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At least 127 records · Page 7

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DOE OSTI2024

Smallness of matter effects in long-baseline muon neutrino disappearance

Current long-baseline accelerator experiments, NOvA and T2K, are making excellent measurements of neutrino oscillations and the next generation of experiments, DUNE and HK, will make measurements at the O ( 1 % ) level of precision. These measurements are a combination of the appearance channel which is more challenging experimentally but depends on many oscillation parameters, and the disappearance channel which is somewhat easier and allows for precision measurements of the atmospheric mass splitting and the atmospheric mixing angle. It is widely recognized that the matter effect plays a key role in the appearance probability, yet the effect on the disappearance probability is surprisingly small for these experiments. Here we investigate both exactly how small the effect is and show that it just begins to become relevant in the high statistics regime of DUNE. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Visualization and quantum control of light-accelerated condensates by terahertz multi-dimensional coherent spectroscopy

Abstract Characterizing and controlling high-order correlation of quantum systems is key for developing quantum devices and switching technologies. Although conventional static and ultrafast spectroscopy gives access to collective excitations characterizing quantum states, more exotic correlations cannot be easily separated from other contributions. Here we develop density matrix simulations to show that seventh-order-wave-mixing peaks with distinct temperature and field dependences in two-dimensional terahertz nonlinear spectra reveal light-induced correlations in non-equilibrium superconducting states. Above critical terahertz driving, these emerging peaks split from conventional peaks along the second axis introduced by pump-probe relative phase in two-dimensional frequency space. They are photo-generated by correlations between two-photon fluctuations and interacting quasi-particle and quasi-particle/Higgs superconductor excitations. By photo-inducing persistent symmetry breaking via light-wave propagation, we also demonstrate seventh-order-wave-mixing sensing of Higgs collective modes. Our theory suggests to use multi-dimensional spectroscopy for quantum sensing of light-driven superconductivity and paves a path for quantum operations by few-cycle-THz-periodic photocurrent modulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Clean Hydrogen: Energy Carrier, Renewables Enabler, and Sector Coupler to Accelerate the Energy Transition and Meet Climate Goals

clean hydrogen↗

DOE OSTI2024

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Forecasting high-dimensional dynamical systems is a fundamental challenge in various fields, such as geosciences and engineering. Neural Ordinary Differential Equations (NODEs), which combine the power of neural networks and numerical solvers, have emerged as a promising algorithm for forecasting complex nonlinear dynamical systems. However, classical techniques used for NODE training are ineffective for learning chaotic dynamical systems. In this work, we propose a novel NODE-training approach that allows for robust learning of chaotic dynamical systems. Here, our method addresses the challenges of non-convexity and exploding gradients associated with underlying chaotic dynamics. Training data trajectories from such systems are split into multiple, non-overlapping time windows. In addition to the deviation from the training data, the optimization loss term further penalizes the discontinuities of the predicted trajectory between the time windows. The window size is selected based on the fastest Lyapunov time scale of the system. Multi-step penalty(MP) method is first demonstrated on Lorenz equation, to illustrate how it improves the loss landscape and thereby accelerates the optimization convergence. MP method can optimize chaotic systems in a manner similar to least-squares shadowing with significantly lower computational costs. Our proposed algorithm, denoted the Multistep Penalty NODE, is applied to chaotic systems such as the Kuramoto-Sivashinsky equation, the two-dimensional Kolmogorov flow, and ERA5 reanalysis data for the atmosphere. It is observed that MP-NODE provide viable performance for such chaotic systems, not only for short-term trajectory predictions but also for invariant statistics that are hallmarks of the chaotic nature of these dynamics.

Chaotic dynamical systems↗

DOE OSTI2026

Adaptive X-ray imaging with reinforcement learning

X-ray imaging is a powerful technique to scan samples in a variety of contexts including biological, environmental and materials science, but commonly requires a synchrotron light source to produce X-rays at sufficient intensity. As these facilities are expensive to operate, the available beam time is limited and always in high demand. Particularly if the illuminated samples are sparse, standard raster scanning methods can be time-consuming, with a majority of that time being spent on areas of the image that carry little information. To increase the efficiency and maximize the information gain for a given time budget, we split the scanning process into a series of steps where previous measurements are used to inform the decision making and adapt the exposure distribution at later stages of the sequence. We formulate this task as a reinforcement learning problem where the goal is to produce a sequence of exposure maps that maximize a predefined scalar metric. We demonstrate the potential of this approach in simulations where the adaptive illumination can accelerate the measurement process by up to an order of magnitude compared with standard raster scanning. Finally, we present the first results from deploying the trained agents on an X-ray fluorescence beamline at the Stanford Synchrotron Radiation Lightsource.

