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DOE OSTI2025

Measurement and analysis of the Doppler broadened energy spectra of gamma radiation originating from the annihilation of positrons incident on clean and adsorbate-covered surfaces

We present measurements and theoretical modeling demonstrating the capability of coincidence Doppler broadened (CDB) annihilation gamma spectroscopy to provide element-specific information from the topmost atomic layer of surfaces. Our measurements show that the energy spectra of Doppler-shifted annihilation gamma photons emitted following the annihilation of positrons from the topmost atomic layers of clean and adsorbate covered surfaces of gold (Au), silver (Ag) and copper (Cu) differ significantly. The shape of the Doppler-broadened gamma spectrum, as analyzed using ratio curves, indicates that the elemental composition of the surface can still be identified despite contributions from positronium annihilation and a significant reduction in core electron annihilation. We estimate the chemical composition of the various probed surfaces by modeling the ratios of the measured Doppler spectra with respect to the Doppler spectra from a clean Cu surface using a linear combination of calculated ratio curves. The fitting of the experimental ratio curves was used to obtain an estimate of the elemental composition of Cu surfaces with sulfur segregation, oxygen adsorption, a thin film of Selenium (Se), and a single layer of graphene (SLG). A similar analysis was performed on the Ag surface with environmental adsorbates, the same surface after argon ion sputtering, as well as a sputter cleaned Au surface. The surface compositions obtained from the analysis of the CDB data were compared to the compositions obtained using positron annihilation induced Auger electron spectroscopy (PAES). Our results show that CDB can detect, identify, and quantify, sub-monolayer adsorbates and a single atomic layer deposited on metal substrates.

Lotfimarangloo, Sima [Univ. of Texas, Arlington, T↗

DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗

DOE OSTI2026

Longitudinal Multi-omics Reveal Phase-Dependent Viral Adaptive Strategies and Functional Potential During Formation of Algal-bacterial Granular Sludge

Virus-host interactions within microbial aggregates critically influence microbiome function and stability, yet how physicochemical stresses shape the interactive dynamics remains largely unexplored. Here, we investigated virus–host dynamics during the transition of algal-bacterial granular sludge (ABGS) from activated sludge under continuous hydraulic shear using integrated metagenomics and metatranscriptomics. Hydraulic stress initially reduced host a-diversity, which coincided with a marked increase in viral lysogenicity. During this host diversity bottleneck, viral microdiversity increased, and genes related to virion structure and DNA packaging were under positive selection (pN/pS >1). As host diversity recovered, viral microdiversity declined, while viral anti-defense systems (ADS) significantly increased in abundance. Lagged correlation analysis revealed a significant positive correlation between viral ADS and host defense systems (DS), suggesting an evolutionary arms race. Furthermore, active lysogenic infections were accompanied by enrichment of DS and auxiliary viral genes (AVGs) involved in genetic information processing and amino acid metabolism, potentially enhancing host fitness. Overall, our study unveils a phase-dependent co-evolutionary interplay between viruses and hosts during ABGS formation, providing insights into the development and maintenance of microbial structural and functional resilience in engineered ecosystems.

Qi, Huiyuan↗

DOE OSTI2025

Alpha ketoacid decarboxylases: Diversity, structures, reaction mechanisms, and applications for biomanufacturing of platform chemicals and fuels

In living cells, alpha-ketoacid decarboxylases (KDCs, EC 4.1.1.-) are a class of enzymes that convert alpha-ketoacids into aldehydes through decarboxylation. These aldehydes serve as either drop-in chemicals or precursors for the biosynthesis of alcohols, carboxylic acids, esters, and alkanes. These compounds play crucial roles in cellular metabolism and fitness and the bioeconomy, facilitating the sustainable and renewable biomanufacturing of platform chemicals and fuels. This review explores the diversity and classification of KDCs, detailing their structures, mechanisms, and functions. We highlight recent advancements in repurposing KDCs to enhance their efficiency and robustness for biomanufacturing. Additionally, we present modular KDC-dependent metabolic pathways for the microbial biosynthesis of aldehydes, alcohols, carboxylic acids, esters, and alkanes. Lastly, we discuss recent developments in the modular cell engineering technology that can potentially be applied to harness the diversity of KDC-dependent pathways for biomanufacturing platform chemicals and fuels.

