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DOE OSTI2025

Enhancing Power Resilience for Remote Communities: A Comprehensive Renewable Energy Solution for Itbayat Island

The island of Itbayat, Philippines, faces significant challenges in maintaining a reliable and resilient power supply due to its current reliance on a vulnerable power distribution system managed by a local electric cooperative. The existing infrastructure, which includes diesel generators and a radial network configuration with some above-ground lines, is highly susceptible to frequent typhoons and adverse weather conditions. These factors, combined with inadequate staffing and high operational costs, result in frequent power outages that disrupt daily life and hinder economic development. This white paper proposes a comprehensive solution to enhance the resilience and reliability of Itbayat's power system by integrating renewable energy sources, specifically solar photovoltaic (PV) systems, battery storage, and a microgrid controller. The proposed solution aims to reduce dependency on diesel fuel, optimize energy use, and provide a sustainable and robust power supply for the island. Key components of the solution include: 1. Solar PV Installation: Deploying solar PV panels to harness abundant solar energy, reducing reliance on diesel fuel. 2. Battery Storage Systems: Installing battery storage to store excess solar energy and ensure a continuous power supply during low solar generation periods. 3. Microgrid Controller: Implementing a microgrid controller to manage and optimize the integration of solar PV, battery storage, and existing diesel generators. The proposed solution addresses several critical issues, including system vulnerability, generator dependency, and operational inefficiencies. By adopting this innovative approach, Itbayat Island can achieve a more resilient, efficient, and sustainable energy infrastructure, ensuring a stable power supply for its residents and enhancing overall energy security.

14 SOLAR ENERGY↗

DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗

DOE OSTI2026

Unraveling Defect-Dependent Conductivity-Type Switching in CuFe 2 O 4 for Enhanced Photoelectrocatalytic Reduction of Benzaldehyde

Photoelectrocatalytic (PEC) reduction provides a sustainable route for upgrading biomass-derived feedstocks with reduced energy requirements, yet remains largely unexplored beyond hydrogen evolution and CO 2 reduction due to the scarcity of stable photocathodes. Here, we report a defect-engineered CuFe 2 O 4 photocathode that enables directly quantified PEC reduction of benzaldehyde to benzyl alcohol using a singlecomponent, earth-abundant oxide. By controlling annealing temperature and oxygen partial pressure, CuFe 2 O 4 is systematically tuned from n-type to p-type conductivity. Electrochemical measurements, X-ray and ultraviolet photoelectron spectroscopy, and firstprinciples defect calculations collectively show that oxygen-rich annealing conditions suppress deep donor-type oxygen vacancies while stabilizing shallow acceptor-type copper vacancies, resulting in enhanced hole concentration and improved charge transport. In a mixed acetonitrile/water electrolyte employing 1,4-benzoquinone as a redox mediator, the optimized CuFe 2 O 4 photocathode achieves stable photoelectrochemical operation over 18 h under continuous illumination with a benzyl alcohol production rate of 2.57 μmol/h at −0.50 V vs Ag/AgNO 3 , corresponding to a Faradaic efficiency of 51.3%. This PEC approach lowers the required applied potential by ∼1 V compared to traditional electrocatalytic methods, offering a more energy-efficient route for carbonyl reduction. These findings establish CuFe 2 O 4 as a viable photocathode platform for sustainable photoelectrocatalytic organic transformations under mild reaction conditions.

benzaldehyde reduction↗

DOE OSTI2024

Best Practices for Smart Grid-Interactive Efficient Building Ready Performance Contracts

Grid-interactive efficient building (GEB) measures reduce costs and optimize energy use for additional grid services by coordinating building energy loads and providing continuous demand management. Incorporating GEB energy conservation measures (ECMs) in performance contracts is reliant upon multiple factors. These factors include site selection with utility tariffs and incentives favorable to GEB, the identification of GEB as a priority in the initial stages of the contracting process, integration of GEB within comprehensive performance contracts with multiple other ECMs, and careful consideration of GEB measurement and verification (M&V) for energy savings performance contracts (ESPCs) and performance assurance for utility energy service contracts (UESCs).

