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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Discovering Elusive Dynamics Across Frontiers (Final Technical Report)

Profound puzzles, such as the nature of dark matter, the origins of the electroweak scale, the mechanism behind the small neutrino mass, and the strong CP problem, suggested new physics beyond the Standard Model and drove the particle physics program in search of the associated new particles. Despite extensive searches, conventional realizations of new physics have not yet provided conclusive evidence. This raised the serious possibility that new dynamics might be more elusive, perhaps due to a richer gauge and matter structure than previously considered. Notably, the existence of dark matter and advancements in understanding the naturalness problem urged exploration into sectors with complex gauge and matter structures. Through experiments like the LHC, DUNE, and small-scale experiments, the robust US HEP program played a critical role in pursuing these well-motivated but under-explored scenarios for new physics. I explored these physics opportunities in depth, focusing on novel searches and significant improvements in parameter space coverage. The elusive dynamics revealed rich information about the underlying theory and were essential in identifying observable opportunities. Understanding the observable consequences required a deep comprehension of the theory, which I also developed. The proposal included essential components aimed at coherently increasing our knowledge in well-motivated elusive dynamics models. Through research on high-quality axions, composite neutrinos, the Higgs boson as a portal to hidden strong dynamics, and new scalar potentials to generate alternative electroweak phase transitions, I focused on identifying new signatures and parameter regions in plausible elusive dynamics models. The exploration emphasized generic possibilities motivated by broad classes of elusive dynamics models. These signatures were not effectively probed previously due to various challenges such as triggering, background suppression, or experimental design. My research involved close interaction with experimental colleagues to overcome these difficulties, leveraging new theoretical and experimental possibilities. These efforts included identifying new observables such as timing information and substructure in calorimetries, new multiple-hit techniques, new scattering events, and new resonance searches in liquid argon detectors. This work created a positive feedback loop: theory and experimental work inspired each other, revealing new exciting opportunities that supported both programs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A Novel Workflow for Coupled Simulation of Hydraulic Stimulation with Simultaneous Injection of Proppant

Enhanced Geothermal Systems (EGS) are located in impermeable host rock. Therefore, to achieve promising circulation, hydraulic stimulation is a key factor. Stimulation involves injecting a combination of fluids and proppant particles into existing fractures under high pressure. The effectiveness of stimulation depends on a variety of different factors, including the pressurization scheme and the optimal choice of carrying fluid and proppant particles to be used. For example, high-density particles can settle out of the injected slurry quickly, whereas lighter particles as well as a dense carrying fluid allow for a long transport into the reservoir. A numerical tool is therefore essential to take all these factors into account and to make robust predictions about the performance of the hydraulic stimulation. We present a novel computational application for coupled numerical simulation of hydraulic stimulation with concurrent injection of proppant in a fractured geothermal reservoir. This application is based on the open-source MOOSE framework that has been enriched with a set of equations to account for the circulation of proppant-laden slurries. It accounts for both the fluid and proppant components of the mixture, as well as particle-driven processes that include gravity settling, particle-particle interactions, and strong density and viscosity contrasts. A particular challenge is the mutual coupling with fracture mechanical processes in a propagating fracture using the FE code Defmod. Aperture and fluid pressure on one side and slurry density and viscosity changes on the other side interact and affect each other. Their basic understanding is therefore essential for the success of the stimulation. We compare our application to common numerical benchmark problems for both slurry flow and hydraulic stimulation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗
DOE OSTI2021

Fabrication of Ionic Covalent Triazine Framework-Linked Membranes via a Facile Sol–Gel Approach