09 BIOMASS FUELS↗

DOE OSTI2024

Predicting RNA structure and dynamics with deep learning and solution scattering

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle x-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA’s conformational plasticity. We introduce a solution conformation predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep learning model, IonNet, designed for predicting Mg 2+ ion binding sites. Validated through benchmarking against 14 experimental data sets, SCOPER significantly improved the quality of SAXS profile fits by including Mg 2+ ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Information theory optimization of signals from small-angle scattering measurements

Small-angle X-ray scattering (SAXS) of particles in solution informs on the conformational states and assemblies of biological macromolecules (bioSAXS) outside of cryo- and solid-state conditions. In bioSAXS, the SAXS measurement under dilute conditions is resolution limited, and through an inverse Fourier transform, the measured SAXS intensities directly relate to the physical space occupied by the particles via the P (r)-distribution. Yet, this inverse transform of SAXS data has been historically cast as an ill-posed, ill-conditioned problem requiring an indirect approach. Here, we show that through the applications of matrix and information theories, the inverse transform of SAXS intensity data is a well-conditioned problem. The so-called ill-conditioning of the inverse problem is directly related to the Shannon number. By exploiting the oversampling enabled by modern detectors, a direct inverse Fourier transform of the SAXS data is possible, provided the recovered information does not exceed the Shannon number. The Shannon limit corresponds to the maximum number of significant singular values that can be recovered in a SAXS experiment, suggesting this relationship is a fundamental property of band-limited inverse integral transform problems. This correspondence reduces the complexity of the inverse problem to the Shannon limit and maximum dimension. We propose a hybrid scoring function using an information theory framework that assesses both the quality of the model-data fit as well as the quality of the recovered P (r)-distribution. The hybrid score utilizes the Akaike information criteria and Durbin-Watson statistic that considers parameter-model complexity, i.e., degrees of freedom, and the randomness of the model-data residuals. The described tests and findings extend the boundaries for bioSAXS by completing the information theory formalism initiated by Peter B. Moore to enable a quantitative measure of resolution in SAXS, robustly determine maximum dimension, and more precisely define the best parameter model appropriately representing the observed scattering data.

Rambo, Robert P. [Science and Technology Facilitie↗

DOE OSTI2025

Ten questions on building stock modeling to inform energy efficiency and sustainability

To enhance economic competitiveness and ensure energy efficiency, resilience, and security, cities and governments are adopting technologies and strategies to improve their existing building stocks. This approach aims to reduce energy use, improve energy affordability, and ensure a reliable power supply while safeguarding occupants during extreme weather events that may disrupt energy services. The effectiveness of these solutions will depend on building stock characteristics, use patterns, weather conditions, evolving technologies and their markets, and a city’s socio-economic conditions. This paper presents ten questions and answers that highlight the most important issues regarding the use of building stock modeling as a powerful tool to provide insights for informing stakeholders’ actions and decision-making on energy efficiency, costs reduction, and resilience of buildings in cities. Building stock modeling should build upon the fit-for-purpose framework, balancing the use case accuracy requirements, level of complexity, and needed resources (expertise, compute). The advancements in Artificial Intelligence (AI), the increasingly available open dataset of building stock in cities, and the more affordable powerful computing will accelerate the adoption of building stock modeling across scales by researchers and practitioners to inform decision making on sustainability and efficiency.

AI↗

DOE OSTI2026

Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable, when interacting with its surroundings. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined law, the entropy production due to internal processes was not included, and the 2nd law was thus practically removed from the Gibbs combined law, so it is only applicable to systems under equilibrium, thus commonly termed as equilibrium or Gibbs thermodynamics. Gibbs further derived the classical statistical thermodynamics in terms of the probability of configurations in a system in the later 1800's and early 1900's. With the quantum mechanics (QM) developed in 1920's, the QM-based statistical thermodynamics was established and connected to classical statistical thermodynamics at the classical limit as shown by Landau in the 1940's. In 1960's the development of density functional theory (DFT) by Kohn and co-workers enabled the QM prediction of properties of the ground state of a system. On the other hand, the entropy production due to internal processes in non-equilibrium systems was studied separately by Onsager in 1930's and Prigogine and co-workers in the 1950's. In 1960's to 1970's the digitization of thermodynamics was developed by Kaufman in the framework of the CALculation of PHAse Diagrams (CALPHAD) modeling of individual phases with internal degrees of freedom. CALPHAD modeling of thermodynamics and atomic transport properties has enabled computational design of complex materials in the last 50 years. Our recently termed zentropy theory integrates DFT and statistical mechanics through the replacement of the internal energy of each individual configuration by its DFT-predicted free energy. The zentropy theory is capable of accurately predicting the free energy of individual phases, transition temperatures and properties of magnetic and ferroelectric materials with free energies of individual configurations solely from DFT-based calculations and without fitting parameters, and is being tested for other phenomena including superconductivity, quantum criticality, and black holes. Those predictions include the singularity at critical points with divergence of physical properties, negative thermal expansion, and the strongly correlated physics. Furthermore, those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of the materials genome®. This has the potential to shift the paradigm of CALPHAD modeling from being heavily dependent on experimental inputs to becoming fully predictive with inputs solely from DFT-based calculations and machine learning models built on those calculations and existing experimental data through newly developed and future open-source tools. Furthermore, through the combined law of thermodynamics including the internal entropy production, it is shown that the kinetic coefficient matrix of independent internal processes is diagonal with respect to the conjugate potentials in the combined law, and the cross phenomena that the phenomenological Onsager flux and reciprocal relationships are due to the dependence of the conjugate potential of a molar quantity on nonconjugate molar quantities and other potentials, which can be predicted by the zentropy theory and CALPHAD modeling.

