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At least 235 records · Page 13

Hygroscopic growth of UO 2 F 2 nanoparticles

Hygroscopicity is an important physicochemical property of aerosol that describes the ability of a particle to uptake water. The hygroscopic properties of uranyl fluoride (UO 2 F 2 ) aerosol generated from a UF 6 hydrolysis reactor was investigated for the first time using a custom-built Humidified Tandem Differential Mobility Analyzer (HTDMA). The HTDMA is capable of measuring UO 2 F 2 nanoparticle growth determined by mobility size over a wide range of atmospheric humidity from dry conditions at <10% relative humidity (RH) to 85% RH. The hygroscopic properties were determined for nanoparticles as small as 3.5 nm in this study. Although the largest size of UO 2 F 2 nanoparticles was 80 nm, monodisperse aerosol with a mobility diameter of up to approximately 500 nm can be investigated using the HTDMA. Anhydrous UO 2 F 2 nanoparticles with a mobility diameter of 3.5 nm were shown to be highly hygroscopic with a deliquescence relative humidity (DRH) of 10%. Hydrates with a larger mobility diameter from 10 to 80 nm were non-hygroscopic with no observable DRH and limited water uptake up to 85% RH. Here, these results demonstrate the hygroscopic properties of UO 2 F 2 nanoparticles are highly variable and based on both the mobility size and hydration state. Hygroscopicity affects the physicochemical properties of UO 2 F 2 nanoparticles, including the aerosol phase state and viscosity, with impacts on aerosol growth, coagulation, and deposition that is critical for understanding the fate and transport of UO 2 F 2 nanoparticles in the atmosphere.

Hygroscopicity↗

High-Resolution LiDAR Observations for Coupled Effects of Dry-Air Entrainment and Haze-Cloud Interactions on Cloud Vertical Structure

This study demonstrates the high-resolution profiling of cloud microphysics in a laboratory chamber using Time-Correlated Single Photon Counting (TCSPC) LiDAR. We present a novel retrieval method to derive vertical extinction (𝜎) profiles, constrained by in situ measurements, to diagnose responses to dry-air entrainment. In clean clouds, the LiDAR signals and retrieved 𝜎 remain relatively uniform, with entrainment effects confined to the upper layer. In contrast, polluted clouds exhibit strong vertical variability and a transition to a water-vapor-limited state. Entrainment significantly enhances the haze number concentration (𝑁ℎ), particularly near the bottom, creating highly height-dependent extinction profiles for polluted clouds. Our results highlight the capability of high-resolution LiDAR in capturing fine-scale vertical inhomogeneities. This approach provides a robust framework for quantifying how aerosol loading modulates entrainment sensitivity, offering new insights into the transition between buffered and water-vapor-limited regimes.

54 ENVIRONMENTAL SCIENCES↗

Conserved Macromolecular Architecture of Poplar Secondary Cell Walls Revealed by ssNMR and Atomistic Modeling

The macromolecular architecture of plant secondary cell walls governs wood's mechanical and biochemical properties, yet its natural intra-species variability remains poorly characterized. Here, we combined 13C solid-state NMR (ssNMR), multivariate statistical analysis, and molecular modeling to profile nanoscale structure across 13 genetically diverse Populus trichocarpa genotypes grown in 13C-enriched atmospheres. SsNMR-derived phenotypes spanning composition, structure, mobility, and inter-polymer proximities reveal a conserved architecture, with a subtle yet coordinated variation organizing into dominant structural and secondary mobility axes. A representative atomistic model captures these features and reproduces experimental metrics. Molecular dynamics simulations support a weak but consistent positive correlation between cellulose abundance and crystalline-like order, with interior cellulose chains enriched in tg (trans-gauche) conformations without expanding crystalline cores. Together, experiment and simulation reveal a genetically buffered, broadly conserved nanoscale architecture across genotypes, where subtle fine-tuning of cellulose bundling and matrix packing balances mechanical performance with biological function.

