research is conducted by a multidisciplinary team consisting of Rutgers University, University of Michigan, Brigham Young University, and the National Renewable Energy Laboratory (NREL). The computational framework, called OpenTurbineCoDe, is designed to integrate, where possible, traditional structural, aerodynamic, aeroelastic models (e.g., OpenFAST) and advanced control algorithms with higher fidelity simulation tools including Reynolds-averaged Navier–Stokes (RANS) solvers, and three-dimensional structural finite element solvers. All the high-fidelity tools used in this research provide numerically exact gradients to facilitate both efficient optimization and local linearization for control implementation.

17 WIND ENERGY↗

DOE OSTI2025

Skyrmion lattice manipulation with electric currents and thermal gradients in MnSi

The skyrmion lattice (SkL) in MnSi was studied using small-angle neutron scattering and under the influence of a radial electric current in a Corbino geometry. In response to the applied current, the SkL undergoes an angular reorientation with respect to the MnSi crystal lattice. The reorientation is nonmonotonic with increasing current, with the SkL rotating first in one direction and then the other. The SkL reorientation was studied at different sample locations and found to depend on the local current density as inferred from a finite-element analysis. Here, the nonmonotonic response indicates the presence of two competing effects on the SkL, most likely due to the presence of both radial electric and thermal currents. Such a scenario is supported by micromagnetic simulations, which show how these effects can act constructively or destructively to drive the SkL rotation, depending on the direction of the electric current. In addition, the simulations also suggest how the direction of the skyrmion flow may affect the SkL orientation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Deep learning assisted jet tomography for the study of Mach cones in QGP

Abstract Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the propagation direction of the parton shower relative to the radial flow because of the distortion caused by the collective expansion of the QGP and the large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and parton-shower propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different path lengths and orientations relative to the radial flow of the QGP in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Design Principles in Engineering of Multigrain Nanocatalysts via Multiscale Electronic Structure Characterization

Engineering grain boundary (GB) strain provides a promising pathway to tune the catalytic properties of nanocrystals. However, structural heterogeneity from random grain orientation and geometry has limited clear structure–property correlations. Here, we utilize a multigrain Co3O4/Mn3O4 core/shell nanocrystal platform as a model system to systematically investigate how geometric misfit strain at GBs serves as catalytically active sites for the oxygen reduction reaction. Through precise subnanometer-level control over grain morphology and by integrating multiscale electronic structure characterization, we identify the electronic structural signature of GB defects and establish a direct correlation between localized strain fields and modified electronic states. Strain modulation at GBs alters the eg orbital energy levels, with elongation along the z-axis combined with shear strain stabilizing the eg states, in contrast to the destabilization observed under pure shear strain. This stabilization mechanism enhances the electrocatalytic activity and selectivity of strained GBs compared with strain-relaxed grain surfaces. Furthermore, we reveal that GBs exhibit a radial strain gradient, producing a spatial energy shift that further modulates local electronic structures, as resolved through the classification of electron energy loss spectroscopy data. Together, these findings demonstrate that geometric misfit strain enables precise tuning of grain geometry and the resulting electronic structures, offering a robust strategy for engineering next-generation nanocatalysts.

Cho, Min Gee↗

DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Microfibril orientation and compositional heterogeneity in fiber and vessel cell walls of poplar xylem studied by AFM-IR and SFG spectroscopy

Understanding the structural organization of cellulose microfibrils (CMFs) within individual plant cell walls is essential for connecting cell wall architecture to its mechanical and physiological functions. However, due to the complex hierarchical structure and nanoscale heterogeneity of cell walls, it remains technically challenging to resolve detailed compositional and orientational information at subcellular levels of individual cell walls. This study investigates the internal 3D structure, chemical composition, and sublayer organization of fiber and vessel cell walls in the xylem tissue of a two-year-old field-grown hybrid poplar tree (Populus alba × P. glandulosa) using photothermal atomic force microscopy coupled with infrared spectroscopy (AFM-IR) and sum frequency generation (SFG) hyperspectral microscopy. AFM-IR provided nanoscale chemical imaging, revealing localized compositional heterogeneity, including variations between adjacent cell walls and transitional layers beyond the traditional S1, S2, and S3 sublayers. SFG microscopy revealed that CMFs in fiber walls are highly aligned along the stem axis, consistent with their role in mechanical support, while vessel cell walls exhibited slightly tilted CMFs, reflecting their function in hydraulic transport. Together, these results offer new insights into cell-type-specific CMF organization and compositional gradients in hybrid poplar xylem. These findings highlight the structural and chemical complexity of secondary cell walls in woody plants and demonstrate the value of AFM-IR and SFG spectroscopy in elucidating plant cell wall architecture.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

