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DOE OSTI2025

Model-independent measurement of the matter-radiation equality scale in DESI 2024

The peak of the matter power spectrum, known as the turnover (TO) scale, is determined by the horizon size at the time of matter-radiation equality. This scale can serve as a standard ruler, independent of other features in the matter power spectrum, such as baryon acoustic oscillations (BAO). Here, in this work, we present the first detection of the turnover in the galaxy autopower spectrum, utilizing the distribution of quasars and luminous red galaxies (LRG) measured by the Dark Energy Spectroscopic Instrument (DESI) during its first year of survey operations in a model-independent manner. To avoid confirmation bias, we first analyze the data using data blinding methods designed for the DESI baryon acoustic oscillation, redshift space distortion and scale-dependent bias signals. We measure the angle-averaged dilation distance 𝐷 V ⁡(𝑧 = 1.651) = (38.1 ± 2.5)⁢𝑟 H from the quasars and 𝐷 V ⁡(𝑧 = 0.733) = (21.8 ± 1.0)⁢𝑟 H from the LRG sample in units of the horizon 𝑟 H at the matter-radiation-equality epoch. Combining these two constraints and assuming a flat Λ⁢CDM model with three standard neutrino species, we can translate this into a constraint of Ω m ⁢ℎ 2 = 0.13⁢9$^{+0.036}_{−0.046}$. We can break the Ω m −𝐻 0 degeneracy with low-redshift distance measurements from type-Ia supernova (SN) data from Pantheon+, we obtain a sound-horizon free estimate of the Hubble-Lemaître parameter of 𝐻 0 = 65.2$^{+4.9}_{−6.2}$ km/s/Mpc, consistent with sound-horizon dependent DESI measurements. On the other hand, combining the DESI BAO and TO, we find a truly DESI-only measurement of 𝐻 0 = 74.0$^{+7.2}_{−3.5}$ km/s/Mpc, in line with DESI-only full-shape results where the sound-horizon scale is marginalized out. This discrepancy in 𝐻 0 can be reconciled in a 𝑤 0 ⁢𝑤 𝑎 ⁢ CDM cosmology, where the combination of DESI BAO and TO data yields 𝐻 0 = 66.5 ± 7.2 km/s/Mpc.

cosmological parameters↗

DOE OSTI2020

Iterative X-ray spectroscopic ptychography

Spectroscopic ptychography is a powerful technique to determine the chemical composition of a sample with high spatial resolution. In spectro-ptychography, a sample is rastered through a focused X-ray beam with varying photon energy so that a series of phaseless diffraction data are recorded. Each chemical component in the material under investigation has a characteristic absorption and phase contrast as a function of photon energy. Using a dictionary formed by the set of contrast functions of each energy for each chemical component, it is possible to obtain the chemical composition of the material from high-resolution multi-spectral images. This paper presents SPA (spectroscopic ptychography with alternating direction method of multipliers), a novel algorithm to iteratively solve the spectroscopic blind ptychography problem. First, a nonlinear spectro-ptychography model based on Poisson maximum likelihood is designed, and then the proposed method is constructed on the basis of fast iterative splitting operators. SPA can be used to retrieve spectral contrast when considering either a known or an incomplete (partially known) dictionary of reference spectra. By coupling the redundancy across different spectral measurements, the proposed algorithm can achieve higher reconstruction quality when compared with standard state-of-the-art two-step methods. It is demonstrated how SPA can recover accurate chemical maps from Poisson-noised measurements, and its enhanced robustness when reconstructing reduced-redundancy ptychography data using large scanning step sizes is shown.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Crystal structure of N-terminally hexahistidine-tagged Onchocerca volvulus macrophage migration inhibitory factor-1

Onchocerca volvulus causes blindness, onchocerciasis, skin infections and devastating neurological diseases such as nodding syndrome. New treatments are needed because the currently used drug, ivermectin, is contraindicated in pregnant women and those co-infected with Loa loa . The Seattle Structural Genomics Center for Infectious Disease (SSGCID) produced, crystallized and determined the apo structure of N-terminally hexahistidine-tagged O. volvulus macrophage migration inhibitory factor-1 (His- Ov MIF-1). Ov MIF-1 is a possible drug target. His- Ov MIF-1 has a unique jellyfish-like structure with a prototypical macrophage migration inhibitory factor (MIF) trimer as the `head' and a unique C-terminal `tail'. Deleting the N-terminal tag reveals an Ov MIF-1 structure with a larger cavity than that observed in human MIF that can be targeted for drug repurposing and discovery. Removal of the tag will be necessary to determine the actual biological oligomer of Ov MIF-1 because size-exclusion chomatographic analysis of His- Ov MIF-1 suggests a monomer, while PISA analysis suggests a hexamer stabilized by the unique C-terminal tails.