09 BIOMASS FUELS↗

Hygroscopic Growth of UO 2 F 2 Particles

Hygroscopicity is an important physicochemical property of aerosol that describes the ability of a particle to uptake water. The hygroscopic properties of uranyl fluoride (UO 2 F 2 ) aerosol generated from a UF 6 hydrolysis reactor was investigated for the first time using a custom-built Humidified Tandem Differential Mobility Analyzer (HTDMA). The HTDMA is capable of measuring UO 2 F 2 particle growth determined by mobility size over a wide range of atmospheric humidity from dry conditions at <10% relative humidity (RH) to 85% RH. The hygroscopic properties were determined for nanoparticles as small as 3.5 nm in this study. Anhydrous UO 2 F 2 particles with a mobility diameter of 3.5 nm were shown to be highly hygroscopic with a deliquescence relative humidity (DRH) of 10%. Hydrates with a larger mobility diameter of 10 to 80 nm were non-hygroscopic with no observable DRH and limited water uptake up to 85% RH. These results demonstrate the hygroscopic properties of UO 2 F 2 particles are highly variable and based on both the mobility size and hydration state. Hygroscopicity affects the physicochemical properties of UO 2 F 2 particles, including the aerosol phase state and viscosity, with impacts on aerosol growth, coagulation, and deposition that is critical for understanding the fate and transport of UO 2 F 2 particles in the atmosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗

Structural Propensities in Cs2MBiX6 (M=Na, Ag; X=Cl, Br) Bismuth Halide Double Perovskites

A previously unreported low-temperature phase transition in the bismuth halide double perovskite Cs2AgBiCl6 is reported, thereby establishing trends in the structural ground state across Cs2NaBiCl6, Cs2AgBiCl6, and Cs2AgBiBr6. Using the combined toolkit of variable-temperature synchrotron X-ray and neutron powder diffraction, Raman spectroscopy, and density-functional theory–based electronic structure modeling, we demonstrate a cubic Fm¯3m → tetragonal I4/m transition upon cooling with distinct onset temperatures. Neutron powder diffraction refinements permit the unambiguously assignment of the low-temperature phase of Cs2NaBiCl6 to I4/m, correcting prior reports of an I4/mmm ground state. Cs2AgBiCl6 is also found to transforms to a structure crystallizing in the I4/m space group at low temperatures. Temperaturedependent Raman data and density-functional theory-based modeling capture the softening and freezing of out-of-phase octahedral-tilt modes and quantify relative instabilities. Solid-state nuclear magnetic resonance spectroscopy at room temperature completes the characterization and helps underpin the subtle differences in covalency across the compounds. Trends in the phase transition temperature Ts and tilt magnitudes emerge from coupled effects of halide identity, M(I)–site bonding character, and a mismatch between interatomic distances. These results establish the structure– dynamics–bonding framework for tuning tilt-driven instabilities in halide double perovskites.

Tian, Haowen↗

High-resolution national mapping of natural gas composition substantially updates methane leakage impacts

Methane is emitted from oil and gas operations alongside heavier hydrocarbons and non-hydrocarbon gases, shaping emissions management decision-making, including air quality impacts. Yet, most assessments assume fixed gas composition, overlooking significant spatial and temporal variations. Here, we generate a high-resolution, data-driven map of natural gas composition across the United States, reconstructing methane, heavier hydrocarbons, and non-hydrocarbon species using spatio-temporal interpolation and oil-and-gas production patterns. Our approach is able to reduce composition prediction errors by 39% in terms of Mean Absolute Error (MAE) compared to standard techniques and reveals that methane loss rates have been underestimated by more than 50% in some regions. Beyond methane, we uncover substantial variability in co-emitted gases, exposing blind spots in current emissions inventories and emissions management frameworks. Our work enables more accurate emissions assessments, guides targeted measurement strategies, and informs emissions management decision-making. It also provides a general framework for prediction in environmental applications that integrate sparse measurements with auxiliary variables.

