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DOE OSTI2024

A First Search for Argon-Bound Neutron-Antineutron Oscillation using the MicroBooNE LArTPC

The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n ₋ n̄) oscillation. The n ₋ n̄ oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n ₋ n̄ oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n ₋ n̄ oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC

The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n – n̄) oscillation. The n – n̄ oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n – n̄ oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n – n̄ oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Solar Energy Innovation Network 2017-2024: Abbreviated Final Technical Report

This material is based upon work supported by the U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office under the Agreement/Award Number 32954 for Solar Energy Innovation Network (SEIN) Project, 2017-2024. SEIN is a dynamic program that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. In conjunction with its partner organizations, NREL implemented the program by providing research, analysis, and technical expertise directly to project teams and groups of teams (cohorts), by facilitating networked learning through cohorts and peer exchange, and by facilitating dissemination and replication of solutions and lessons learned among stakeholders across the U.S. with similar challenges.

14 SOLAR ENERGY↗

DOE OSTI2025

Influence of Initial fabric and water-wetting on particle fracture and force chain evolution in natural silica sand

Particle fracture has a significant influence on the engineering behavior of granular materials. However, the combined influence of the initial fabric and wetting on particle fracture and force chain evolution in granular materials, particularly in sands, remains insufficiently explored in the current literature. Here, in this paper, one-dimensional (1D) confined compression experiments were conducted on dry and wet specimens, and particle-level fracture was captured using three-dimensional (3D) in-situ Synchrotron Micro-Computed Tomography (SMT). A quantitative assessment of particle fracture revealed that wet specimens, which have disturbed fabric, exhibited a higher fracture percentage in comparison to dry specimens. Complementary 3D finite element (FE) simulations were performed using dry and wet (only solid fabric was considered and the influence of pore water is not considered) sand assemblies to assess interparticle contact forces and particle-scale stress distributions within the specimens. Force chain analysis was subsequently conducted, encompassing quantification of interparticle forces, characterization of force network structures, and monitoring the dynamic evolution of force chains under different strain levels. The results show that the specimens with disturbed fabric led to a more dynamic and less persistent force network, more fabric instability, and thus more reorganization of force chain structures. In addition, the frequent rearrangement of the force network in the presence of water (with reduced inter-particle contact friction) likely exacerbates localized stress concentrations, promoting failure in previously unengaged or weakly connected particles. The results reported in this paper offer a new insight into how the initial fabric and wetting cause different fracture behavior. The findings can also pave the way for more in-depth future investigations into the mechanics governing particle fracture in granular assemblies.

Finite element analysis↗

DOE OSTI2025

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3σ$) in the $Ω_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $Ω_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Prat, J. [Nordita; Royal Inst. Tech., Sodertalje; ↗

DOE OSTI2024

Clean Energy Workforce and Employment Gap Analysis in the Hill District of Pittsburgh, PA [Slides]

Through Communities LEAP, a community coalition focused on the Hill District neighborhood of Pittsburgh is working with a technical assistance provider network led by the National Renewable Energy Laboratory (NREL). The coalition includes community organizations, nonprofits, the city government, and the utility. Technical assistance provides analysis and information to support Hill District stakeholders in their goals to create informed residential energy efficiency and renewable energy transition strategies that improve housing conditions and lower energy bills, incorporate energy efficiency and renewable energy strategies into existing, community-driven development efforts; and generate quality local jobs. This presentation provides a summary of potential employment impacts of residential energy efficiency investments in the Hill District, aligned with NREL's housing stock analysis; a scan of existing energy efficiency and clean energy workforce and education stakeholders in and around the Hill District; and gaps and potential opportunities for new or expanded training to align with energy efficiency and clean energy goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Critical needs to close monitoring gaps in pan-tropical wetland CH 4 emissions

Global wetlands are the largest and most uncertain natural source of atmospheric methane (CH 4 ). The FLUXNET-CH 4 synthesis initiative has established a global network of flux tower infrastructure, offering valuable data products and fostering a dedicated community for the measurement and analysis of methane flux data. Existing studies using the FLUXNET-CH 4 Community Product v1.0 have provided invaluable insights into the drivers of ecosystem-to-regional spatial patterns and daily-to-decadal temporal dynamics in temperate, boreal, and Arctic climate regions. However, as the wetland CH 4 monitoring network grows, there is a critical knowledge gap about where new monitoring infrastructure ought to be located to improve understanding of the global wetland CH 4 budget. Here we address this gap with a spatial representativeness analysis at existing and hypothetical observation sites, using 16 process-based wetland biogeochemistry models and machine learning. We find that, in addition to eddy covariance monitoring sites, existing chamber sites are important complements, especially over high latitudes and the tropics. Furthermore, expanding the current monitoring network for wetland CH 4 emissions should prioritize, first, tropical and second, sub-tropical semi-arid wetland regions. Considering those new hypothetical wetland sites from tropical and semi-arid climate zones could significantly improve global estimates of wetland CH 4 emissions and reduce bias by 79% (from 76 to 16 TgCH 4 y -1 ), compared with using solely existing monitoring networks. Our study thus demonstrates an approach for long-term strategic expansion of flux observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Analysis and Overview of Hybrid Wired and Wireless Bi-Directional EV Charger Systems