Kimble, Amber D. (ORCID:0000000167851596)↗

DOE OSTI2026

A Physics-Aligned Multi-Domain Machine Learning Framework for Time-Localised Diagnosis of Power Electronics Faults

This paper presents a physics-aligned framework for fault diagnosis in multi-phase power-electronic systems using cycle-synchronous windowing and multi-domain features derived from Fourier, wavelet, and Hilbert–Huang representations. While both logistic regression and multilayer perceptron (MLP) models achieve perfect performance under standard evaluation, blind unseen testing reveals a critical failure in a baseline MLP. This is shown to arise from model selection based on validation accuracy. Using validation-loss-based selection restores correct unseen performance and improves confidence. Feature ablation shows that Fourier and wavelet features dominate, while computational analysis indicates that feature extraction, particularly HHT, governs runtime.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗

DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗

DOE OSTI2020

Non-Intrusive Appliance Identification with Appliance-Specific Networks

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. Here, we adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.

42 ENGINEERING↗

DOE OSTI2022

Cybersecurity Enhancement for Multi-Infeed High-Voltage DC Systems

Composed of multiple two-terminal high-voltage DC (HVDC) transmission systems, a multi-infeed HVDC (MIDC) system exchanges massive power among multiple asynchronous AC systems. However, as an intrinsically cyber-physical system, an MIDC system could suffer from cyber-attacks, leading to massive power mismatches in multiple AC systems, and resulting in catastrophic consequences. Since the sequential responses of an MIDC system and interconnected AC systems are in different timescales, this paper first establishes a two-timescale model to evaluate the sequential impacts caused by cyber-attacks. Then, an event-triggered cyber-defense strategy is proposed to enhance the cybersecurity of an MIDC system by mitigating multiple non-simultaneous cyber-attacks. Whenever new cyber-attack events occur, the proposed cyber-defense strategy, which is mathematically modeled as a mixed-integer quadratic programming problem, is executed on-line and updated in an event-triggered manner. Here, simulation results on an MIDC system demonstrate that the low-cost and almost blind cyber-attacks can cause severe frequency deviations, and the proposed strategy can mitigate multi-cyber-attacks effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis . Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Effect of dehydroepiandrosterone administration before in vitro fertilization on the live birth rate in poor ovarian responders according to the Bologna criteria: A randomised controlled trial

Abstract Objective To evaluate the effect of dehydroepiandrosterone (DHEA) pre‐treatment before in vitro fertilization and embryo transfer (IVF‐ET) on the live birth rate in infertile women with poor ovarian response (POR) defined according to the Bologna criteria. Design A randomized, double‐blind, placebo‐controlled trial. Setting Nine reproductive medical centers in China. Population A total of 821 participants with POR defined according to the Bologna criteria were enrolled in the study between April 2016 and December 2018. Methods Eligible participants were randomly assigned to receive either DHEA ( n = 410) or placebo ( n = 411) treatments for 4–12 weeks prior to IVF‐ET, in a 1:1 ratio. Main outcome measures Live birth rate after the first embryo transfer. Results Thirty‐six (8.8%) of 410 women in the DHEA group and 37 (9.0%) of 411 women in the placebo group had a live birth, with no significant difference observed between groups (relative risk, 0.98, 95% CI, 0.63–1.51; p = 0.911). There were no significant differences in the number of retrieved oocytes, and the rates of clinical pregnancy, pregnancy loss, and cumulative live births between the two groups. Conclusions DHEA administration prior to IVF‐ET had no beneficial effect on the live birth rate relative to placebo in women with POR defined according to the Bologna criteria. Tweetable Abstract No benefit was found in poor ovarian responders who received DHEA administration prior to IVF.