51 V NMR evidence for interlayer-modulated charge order and a first-order low-temperature transition in CsV 3 ⁢Sb 5

Charge order in the kagome superconductor CsV 3 ⁢Sb 5 exhibits a complex three-dimensional organization and intermediate-temperature anomalies whose bulk character has remained unsettled. We use orientation-dependent 51 V NMR as a site-selective probe to determine the stacking of the charge density wave (CDW) state and its thermal evolution. Below 𝑇 CDW ≈ 94K, the field-linear splitting of the 51 V central transition together with the anisotropy of the Knight shift tensor identify an interlayer-modulated 3⁢𝑞 CDW whose local environments are consistent with a four-layer 2 × 2 × 4 stacking with mixed trihexagonal/Star-of-David distortions, in agreement with synchrotron x-ray determinations. For comparison, RbV 3 ⁢Sb 5 serves as a reference exhibiting a uniform trihexagonal 2 × 2 × 2 stacking, allowing us to isolate features unique to the 2 × 2 × 4 state in CsV 3 ⁢Sb 5 . With 𝐻 0 ∥ 𝑐, the 51 V quadrupolar satellites through the intermediate temperature scale near 𝑇 CO ≈ 65K reorganize into two well-resolved electric-field-gradient manifolds that coexist over a finite interval; their relative spectral weights interchange on cooling while the total integrated satellite intensity remains conserved and 𝜈 𝑄 within each manifold is nearly temperature independent. The coexistence without critical broadening, together with conserved intensity, provides bulk evidence consistent with a first-order charge-order transition near 𝑇 CO . Our measurements do not resolve whether this lower-temperature transition corresponds to a distinct in-plane order or a reorganization of the 3⁢𝑞 state; rather, they delimit this window and provide bulk, site-resolved constraints that connect prior reported anomalies to a thermodynamic first-order transition.

Wang, Xiaoling [California State Univ., East Bay, ↗

DOE OSTI2021

Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor

Reduced models describing the Lagrangian dynamics of the velocity gradient tensor (VGT) in homogeneous isotropic turbulence (HIT) are developed under the physics-informed machine learning (PIML) framework. We consider the VGT at both Kolmogorov scale and coarse-grained scale within the inertial range of HIT. Building reduced models requires resolving the pressure Hessian and subfilter contributions, which is accomplished by constructing them using the integrity bases and invariants of the VGT. The developed models can be expressed using the extended tensor basis neural network (TBNN) introduced by Ling et al. [J. Fluid Mech. 807, 155 (2016)]. Physical constraints, such as Galilean invariance, rotational invariance, and incompressibility condition, are thus embedded in the models explicitly. Our PIML models are trained on the Lagrangian data from a high-Reynolds number direct numerical simulation (DNS). To validate the results, we perform a comprehensive out-of-sample test. We observe that the PIML model provides an improved representation for the magnitude and orientation of the small-scale pressure Hessian contributions. Statistics of the flow, as indicated by the joint PDF of second and third invariants of the VGT, show good agreement with the “ground-truth” DNS data. A number of other important features describing the structure of HIT are reproduced by the model successfully. We have also identified challenges in modeling inertial range dynamics, which indicates that a richer modeling strategy is required. This helps us identify important directions for future research, in particular towards including inertial range geometry into the TBNN.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Interplay between electron localization, magnetic order, and Jahn-Teller distortion dictates LiMnO2 phase stability

