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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Improved $^{95}\mathrm{Mo}$ neutron resonance parameters and astrophysical reaction rates

We report improved 95 Mo neutron resonance parameters and reaction rates are important for nuclear astrophysics, testing nuclear models, and nuclear criticality safety. However, despite many previous neutron-capture and total cross-section measurements on this nuclide, there still is much room for improvement as well as several discrepancies. For example, there are very few firm resonance spin and parity assignments; average resonance parameters are available only for each parity, the currently recommended astrophysical reaction rate results in disagreements between stellar models and meteoric isotopic anomalies, and there are substantial disagreements in the neutron-capture cross section at low energies important for nuclear criticality safety. To obtain an improved set of neutron resonance parameters and astrophysical reaction rates for 95 Mo. High-resolution neutron-capture and transmission data were measured at the Oak Ridge Electron Linear Accelerator (ORELA) using highly isotopically enriched 95 Mo samples. The neutron-capture apparatus, data reduction, and analysis were improved so that information contained in the γ-ray cascade following neutron capture were used to assign resonance J π values. Following this, simultaneous analysis of the new neutron-capture and transmission data was used to obtain resonance energies, gamma widths, and neutron widths and their uncertainties to a maximum energy of 10 keV. Accurate neutron-capture cross sections also were obtained for the unresolved resonance region to a maximum energy of 500 keV and, together with the new resonance parameters, used to calculate the astrophysical reaction rates in the temperature range from 5 to 30 keV. A vastly improved set of 95Mo neutron resonance parameters and an astrophysical reaction rate accurate to about 3% were obtained. Firm J π assignments were determined for 261 of the 314 observed resonances. This is a very large improvement over the previously published 32 firm J π assignments for 108 resonances. Also, the number of resonances having both firm J π assignments and Γ γ values was increased by almost a factor of 24—from 11 to 261. Neutron- and total-radiation-width distributions and average resonance spacings, average total radiation widths, and neutron strength functions were obtained for the six different s- and p-wave possibilities. Parameters for the lowest s-wave resonance, which is most important for criticality benchmarks, were obtained with high accuracy. Simple modification of the neutron-capture apparatus and expansion and improvement of data analysis techniques led to a large increase in firm J π assignments for 95 Mo neutron resonances. The resulting astrophysical reaction rate is 20%–30% larger than the currently recommended rate at s-process temperatures, which should lead to better agreement between stellar models and meteoric isotopic anomalies. The neutron-capture cross section at low energies is substantially larger than recommended in the latest evaluation, which is problematical for criticality benchmarks. The average resonance spacing as a function of spin and parity is significantly different from current models. The total-radiation-width distributions are significantly broader than predicted by theory and show significant departures from the expected Gaussian shapes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Characterization of a Low-Temperature Plasma (LTP) Ambient Ionization Source Using Temporally Resolved Monochromatic Imaging Spectrometry

The low-temperature plasma (LTP) probe is a common plasma-based source used for ambient desorption–ionization mass spectrometry (MS). While the LTP probe has been characterized in detail with MS, relatively few studies have used optical spectroscopy. In this paper, two-dimensional (2D) imaging at selected wavelengths is used to visualize important species in the LTP plasma jet. First, 2D steady-state images of the LTP plume for N 2 + (391.2 nm), He I (706.5 nm), and N 2 (337.1 nm) emissions were recorded under selected plasma conditions. Second, time-resolved 2D emission maps of radiative species in the LTP plasma jet were recorded through the use of a 200 ns detection gate and varying gate delays with respect to the LTP trigger pulse. Emission from He I, N 2 + , and N 2 in the plasma jet region was found to show a transient behavior (often referred to as plasma bullets) lasting only a few microseconds. The N 2 + and He I maps were highly correlated in spatial and temporal structure. Further, emission from N 2 showed two maxima in time, one before and one after the maximum emission for N 2 + and He I, due to an initial electronic excitation wave and ion–electron recombination, respectively. Third, the interaction of the LTP probe with a sample substrate and an electrically grounded metallic needle was studied. Emission from a fluorophore on the sample substrate showed an initial photon-induced excitation from plasma-generated photons followed by electronic excitation by other plasma species. The presence of a grounded needle near the plasma jet significantly extended the plasma jet lifetime and also generated a long-lived corona discharge on the needle. The effect of LTP operating parameters on emission spectra was correlated with mass-spectral results including reagent-ion signals. Lastly, five movies provide a side-by-side comparison of the temporal behavior of emitting species and insights into the interactions of the emission clouds with a sample surface as well as an external needle. Temporally and spatially resolved imaging provided insights into important processes in the LTP plasma jet, which will help improve analyte ion sampling in LTP–MS.