building energy loads↗

DOE OSTI2026

Parametric optimization of PCM-enhanced underground thermal energy storage for buildings in moderate cold climates

This study presents a novel underground thermal energy storage (UTES) system designed for space heating in buildings located in moderate cold climates. The proposed UTES features a borehole with a depth of 25 ft. (7.62 m) and a diameter of 3 ft. (0.91 m), containing two helical pipe loops-one for discharge fluid and another for recharge fluid-and a thermally enhanced phase change material (PCM) layer that provides high energy storage capacity. The system requires daily thermal recharging using a low-grade heat source for a few hours and delivers continuous heating at a nearly constant discharge rate without significant performance degradation over a 24-h period. This study demonstrates that the optimized UTES, when recharged with hot fluid at an inlet temperature of 40 degrees C for 6 h daily, can provide a continuous heat discharge rate of approximately 2.9 kW for 24 h or 3.4 kW if operated under a shorter discharge period of 15 h (17:00-08:00). With a round-trip efficiency of 72-97%, the proposed UTES offers an efficient and reliable solution for short- and long-duration thermal energy storage technology, making it a promising technology for cold climates.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Optimizing 2D passivation for enhancing performance of fully air-processed carbon electrode-based perovskite solar cells

Air-processed carbon-based perovskite solar cells (C-PSCs) offer scalable and cost-effective photovoltaic manufacturing but face efficiency loss compared to metal-contact perovskite solar cells. Surface passivation of three-dimensional (3D) perovskites with two-dimensional (2D) perovskite layers has emerged as a promising strategy to enhance device performance. However, the mechanisms by which 2D perovskites more effectively improve C-PSC efficiency and stability remain underexplored. This study investigates the efficacy of 2D/3D heterostructures using n-hexylammonium bromide (C6Br), phenethylammonium iodide (PEAI), and n-octylammonium iodide (OAI) as surface passivators for C-PSCs. C-PSCs treated with C 6 Br achieved a champion power conversion efficiency (PCE) of 21.0%. This enhancement is attributed to superior defect passivation, improved charge extraction, and suppressed non-radiative recombination. Transient ion-drift characterization demonstrates that C 6 Br and OAI reduce ionic conductivity by 2–3 orders of magnitude, correlating with enhanced operational stability under continuous illumination. Our findings highlight the role of short-chain bromide cations (C 6 Br) in optimizing halide-mediated defect healing and interfacial band alignment, positioning 2D-passivated C-PSCs as viable competitors to conventional metal-contact perovskite solar cells.

14 SOLAR ENERGY↗

DOE OSTI2024

Adaptive immersed isogeometric level-set topology optimization

Here, this paper presents for the first time an adaptive immersed approach for level-set topology optimization using higher-order truncated hierarchical B-spline discretizations for design and state variable fields. Boundaries and interfaces are represented implicitly by the iso-contour of one or multiple level-set functions. An immersed finite element method, the eXtended IsoGeometric Analysis, is used to predict the physical response. The proposed optimization framework affords different adaptively refined higher-order B-spline discretizations for individual design and state variable fields. The increased continuity of higher-order B-spline discretizations together with local refinement enables direct control over the accuracy of the representation of each field while simultaneously reducing computational cost compared to uniformly refined discretizations. A flexible mesh adaptation strategy enables local refinement based on geometric measures or physics-based error indicators. These adaptive discretization and analysis approaches are integrated into gradient-based optimization schemes, evaluating the design sensitivities using the adjoint method. Numerical studies illustrate the features of the proposed framework with static, linear elastic, multi-material, two- and three-dimensional problems. The examples provide insight into the effect of refining the design variable field on the optimization result and the convergence rate of the optimization process. Using coarse higher-order B-spline discretizations for level-set fields promotes the development of smooth designs and suppresses the emergence of small features. Moreover, adaptive mesh refinement for state variable fields results in a reduction of overall computational cost. Higher-order B-spline discretizations are especially interesting when evaluating gradients of state variable fields due to their higher inter-element continuity.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Fuel Behavior Implications of Reactor Design Choices in Pressurized Water SMRs