Wang, Ze↗

DOE OSTI2022

Identification and distribution of pathogens coinfecting with Brucella spp., Coxiella burnetii and Rift Valley fever virus in humans, livestock and wildlife

Zoonotic diseases, such as brucellosis, Q fever and Rift Valley fever (RVF) caused by Brucella spp., Coxiella burnetii and RVF virus, respectively, can have devastating effects on human, livestock, and wildlife health and cause economic hardship due to morbidity and mortality in livestock. Coinfection with multiple pathogens can lead to more severe disease outcomes and altered transmission dynamics. These three pathogens can alter host immune responses likely leading to increased morbidity, mortality and pathogen transmission during coinfection. Developing countries, such as those commonly afflicted by outbreaks of brucellosis, Q fever and RVF, have high disease burden and thus common coinfections. A literature survey provided information on case reports and studies investigating coinfections involving the three focal diseases. Fifty five studies were collected demonstrating coinfections of Brucella spp., C. burnetii or RVFV with 50 different pathogens, of which 64% were zoonotic. While the literature search criteria involved ‘coinfection’, only 24/55 studies showed coinfections with direct pathogen detection methods (microbiology, PCR and antigen test), while the rest only reported detection of antibodies against multiple pathogens, which only indicate a history of co-exposure, not concurrent infection. These studies lack the ability to test whether coinfection leads to changes in morbidity, mortality or transmission dynamics. We describe considerations and methods for identifying ongoing coinfections to address this critical blind spot in disease risk management.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Corrosion of Rare-Earth Disilicate Environmental Barrier Coatings in Gas Turbine Environments—Part 2: Microstructure Model

A model for the volatization of ytterbium disilicate (YbDS) by water vapor is developed. The new model is based on the Deal & Grove theory of the thermal oxidation of silicon [1], generalized here to account for the multi-step, second-order chemical reactions for silica depletion, multi-component mixture effects, and the unsteady porous microstructural evolution of the corroded inner zone. With the new model, the fundamental kinetic rate and standard porous media parameters are determined using the atmospheric steam jet impingement experiment of Ridley & Opila [2] and compared to results from the original Deal & Grove model. Fixing the input parameters in each respective model, a validation study (blind prediction) is presented for the high Reynolds number (1 × 105) and high pressure (14.7 atm) burner rig experiment of Wan et al. [3].

Cha, Chong M [Rolls Royce]↗

DOE OSTI2020

Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.

09 BIOMASS FUELS↗

DOE OSTI2021

Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations

Abstract The use of biomass-derived additives in diesel fuel mixtures has the potential to increase the fuel’s efficiency, decrease the formation of particulate matter during its combustion, and retain the fuel’s behavior in cold weather. To this end, identifying compounds that enable these behaviors is paramount. The present work utilizes a series of linear and non-linear equations in series with artificial neural networks to predict the cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value of multi-component blends. Property values of pure components are predicted using artificial neural networks trained with existing experimental data, and these predictions and their expected errors are propagated through linear and non-linear equations to obtain property predictions for multi-component blends. Individual component property prediction errors, defined by blind prediction median absolute error, are 4.91 units, 7.84 units, 0.06 cSt, 4.00 °C, and 0.55 MJ/kg for cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value respectively. On average, property predictions for blends are shown to be accurate to within 6% of the blends’ experimental values. Further, a multitude of compounds expected to be produced from catalytically upgrading products of fast pyrolysis are evaluated with respect to their behavior in diesel fuel blends.

09 BIOMASS FUELS↗

DOE OSTI2022

Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives

Abstract Discovering renewable fuels and fuel additives is paramount in reducing carbon emissions from internal combustion engines. Terpenes, a group of compounds that can be synthesized from plant matter and microorganisms, have gained significant interest in recent years as promising candidates for fuels/additives. Terpenes are a diverse class of compounds that contain rings and methyl branches, resulting in high energy densities and optimal cold weather behavior. Their variation in bond order, carbon chains, and functional groups lead to varying degrees of soot formation and performance in existing engines. The present work leverages predictive models, namely artificial neural networks, to predict the cetane number (CN), sooting tendency (quantified with yield sooting index, YSI), and energy density (quantified with lower heating value, LHV) of terpenes and hydrogenated terpenes whose sooting propensities were previously determined through experimental means. Predicted sooting propensities of these terpenes are compared with experimental values, and predicted cetane numbers and energy densities are used to comment on the compounds’ ability to act as fuels/additives. Expected prediction errors for CN, YSI, and LHV, defined by blind test set median absolute error, are within 5.56 cetane units, 3.63 yield sooting index units, and 0.77 MJ/kg respectively. Additionally, the present work investigates a variety of correlation/dependence metrics for property-property relationships, furthering our understanding of how combustion-relevant properties are related.