03 NATURAL GAS↗

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗

Photoinitiated Reactions of Molecules and Radicals in Molecular Beams

The UV photochemistry of organic molecules is a fundamental process that governs reactions in the atmosphere, synthetic chemistry, processing of organic aerosols, and biological damage in living tissues. In many organic molecules, the ensuing evolution involves pathways that are in competition, giving rise to different products, isomerization, coupling to other electronic states, and secondary reactive collisions. Because photodissociation is usually fast (picoseconds to microseconds), these processes are far from equilibrium and are controlled by kinetic competition and dynamical forces. The work accomplished was focused primarily on the photochemistry of alpha-keto carboxylic acids, a group of acids produced from natural sources, which are implicated in aerosol formation and biological processes. In the atmosphere, they are destroyed mainly by solar radiation. Studying their photochemistry has been surprisingly difficult because of the complexity of their excited electronic states, and the effect of collisions and secondary reactions. The second project investigated the lifetime of excited electronic states of pyrazine and picoline and had both experimental and theoretical aspects. The work focused on a model molecule, pyruvic acid (PA), because it was hypothesized that it should be a good source of the unstable carbene, methylhydroxycarbene (MHC). PA has an internal hydrogen bond that controls the evolution of its decomposition. The decomposition was studied following excitation to two of its lowest excited states reached by laser irradiation at 351 and 193 nm. The photodissociation dynamics in the absence and presence of collisions was monitored and compared. Understanding the UV photochemistry requires the use of complementary experimental approaches. Two methods were enlisted, which together generated a comprehensive and detailed set of results: (i) The time-sliced velocity map imaging (SVMI) instrument at USC was exploited to determine kinetic energy release of fragments, fast dissociation timescales, and internal state distributions of fragments for which Resonance Enhanced Multiphoton Ionization (REMPI) schemes exist; and (ii) The multiplexed photoionization mass spectrometer (MPIMS) setup developed at the Sandia Combustion Research Facility was used for product discovery, achieved by exploiting tunable narrowband VUV radiation at the Advanced Light Source (ALS), and to follow in real time their subsequent unimolecular and bimolecular reactions. The experimental conditions ranged from collisionless molecular beams to study nascent products to flow reactors of variable pressures to study subsequent bimolecular reactions of the products. The goals stated above were successfully accomplished. By exciting PA to its lowest excited state, MHC was identified as the only primary product and its isomerization to vinylalcohol and acetaldehyde was directly observed in real time. Moreover, we reported the first bimolecular reaction of MHC, which was with acetaldehyde, and identified its reaction product. This paves the way for the study of other reactions of this important carbene intermediate. When excitation was carried out at 193 nm, which imparted much higher energy to PA, many more products were directly observed in real time, some deriving from three-body dissociation. Quantitative branching ratios and secondary reactions of radical products were also determined and analyzed. The studies of pyrazine and picoline focused on the second excited state of these molecules and showed (via ionization studies) that their excited states were very short lived (<100 fs) because of efficient couplings to lower electronic states via vibronic coupling. Theoretical work on modeling the absorption spectrum and decay mechanisms of the excited states are in progress in collaboration with theoreticians.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Continuous-variable quantum computation of the O(3) model in 1+1 dimensions

We formulate the $O(3)$ non-linear sigma model in $1+1$ dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous variable (CV) approach to quantum computing. Here we construct the ground state and excited states using the coupled cluster ansatz and find excellent agreement with the exact diagonalization results for a small number of lattice sites. We then present the simulation protocol for the time evolution of the model using CV gates, estimate the discretization error, and present numerical results obtained from a photonic quantum simulator. We expect that the methods developed in this work will be useful for exploring interesting dynamics for a wide class of sigma models and gauge theories, as well as for simulating scattering events on quantum hardware in the coming decade.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Is There a Scalar Atmospheric Surface Layer Within a Convective Boundary Layer? Implications for Flux Measurements

