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DOE OSTI2022

Electrolytic Renewable Fuel Production Optimal Operation Investigation - H2@Scale: Cooperative Research and Development Final Report, CRADA Number CRD-19-00818

This work explored the optimal design and operation of electrolytic hydrogen production from renewable power. While there are many financial incentives for renewable hydrogen, this work investigated the effects of Low Carbon Fuel Standards (LCFS) on the hydrogen breakeven cost. To this end, this project developed optimized operational strategies for electrolytic hydrogen facilities in the California. Four specific model projects were used as the basis for the analysis. Three model projects will be located in California and one in Texas or the Midwest to be determined during the project. All projects include interconnection to the natural gas grid as a method of transport for the product fuel and the hydrogen cases will also consider alternative modes of transport. The operational optimization will maximize project return through fuel production and grid services based on future scenarios for the value of each. The effort will rely on modeling tools developed by NREL and the UCI for grid modeling as well as the integrated resource planning tool (IRP), RESOLVE, which is the official IRP tool used by the California Public Utility Commission (CPUC). The development of this project can help to inform future hydrogen deployment, policy and regulation makers, research and development decisions, and private and public investment.

08 HYDROGEN↗

DOE OSTI2025

LAMP Technical Readiness Evaluation Report

An internal preliminary evaluation of Critical Technology Elements (CTEs) for the LANSCE Modernization Project (LAMP) was completed in 2023. This included determining corresponding Technical Readiness Levels (TRLs) for all subsystems using the criteria of DOE G 413.3-4A, Technical Readiness Assessment Guide. This revised report includes a summary of the recent design modifications required to meet the project Key Performance Requirements (KPPs), some of which may reduce technical risk to the project. These recent design modifications include: • Further optimization of the low-energy and medium-energy beam transport regions (LEBT and MEBT, respectively), including relocation of various functional elements (ie choppers, kickers, and bunchers). • An additional H - ion source to separate ion-source function based on beam delivery requirements. • A high-repetition-rate pulsed kicker magnet to select/merge the two H ion beams into a common low-energy beam transport. • Modification and further optimization to a more conventional RFQ design. Performance of the RFQ has been optimized to deliver the required three types of beams while meeting the project KPPs. • The addition of a second chopper in the medium-energy beam transport (MEBT) line to reduce the required pulser voltages. The scope of the evaluation was limited to the project Work Breakdown Structure (WBS) elements as defined for the RFQ Injector and Drift Tube Linac (DTL) systems only. Integration of Instrumentation and Controls (I&C) and Safety Systems was not considered, although specific technologies as related to the RFQ and DTL systems were included. Other elements of the project such as Shielding, System Design, Technical Management, and additional facility integration needed to enable off-line testing and pre-installation commissioning were also not evaluated. Each technical subsystem element was evaluated for technical readiness, however, not all were found to meet the criteria for a CTE. Three subsystem elements were determined to meet the CTE criteria. Their associated TRLs are summarized in the table below. These subsystem elements of the project have the lowest technical readiness due to either being new, novel or modified, requiring additional R&D before being capable of meeting the project Key Performance Parameters (KPPs) and subsystem requirements, or present technology exists but has not yet been demonstrated in a relevant environment. All other subsystems were determined to have a TRL of 8, indicating that actual operating systems exist having similar performance requirements as needed for LAMP. Details of the technical readiness evaluation for each subsystem is given in the following sections of this report.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Impact of Stabilizing Cations on Lithium Intercalation in Tunneled Manganese Oxide Cathodes