Covalent triazine framework (CTF)-based membranes have shown unique properties and wide applications in energy-related fields, but there is still no efficient strategy capable of affording CTF membranes functionalized with ionic moieties, which will bring extra merits and application possibilities. In this work, a robust CTF membrane with ionic functionalities was fabricated via a sol–gel approach promoted by a superacid (FSO 3 H). The CTF skeleton was constructed via the trimerization of cyano groups in the monomer, and piperazine moieties were introduced as reactive sites for the formation of ammonium cations coupled with FSO 3 – anions within the backbone. The obtained transparent and red protonated polymeric CTF-based membrane (PP-CTF) exhibited significant absorption at 646 nm in the solid-state ultraviolet–visible diffuse reflectance spectrum. Comparatively, the neutralized deprotonated counterparts turned to yellow color with a significant blueshift absorption to 492 nm. The theoretical calculation demonstrated that PP-CTF and deprotonated polymeric CTF membrane (DP-CTF) were stable with AB-stacking mode, and PP-CTF had a smaller band gap (0.90 eV) than that of DP-CTF (2.17 eV). The significant optical absorption and emission behavior change of PP-CTF and its robust chemical stability endowed it with the capability to act as pH indicators. The synthetic pathway developed in this work and the performance of the resultant ionic membrane opens new opportunities in the aspects of membrane design, fabrication, and application.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗
DOE OSTI2024

Electron-mediated entanglement of two distant macroscopic ferromagnets within a nonequilibrium spintronic device

Using the nascent concept of quantum spin-transfer torque, we demonstrate that a current pulse can be harnessed to entangle quantum localized spins of two spatially separated ferromagnets (FMs) which are initially unentangled. The envisaged setup is composed of a spin-polarizer (FM p ) and a spin-analyzer (FM a ) FM layers separated by a normal metal (NM) spacer. The injection of a current pulse into the device leads to a time-dependent superposition of many-body states characterized by a high degree of entanglement between the spin degrees of freedom of the two distant FM layers. The nonequilibrium dynamics are due to the transfer of spin angular momentum from itinerant electrons to the localized spins via a quantum spin-torque mechanism that remains active even for collinear but antiparallel arrangements of the FM p and FM a magnetizations (a situation in which the conventional spin torque is absent). We quantify the mixed-state entanglement generated between the FM layers by tracking the time evolution of the full density matrix and analyzing the build-up of the mutual logarithmic negativity over time. The effect of decoherence and dissipation in the FM layers due to coupling to bosonic baths at finite temperature, the use of multielectron current pulses, and the dependence on the number of spins are also considered in an effort to ascertain the robustness of our predictions under realistic conditions. Lastly, we propose a “current-pump–x-ray-probe” scheme, utilizing ultrafast x-ray spectroscopy, that can witness nonequilibrium and transient entanglement of the FM layers by extracting its time-dependent quantum Fisher information.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Inferring Quantum Network Topology Using Local Measurements

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities-namely, the von Neumann entropy and the measured mutual information-as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Hydrogen and Ammonia Blending With Natural Gas: Ignition Delay Times and Chemical Kinetic Model Validation at Gas Turbine Relevant Conditions

Abstract Ignition delay times from undiluted mixtures of natural gas (NG)/H2/Air and NG/NH3/Air were measured using a high-pressure shock tube at the University of Central Florida. The combustion temperatures were experimentally tested between 1000–1500 K near a constant pressure of 25 bar. As mentioned, mixtures were kept undiluted to replicate the same chemistry pathways seen in gas turbine combustion chambers. Recorded combustion pressures exceeded 200 bar due to the large energy release, hence why these were performed at the high-pressure shock tube facility. The data is compared to the predictions of the NUIGMech 1.1 mechanism for chemical kinetic model validation and refinement. An exceptional agreement was shown for stoichiometric conditions in all cases but strayed at lean and rich equivalence ratios, especially in the lower temperature regime of H2 addition and all temperature ranges of the baseline NG mixture. Hydrogen addition also decreased ignition delay times by nearly 90%, while NH3 fuel addition made no noticeable difference in ignition time. NG/NH3 exhibited similar chemistry to pure NG under the same conditions, which is shown in a sensitivity analysis. The reaction CH3 + O2 = CH3O + O is identified and suggested as a possible modification target to improve model performance. Increasing the robustness of chemical kinetic models via experimental validation will directly aid in designing next-generation combustion chambers for use in gas turbines, which in turn will greatly lower global emissions and reduce greenhouse effects.