Chan, George C. -Y.↗
DOE OSTI2020

Estimating the Timing of Early Simian-Human Immunodeficiency Virus Infections: a Comparison between Poisson Fitter and BEAST

Many HIV prevention strategies are currently under consideration where it is highly informative to know the study participants’ times of infection. These can be estimated using viral sequence data sampled early in infection. However, there are several scenarios that, if not addressed, can skew timing estimates. These include multiple transmitted/founder (TF) viruses, APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like)-mediated mutational enrichment, and recombination. Here, we suggest a pipeline to identify these problems and resolve the biases that they introduce. We then compare two modeling strategies to obtain timing estimates from sequence data. The first, Poisson Fitter (PF), is based on a Poisson model of random accumulation of mutations relative to the TF virus (or viruses) that established the infection. The second uses a coalescence-based phylogenetic strategy as implemented in BEAST. The comparison is based on timing predictions using plasma viral RNA (cDNA) sequence data from 28 simian-human immunodeficiency virus (SHIV)-infected animals for which the exact day of infection is known. In this particular setting, based on nucleotide sequences from samples obtained in early infection, the Poisson method yielded more accurate, more precise, and unbiased estimates for the time of infection than did the explored implementations of BEAST. The inference of the time of infection is a critical parameter in testing the efficacy of clinical interventions in protecting against HIV-1 infection. For example, in clinical trials evaluating the efficacy of passively delivered antibodies (Abs) for preventing infections, accurate time of infection data are essential for discerning levels of the Abs required to confer protection, given the natural Ab decay rate in the human body. In such trials, genetic sequences from early in the infection are regularly sampled from study participants, generally prior to immune selection, when the viral population is still expanding and genetic diversity is low. In this particular setting of early viral growth, the Poisson method is superior to the alternative approach based on coalescent methods. This approach can also be applied in human vaccine trials, where accurate estimates of infection times help ascertain if vaccine-elicited immune protection wanes over time.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model

Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced-order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF-UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension-reduced model, we propose a new adaptive framework consisting of four principles: (a) re-parameterize the model first to reduce obvious non-identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially-distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

It is not σ 8 : constraining the non-linear matter power spectrum with the Dark Energy Survey Year-5 supernova sample

The weak gravitational lensing magnification of Type Ia supernovae (SNe Ia) is sensitive to the matter power spectrum on scales $k\gt 1 h$ Mpc$^{-1}$, making it unwise to interpret SNe Ia lensing in terms of power on linear scales. We compute the probability density function of SNe Ia magnification as a function of standard cosmological parameters, plus an empirical parameter $A_{\rm mod}$ which describes the suppression or enhancement of matter power on non-linear scales compared to a cold dark matter only model. While baryons are expected to enhance power on the scales relevant to SN Ia lensing, other physics such as neutrino masses or non-standard dark matter may suppress power. Using the Dark Energy Survey Year-5 sample, we find $A_{\rm mod} = 0.77^{+0.69}_{-0.40}$ (68 per cent credible interval around the median). Although the median is consistent with unity there are hints of power suppression, with $A_{\rm mod} \lt 1.09$ at 68 per cent credibility.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Kinematics of the H α and H β broad-line region in an SDSS sample of type-1 AGNs

Here, we investigate the kinematics of the part of the broad-line region (BLR) in active galactic nuclei (AGNs) emitting H β and H α emission lines. We explore the widths and asymmetries of the broad H β and H α emission lines in a sample of high-quality (i.e. high signal-to-noise ratio) spectra of type-1 AGN taken from the Data Release 16 of the Sloan Digital Sky Survey in order to explore possible deviation from the gravitationally bound motion. To find only the broad component of H β and H α, we use the FANTASY (Fully Automated pythoN Tool for AGN Spectra analYsis) code for the multicomponent modelling of the AGN spectra and for careful extraction of the broad emission-line parameters. Here, we show that based on the broad-line profiles widths and asymmetries, the BLR gas emitting H β and H α lines follows similar kinematics, and seems to be virialized in our sample of type-1 AGN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Active Learning‐Driven Inkless Additive Nanomanufacturing for Printed Electronics

Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high-dimensional print parameter space makes tuning print parameters time-intensive. We present a Bayesian optimization study that constructs a digital twin from printed-silver data to benchmark surrogate models, acquisition functions, and batch sizes head-to-head to achieve user-specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Altogether, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.