Stabilizing cations such as K + , Ba 2+ , and Ag + are known to provide charge neutrality and enhance structural stability in low-cost tunneled manganese dioxide (MnO 2 ) cathodes for Li ion batteries. However, a fundamental understanding of the role of these cations in the electrochemical performance of tunneled MnO 2 cathodes remains unclear, especially at low stabilizing cation concentrations. Here, we employ density functional theory (DFT + U) calculations to reveal the impact of stabilizing potassium cation (K + ) concentration on the structural stability, electronic properties, and kinetics of lithium transport in 2 x 2 tunneled manganese oxide (α-K y Mn 8 O 16 , at y = 0, 1, and 2) battery cathodes during lithium intercalation. Specifically, we provide insights into the effect of K + ions on several critical factors governing the electrochemical storage performance of tunneled MnO2 cathodes, including (a) energetically favorable Li+ host sites, (ii) Li + and electron transport capabilities, (iii) optimal intercalation pathways, crystal distortion, microstructural stability, and tunneled-to-layer phase transformation as a function of lithium content, and (iv) cell output voltage profile. Interestingly, we find that low K + concentrations (y ≤ 1) yield partially cation-deficient tunnels in the MnO 2 cathode. Such unique tunnel structures in the cathode enable (a) low kinetic barriers for Li transport, (b) excellent thermodynamic stability of the tunneled structure even at a high Li + loading (up to ~ 0.625 Li/Mn), and (c) good electronic conductivity facilitated by Jahn-Teller distortions; all of which are critical for achieving high capacity batteries with enhanced rate capability. Additionally, these results provide perspectives to design low-cost transition metal oxide cathodes for high-performance Li-ion batteries with excellent cycle life.

25 ENERGY STORAGE↗

DOE OSTI2026

LAMP Technical Readiness Evaluation Report: LAMP-ENG-RPT-003 (Revision 2)

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Discovering Reactant Supply Pathways at Electrode/PEM Reaction Interfaces Via a Tailored Interface-Visible Characterization Cell

In situ and micro-scale visualization of electrochemical reactions and multiphase transports on the interface of porous transport electrode (PTE) materials and solid polymer electrolyte (SPE) has been one of the greatest challenges for electrochemical energy conversion devices, such as proton exchange membrane electrolyzer cells (PEMECs), CO 2 reduction electrolyzers, PEM fuel cells, etc. Here, an interface-visible characterization cell (IV-CC) is developed to in situ visualize micro-scaled and rapid electrochemical reactions and transports in PTE/SPE interfaces. Taking the PEMEC of a green hydrogen generator as a study case, the unanticipated local gas blockage, micro water droplets, and their evolution processes are successfully visualized on PTE/PEM interfaces in a practical PEMEC device, indicating the existence of unconventional reactant supply pathways in PEMs. Further comprehensive results reveal that PEM water supplies to reaction interfaces are significantly impacted with current densities. Here these results provide critical insights about the reaction interface optimization and mass transport enhancement in various electrochemical energy conversion devices.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms

We report optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Strong Potential Gradients and Electron Confinement in ZnO Nanoparticle Films: Implications for Charge-Carrier Transport and Photocatalysis

We report that zinc oxide (ZnO) nanomaterials are promising components for chemical and biological sensors and photocatalytic conversion and operate as electron collectors in photovoltaic technologies. Many of these applications involve nanostructures in contact with liquids or exposed to ambient atmosphere. Under these conditions, single-crystal ZnO surfaces are known to form narrow electron accumulation layers with few nanometer spatial penetration into the bulk. A key question is to what extent such pronounced surface potential gradients can develop in the nanophases of ZnO, where they would dominate the catalytic activity by modulating charge-carrier mobility and lifetimes. Here, we follow the temperature-dependent surface electronic structure of nanoporous ZnO with photoemission spectroscopy to reveal a sizable, spatially averaged downward band bending for the hydroxylated state and a conservative upper bound of < 6 nm for the spatial extent of the associated potential gradient. This nanoscale confinement of conduction-band electrons to the nanoparticle film surface is crucial for a microscopic understanding and further optimization of charge transport and photocatalytic function in complex ZnO nanomaterials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications

Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure, input velocity, bed porosity, column length, and radius were optimized to minimize the capture cost. After optimization, a sensitivity analysis revealed the complex interplay between the decision variables and their effect on the specific energy and cost of removing the CO 2 . We optimized the capture cost while taking into account the trade-off between energy consumption and productivity. The resulting minimum capture cost was determined to be 265.2 $/t-CO 2 , which aligns with expected values reported in the literature. Numerical results suggest the effectiveness of the optimization strategies applied, and underscore the importance of simultaneous decision variable selection in improving the performance in direct air capture processes. We also extend the modeling approach to a 2D axisymmetric model to better visualize CO₂ uptake and temperature profiles, revealing significant radial gradients during the regeneration step. As a main drawback, this enhanced model comes with a computational cost approximately 40 times higher than that of the 1D model.