09 BIOMASS FUELS↗

DOE OSTI2023

Broadband, 920-nm mirror thin film damage competition

This year’s competition proposed to survey the state-of-the-art broadband, near-IR multilayer dielectric (MLD) mirrors designed for ultra-short, pulsed laser applications. The requirements for the coatings were a minimum reflection of 99.5% at 45-degree incidence angle for S-polarization from 830 nm to 1010 nm and group delay dispersion (GDD) < ± 50 fs 2 . The participants in this effort selected the coating materials, coating design, and deposition method. Samples were damage tested at a single testing facility to enable direct comparison among the participants using a 25 ± 5 fs OPCPA laser system operating at 5 Hz. A double blind test assured sample and submitter anonymity. The damage performance results, sample rankings, details of the deposition processes, coating materials and substrate cleaning methods are shared here. We found that multilayer coatings using tantala and/or hafnia as high index materials were top performers within several coating deposition groups. Specifically, dense coatings by ion-beam sputtering (IBS), magnetron sputtering (MS), and electron-beam ion assisted deposition (e-beam IAD) exhibited highest damage initiation onset (LIDT) while e-beam coatings were low performers. In addition, damage growth onset (LDGT) was also examined and the results are reported here for all samples as this performance metric plays an important role in establishing the safe operational conditions for larger aperture, ultrashort pulsed lasers. As a result, not all coating samples in the survey met the GDD requirements stated above and associated measurements are discussed in the context of the present and past competitions focused on similar broadband, near-IR MLD coatings.

42 ENGINEERING↗

DOE OSTI2023

Time-resolving state-specific molecular dissociation with XUV broadband absorption spectroscopy

The electronic and nuclear dynamics inside molecules are essential for chemical reactions, where different pathways typically unfold on ultrafast timescales. Extreme ultraviolet (XUV) light pulses generated by free-electron lasers (FELs) allow atomic-site and electronic-state selectivity, triggering specific molecular dynamics while providing femtosecond resolution. Yet, time-resolved experiments are either blind to neutral fragments or limited by the spectral bandwidth of FEL pulses. Here, we combine a broadband XUV probe pulse from high-order harmonic generation with an FEL pump pulse to observe dissociation pathways leading to fragments in different quantum states. We temporally resolve the dissociation of a specific O 2 + state into two competing channels by measuring the resonances of ionic and neutral fragments. This scheme can be applied to investigate convoluted dynamics in larger molecules relevant to diverse science fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Trapping and imaging dynamic battery nanointerfaces via electrified cryo-EM

The electrified interface between a liquid and a solid underpins diverse phenomena, from ion-transfer during battery operation to action potentials enabling biological communication. However, conventional tools are blind to the nanoscale dynamics of this metastable interface. Here, we leverage electrified cryo–electron microscopy (eCryo-EM), a technique that rapidly freezes and kinetically traps these dynamic, nonequilibrium states during battery operation for nanoscale characterization. Collective snapshots of the electrified interface at controlled time intervals quantifies early-stage growth kinetics of the solid electrolyte interphase (SEI), a passivation film that governs electron and ion transport. Unexpectedly, the diffusivity of charged species of the two SEI films with differing chemistry and performance are estimated to be within 10% of the other, indicated by the slope of their diffusion-limited SEI growth regimes. Instead, the slope of the reaction-limited SEI growth regimes differs by a factor of 3, suggesting that lowered reactivity of the high-performance electrolyte is largely responsible for its high coulombic efficiency.

Science & Technology - Other Topics↗

DOE OSTI2021

Geometric deep learning of RNA structure

RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.

Townshend, Raphael J. L.↗