Reinforcement Learning↗

DOE OSTI2020

The Fast-Track Water Oxidation Channel on BiVO 4 Opened by Nitrogen Treatment

BiVO 4 is one of the most promising photoanode materials for water-splitting systems. Nitrogen incorporation into a BiVO 4 surface overcomes the known bottleneck in its charge-transfer kinetics into the electrolyte. We explored the role of nitrogen in the surface charge recombination and charge-transfer kinetics by employing transient photocurrent spectroscopy at the time scale of surface recombination and water oxidation kinetics, transient absorption spectroscopy, and X-ray photoelectron spectroscopy. We attributed the activity enhancement mechanism to the accelerated V 5+ /V 4+ redox process, in which incorporated nitrogen suppresses a limiting surface recombination channel by increasing the oxygen vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Probing the 𝑗 dependence of angular distributions and 𝑁=20 shell rigidity via the 36 S (𝑝,𝑑) 35 S reaction

An investigation of the N=20 36 S nucleus has been performed through a detailed study of the 3 6S (p,d)3 5S neutron-removal reaction, employing a 66-MeV proton beam at iThemba Laboratory for Accelerator Based Sciences and an innovative target design. Using the high-resolution K = 600 magnetic spectrometer, 98 states in 3 5S were identified up to 16-MeV excitation energy, including 47 previously unobserved states. Angular distributions and spectroscopic factors, including isobaric analog-state contributions, were extracted for 81 levels. A pronounced j dependence in the angular distributions of ℓ=2 states provides refined insights into the spin-orbit splitting. Finite-range adiabatic distorted-wave approximation calculations qualitatively reproduce the observed j dependence. Comparisons of the measured 1d 5/2 spectroscopic strength distribution with large-scale shell-model and ab initio calculations show good agreement overall, and the robustness of the N=20 shell closure in 36 S is confirmed when comparing the relatively low fp orbital occupancies in 40 Ca and 3 6S across the Fermi surface. This study underscores the utility of neutron-removal reactions in probing nuclear structure and the Fermi surface of sd nuclei and beyond. The findings advance our understanding of shell evolution and offer constraining data for theoretical models.

20 ≤ A ≤ 38↗

DOE OSTI2024

Experimental Investigation on Heating Performance of a Cold Climate Thermoelectric-Assisted Heat Pump

To accelerate the electrification of air source heat pumps (ASHPs) in cold climates across the United States, various initiatives have been launched to enhance the effectiveness of ASHPs. One avenue of research involves incorporating thermoelectric (TE) technology into vapor compression refrigeration cycles. This study aims to assess the heating performance of a cold climate ASHP by employing TE modules as a liquid line subcooler. The tested system is a nominal 4.5-ton split heat pump utilizing R410A, equipped with a scroll compressor and an accumulator. An electronic expansion valve was employed for both cooling and heating modes. Two configurations of TE sub-coolers, one utilizing 2 TE bundles and the other 4 TE bundles, were integrated into the liquid line of the tested system. The heating performance of these configurations was evaluated. The results revealed that activating the TE subcooler led to a notable increase in total heat capacity, reaching 1318 W at -15.0 °C and 1164 W at -19.0 °C. The corresponding TE coefficients of performance (COPs) were 1.76 and 1.63, respectively. The activation of the TE sub-cooler resulted in a slight reduction in the overall system COP, with a decrease ranging from -2.6% to -4.2% for these two temperatures. The system COPs were measured at 2.10 and 1.86 for -15.0 °C and -19.0 °C, respectively. This prototype demonstrated a significant augmentation in heating capacity with a minimal sacrifice in COP.

