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DOE OSTI2025

Search for the Anomalous Events Detected by ANITA Using the Pierre Auger Observatory

A dedicated search for upward-going air showers at zenith angles exceeding 110° and energies E > 0.1 EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two “anomalous” radio pulses observed by the ANITA flights I and III that appear inconsistent with the standard model of particle physics. Using simulations of both regular cosmic-ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1–33] EeV. One event has been found in the search period between January 1, 2004, and December 31, 2018, consistent with an expected background of 0.27 ± 0.12 events from misreconstructed cosmic-ray showers. This translates to an upper bound on the integral flux of ( 7.2 ± 0.2 ) × 10 − 21 cm − 2 sr − 1 y − 1 and ( 3.6 ± 0.2 ) × 10 − 20 cm − 2 sr − 1 y − 1 for an E − 1 and E − 2 spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an E − 3 spectrum and over 8.1 events for a conservative E − 5 spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers. Published by the American Physical Society 2025

Abdul Halim, A.↗

DOE OSTI2022

LANL Regular Employee Population and Demographics, FY15-21

This document summarizes an effort to compile and analyze the LANL regular employee population’s demographic attributes and trends between FY15-FY21 using MicroStrategy, a web-based data analytics and visualization tool. LANL’s HR Division collected and prepared data for the Weapons Program’s annual reporting activities for the 2017 through 2023 NNSA Stockpile Stewardship and Management Plan (SSMP). Los Alamos workforce data represent the fiscal year-end snapshot of the permanent employee population who are categorized by the Common Occupational Classification System (COCS). This work enables further analyses, including trends for all workforce attributes presented in the SSMPs as well as a definition of a crosswalk between COCS categorization and the internal laboratory job classification hierarchy. Data for other sites and the federal workforce, as shown in the SSMPs, are included and corresponding figures can be visualized within the MicroStrategy tool. A portable, Excel-based version of the tool was developed and shared with the multi-site workforce working group. This summary highlights selected aspects of the LANL regular workforce. The data analyses and visualizations communicate insights within three major themes regarding permanent LANL employees: 1) demographic transformation, 2) attrition, and 3) skills. Regarding demographic transformation, and consistent with the general trend across the enterprise, early career regular employees are the fastest growing group, with growth ranging between 12-34% year-to-year over the seven-year period analyzed. The data also reflect effects of internal and external factors on attrition; the large spike in separations in FY18 aligns with the contract transition from LANS to Triad and the dip in attrition between FY20-FY21 is likely a result of the global pandemic. The population of regular employees in general management, engineer, and operator COCS categories have the fastest growth rates amongst all the occupational categories. The MicroStrategy and Excel tools provide additional details and trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

K-space algorithmic reconstruction (KAREN): a robust statistical methodology to separate Bragg and diffuse scattering

Diffuse scattering occurring in the Bragg diffraction pattern of a long-range-ordered structure represents local deviation from the governing regular lattice. However, interpreting the real-space structure from the diffraction pattern presents a significant challenge because of the dramatic difference in intensity between the Bragg and diffuse components of the total scattering function. In contrast to the sharp Bragg diffraction, the diffuse signal has generally been considered to be a weak expansive or continuous background signal. In this paper, using 1D and 2D models, it is demonstrated that diffuse scattering in fact consists of a complex array of high-frequency features that must not be averaged into a low-frequency background signal. To evaluate the actual diffuse scattering effectively, an algorithm has been developed that uses robust statistics and traditional signal processing techniques to identify Bragg peaks as signal outliers which can be removed from the overall scattering data and then replaced by statistically valid fill values. This method, described as a 'K-space algorithmic reconstruction' (KAREN), can identify Bragg reflections independent of prior knowledge of a system's unit cell. KAREN does not alter any data other than that in the immediate vicinity of the Bragg reflections, and reconstructs the diffuse component surrounding the Bragg peaks without introducing discontinuities which induce Fourier ripples or artifacts from underfilling `punched' voids. The KAREN algorithm for reconstructing diffuse scattering provides demonstrably better resolution than can be obtained from previously described punch-and-fill methods. The superior structural resolution obtained using the KAREN method is demonstrated by evaluating the complex ordered diffuse scattering observed from the neutron diffraction of a single plastic crystal of CBr 4 using pair distribution function analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Advanced Characterization Techniques for Overcoming Challenges of Perovskite Solar Cell Materials