42 ENGINEERING↗

DOE OSTI2024

Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A miniature jet pump for slurries

Here, this work reports on the design and performance of a jet pump system to pump slurries. The jet pump uses a high-velocity water jet to entrain and pump the slurry. This pumping action requires no valves and does not use moving solid parts. Hence, the system can be readily sealed, and the pumping action creates no observable mechanical damage to slurry particles. We tested carbon black beads suspended in water at concentrations of 0.0, 0.2, and 2.0 wt% at primary jet flow rates of 50 and 100 mL/min. Our measurements show an approximately linear decrease in entrainment ratio (slurry-to-jet flow rate) and absolute slurry flow rate with increasing adverse (load) pressure difference. The entrainment ratio is a strong function of the absolute jet flow rate. Pump performance was insensitive to slurry composition. Micron-resolution particle image velocimetry (micro-PIV) revealed qualitative changes in the pump’s internal flow due to changes in absolute jet flow rate, including evidence of the suppression of a vortex at higher flow rates. These findings establish key operational parameters for the jet pump and highlight its potential in a variety of slurry flow pumping applications. The pump is particularly suited for flow electrode slurries (e.g. as used for energy storage and water treatment) as it maintains particle integrity and can be used with standard tubing, tube fittings, and sealing methods.

42 ENGINEERING↗

DOE OSTI2026

Kinetic model development for single step ethanol to butene rich olefin process over Cu-Y/Beta catalysts

Here, this study presents the first intrinsic kinetic model for the single-step conversion of ethanol to butene-rich olefins over bifunctional Cu-Y/Beta catalysts, addressing a critical gap in the design and scale-up of Sustainable Aviation Fuel (SAF) processes. The reaction network comprises ten global steps involving dehydrogenation, aldol condensation, hydrogenation, and dehydration reactions, distributed across Cu and rare-earth (Y) active sites. The model incorporates dual-site functionality (Cu and Y site) and explicitly accounts for key intermediates such as crotonaldehyde and butanal. Reaction rates are formulated using Langmuir–Hinshelwood–Hougen–Watson (LHHW) kinetics. Kinetic parameters are extracted by fitting the model to lab-scale packed-bed reactor data across a wide range of temperatures and space velocities, demonstrating strong agreement in ethanol conversion and product selectivity. The reaction kinetics developed in this work provide a foundational basis for constructing reactor models that enable process optimization and scale-up of ethanol-to-jet fuel technologies.

Cu-Y/Beta catalyst↗

DOE OSTI2025

Quantification of the REE 3+ aqua ions and chloride species in aqueous fluids by in situ Raman spectroscopy using perturbations of the water band

Acidic NaCl-rich aqueous fluids play a crucial role in forming hydrothermal rare earth elements (REE) mineral deposits. Aqueous REE mobility is mostly controlled by the stabilities of REE 3+ and REE chloride species. Our current knowledge of REE speciation is based on solubility data, thermodynamic models and in situ spectroscopic measurements, sometimes coupled with molecular simulations. Here, in this study, we investigate Nd and Yb speciation in pH2 Cl-bearing solutions at 25 °C and 0.1 MPa with variable Cl/REE ratios using Raman Spectroscopy in solutions with 0.1 to 0.6 mol/kg NdCl 3 or YbCl 3 and 0.2 to 3.2 mol/kg NaCl. Due to the challenges in resolving the REE-Cl band, we developed a new method using the water vibrational mode and multivariate curve resolution (MCR) analysis. The Raman spectra for the vibrational band of water (2700 to 3900 cm –1 ) were collected at 25 °C and fitted by three Gaussian sub-peaks, then quantified using MCR analysis to de-convolute the water band into bulk H 2 O and the perturbations caused by of Cl – , REE 3+ , and REE chloride species. REE speciation based on the perturbations of the water band indicates that REE 3+ aqua ions dominate acidic solutions at 25 °C, but up to ~20 mol% YbCl 2+ forms at high YbCl 3 concentrations. The new method is promising for quantifying in situ speciation of the REE 3+ aqua ions and REE chloride species in aqueous fluids while providing information on the hydration of ions. This method improves our molecular level understanding of REE aqueous species stability and their role in REE mobilization during fluid-rock interaction.