Small pressurized water reactors (PWRs) can feature boron free operation, natural circulation mode, reduced height assemblies and/or long refueling cycles. This paper attempts to explore core design optimization for each of these design evolutions. In consequence, five core design layouts are developed incorporating boron free operation with continuous control rods insertion, natural circulation with low burnup/low power density design, natural circulation with high burnup/low power density design, forced circulation with standard core power density design, and forced circulation with high power density design. These cores’ performance is compared to a standard 4-loop PWR. The design process aims to improve the fuel cycle cost under safety constraints through core design optimization using CASMO4E/SIMULATE3 reactor physics codes and FRAPCON4.1 fuel performance assessment tool. Core modeling assumes standard 17x17 PWR fuel assemblies loaded with low enriched uranium (LEU) up to 5wt% or LEU+ (i.e., below 10wt% enrichment) pellets with gadolinium oxide (Gd2O3) as the burnable poison. Satisfactory core and fuel performances are obtained for all the designed cores under steady state and considered overpower transients. For low power density operation, long cycle lengths are achieved reaching a 2.5- and a 5-year cycles and peak rod-average burnup is pushed to 83 MWd/kgU. Other cycle lengths are maintained at 18 months. Boron free operation exhibits the ability to achieve longer cycle lengths at the cost of higher peaking factors leading to high local power and fuel temperatures which prevents sizable power uprates and is deemed uneconomical. Fuel assembly height reduction allows coolant velocity retrofit which enables higher core power density without violating structural integrity of the fuel assembly. As a result, a core power density of 123 kW/l is reached where total cladding hoop strain becomes the limiting parameter.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Determining Optimal Magnetometer Configuration on MAGIS-100

Long-baseline atom interferometers such as the Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) require stringent control and continuous characterization of background magnetic fields and spatial gradients to prevent systemic phase shifts that mimic ultralight dark matter or gravitational wave signatures. Because direct sensor placement within the ultra-high vacuum beam pipe is infeasible, in-situ magnetic field monitoring relies on external sensor arrays situated in the surrounding annular region. This work demonstrates a field reconstruction framework for a 5.3-meter MAGIS-100 modular section using finite-element Opera simulations. Transverse magnetic fields are expanded using a cylindrical multipole framework as informed by Fermilab’s Muon g-2 experiment, with magnetometer array configurations optimized via Fisher information matrix D-optimality. Inverting external sensor readings through a Gauss-Newton scheme recovers interior tube fields across distinct axial positions. In the discontinuity-averse uniform region (slice pair P4), the model achieves sub-noise-floor performance with a cross-validated root-mean-square error (RMSE) of $6.7227 \times 10^{-4}\text{ A/m}$ ($0.845\times$ sensor noise floor) and an interior field coefficient of variation of $1.71\%$. An elbow criterion in the Fisher bounds establishes $n_{\text{max}} = 2$ as the optimal multipole truncation order to prevent noise amplification from over-parameterization, with $n_{\text{max}} = 3$ (sextupole) order chosen for analysis to demonstrate further complexity and cross-pair comparison. Furthermore, analytical differentiation of the fitted multipole coefficients yields dense spatial maps of the transverse Jacobian gradient matrix $\nabla \mathbf{H}$ along with propagated $1\sigma$ uncertainty bounds across the beam region ($r \le 2.75\text{ in}$). This operational framework confirms that external magnetometer arrays can reliably monitor magnetic field uniformity and spatial gradients along the 100-meter flight path given appropriate sampling for any complexity order.