Bevel, Colton [Auburn University, AL (United State↗
DOE OSTI2026

Competing magnetic phases in Cr 3+𝛿 ⁢Te 4 are spatially segregated

Cr 1+𝑥⁢ Te 2 is a self-intercalated van der Waals system that is of current interest for its room-temperature room-temperature ferromagnetic (FM) phases and tunable topological properties. In bulk samples, the strain from the interstitial Cr ions leads to distinct structural phases for different ranges of 𝑥. Early neutron powder diffraction (NPD) measurements on the monoclinic phase Cr 3 ⁢Te 4 (𝑥=0.5) presented evidence for competing FM and antiferromagnetic (AFM) phases. Here we apply neutron diffraction to a single crystal of Cr 3+𝛿 ⁢Te 4 with 𝛿=−0.10 and discover that it consists of two distinct monoclinic phases, one with FM order below 𝑇 C ≈321 K and another that develops AFM order below 𝑇 N ≈86 K. In contrast, we find that a crystal with 𝛿=−0.26 exhibits only FM order below 𝑇 C ≈285 K. The single-crystal analysis is complemented by results obtained with NPD, x-ray powder diffraction, and transmission electron microscopy (TEM) measurements on the 𝛿=−0.10 composition. From observations of spontaneous magnetostriction of opposite sign at 𝑇 C and 𝑇 N , along with the TEM evidence for both monoclinic phases in a single thin ( ≈100 nm) grain, we conclude that the two phases must have a fine-grained ( ≲100 nm) intergrowth character, as might occur from high-temperature spinodal decomposition during the growth process. Calculations of the relaxed lattice structures for the FM and AFM phases with density functional theory provide a rationalization of the observed spontaneous magnetostrictions. Correlations between the magnitude and orientation of the magnetic moments with lattice parameter variation demonstrate that the magnetic orders are sensitive to strain, thus explaining why magnetic ordering temperatures and anisotropies can be different between bulk and thin-film samples, when the latter are subject to epitaxial strain. Our results point to the need to investigate the supposed coexistence FM and AFM phases reported elsewhere in the Cr 1+𝑥 ⁢Te 2 system, such as in the Cr 5 ⁢Te 8 phase (𝑥=0.25).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

An anisotropic viscoplasticity model for shale based on layered microstructure homogenization

Viscoplastic deformation of shale is frequently observed in many subsurface applications. Many studies have suggested that this viscoplastic behavior is anisotropic—specifically, transversely isotropic—and closely linked to the layered composite structure at the microscale. In this work, we develop a two-scale constitutive model for shale in which anisotropic viscoplastic behavior naturally emerges from semianalytical homogenization of a bilayer microstructure. The microstructure is modeled as a composite of soft layers, representing a ductile matrix formed by clay and organics, and hard layers, corresponding to a brittle matrix composed of stiff minerals. This layered microstructure renders the macroscopic behavior anisotropic, even when the individual layers are modeled with isotropic constitutive laws. Using a common correlation between clay and organic content and magnitude of creep, we apply a viscoplastic modified Cam-Clay plasticity model to the soft layers, while treating the hard layers as a linear elastic material to minimize the number of calibration parameters. We then describe the implementation of the proposed model in a standard material update subroutine. The model is validated with laboratory creep data on samples from three gas shale formations. We also demonstrate the computational behavior of the proposed model through simulation of time-dependent borehole closure in a shale formation with different bedding plane directions.

58 GEOSCIENCES↗
DOE OSTI2022

Study of quark and gluon jet substructure in Z+jet and dijet events from pp collisions

Measurements of jet substructure describing the composition of quark- and gluon-initiated jets are presented. Proton-proton (pp) collision data at √s = 13 TeV collected with the CMS detector are used, corresponding to an integrated luminosity of 35.9 fb -1 . Generalized angularities are measured that characterize the jet substructure and distinguish quark- and gluon-initiated jets. These observables are sensitive to the distributions of transverse momenta and angular distances within a jet. The analysis is performed using a data sample of dijet events enriched in gluon-initiated jets, and, for the first time, a Z+jet event sample enriched in quark-initiated jets. The observables are measured in bins of jet transverse momentum, and as a function of the jet radius parameter. Additionally, each measurement is repeated applying a “soft drop” grooming procedure that removes soft and large angle radiation from the jet. Using these measurements, the ability of various models to describe jet substructure is assessed, showing a clear need for improvements in Monte Carlo generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Comparison of source-location algorithms for atmospheric samplers

Numerous algorithms have been developed to determine the source characteristics for an atmospheric release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. An unnamed model developed at Pacific Northwest National Laboratory (herein called BAYEST) uses a Bayesian formulation (Eslinger et al., 2019; Eslinger et al., 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021; Pieter De Meutter, 2021). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133 Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2023

Development of a continuous synthesis process for carbamazepine using validated in-line Raman spectroscopy and kinetic modelling for disturbance simulation