The development of manganese (Mn)-rich cathodes for Li-ion batteries promises to alleviate potential supply chain bottlenecks in battery manufacturing. Fundamental challenges in Mn-rich cathodes arise from phenomena such as structural changes due to cooperative Jahn-Teller (JT) distortions of in octahedral environments, Mn migration, and phase transformations to spinel-like order, all of which affect the electrochemical performance. These physically complex phenomena motivate an re-examination of the Li-Mn-O rock-salt space, with a focus on the thermodynamics of the prototypical, polymorphs. It is found that the generalized gradient approximation (GGA-PBEsol) and meta-GGA ( ) density functionals with empirically fitted on-site Hubbard corrections yield spurious stable phases for , such as predicting a phase with -like order ( ) to be the ground state instead of the orthorhombic (Pmmn) phase, which is the experimentally known ground state. Accounting for antiferromagnetic order in each structure is shown to have a substantial effect on the total energies and resulting phase stability. By using hybrid-GGA (HSE06) and GGA with self-consistent Hubbard parameters (on-site and inter-site ) calculated from linear response theory, the experimentally observed phase stability trends are recovered. The calculated on-site between Mn- states in the experimentally observed orthorhombic, layered, and spinel phases are significantly smaller than in and disordered layered structures, by within GGA. The smaller values of are shown to be correlated with a collinear ordering of JT distortions, in which all orbitals are oriented in the same direction. This cooperative JT effect can lead to greater electron delocalization from Mn along the states due to increased Mn-O covalency, which contributes to the greater electronic stability compared to the phases with noncollinear JT arrangements. The structures with collinear ordering of JT distortions also generate greater vibrational entropy, which helps stabilize these phases at high temperature. These phases are shown to be strongly insulating with large calculated band gaps , which are computed using HSE06 and .

Kam, Ronald L↗

DOE OSTI2020

Azimuthally averaged wind and thermodynamic structures of tropical cyclones in global climate models and their sensitivity to horizontal resolution

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. Further, when TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud-radiative feedbacks during the early developmental phases of TCs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Measurements of Beam Spin Asymmetries in p+p0 and p´p0 Dihadron Production at CLAS12

Semi-Inclusive Deep Inelastic Scattering (SIDIS) is a powerful experimental tool for studying the internal structure and dynamics of the proton, revealing how quarks and gluons are distributed and interact within it. SIDIS describes a process where an elec tron scatters off one of the constituent quarks within the proton, causing it to undergo hadronization, creating multiple hadrons in the final state. Through factorization, the full process can be split into probabilistic components: one which describes the internal structure of the proton using Parton Distribution Functions (PDFs), and another which describes the hadronization process using Fragmentation Functions (FFs). These functions are non-perturbative quantities of Quantum Chromodynamics (QCD), meaning they cannot be calculated directly from first principles and must instead be extracted from experimental measurements. Acommon approach for accessing PDFs and FFs using SIDIS is to measure asymmetries. In this context, asymmetries correspond to subtle differences in the angular distribution of outgoing particles that arise when the spin orientation of the incoming beam or target is reversed. Because many of these effects only appear when spin is involved, they isolate specific, nuanced properties of the proton’s spin-structure that are otherwise hidden in spin averaged measurements. In practice, they show up as specific azimuthal modulations (e.g., sin ¿R, sin(¿h ´ ¿R)), whose amplitudes isolate convolutions of PDFs and FFs at leading and subleading twist. Non-zero asymmetries of these angular distributions can be traced back to unique combinations of PDFs and FFs, offering a way to probe them directly. In this work, we measure SIDIS by analyzing high energy electron-proton scattering events using the CLAS12 detector at Jefferson Lab. This study focuses on subset of SIDIS referred to as dihadron SIDIS, where pairs of hadrons — here p+p0 and p´p0 — are observed. We analyzed these dihadrons using detector data collected during Fall 2018 and Spring 2019, where longitudinally polarized electrons from the CEBAF accelerator were incident on a liquid hydrogen target. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained using Monte Carlo simulations to reduce the amount of iv false combinatorial background p0’s. When deployed on experimental data, the model in creases our dihadron statistics by up to five-fold compared to previous CLAS12 p0 analyses. This work reports the first measurements of beam spin asymmetries for p+p0 and p´p0 dihadron production in SIDIS. The measured asymmetries offer new insights to the spin-dependent structure and dynamics within the proton, as well as the spin-dependent properties of quark fragmentation. Non-zero twist-3 sin¿R amplitudes are observed, pro viding sensitivity to the subleading twist PDF e(x). The PDF e(x) encodes quark-gluon correlations within the proton — a property that is otherwise inaccessible at leading twist. Additionally, this work measured significant twist-2 modulations carried by sin(¿h ´ ¿R) and sin(2¿h ´2¿R), providing experimental access to the helicity dihadron fragmentation function (DiFF) GK 1 . Because there is no equivalent quark helicity-dependent FF in single pion SIDIS, the DiFF GK 1 offers a unique lens into novel spin-dependent fragmentation. For instance, the twist-2 modulations observed in this study are enhanced by vector mesons created during fragmentation — a behavior predicted by phenomenological models. This study broadens our understanding of dihadron fragmentation, revealing new details about the flavor and charge dependence of hadronization.