Top‐down entrainment shapes the vertical gradients of sensible heat, latent heat, and CO 2 fluxes, influencing the interpretation of eddy covariance (EC) measurements in the unstable atmospheric surface layer (ASL). Using large eddy simulations for convective boundary layer flows, we demonstrate that decreased temperature gradients across the entrainment zone increase entrainment fluxes by enhancing the entrainment velocity, amplifying the asymmetry between top‐down and bottom‐up flux contributions. These changes alter scalar flux profiles, causing flux divergence or convergence and leading to the breakdown of the constant flux layer assumption (CFLA) in the ASL. As a result, EC‐measured fluxes either underestimate or overestimate “true” surface fluxes during divergence or convergence phases, contributing to energy balance non‐closure. The varying degrees of the CFLA breakdown are a fundamental cause for the non‐closure issue. These findings highlight the underappreciated role of entrainment in interpreting EC fluxes, addressing non‐closure, and understanding site‐to‐site variability in flux measurements.

eddy covariance fluxes of scalars↗

Development of chemometric models to classify solid-state U materials by micro-Raman spectroscopy

Discerning uranium (U) particles found in environmental sampling is of interest for monitoring the peaceful use of nuclear material. In this study, a soft independent modeling of class analogy (SIMCA) library was successfully developed for the classification of a four-class system consisting of α-U 3 O 8 , UO 2 , UO 2 (NO 3 ) 2 ·6H 2 O (UNH), and UO 2 O 2 ·4H 2 O (studtite) by Raman spectroscopy in the presence of matrix particulates and additional outliers. Spectral variability between numerous particles of each type revealed appreciable differences as a function of particle size with respect to hydration state and potential oxide phase within each class. Interclass variability was accounted for using both unsupervised and supervised chemometric models. The supervised SIMCA model displayed reasonable sensitivity for each U class and a high degree of specificity by returning whether a spectrum belonged to one class or not. This work demonstrates how Raman spectral features and chemometrics can be used to distinguish U materials from one another and from matrix materials such as flint clay. Combining the outlined chemometric approach with Raman mapping sequences could provide a rapid, nondestructive technique to characterize the chemical composition of a diverse collection of U compounds amid background samples for environmental sampling, nuclear forensics, and industrial applications.

Actinide↗

Leveraging Intramolecular π-Stacking to Access an Exceptionally Long-Lived 3 MC Excited State in an Fe(II) Carbene Complex

The ability to manipulate excited-state decay cascades using molecular structure is essential to the application of abundant-metal photosensitizers and chromophores. Ligand design has yielded some spectacular results elongating charge-transfer excited state lifetimes of Fe(II) coordination complexes, but triplet metal-centered ( 3 MC) excited states - recently demonstrated to be critical to the photoactivity of isoelectronic Co(III) polypyridyls - have to date remained elusive, with temporally isolable examples limited to the picosecond regime. Here, with this report, we show how strong-field donors and intramolecular π-stacking can conspire to stabilize a long-lived 3 MC excited state for a remarkable 4.1 ± 0.3 ns in fluid solution at ambient temperature. Analysis of variable-temperature time-resolved absorption data using theoretical models ranging from Arrhenius to semiclassical Marcus theory, combined with computational modeling and X-ray crystallography, reveal a Jahn−Teller stabilized excited state with a high activation barrier for ground-state recovery. The net result is a chromophore with a 3 MC excited-state lifetime that is orders of magnitude longer than anything yet observed for an Fe(II) complex.

carbene compounds↗

Broadband multiwavelength properties of the archetypal blazar 3C 279 during the 2017 Event Horizon Telescope campaign