Abstract In the last 10 years, organic–inorganic hybrid perovskite solar cells have achieved unprecedented advances, to the point where they now exhibit extremely high efficiency. However, long‐term stability and areal scalability limitations impede the commercial application of perovskite materials, and appropriate diagnosistic tools have become necessary to evaluate perovskite materials. Characterization of perovskite materials is regularly misinterpretated, due to unique intrinsic and extrinsic factors: degradation from the measurement source, ion migration, phase transition, and separation. Herein, studies on perovskites are reviewed that have used advanced characterization tools to overcome characterization challenges. Cryogenic temperature assisted measurements mitigate degradation or phase transitions induced by the measurement source. In situ measurements can track the variation of perovskite materials depending on external stimuli. Spatial material properties are able to be evaluated by the use of multidimensional mapping techniques. An overview of these advanced characterization tools that can overcome the challenges associated with established tools provides the opportunity for further understanding perovskite materials and solving the remaining challenges on the road to commercialization.

Kim, Min‐cheol↗

DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗

DOE OSTI2022

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it remains notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes the first GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs' inference efficiency. Specifically, on the algorithm level, GCoD integrates a divide and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently outperforms state-of-the-art designs in terms of accelerator efficiency while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices to better understand its advantages. All codes and pre-trained models will be released upon acceptance.

You, Haoran↗

DOE OSTI2021

Synthesis and Characterization of Macrocyclic Ionic Liquids for CO 2 Separation

In this work, a series of ionic liquids with symmetric, macrocyclic cations composed of N-heterocycles connected by alkyl or ether linkages coupled with bis(trifluoromethylsulfonyl)imide or bis(pentafluoroethanesulfonyl)amide anions were synthesized to explore their application in CO 2 separation. These novel ionic liquids showed selective physical absorption of CO 2 compared to N 2 making them potential candidates for carbon capture. We examined the effect of cation structure on the physicochemical and separations properties of the ionic liquid and showed that these ionic liquids exhibit an inverse relationship between molar volume and CO 2 sorption capacity following the trend predicted by models based on regular solution theory. The fractional free volumes of the ionic liquids were lower than typical alkyl-imidazolium ionic liquids despite the cyclic structure of the cations which could be related to the flexibility of the alkyl and ether linkages. These results provide added insight into our understanding of structure–property relationships in CO 2 solubility in ionic liquids and afford a path toward enhancing the separations properties based on structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Influence of graphite geography on the yield of mechanically exfoliated few-layer graphene

Some of graphene's exciting properties that inspired countless studies over the last two decades are exclusive to defect-free, few-layer graphene (FLG, ≤4 layers) produced by mechanical exfoliation. Despite graphite exfoliation having been thoroughly studied, the influence of the graphite source on the successful exfoliation of FLG remains unexplored. Here, in this work, we examine the physicochemical properties and exfoliation performance of various graphite types (e.g., natural crystalline flake, natural vein, and synthetic) from around the globe (e.g., Alaska, Tanzania, China, etc.). We first established a normalization method for the facile comparison of FLG (Φ) yields between separately exfoliated graphites. Using this approach, we find that the relative yield of FLG ranges from 0% to 22% across graphite types, with regular natural crystalline flakes (NCF-R) performing the best overall (Φ NCF-R = 16% ± 5%). FLG yield was also found to depend on source geography, evidenced by Φ NCF-R ranging from 8% to 22% for NCF-R sourced from Alabama and Canada, respectively. We employed machine learning and Pearson correlation analysis to determine the graphite characteristics that govern FLG yield. Graphite source-dependent properties such as graphite surface area and mineral impurities, including aluminum and calcium, were found to be critically important. Our findings highlight the importance of the graphite source when trying to maximize the yield of mechanically exfoliated FLG and represent an important step toward producing higher quantities of defect-free FLG for graphene-based research.

42 ENGINEERING↗

DOE OSTI2024

Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O 3 . Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.