Adsorption-desorption process↗

DOE OSTI2021

Optimized molecular aggregation via incorporating fluorinated unit in the polymer donor for 17.3% efficiency organic solar cells

Regulating molecular aggregation state via chemical modification of photovoltaic materials to optimize the blend morphology is an effective and challenging strategy to improve the photovoltaic performance of organic solar cells (OSCs). Here, in this work, we demonstrate a random terpolymerization strategy by incorporating 1,3-bis(5-bromo-4-fluorothiophen-2-yl)-5,7-bis(2-ethylhexyl)-4H,8H-benzo[1,2-c:4,5-c']bisthiophene-4,8-dione (BFT, 30 mol%) unit with fluorinated π-bridges into the molecular backbone of PM6 and develop a terpolymer PF1. Benefiting from the strong electronegativity of fluorine atom and noncovalent interaction of F---S, the energy levels, molecular ordering, and aggregation properties of the polymer donors are precisely regulated. Notably, in the active layer film based on PF1: Y6, the π-π stacking distance can reduce to 3.51 Å, which is significantly smaller than that of PM6:Y6 (3.65 Å) and boosts the effective exciton dissociation and charge transport, thereby effectively optimize the photovoltaic properties of the related devices. As a result, the PF1:Y6-based OSCs achieved a power conversion efficiency (PCE) of up to 17.3%, which is much higher than that of the controlled OSCs based on PM6:Y6 (PCE = 16.2%). These results demonstrate high-efficiency OSCs enabled by regulating molecular aggregation via terpolymerization strategy.

14 SOLAR ENERGY↗

DOE OSTI2023

Exploring Demographic Factors Behind the User Preferences in Ridesharing Services

Ridesharing is an increasingly prevalent form of transportation. Personal and pooled rideshare services are the two modes of ridesharing available on the market. While personal rideshare features higher time efficiency and better service experience, especially in terms of privacy, pooled rideshare has better energy efficiency and can reduce traffic congestion. To encourage users to utilize pooled rideshare, it is crucial to understand the factors behind preferences between pooled and personal rideshare services. This study conducted a survey study on a sample of 2,884 participants from the U.S. The stated-choice method was employed to collect the usage rate of pooled rideshare in designed scenarios. A detailed analysis of pooled rideshare usage rates among participants from different demographic backgrounds was performed. The results demonstrated demographic factors related to variations in pooled rideshare usage rates. Findings from this study explore the impacts of demographic factors on people's choices in ridesharing and provide important information for the modeling and optimization of ridesharing transportation systems.

Su, Haotian↗

DOE OSTI2021

Processable High Electron Mobility {pi}-Copolymers via Mesoscale Backbone Conformational Ordering

The synthesis and experimental/theoretical characterization of a new series of electron-transporting copolymers based on the naphthalene bis(4,8-diamino-1,5-dicarboxyl)amide (NBA) building block are reported. Comonomers are designed to test the emergent effects of manipulating backbone torsional characteristics, and density functional theory (DFT) analysis reveals the key role of backbone conformation in optimizing electronic delocalization and transport. The NBA copolymer conformational and electronic properties are characterized using a broad array of molecular/macromolecular, thermal, optical, electrochemical, and charge transport techniques. All NBA copolymers exhibit strongly aggregated morphologies with significant nanoscale order. Copolymer charge transport properties are investigated in thin-film transistors and exhibit excellent electron mobilities ranging from 0.4 to 4.5 cm(2) V-1 s(-1). Importantly, the electron transport efficiency correlates with the film mesoscale order, which emerges from comonomer-dependent backbone planarity and extension. These results illuminate the key NBA building block structure-morphology-bulk property design relationships essential for processable, electronics-applicable high-performance polymeric semiconductors.

n‐↗

DOE OSTI2025

Directed Self-Assembly of the Organic Semiconductor C8-BTBT-C8 in Anodic Aluminum Oxide Nanopores