Here, this paper analyzes and overviews hybrid wired and wireless bi-directional Electric Vehicle (EV) charging systems with a primary focus on resonant compensation methods that enable a unified power conversion architecture. Four compensation configurations based on series–series and LCC–LCC resonant networks are systematically evaluated for both wired transformer-based and wireless coupler-based operation. The analysis examines how coupling conditions, resonant component selection, and auxiliary compensation tuning influence voltage gain characteristics, resonant tank current magnitude and phase, and operating frequency requirements. Normalized frequency-domain results are presented to directly compare reactive current behavior and voltage regulation capability under wired and wireless operating conditions. A 60 kW bi-directional charger case study is used to demonstrate the feasibility of retaining a common hardware platform while accommodating distinct coupling scenarios through compensation tuning rather than structural modification. The presented results provide design-oriented insights into resonant network selection and compensation strategies for scalable and flexible hybrid EV charging systems.

Hybrid↗

DOE OSTI2024

Robust Self-Healing Adhesives Based on Dynamic Urethane Exchange Reactions

Thermoset polyurethanes (PUs) have been successfully reprocessed as covalent adaptable networks (CANs) by catalyzing carbamate exchange. Here we extend bond exchange beyond the internal network cross-links to create a dynamic urethane adhesive. Interfacing PU CANs to substrates with nucleophilic functional groups creates adhesives capable of reversible transcarbamoylation with the substrate, which has not been demonstrated previously by CAN adhesives. Two types of thermoset PU films were synthesized, one containing the green carbamate exchange catalyst Zr(tmhd)4 and the other containing no catalyst. Although otherwise identical in chemical and network properties, as indicated by FT-IR spectroscopy and dynamic mechanical thermal analysis (DMTA), the film containing catalyst showed dynamic bond exchange behavior through stress relaxation analysis. When evaluated as an adhesive, the CAN film exhibited self-healing properties and retained its adhesive strength for five cycles, which is attributed to reversible covalent bonding to the glass substrate. Furthermore, this work expands industrially relevant CANs to structural adhesives and demonstrates their potential value in an application that presently employs PUs as single-use materials.

adhesive↗

DOE OSTI2026

Single-cell proteomics of Arabidopsis leaf mesophyll reveals dynamic protein responses to water-deficit stress

Background The application of single-cell omics tools to biological systems can provide unique insights into diverse cellular populations and their heterogeneous responses to internal and external perturbations. Thus far, most single-cell studies in plant systems have been limited to RNA-sequencing approaches, which only provide indirect readouts of cellular functions. Results Here, we present a single-cell proteomics workflow for plant cells that integrates tape-sandwich protoplasting, piezoelectric cell sorting, nanoPOTS sample preparation, and ion mobility-based MS data acquisition method for label-free single-cell proteomics analysis of Arabidopsis leaf mesophyll cells. From a single leaf protoplast, over 3,000 proteins were quantified with high precision. The workflow is demonstrated to identify stress associated changes in protein abundance by analyzing 117 protoplasts from well-watered and water-deficit stressed plants. Additionally, we describe a new approach for constructing covarying protein networks at the single-cell level and demonstrate how single-cell protein covariation analysis can reveal previously unrecognized protein functions while also capturing stress-induced changes in protein–protein dynamics. Conclusions The label-free scProteomic approach presented here represents a significant advance through the demonstration of a facile protoplast isolation method combined with deep and precise proteomic coverage of Arabidopsis leaf mesophyll cell types. We believe this study will serve as an informative reference to future plant scProteomic investigations.