Pierro, Michael↗
DOE OSTI2025

Phenotypically anchored transcriptomics across diverse agrichemicals reveals conserved pathways and unique gene expression signatures in zebrafish

Agrichemicals such as herbicides, fungicides, insecticides, and biocides are widely used in agriculture, yet some are associated with adverse effects in humans and the environment. While many of these chemicals have been extensively studied in vitro and are included in the EPA’s ToxCast program, comprehensive in vivo comparisons using RNA sequencing across structurally diverse agrichemicals, in a single screening platform, are lacking. In this study, we examined structurally diverse agrichemicals found in the U.S. Environmental Protection Agency’s (EPA) Toxcast Phase I and II library by statically exposing early life stage zebrafish at 6 h post fertilization (hpf) until 120 hpf at concentrations ranging from 0.25 to 100 µM. Morphological outcomes were assessed at 120 hpf across 10 endpoints, including yolk sac edema, craniofacial malformations, and axis abnormalities. Chemicals that produced robust concentration-response relationships were selected for transcriptomic profiling. For transcriptomic analysis, zebrafish were statically exposed to each chemical and sampled at 48 hpf, prior to the onset of morphological effects observed at 120 hpf. Differential expression analysis identified between 0 and 4,538 differentially expressed genes (DEGs) per chemical, with no clear correlation to morphological severity. Both DEG and co-expression network analyses revealed chemical-specific expression patterns that converged on shared biological pathways, including neurodevelopment and cytoskeletal organization. Key regulatory genes such as mylpfa and krt4 were identified within co-expression modules, suggesting their potential role in conserved toxicity mechanisms. Semantic similarity analysis of enriched gene ontology (GO) terms, when compared to existing datasets, highlighted gaps in the annotation of neurodevelopmental processes, indicating that some in vivo effects may not be fully captured by current curated resources. The results provide new insights into the modes of action of diverse agrichemicals and establish a framework for understanding how agrichemical structure relates to biological function in a vertebrate model.

agrichemical↗
DOE OSTI2026

Global hotspots of particulate organic carbon losses under climate change

Soil organic carbon (SOC) comprises particulate (POC) and mineral-associated organic carbon (MAOC), which differ in formation, stabilization, and loss mechanisms. While the current global distribution of POC and MAOC is characterized, their vulnerability under future climate scenarios remains unclear. Using 3284 topsoil (0-30 cm) observations from six continents, we identify high-latitude soils as global hotspots of SOC vulnerability under shared socioeconomic pathway scenarios (SSP126, SSP245, and SSP585). Under a high-emission scenario (SSP585), high-latitude soils are projected to lose substantial POC by 2100, accounting for about 81 ± 10% of total SOC losses. These declines are driven by the high proportion of SOC stored as POC (f POC ) and its high temperature sensitivity. We show that f POC is a robust indicator of SOC vulnerability to climate change. Globally, the projected POC decline corresponds to a cumulative carbon dioxide (CO 2 ) release of 81.34 Pg CO 2 -equivalent by 2100, highlighting the importance of preserving POC to mitigate climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings

Environmental barrier coatings (EBC) are crucial for the use of SiC-based ceramic matrix composites in high-temperature combustion environments, yet knowledge of oxygen diffusion in these coatings is limited. This study investigates oxygen diffusion dynamics in the β-RE 2 Si 2 O 7 system to minimize oxygen penetration in rare earth disilicates. We analyze defect formation energy under varying oxygen conditions, identifying key diffusion mechanisms. In oxygen-rich environments, the most favorable neutral interstitial oxygen diffuses along the [110] direction. In oxygen-poor conditions, neutral oxygen vacancies rotate around Y and Si atoms, exhibiting a diffusivity of 6.59×10 −22 m 2 /s at 1500 K for β-Yb 2 Si 2 O 7 . Under intermediate oxygen levels, charged interstitial oxygen diffuses via concerted interstitialcy along the [001] direction with a diffusivity of 6.21×10 −17 m 2 /s. Additionally, alloying rare earth Y with Er and Yb increases diffusion barriers, contributing to improved EBC performance in extreme environments. The insights gained provides valuable guidance for designing robust coatings tailored to withstand extreme operational environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Implementation of scalable suspended superinductors