Hu, Yifeng↗

DOE OSTI2021

Receiving flat bands near the Fermi level in 1T-VSe2 by using angle resolved photoemission spectroscopy

Here, we study the surface electronic structure of 1T-VSe 2 by means of angle resolved photoemission spectroscopy and uncover a dispersion-less emission located in the vicinity of the Fermi level. Its crystal momentum dependency reveals that it occupies large portions of the Brillouin zone (BZ), where no bulk band is expected. Upon electron doping (deposition of Rb-atoms), the system evolves in a surprising way. Besides the expected down-shifting of the bands, a splitting of both bulk and dispersion-less emission is observed. This peculiar behavior strongly suggests the intrinsic nature of the dispersion-less emission. Its characterization may therefore be relevant to a deeper understanding of the physics of transition metal dichalcogenides.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

New NOvA Results with 10 Years of Data

NOvA is a two-detector accelerator neutrino oscillation experiment. Using Fermilab's newly Megawatt-capable NuMI neutrino beam, NOvA measures the disappearance of muon (anti)neutrinos and the appearance of electron (anti)neutrinos at the far detector, 810 km from the source. These oscillations are observed relative to the unoscillated beam composition measured at the functionally equivalent near detector, also located at Fermilab, which enables significant cancellation of systematic uncertainties. From these, we obtain precision measurements of the larger neutrino mass splitting and the largest neutrino mixing angle, as well as constraints on the octant of that angle, the neutrino mass ordering, and neutrino CP violation. In this talk I will present new measurements of these parameters using 10 years of NOvA data collected between 2013 and 2023, which includes twice the neutrino-mode exposure of our previous results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Experimental confirmation of the magnetic ordering transition induced by an electronic structure change in the metallic triangular antiferromagnet Co 1/3⁢ TaS 2

We report angle-resolved photoemission spectroscopy (ARPES) studies combined with DFT+DMFT calculations to confirm that the magnetic ordering vector transition from Q=(1/2,0,0) to Q=(1/3,0,0) in the metallic triangular antiferromagnets Co 1/3±𝜀⁢ TaS 2 (𝜀 ≈ 0.007) is induced by the electronic structure change in the system. The ARPES-measured Fermi surface (FS) maps of Co 0.325 ⁢TaS 2 show two hexagonal and one circular holelike FSs around Γ, which matches well with the triple- Q state by taking into account the contribution of nesting vectors occurring between Co 3⁢𝑑 and Ta 5⁢𝑑 orbitals. In the case of Co 0.340 ⁢TaS 2 , a new electron pocket around K appears, and the FS geometry changes as a result of the correlation effect of Co 4 ⁢S 18 tripods forming in the system. The magnetic susceptibility calculations based on the charge-self-consistent DFT+DMFT band structures and the random phase approximation indicate that the most stable magnetic ordering vector (1/2,0,0) split into (1/6,0,0) and (1/2,0,0), which is consistent with the magnetic phase transition around 𝑥=1/3 in Co 𝑥 ⁢TaS 2 .

Fermi surface↗

DOE OSTI2025

Splitting amplitudes at N 3 LO in QCD

In the limit where partons become collinear to each other, scattering amplitudes factorize into a product of universal, process-independent building blocks and scattering amplitudes involving fewer partons. We compute these universal building blocks — known as splitting amplitudes — for two collinear QCD partons up to third loop order in QCD. Our results describe arbitrary time-like splitting processes. Due to the violation of strict collinear factorization in space-like splitting processes, we specifically present space-like splitting amplitudes for three-parton QCD scattering amplitudes at third loop order. To achieve our results, we perform a collinear expansion of three-loop scattering amplitudes using a new expansion-by-subgraph technology, which is based on the method of regions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Clean Hydrogen Production R&D at NREL

Comprehensive, concerted efforts supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Hydrogen and Fuel Cell Technologies Office (HFTO) are advancing research and development to demonstrate clean hydrogen production and industrial decarbonization pathways. These pathways enable an economically competitive and environmentally beneficial future energy system across sectors and can address specific applications that are difficult to decarbonize. NREL's research accelerates development, integration, and scale up of hydrogen and fuel cell technologies to enable widespread deployment across multiple energy sectors. Our work helps industry overcome technical challenges and supports DOE's H2@Scale vision for clean hydrogen across multiple applications and economic sectors. We also bridge technologies with other research areas across the lab and through multiple DOE and national lab research initiatives, consortia, and collaborations including: H2NEW: Hydrogen from Next-generation Electrolyzers of Water Consortium, HydroGEN: Advanced Water Splitting Materials Consortium, H2@Scale CRADA, and helping de-risk the hydrogen systems via large-scale validation and demonstration.