Arabidopsis↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

High-strength 3D printed poly(lactic acid) composites reinforced by shear-aligned polymer-grafted cellulose nanofibrils

This work demonstrates the application of pilot-scale surface functionalization of cellulose nanofibrils (CNFs) by aqueous grafting-through polymerization and subsequent spray drying in 3D printed poly(lactic acid) (PLA) composites. Grafted-CNF composites attain an ultimate tensile strength of 88 ± 3 MPa and a tensile modulus of elasticity of 7.8 ± 1.3 GPa in the printing direction at 20 wt% reinforcement loading. These increases, 42% and 139% over neat PLA, respectively, represent the strongest reported 3D printed CNF/PLA composite to date in the literature. The mechanisms behind these improvements are investigated by comparisons to neat PLA and unmodified spray-dried CNF/PLA controls using melt rheology, dynamic mechanical analysis, and assessment of the reinforcement dispersion. These experiments reveal that improved network formation and shear-induced alignment of the grafted CNFs facilitate the remarkable tensile properties of the printed composites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Interpretation of autoencoder-learned collective variables using Morse–Smale complex and sublevelset persistent homology: An application on molecular trajectories

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse–Smale complex and sublevelset persistent homology. Furthermore, this approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Time projection chamber for GADGET II

The established Gaseous Detector with Germanium Tagging (GADGET) detection system is used to measure weak, low-energy 𝛽-delayed proton decays. It consists of the Gaseous Proton Detector equipped with a MICROMEGAS (MM) readout to detect protons and other charged particles calorimetrically, surrounded by the Segmented Germanium Array (SeGA) for high-resolution detection of prompt 𝛾 rays. To upgrade GADGET's Proton Detector to operate as a compact time projection chamber (TPC) for the detection, three-dimensional imaging and identification of low-energy 𝛽-delayed single- and multiparticle emissions mainly of interest to astrophysical studies. A new high granularity MM board with 1024 pads has been designed, fabricated, installed, and tested. A high-density data acquisition system based on generic electronics for TPCs (GET) has been installed and optimized to record and process the gas avalanche signals collected on the readout pads. The TPC's performance has been tested using a 220 Rn 𝛼-particle source and cosmic-ray muons. In addition, decay events in the TPC have been simulated by adapting the attpcroot data analysis framework. Furthermore, a novel application of two-dimensional convolutional neural networks for GADGET II event classification is introduced. The optimization of data throughput is also addressed. The GADGET II TPC is capable of detecting and identifying 𝛼 particles as well as measuring their track direction, range, and energy. The extracted energy resolution of the GADGET II TPC using P10 gas is about 5.4% at 6.288 MeV ( 220 Rn 𝛼 events), computed using charge integration. Based on a systematic simulation study, we estimated the detection efficiency of the GADGET II TPC for protons and 𝛼 particles, respectively. It has also been demonstrated that the GADGET II TPC is capable of tracking minimum-ionizing particles (i.e., cosmic-ray muons). From these measurements, the electron drift velocity was measured under typical operating conditions. In addition to being one of the first generation of micropattern gaseous detectors (MPGDs) to utilize a resistive anode applied to low-energy nuclear physics, the GADGET II TPC will also be the first TPC surrounded by a high-efficiency array of high-purity germanium 𝛾-ray detectors. As a result, the TPC of GADGET II has been designed, fabricated, and tested and is ready for operation at the Facility for Rare Isotope Beams for radioactive-beam-line experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Low Activity Tritium Detection in CCDs Using Deep Learning Techniques

Here, this study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual approach utilizing both measured CCD data and detailed Geant4 simulations. Our analysis compares classical techniques with advanced deep learning methods, including convolutional neural networks (CNNs), autoencoders trained exclusively on tritium data, and preliminary studies on boosted decision trees (BDTs). The CNN, trained on mixed signal/background datasets, demonstrates superior classification performance, while the autoencoder shows the potential of unsupervised, background-agnostic strategies when background characteristics are poorly defined. These results highlight the excellent sensitivity achievable thanks to the background rejection made possible by information-rich CCD data, paving the way for improved portable tritium monitoring.

Autoencoder↗

DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗

DOE OSTI2026

Neutrino Physics with Deep Learning on NOvA

The NOvA experiment has made both νμ \nu_\mu disappearance and νe \nu_e appearance measurements in Fermilab's NuMI beam, and is working on cross section measurements using near detector data. At the core of NOvA's measurements is the use of deep learning algorithms for identification and reconstruction of the neutrino flavor and energy. These algorithms, used for the first time on NOvA in 2016, yielded large improvements in selection efficiency, and will be applied to our first anti-neutrino results to be released this year. Presented here is the extension of our deep learning efforts for identification of neutrino signal events, final state identification, single particle tagging, and reconstruction using instance segmentation techniques. We will describe the new implementations of modified Convolutional Neural Networks for anti-neutrino events, single particles and their performance for analysis final states selection, standard candle measurements, and reconstruction.

Psihas, Fernanda [Indiana U.]↗