Controlling the self-assembly of organic semiconductors at the nanoscale is critical for advancing high-performance electronic and photonic devices, yet it remains challenging due to their intrinsic anisotropic crystallization and sensitivity to processing conditions. Here, in this study, we demonstrate that cylindrical nanoconfinement within anodic aluminum oxide membranes provides a versatile platform to precisely tune the molecular orientation and phase behavior of the prototypical organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT-C8). Combining temperature-dependent high-resolution synchrotron X-ray scattering with optical birefringence measurements, we uncover that confinement geometries (pore diameters 25–180 nm) and surface chemistry govern the emergence of distinct smectic A textures, featuring molecular layers either parallel or perpendicular to the pore axis. The competition between axial and radial smectic layering is modulated by pore size, surface hydrophilicity, and thermal history, enabling reversible control over domain orientations and transitions between liquid crystalline and crystalline states. Notably, nanoconfinement stabilizes the smectic phase over an expanded temperature range compared to bulk, while inducing complex multidomain configurations owing to geometric constraints and anchoring conditions. Our results elucidate fundamental mechanisms by which anisotropic nanoscale confinement directs the self-organization of highly conjugated organic molecules, with implications for optimizing directional charge transport and anisotropic optical responses in organic–inorganic hybrid nanoarchitectures. This study establishes nanoconfinement as a powerful strategy to engineer morphology and functional properties in organic semiconducting materials with nanoscale precision.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Establishing the Role of Operating Potential and Mass Transfer in Multicarbon Product Generation for Photoelectrochemical CO 2 Reduction Cells Using a Cu Catalyst

There is increasing interest in the possibility of photoelectrochemical (PEC) reduction of CO 2 to C 2+ products; however, the criteria for maximizing PEC solar-to-C 2+ (STC 2+ ) rates are not well understood. We report here a continuum-scale model of PEC CO 2 reduction (CO 2 R) on Cu in 0.1 M CsHCO 3 and use it to optimize the design and operating conditions for generating C 2+ products. Furthermore, we demonstrate that the potential-dependent product distribution of CO 2 R on Cu requires operating near the potential that maximizes C 2+ generation rates ($V$ id ), unlike PEC water splitting, which desires operation at the maximum photocurrent density. Because of this requirement, the criterion for a high STC 2+ rate includes high-photocurrent semiconductors with photovoltages near $V$ id and low series resistance. The STC 2+ rate in these systems is enhanced by optimal CO 2 transport and exhibits low sensitivity to dirunal solar irradiance variations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Overlook the Spatial Variability of Miscanthus-Derived Sustainable Aviation Fuel

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. Here, this work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate that GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Ohnishi, Masato [University of Tokyo (Japan); Inst↗

DOE OSTI2025

Observing differential spin currents by resonant inelastic X-ray scattering

Controlling spin currents, that is, the flow of spin angular momentum, in small magnetic devices, is the principal objective of spin electronics, a main contender for future energy-efficient information technologies. A pure spin current has never been measured directly because the associated electric stray fields and/or shifts in the non-equilibrium spin-dependent distribution functions are too small for conventional experimental detection methods optimized for charge transport. Here we report that resonant inelastic X-ray scattering (RIXS) can bridge this gap by measuring the spin current carried by magnons—the quanta of the spin wave excitations of the magnetic order—in the presence of temperature gradients across a magnetic insulator. This is possible due to the sensitivity of the momentum- and energy-resolved RIXS intensity to minute changes in the magnon distribution under non-equilibrium conditions. Furthermore, we use the Boltzmann equation in the relaxation time approximation to extract transport parameters, such as the magnon lifetime at finite momentum, essential for the realization of magnon spintronics.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Integral Nuclear Data and Benchmarking Needs for Fusion Energy Systems

Fusion energy systems are currently being designed and optimized using radiation transport codes. To deal with the unique environment inside a fusion-based system, many of these designs incorporate novel materials able to withstand the high radiation fields, ensure adequate cooling and thermal protection, and produce tritium. Validation plays a vital role in building trust in the predictive power of these models and computational methods. Validation of a code consists of modeling documented real-world experiments and comparing the code-predicted response to the measured response. Adequate validation requires measured responses from real-world experiments, also known as integral data, that mimic the system being designed, including materials, impinging radiation, and temperature, among other variables. The most trusted integral data are experimental responses that have been through a rigorous benchmarking process that develops a recommended computational model and evaluates all experimental uncertainties. Finally, there are a few research groups around the world that have been producing integral data for fusion applications, but a substantial investment is needed to address the unique validation needs of the fusion community.

Fusion↗