36 MATERIALS SCIENCE↗

DOE OSTI2022

OC6 Phase Ia Definition Document: Validation of Nonlinear Hydrodynamic Loading on the DeepCwind Semisubmersible

The objective of OC6 Phase I is to investigate the persistent under-prediction (about 20% on average) of the structural loads in the OC5-DeepCwind floating semisubmersible wind system, as observed within the previous OC5 Phase II project. The OC5 results indicate that much of this under-prediction originates from the low-frequency response of the system at the surge/pitch natural frequencies, resulting from nonlinear hydrodynamic loading and/or overprediction of hydrodynamic damping. This work package therefore focuses on better understanding this low-frequency response behavior, the applicability of computational-fluid-dynamics (CFD) models and engineering-level hydrodynamic models for predicting the nonlinear hydrodynamic loading, and the level of uncertainty in the measured response characteristics. To address these objectives, two experimental campaigns were developed and performed in 2017 and 2018 by a sub-group of the OC5 project: Floating test campaign: A simplified OC5-DeepCwind semisubmersible model (no turbine, rigid tower) was moored to three taut-spring lines and subjected to motion decay and wave tests (regular and irregular waves), during which motion amplitudes of the model were measured (see Figure 1a). The purpose of this test campaign was to examine the repeatability of the low-frequency response of the system to wave excitation and determine the level of uncertainty in the response behavior.Constrained test campaign: The same model, but without the tower present, was fixed to a carriage and subjected to towing, forced oscillation and wave tests (regular and irregular waves), during which forces exerted upon the model were measured (see Figure 1b). The purpose of this test campaign was to examine the hydrodynamic loading contributions by separating the wave excitation and radiation forces. Both model test campaigns were performed in the concept basin of the Maritime Research Institute Netherlands (MARIN) in Wageningen, the Netherlands and the first test was within the framework of the MARINET2 project. Both experimental campaigns were examined within Phase Ia of the OC6 project. The purpose of this report is to supply the needed information to participants of the OC6 project to build and simulate the experimental campaigns.

17 WIND ENERGY↗

DOE OSTI2023

Integral and Separate Effects Test Facilities To Support Water Cooled Small Modular Reactors: A Review

This study reviews previous experimental facilities and test programs relevant to water-cooled reactor system design and analysis to meet regulatory compliances. This study aims to find the best solution for designing the required experiments, obtaining necessary test data, and verifying the developed computer code/models to support the new reactor design and development while minimizing cost and time while leveraging experiences from previous facilities to minimize. Nuclear reactor licensing requires supportive design, analysis, and experimental results to ensure the safety of the full-scale prototype reactor in regular operation, as well as during postulated accident scenarios. These reactor design analyses are generally performed using system codes and other associated simulation tools that require assessment, verification and validation using an appropriate experimental dataset. Experimental facilities used for reactor system safety analysis and system code assessments are categorized into integral effects test (IET) and separate effects test (SET) facilities. Further, these IET and SET experiments and studies use geometrically scaled systems to reproduce the prototype system behavior at a reasonable cost, albeit with some scaling-related distortions. The design challenge of these model facilities is to identify and minimize scaling distortions while reproducing the most important operational phenomena in steady-state operation and in postulated accident scenarios. Lessons learned from previous experimental facilities, models, and correlations can support the development of new multipurpose, scaled, hybrid, integrated, and modular experimental facilities for advanced light water-cooled small modular reactors (SMRs). Successful operation of these facilities can significantly reduce upfront reactor development and demonstration costs and time to deployment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Sequence-dependent self-assembly of supramolecular nanofibers in periodic dynamic block copolymers

Block copolymers (BCPs) can spontaneously self-assemble into various nanostructured morphologies which enable their diverse applications in selective membranes, polymer electrolytes, and optoelectronics. To expand the range of nanostructures accessible to block copolymers, we designed dynamic block copolymers (DBCPs) that combine the phase separation of traditional block copolymers with the supramolecular self-assembly of periodic dynamic polymers. We demonstrate that DBCPs synthesized with a periodic block sequence self-assemble into high aspect ratio supramolecular nanofibers with well-ordered PEG and PDMS domains, in contrast to those synthesized with a random sequence which do not form nanofibers but have disordered morphologies. The periodicity of the block sequence ensures regular placement of dynamic bonds along the polymer chain, which enables stacking of the hydrogen bonding units into ordered 1D supramolecular nanofibers and delays the onset of terminal flow by up to 60 °C compared to the random block sequence. The hierarchically assembled supramolecular nanofibers display complex mechanical and thermal phase behavior arising from the interplay between phase separation of the dissimilar polymer backbones and supramolecular interactions between dynamic bonds. Despite identical bulk composition, the periodic DBCPs demonstrate ionic conductivity values over two orders of magnitude higher than their random counterparts due to the formation of well-ordered, interconnected, high aspect ratio ion-transporting PEG domains. In conclusion, these results highlight the potential for DBCPs as an emerging material platform to achieve advanced, self-assembled nanostructured morphologies.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Regio‐Regular Polymer Acceptors Enabled by Determined Fluorination on End Groups for All‐Polymer Solar Cells with 15.2 % Efficiency