Matousek, Gregory [Duke Univ., Durham, NC (United ↗

DOE OSTI2022

Strong yet ductile nanolamellar high-entropy alloys by additive manufacturing

Additive manufacturing produces net-shaped components layer by layer for engineering applications. The additive manufacture of metal alloys by laser powder bed fusion (L-PBF) involves large temperature gradients and rapid cooling, which enables microstructural refinement at the nanoscale to achieve high strength. However, high-strength nanostructured alloys produced by laser additive manufacturing often have limited ductility. In this work, we use L-PBF to print dual-phase nanolamellar high-entropy alloys (HEAs) of AlCoCrFeNi 2.1 that exhibit a combination of a high yield strength of about 1.3 gigapascals and a large uniform elongation of about 14 percent, which surpasses those of other state-of-the-art additively manufactured metal alloys. The high yield strength stems from the strong strengthening effects of the dual-phase structures that consist of alternating face-centred cubic and body-centred cubic nanolamellae; the body-centred cubic nanolamellae exhibit higher strengths and higher hardening rates than the face-centred cubic nanolamellae. The large tensile ductility arises owing to the high work-hardening capability of the as-printed hierarchical microstructures in the form of dual-phase nanolamellae embedded in microscale eutectic colonies, which have nearly random orientations to promote isotropic mechanical properties. The mechanistic insights into the deformation behaviour of additively manufactured HEAs have broad implications for the development of hierarchical, dual- and multi-phase, nanostructured alloys with exceptional mechanical properties.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Self-similar mesocrystals form via interface-driven nucleation and assembly

Crystallization by particle attachment (CPA) is a frequently occurring mechanism of colloidal crystallization that results in hierarchical morphologies. CPA has been exploited to create nanomaterials with unusual properties and is implicated in the development of complex mineral textures. Oriented attachment–a form of CPA in which particles align along specific crystallographic directions–produces mesocrystals that diffract as single crystals do, although the constituent particles are still discernible. The conventional view of CPA is that nucleation provides a supply of particles that aggregate via Brownian motion biased by attractive interparticle potentials. However, mesocrystals often exhibit regular morphologies and uniform sizes. Although many crystal systems form mesocrystals and individual attachment events have been directly visualized10, how random attachment events lead to well defined, self-similar morphologies remains unknown, as does the role of surface-bound ligands, which are ubiquitous in nanoparticle systems. Attempts to understand mesocrystal formation are further complicated in many systems by the presence of precursor nanoparticles with a phase distinct from that of the bulk. Some studies propose that such particles convert before attachment, whereas others attribute conversion to the attachment process itself and yet others conclude that transformation occurs after the mesocrystals exceed a characteristic size. Here we investigate mesocrystal formation by iron oxides, which are important colloidal phases in natural environments and classic examples of systems forming ubiquitous precursor phases and undergoing CPA accompanied by phase transformations. Combining in situ transmission electron microscopy (TEM) at 80 degrees Celsius with ‘freeze-and-look’ TEM, we tracked the formation of haematite (Hm) mesocrystals in the presence of oxalate (Ox), which is abundant in soils, where iron oxides are common. In this work, we find that isolated Hm particles rarely appear, but once formed, interfacial gradients at the Ox-covered surfaces drive Hm particles to nucleate repeatedly about two nanometres from the surfaces, to which they then attach, thereby generating mesocrystals. Comparison to natural and synthetic systems suggests that interface-driven pathways are widespread.