The archetypal blazar 3C 279 hosts a prominent relativistic jet and exhibits strong broadband variability across the electromagnetic spectrum. In April 2017, the Event Horizon Telescope (EHT) observed 3C 279, alongside one of the most extensive quasi-simultaneous multiwavelength (MWL) campaigns ever conducted. With the aim of investigating the physical processes governing 3C 279, we analyzed individual observations and multiband light curves, and constructed a new quasi-simultaneous MWL spectrum. We also performed phenomenological modeling using the turbulent extreme multi-zone (TEMZ) model to constrain the fundamental physical properties of the source. The EHT observations reveal a clear flux increase in the innermost core between April 5 and 11, 2017. Over a broader timescale, radio measurements at longer wavelengths show concurrent enhancements in core flux and polarization around mid-April, coinciding with the ejection of a superluminal knot. Record UV-optical flares with strong polarization variability occurred in late March, followed by gamma-ray activity that declined before the end of the EHT observing period. During this interval, the source remained in a low X-ray state and showed no detectable VHE emission. The TEMZ modeling suggests that the broadband spectrum and variability of 3C 279 can be explained within a jet scenario in which turbulent plasma cells are compressed by a stationary conical shock. However, alternative interpretations, such as magnetic reconnection or a moving shock-in-jet event, remain plausible. This coordinated MWL campaign advances our understanding of the origin of jet and gamma-ray emission in 3C 279, while also providing a comprehensive publicly available dataset that will serve as a valuable reference for future studies.

Principe, G. [Trieste U.; INFN, Trieste; Bologna, ↗

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗

Using intrusive approaches as a step towards accounting for stochasticity in wind turbine design

Current wind turbine design methods require tens of thousands of time-domain simulations and use different random seeds to account for the stochasticity of the environmental conditions. The account of stochasticity is nonintrusive because the sampling method calls a deterministic model multiple times without changing its underlying equations. In this work, we investigate and demonstrate using simple proof of concepts how intrusive approaches can be used to directly account for stochasticity in the equations representing a mechanical system. Our long term goal is to apply such methodology to the design of wind turbines without requiring an excessive number of simulations. Intrusive methods manipulate stochastic variables directly to provide the probability density functions (PDFs) of the states and outputs at any time as functions of the PDFs of the inputs. We illustrate how different methods can be used with a reduced-order model of a wind turbine with one degree of freedom and for linear and nonlinear models. We discuss how the methods can be extended and what it will take to apply them to a level of fidelity similar to current state-of-the-art wind turbine design tools.

17 WIND ENERGY↗

FY24 Integrated Results for High-Temperature Mechanical Testing of LPBF 316H Stainless Steel

This report provides the mechanical test data of laser powder bed fusion (LPBF) 316H stainless steel (SS) collected during Fiscal Year 2024 (FY 24) under the US Department of Energy, Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies program, along with the current state of microstructural-based understandings of the behaviors. Materials with variabilities in manufacturing site, machine, laser parameters, powder chemistry, porosity, specimen geometry and heat treatment were tested in high-temperature tension, creep, fatigue and creep-fatigue. Electron microscopy and optical microscopy were performed on selected materials before and after the tests to provide microstructural-based understandings to the mechanical behavior. It was discovered that the as-built (AB) and stress-relieved (SR) materials exhibit similar behaviors in tension and creep, as do the solution-annealed (SA) and hot-isostatic pressed (HIP) materials. The AB and SR materials are softer but more ductile than the SA and HIP materials in tension. In creep, the LPBF materials have comparable rupture times but lower rupture strains compared to the wrought materials. The AB and SR materials have low creep rupture strains (<10%) when tested at 720°C and 800°C, while the SA and HIP materials are much more ductile. The LPBF materials exhibit large scatter in fatigue and creep-fatigue lives. The cyclic lives of creep-fatigue tests are lower than those of fatigue tests. In fatigue and creep-fatigue, SR materials outperform SA materials. A common observation from the cyclic tests is that, given the same post-build heat treatment, a lower initial peak stress generally results in a longer cyclic life regardless of hold time. It was also discovered that the batch variation can be more impactful than heat treatment, as demonstrated by the overall better performance of one batch of material than another, regardless of the heat treatment. The results provided insights into how different factors impact the behaviors of LPBF 316H SS. An outlook to FY 25 work scope is provided.

36 MATERIALS SCIENCE↗