Abstract Polymerization sites of small molecule acceptors (SMAs) play vital roles in determining device performance of all‐polymer solar cells (all‐PSCs). Different from our recent work about fluoro‐ and bromo‐ co‐modified end group of IC‐FBr (a mixture of IC‐FBr1 and IC‐FBr2), in this paper, we synthesized and purified two regiospecific fluoro‐ and bromo‐ substituted end groups (IC‐FBr‐ o & IC‐FBr‐ m ), which were then employed to construct two regio‐regular polymer acceptors named PYF‐T‐ o and PYF‐T‐ m , respectively . In comparison with its isomeric counterparts named PYF‐T‐ m with different conjugated coupling sites, PYF‐T‐ o exhibits stronger and bathochromic absorption to achieve better photon harvesting. Meanwhile, PYF‐T‐ o adopts more ordered inter‐chain packing and suitable phase separation after blending with the donor polymer PM6, which resulted in suppressed charge recombination and efficient charge transport. Strikingly, we observed a dramatic performance difference between the two isomeric polymer acceptors PYF‐T‐ o and PYF‐T‐ m . While devices based on PM6:PYF‐T‐ o can yield power conversion efficiency (PCE) of 15.2 %, devices based on PM6:PYF‐T‐ m only show poor efficiencies of 1.4 %. This work demonstrates the success of configuration‐unique fluorinated end groups in designing high‐performance regular polymer acceptors, which provides guidelines towards developing all‐PSCs with better efficiencies.

Yu, Han↗

DOE OSTI2021

Regio‐Regular Polymer Acceptors Enabled by Determined Fluorination on End Groups for All‐Polymer Solar Cells with 15.2 % Efficiency

Yu, Han↗

DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Particulate Effluent Characterization (Final Report)

Understanding particulate emissions from nuclear facilities could help differentiate between normal nuclear operations and potential nuclear accidents or nuclear weapons tests. Oak Ridge National Laboratory (ORNL) is unique in that there are several types of nuclear facilities on site: an operating production reactor, radiochemical separation facilities, and a spallation neutron source. This project deployed a high-volume particulate air filter sampler to collect airborne particulate effluent from the nuclear facilities on site at ORNL. Collections occurred regularly from November 1, 2024, through June 30, 2025, and were analyzed via gamma spectroscopy in the laboratory. The radioisotope iodine-123 ( 123 I) was detected in several samples throughout the collection period. Detailed atmospheric transport modeling was performed on all detections for source attribution, and the most likely source of the 123 I was determined to be the Spallation Neutron Source. The project demonstrated the viability of ORNL as a test bed for effluent monitoring studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Fermions, quantum gravity, and holography in two dimensions

We study a model comprising N flavors of Kähler Dirac fermion propagating on a triangulated two-dimensional disk which is constrained to have a negative average bulk curvature. Dirichlet boundary conditions are chosen for the fermions. Quantum fluctuations of the geometry are included by summing over all possible triangulations consistent with these constraints. We show in the limit N → ∞ that the partition function is dominated by a regular triangulation of two-dimensional hyperbolic space. We use strong coupling expansions and Monte Carlo simulation to show that in this limit boundary correlators of the fermions have a power law dependence on boundary separation as one expects from holography. However, we argue that this behavior breaks down for any finite number of massive fields in the thermodynamic limit and quantum fluctuations of the bulk geometry drive the theory into a nonholographic phase. In contrast, for massless fermions, we find evidence that the boundary is conformal even for finite N . This is consistent with theoretical results in quantum Liouville theory. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2024

LATTE: Los Alamos TravelTime package based on Eikonal equation

This Fortran code focuses on traveltime computation and tomography based on eikonal equation. Specifically, the package provides three major functionalities: (1) forward modeling of traveltime from single-point or ensemble source based on factorized eikonal equation, (2) adjoint-state first-arrival traveltime tomography based on picked first arrival traveltime using steepest descent, conjugate gradient, or limited-memory BFGS inversion scheme, and (3) adjoint-state joint transmission-reflection tomography based on picked first-arrival and reflection traveltimes. The package applies to forward modeling and tomography based on traveltime in 2D and 3D isotropic regular-grid models. We name this package LATTE – Los Alamos TravelTime package based on Eikonal equation. * The code is for accompanying a journal paper under preparation. The paper will be submitted via LA-UR separately later.

Gao, Kai↗