Superinductors have become a crucial component in the superconducting circuit toolbox, playing a key role in the development of more robust qubits. Enhancing the performance of these devices can be achieved by suspending the superinductors from the substrate, thereby reducing stray capacitance. Here, we present a fabrication framework for constructing superconducting circuits with suspended superinductors in planar architectures. To validate the effectiveness of this process, we systematically characterize both resonators and qubits with suspended arrays of Josephson junctions, ultimately confirming the high quality of the superinductive elements. In addition, this process is broadly compatible with other types of superinductors and circuit designs. Furthermore, our results not only pave the way for scalable superconducting architectures utilizing superinductors but also provide the primitive for future investigation of loss mechanisms associated with the device substrate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

X-ray free-electron laser wavefront sensing using the fractional Talbot effect

Wavefront sensing at X-ray free-electron lasers is important for quantitatively understanding the fundamental properties of the laser, for aligning X-ray instruments and for conducting scientific experimental analysis. A fractional Talbot wavefront sensor has been developed. This wavefront sensor enables measurements over a wide range of energies, as is common on X-ray instruments, with simplified mechanical requirements and is compatible with the high average power pulses expected in upcoming X-ray free-electron laser upgrades. Single-shot measurements were performed at 500 eV, 1000 eV and 1500 eV at the Linac Coherent Light Source. These measurements were applied to study both mirror alignment and the effects of undulator tapering schemes on source properties. The beamline focal plane position was tracked to an uncertainty of 0.12 mm, and the source location for various undulator tapering schemes to an uncertainty of 1m, demonstrating excellent sensitivity. These findings pave the way to use the fractional Talbot wavefront sensor as a routine, robust and sensitive tool at X-ray free-electron lasers as well as other high-brightness X-ray sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Polyimide aerogels for ballistic impact protection

The ballistic performance of edge-clamped monolithic polyimide aerogel blocks (12 mm thickness) has been studied through a series of impact tests using a helium-filled gas gun connected to a vacuum chamber and a spherical steel projectile (approximately 3 mm diameter) with an impact velocity range of 150–1300 m s -1 . The aerogels had an average bulk density of 0.17 g cm -3 with high porosity of approximately 88%. The ballistic limit velocity of the aerogels was estimated to be in the range of 175–179 m s -1 . Moreover, the aerogels showed a robust ballistic energy absorption performance (e.g., at the impact velocity of 1283 m s -1 at least 18% of the impact energy was absorbed). At low impact velocities, the aerogels failed by ductile hole enlargement followed by a tensile failure. By contrast, at high impact velocities, the aerogels failed through an adiabatic shearing process. Given the substantially robust ballistic performance, the polyimide aerogels have a potential to combat multiple constraints such as cost, weight, and volume restrictions in aeronautical and aerospace applications with high blast resistance and ballistic performance requirements such as in stuffed Whipple shields for orbital debris containment application.

42 ENGINEERING↗
DOE OSTI2023

Photophysics of Intrinsic Single‐Photon Emitters in Silicon Nitride at Low Temperatures

A robust process for fabricating intrinsic single-photon emitters in silicon nitride is recently established. These emitters show promise for quantum applications due to room-temperature operation and monolithic integration with technologically mature silicon nitride photonics platforms. Here, the fundamental photophysical properties of these emitters are probed through measurements of optical transition wavelengths, linewidths, and photon antibunching as a function of temperature from 4.2 to 300 K. Important insight into the potential for lifetime-limited linewidths is provided through measurements of inhomogeneous and temperature-dependent broadening of the zero-phonon lines. At 4.2 K, spectral diffusion is found to be the main broadening mechanism, while spectroscopy time series reveal zero-phonon lines with instrument-limited linewidths.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