36 MATERIALS SCIENCE↗

DOE OSTI2021

New Mechanism for Yield Point Phenomena

Yield point phenomena (YPP) are widely attributed to discrete dislocation locking by solute atmospheres. An alternate YPP mechanism was recently suggested by simulations of Ta single crystals without any influence of solutes or discrete dislocations. The general meso-scale (GM) simulations consist of crystal plasticity (CP) plus accounting for internal stresses of geometrically necessary dislocation content. GM predicted the YPP while CP did not, suggesting a novel internal stress mechanism. The predicted YPP varied with crystal orientation and boundary conditions, contrary to expectations for a solute mechanism. The internal stress mechanism was probed by experimentally deforming oligocrystal Ta samples and comparing the results with independent GM simulations. Strain distributions of the experiments were observed with high-resolution digital image correlation. A YPP stress–strain response occurred in the 0–2% strain range in agreement with GM predictions. Shear bands appeared concurrent with the YPP stress–strain perturbation in agreement with GM predictions. At higher strains, the shear bands grew at progressively slower rates in agreement with GM predictions. It was concluded that the internal stress mechanism can account for the existence of YPP in a wide variety of materials including ones where interstitial-dislocation interactions and dislocation transient avalanches are improbable. Furthermore, the internal stress mechanism is a CP analog of various micro-scale mechanisms of discrete dislocations such as pile-up or bow-out. It may operate concurrently with strain aging, or either mechanism may operate alone. A suggestion was made for a future experiment to answer this question.

42 ENGINEERING↗

DOE OSTI2025

Reproductive and leaf litterfall fluxes in forest ecosystem sites globally (1950-2022)

Forest allocation of net primary productivity (NPP) to reproduction is poorly quantified globally, despite its critical role in forest regeneration and a well-supported trade-off with allocation to growth. Although field measurements of total NPP are rare, our work finds that a proxy for reproductive carbon allocation constructed from leaf (L) and reproductive (R) litterfall fluxes, R/(R+L), is strongly correlated with R/NPP, facilitating analysis across a wide range of sites where biometric estimates of NPP are not available (R² = 0.85; Hanbury-Brown et al., 2022, Ward et al., in prep). To investigate relationships between ecosystem-scale reproductive allocation (RA) and climate, soil fertility, and stand age gradients, we conducted a literature search and synthesized 824 observations of annual average leaf and reproductive litterfall fluxes across forest sites globally. The zip file includes 1) a folder Data/ containing the litterfall data ("GlobalForestRA_data.csv") and metadata ("GlobalForestRA_metadata.doc") files. The data file includes geographic coordinates, long-term mean annual temperature and precipitation (1970-2000, extracted from WorldClim2.1), leaf and reproductive litterfall fluxes, sampling interval and protocols, forest characteristics (dominant leaf morphology, information pertaining to forest age and successional stage, and disturbance history) and soil properties (% sand, %silt, %clay, total phosphorus (P), nitrogen (N), cation exchange capacity (CEC) and pH) extracted from SoilGrids250 and from on-site measurements, where available. The metadata file contains information about each variable reported in the data file, including data sources, processing methods, and all references. The Data folder contains two additional files used to create Figure 1; these are described in greater detail in the README.2) R scripts GloalForestRA_analysis.r and GlobalForestRA_SI.r and a folder /Functions used to produce results, figures, and tables in the manuscript Ward et al. (in press)3) a README file describing how the data and R scripts can be used to reproduce statistical results, figures, and tables found in the manuscript. Ward et al. (in press)This repository can also be found at: https://github.com/r-ward/Global_Analysis_ForestRA.Ward, R.E., Zhang-Zheng, H. Aernethy, K., Adu-Bredu, S., Arroyo, L., Bailey, A. et al. (in press). Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally. Ecology Letters. Hanbury-Brown, A.R., Ward, R.E. & Kueppers, L.M. (2022). Forest regeneration within Earth system models: current process representations and ways forward. New Phytol., 235, 20–40.Ward et al. (2025), Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally, in prep.

54 ENVIRONMENTAL SCIENCES↗