clean hydrogen↗

DOE OSTI2023

Clean Hydrogen Production R&D

Comprehensive, concerted efforts supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Hydrogen and Fuel Cell Technologies Office (HFTO) are advancing research and development to demonstrate clean hydrogen production and industrial decarbonization pathways. These pathways enable an economically competitive and environmentally beneficial future energy system across sectors and can address specific applications that are difficult to decarbonize. NREL's research accelerates development, integration, and scale up of hydrogen and fuel cell technologies to enable widespread deployment across multiple energy sectors. Our work helps industry overcome technical challenges and supports DOE's H2@Scale vision for clean hydrogen across multiple applications and economic sectors. We also bridge technologies with other research areas across the lab and through multiple DOE and national lab research initiatives, consortia, and collaborations including: H2NEW: Hydrogen from Next-generation Electrolyzers of Water Consortium, HydroGEN: Advanced Water Splitting Materials Consortium, and BioH2. Within the plenary panel called: From the Classroom to the Lab to the Board Room, I will represent the Lab in this panel and will be talking about hydrogen technology at NREL, the lab's role in bridging university research with industry commercialization. I will also talk about my personal career path and what it's like to work at NREL, along with work force development and DEIA programs at NREL.

BIL↗

DOE OSTI2021

Joint ptycho-tomography with deep generative priors

Abstract Joint ptycho-tomography is a powerful computational imaging framework to recover the refractive properties of a 3D object while relaxing the requirements for probe overlap that is common in conventional phase retrieval. We use an augmented Lagrangian scheme for formulating the constrained optimization problem and employ an alternating direction method of multipliers (ADMM) for the joint solution. ADMM allows the problem to be split into smaller and computationally more efficient subproblems: ptychographic phase retrieval, tomographic reconstruction, and regularization of the solution. We extend our ADMM framework with plug-and-play (PnP) denoisers by replacing the regularization subproblem with a general denoising operator based on machine learning. While the PnP framework enables integrating such learned priors as denoising operators, tuning of the denoiser prior remains challenging. To overcome this challenge, we propose a denoiser parameter to control the effect of the denoiser and to accelerate the solution. In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Voltage and temperature effects on low cobalt lithium-ion battery cathode degradation

Degradation of low cobalt lithium-ion cathodes was tested using a full factorial combination of upper cut-off voltage (4.0 V and 4.3 V vs. Li/Li + ) and operating temperature (25 °C and 60 °C). Half-cell batteries were analyzed with electrochemical and microstructural characterization methods. Electrochemical performance was assessed with galvanostatic cycling, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) supported by distribution of relaxation times (DRT) analysis. Electrode microstructure was characterized with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray absorption near edge structure (XANES) imaging. Higher cut-off voltage cycling shows presence of NiO x formation, a low diffusivity rock-salt phase, in both CV and XRD data. XRD patterns confirmed that the rock-salt phase was beginning to form at the low cut-off voltage at high temperature, but in much lower intensity than at the high cut-off voltage. Higher temperature accelerates degradation processes at both voltages. Degradation factors at high temperature include NiO x formation, cathode material dissolution, and electrolyte decomposition. SEM analysis suggests that supporting phases may isolate and disconnect active material particles reducing capacity retention and battery life cycle. DRT analysis and XANES imaging show that both high temperature samples revealed a NiO x phase based on an increased diffusive impedance and a visible shift in the XANES spectra. The low cut-off voltage, high temperature sample showed a split peak and shift to lower energies indicating early formation of the NiO x phase. The diffusive impedance, which hinders intercalation and deintercalation, is driven by the formation of the NiO x phase. While primarily driven by cut-off voltage, elevated temperature also contributes to this degradation mechanism.

electrochemical impedance spectroscopy↗