Appleby, Darwin [William Rainey Harper Coll.] (ORC↗

DOE OSTI2026

Determining Optimal Magnetometer Configuration on MAGIS-100

Appleby, Darwin [William Rainey Harper Coll.] (ORC↗

DOE OSTI2025

Conceptual design of ELM control coils for the TCABR tokamak

An upgrade of the Tokamak à Chauffage Alfvén Brésilien (TCABR) is being designed to make it capable of creating a well controlled environment where the impact of resonant magnetic perturbation (RMP) fields on edge localised modes can be addressed over a wide range of (i) plasma shapes, (ii) divertor configurations, (iii) RMP coil geometries and (iv) perturbed magnetic field spectra. To this end, a unique set of in-vessel RMP coils is being designed and, in this work, their conceptual design is presented. This unique set of coils is composed of three toroidal arrays of coils on the low field side and three toroidal arrays of coils on the high field side. Each of these six toroidal arrays is composed of 18 coils thus allowing for the creation of RMP fields with toroidal mode numbers n ≤ 9 and with increased control of the poloidal mode number spectrum. To study dynamical effects of RMP fields of different toroidal mode numbers, all rotating simultaneously with different velocities, each of the 108 RMP coils will be powered independently by power supplies that can provide voltages of up to 4 kV and electric currents of up to 2 kA, with frequencies varying continuously from 0Hz up to 10kHz. A set of physical criteria were used to determine the optimal coil geometry and their respective number of turns to reduce the coil currents and voltages during operation with alternate current. Further, the conceptual design was carried out using both the vacuum approach (no plasma response) and the single-fluid response approach, which accounts for the response of a linear, single-fluid, visco-resistive plasma calculated using the M3D-C 1 code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Multi-parametric analysis for mixed integer linear programming: An application to transmission upgrade and congestion management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. Here, we compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

ODU Optimization of Nb3Sn supttering on SRF structures

High efficiency Superconducting radio-frequency (SRF) cavities are the key technology to cost-effective Continuous Wave (CW) SRF accelerators. Recent advances in Nb3Sn coatings on the inner surface of SRF cavities showed the potential for this material to significantly cut the capital and operating cost of SRF accelerators. The best results are presently achieved with Nb3Sn films coated by using the so-called vapor diffusion technique. Several alternative approaches have been and are being investigated such as electrochemical deposition, bronze routes, and sputtering. Among these techniques, magnetron sputtering is a versatile technique, which has been demonstrated to deposit Nb3Sn films on SRF cavities. The technique requires further development to compete with the results demonstrated on the cavities coated with the vapor diffusion techniques. The team to develop the novel technique includes a graduate student from Old Dominion University (ODU) and other experts. ODU Prof. Elsayed-Ali directs the graduate student to coat samples and cavities in the new coating system and analyze samples with different surface science techniques. Dr. Grigory Eremeev from Fermilab assists with 2.6 GHz cavities for coating, cavity characterization at cryogenic temperature, and result analysis. The research is funded by the Office of High Energy Physics at the US Department of Energy at both Fermilab and ODU

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Dense, continuous membrane labeling and expansion microscopy visualization of ultrastructure in tissues

Abstract Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines an innovative membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validate the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supports the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrate the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yields ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

Science & Technology - Other Topics↗

DOE OSTI2024

Exploring Multidimensional Spatial-Temporal Hydropower Operational Flexibilities by Modeling and Optimizing Water-Constrained Cascading Hydroelectric Systems

Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.

13 HYDRO ENERGY↗

DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗

DOE OSTI2026

DIVA/DeviceEditor v6.1.2

DIVA is an end-to-end DNA design and construction management platform that streamlines how researchers design, build, and receive sequence-verified DNA constructs. Through a web-based BioCAD interface (DeviceEditor), researchers independently design DNA constructs and submit them to a centralized queue with a single action. Designs progress transparently through standardized states which allow researchers to track status and access finished constructs via a central DNA repository. Submitted designs are reviewed by dedicated staff for feasibility and optimization, reducing costly failures and improving downstream execution. Automated DNA assembly software optimizes construction strategies by reusing existing parts where possible and sourcing synthetic DNA only when needed. Standardized, sequence-agnostic assembly methods enable many independent constructs to be built in parallel using lab automation, dramatically increasing throughput. High-throughput next-generation sequencing is used to verify construct accuracy, with flexible platforms selected based on task requirements. Throughout the process, detailed success and failure data are captured and analyzed, enabling continuous improvement of assembly protocols. Compared to traditional, manual DNA construction workflows, DIVA offers higher scalability, transparency, reproducibility, and data-driven optimization.

Plahar, Hector [Lawrence Berkeley National Laborat↗