58 GEOSCIENCES↗

DOE OSTI2025

Rate Limiting Regimes in Photochemical H2 Generation by Complexes of Colloidal CdS Nanorods and Hydrogenase

Driving redox enzyme catalysis with photoexcited semiconductor nanocrystals is a compelling approach for chemical conversion. We examined how the interplay of the many chemical steps involved determines the rates of photochemical H2 production with complexes of colloidal CdS nanorods and an [FeFe]-hydrogenase. We elucidated the roles of three critical and previously elusive processes-scavenging of photoexcited holes from nanorods, back-electron transfer, and H2 oxidation. Kinetic Monte Carlo simulations and fitting to experimental data revealed that hole transfer becomes the rate-limiting step at high illumination intensities. Comparisons of simulations to experimental H2 production showed that both back-electron transfer and H2 oxidation play an efficiency-limiting role at high catalyst loadings. This work provides guiding principles for tuning experimental parameters to minimize energy-wasting pathways and optimize photochemical product formation. More broadly, we demonstrate how critical but elusive chemical steps in photochemical reactions can be probed with a combination of experiments and simulations.

08 HYDROGEN↗

DOE OSTI2024

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.

Data-driven constitutive models↗

DOE OSTI2024

Nonlinear elasticity with the Shifted Boundary Method

Here, we propose a new unfitted/immersed computational framework for nonlinear solid mechanics, which bypasses the complexities associated with the generation of CAD representations and subsequent body-fitted meshing. This approach allows to speed up the cycle of design and analysis in complex geometry and requires relatively simple computer graphics representations of the surface geometries to be simulated, such as the Standard Tessellation Language (STL format). Complex data structures and integration on cut elements are avoided by means of an approximate boundary representation and a modification (shifting) of the boundary conditions to maintain optimal accuracy. An extensive set of computational experiments in two and three dimensions is included.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗

DOE OSTI2024

Structural characterization of high-protein system through ultra-small and small-angle X-ray scattering

High-protein systems exhibit a hierarchical structure consisting of interconnected multiscale length assemblies. Recent investigations are directed toward identifying these structural units under various processing and environmental conditions to establish structure–function correlations. Ultra-small and small-angle X-ray scattering (USAXS/SAXS) has become a crucial tool for characterizing the structures of proteins and their clusters/aggregates, ranging from nanometers to micrometers, with minimal disruption to their original state. Here, this review first describes the facilities and principles of X-ray scattering, followed by discussions on the analysis of scattering data, including the interpretation of fitting models. It then delves into the main applications of USAXS/SAXS in plant and dairy protein-rich systems. Future strategies to enhance the utilization of scattering techniques for elucidating the structure of high-protein systems are also included.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Sublimation and oxidation measurements of graphite and carbon black at high temperatures in a shock tube using absorption imaging and thermal emission

Surface mass loss rates due to sublimation and oxidation at temperatures of 3000–7000 K have been measured in a shock tube for graphite and carbon black (CB) particles. Diagnostics are presented for measuring surface mass loss rates by diffuse backlit illumination extinction imaging and thermal emission. Here, the surface mass loss rate is found by regression fitting extinction and emission signals with an independent spherical primary particle assumption. Measured graphite sublimation and oxidation rates are reported to be an order of magnitude greater than CB sublimation and oxidation rates. It is speculated that the difference between CB and graphite surface mass loss rates is largely due to the primary particle assumption of the presented technique which misrepresents the effective surface area of an aggregate particle where primary particles overlap and shield inner particles. Measured sublimation rates are compared to sublimation models in the literature, and it is seen graphite shows fair agreement with the models while CB underestimates, likely a result of the particle shielding affect not being considered in the sublimation model.

36 MATERIALS SCIENCE↗