Mitigation of failure modes in the continuous synthesis (CS) of a drug substance (DS) has the potential to widen the adoption of continuous manufacturing (CM) technologies by the pharmaceutical industry. Here, this work demonstrates the development of a robust continuous process for the synthesis of carbamazepine (CBZ), an essential medicine as per the World Health Organization (WHO), facilitated by kinetic modelling and monitored by in-line Raman spectroscopy. Accurate kinetic modelling and the use of validated process analytical technology (PAT) models for quantitative measurement were found to play an important role in developing CS of drug substances. Kinetic data for the formation of CBZ from iminostilbene (ISB) were collected by batch reaction sampling and high-performance liquid chromatography (HPLC) analysis. A non-linear solver and iterative method was applied to determine two sets of Arrhenius parameters simultaneously for the reaction system by minimizing the standard error of the model fit. The start-up and dynamic equilibrium stages for the CS of CBZ using a continuous stirred tank reactor (CSTR) were modelled based on the batch kinetic data and employed to optimize conversion and simulate process disturbances. An in-line Raman spectroscopy method was successfully developed, validated, and integrated to determine the concentrations of CBZ and ISB within the operating range for the CS. The CS kinetic model was evaluated experimentally from startup to dynamic equilibrium over 10 residence times with monitoring by HPLC and in-line Raman spectroscopy. The developed kinetic model in tandem with in-line Raman spectroscopy successfully predicted disturbances due to changes in process variables and can serve as a useful tool in the future design of advanced process control strategies for the continuous synthesis of CBZ.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Epitaxial (Al x Ga 1-x-y In y ) 2 O 3 Alloys Lattice Matched to Monoclinic Ga 2 O 3 Substrates

We have epitaxially stabilized a series of monoclinic (Al x Ga 1-x-y In y ) 2 O 3 alloys by careful choice of molecular beam epitaxy growth conditions, which balance alloy growth with suboxide desorption. The films are pseudomorphic to (010) β-Ga 2 O 3 substrates at thicknesses up to 150 nm with compositions ranging from (Al 0.01 Ga 0.83 In 0.16 ) 2 O 3 to (Al 0.24 Ga 0.75 In 0.03 ) 2 O 3 . The absorption edge shifts from approximately 4.62-5.14 eV with coincidently increasing Al and decreasing In mole fractions. J-V measurements reveal an increase in resistivity over four orders of magnitude with a maximum value of 4.2 x 10 5 Ω-cm for (Al 0.17 Ga 0.76 In 0.07 ) 2 O 3 , which has nearly identical lattice parameters (both in-plane and out-of-plane) to the underlying β-Ga 2 O 3 . Scanning transmission electron microscopy of this sample reveals a mostly uniform and single crystalline film, though we identify areas of non-uniform In incorporation and some γ-phase inclusions. This work demonstrates the feasibility of thick layers lattice-matched to β-Ga 2 O 3 with increased bandgap compared to phase-separation limited (Al,Ga) 2 O 3 . These alloys can enable higher bandgap epitaxial dielectrics and high sheet charge density transistors by increasing the conduction band offset with respect to β-Ga 2 O 3 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Magnetic edge fields in UTe 2 near zero background fields

Chiral superconductors are theorized to exhibit spontaneous edge currents. Here, in this study, we found magnetic fields at the edges of UTe 2 , a candidate odd-parity chiral superconductor, that seem to agree with predictions for a chiral order parameter. However, we did not detect the chiral domains that would be expected, and recent polar Kerr and muon spin relaxation data in nominally clean samples argue against chiral superconductivity. Our results show that hidden sources of magnetism must be carefully ruled out when using spontaneous edge currents to identify chiral superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Search for hadronic decays of feebly-interacting particles at NA62

The NA62 experiment at CERN has the capability to collect data in a beam-dump mode, where 400 GeV protons are dumped on an absorber. In this configuration, New Physics particles, including dark photons, dark scalars, and axion-like particles, may be produced in the absorber and decay in the instrumented volume beginning approximately 80 m downstream of the dump. A search for these particles decaying in flight to hadronic final states is reported, based on an analysis of a sample of 1.4 x 10 17 protons on dump collected in 2021. No evidence of a New Physics signal is observed, excluding new regions of parameter spaces of multiple models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Time-series dissolved oxygen, other bigeochemically-relevant analytes, and pressure gradients associated with the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”

This dataset contains time-series data from a vertical profile within the bed and banks of the Columbia river near Richland, WA. Water was sampled through 3 small tubes embedded in the sediment at 50,100, and 200 cm below the sediment-water interface. The goal of this study was to observe the correlations between hydraulic drivers and biogeochemical responses. The results of this study are published in the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”. The file types included in the data package are all time-series spreadsheet data, including hydraulic head gradients, physical parameters (temperature, pressure, SpC (specific conductivity)), and biogeochemical parameters (dissolved oxygen, pH, NO3 (nitrate) and ORP (oxidation-reduction potential)).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