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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Self-assembly of co-continuous nanostructured copolymer templates with compositional and architectural dispersity

In our previous DOE-supported project (DE-SC0016208), we demonstrated that a particular architecture dubbed ‘randomly end-linked copolymer networks’ (RECNs) led to especially robust self-assembly of disordered cocontinuous nanostructures. Such structures, wherein both microphases are fully percolating in three dimensions, are attractive for a wide-variety of energy-relevant applications including purification membranes, separators in batteries and fuel cells, catalyst supports, and high surface area electrodes. In the current project we sought to substantially extend our fundamental understanding of how different types of randomness in blocky copolymer architecture contribute to the robust formation of disordered cocontinuous nanostructures. To do so, we filled in the sizable gap between the previously studied cases of randomly linked linear multiblock copolymers and RECNs by studying two intermediate cases: (i) random miktoarm star polymers, which contain dispersity in the number of the two chemically-incompatible strands located at each junction and therefore the preferred interfacial curvature of each polymer, and (ii) randomly branched copolymers, which also possess bridging strands that stretch across domains and apply local ‘elastic’ forces to the interfaces. In addition, we studied a different network architecture, statistically crosslinked copolymer networks, that is more straightforward to prepare using chain growth polymerization methods, enabling more facile formation of cocontinuous nanostructures. Finally, we extended our approaches from model systems based on polystyrene and poly(lactic acid) to copolymer architectures containing either (i) polysulfone, an engineering polymer that provides excellent mechanical properties for membrane applications, or (ii) polyacrylonitrile, providing a convenient route to nanoporous carbon materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗
DOE OSTI2020

Understanding Superionic Conductivity in Lithium and Sodium Salts of Weakly Coordinating Closo -Hexahalocarbaborate Anions

Solid-state ion conductors based on closo-polyborate anions combine high ionic conductivity with a rich array of tunable properties. Cation mobility in these systems is intimately related to the strength of the interaction with the neighboring anionic network and the energy for reorganizing the coordination polyhedra. In this paper, we explore such factors in solid electrolytes with two anions of the weakest coordinating ability, [HCB 11 H 5 Cl 6 ] – and [HCB 11 H 5 Br 6 ] – , and a total of 11 polymorphs are identified for their lithium and sodium salts. Our approach combines ab initio molecular dynamics, synchrotron X-ray powder diffraction, differential scanning calorimetry, and AC impedance measurements to investigate their structures, phase-transition behavior, anion orientational mobilities, and ionic conductivities. We find that M(HCB 11 H 5 X 6 ) (M = Li, Na, X = Cl, Br) compounds exhibit order–disorder polymorphic transitions between 203 and 305 °C and display Li and Na superionic conductivity in the disordered state. Through detailed analysis, we illustrate how cation disordering in these compounds originates from a competitive interplay among the lattice symmetry, the anion reorientational mobility, the geometric and electronic asymmetry of the anion, and the polarizability of the halogen atoms. These factors are compared to other closo-polyborate-based ion conductors to suggest guidelines for optimizing the cation–anion interaction for fast ion mobility. This study expands the known solid-state poly(carba)borate-based materials capable of liquid-like ionic conductivities, unravels the mechanisms responsible for fast ion transport, and provides insights into the development of practical superionic solid electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Thermal Conductivity of a Novel Salt-Hydrogel Complex as a Phase Change Material for Building Thermal Management

Use of electricity for space heating, ventilation and air conditioning in buildings may be better managed through temporary energy storage. Compared with electrochemical storage, the levelized cost of thermal energy storage systems can be lower depending on geographical location and thermophysical properties [1]. In any phase change material (PCM), the rate of absorption and release of thermal energy increases with higher thermal conductivity [2]. However, leading PCMs like paraffin waxes have thermal conductivities on the order of 0.1 W/mK. Glauber’s salt, a salt hydrate, possesses a relatively high thermal conductivity but suffers from supercooling and phase segregation. Research has shown that polymer-based composites with salt hydrates can prevent phase segregation and limit supercooling over hundreds of cycles [3] though the thermal conductivity of the composite may be reduced leading to low energy storage rates. Here, we report thermal conductivity measurements on polymer hydrogels which are stable in solutions of Glauber’s salt. To measure thermal conductivity of soft gels, we use a 3-omega method [4] where heat flows bidirectionally through a substrate below and through a gel placed on top. This suits measurements of both gels and liquids unlike transient plane heat source or transient hot wire methods. We report measurements of thermal conductivity of salt-hydrogel complexes as a function of temperature, cross-linking and salt concentration. In addition, we present modeling of thermal conductivity of sodium sulfate solutions with available theories. We finally employ polarized optical microscopy to observe inside the transparent hydrogel networks, showing that precipitating crystals may influence thermal conductivity measurements for high salt concentrations. Our thermal conductivity results combined with enthalpy of phase change, mass density and specific heat are essential to accurately design thermal storage systems for energy-efficient buildings.

composite, thermal conductivity↗
DOE OSTI2022

Design of a neutral thermal scattering (NeTS) module for hydrogen in light water

The accurate representation of thermal scattering law (TSL) data is integral to the design and characterization of many modern nuclear systems, particularly those using light water as a moderator/coolant. As a material-dependent distribution over energy-momentum phase space, the TSL may exhibit a variety of unique and relevant conditional (e.g., temperature, pressure) and compositional (e.g., porosity, stoichiometry, radiation damage) dependencies. Currently, there are various approaches to incorporate temperature dependence, which is especially important in coupled neutronic-thermal hydraulic simulations. In each approach, there is an inherent tradeoff between memory consumption and accuracy. Some techniques require tens to hundreds of MBs or more, while others fail to reproduce the underlying data to within 10% error over the considered input domain, despite having a reduced storage burden. This work aims to address both sides of the tradeoff simultaneously by implementing a novel deep learning (DL) approach to TSL representation. The neural thermal scattering (NeTS) concept, which is amenable to an arbitrary number of dependencies, is demonstrated via the inclusion of temperature dependence into a highly compact, highly accurate functional form of the multi- variate TSL (i.e., S(α, β, T)) for hydrogen in light water. Resulting storage requirements are on the order of 100 kB, and median and maximum percent deviations are on the order of 0.1% and 1%, respectively. These measures represent a step improvement over previous techniques. Notably, the developed neural network and feature methodology build on those employed in prior work on beryllium oxide. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Interfacial Electromechanics Predicts Phase Behavior of 2D Hybrid Halide Perovskites

Quasi-two-dimensional (2D) mixed-cation hybrid halide perovskites (A' 2 A N–1 M N X 3N+1 ; A' = large organic molecule with cationic group, A = [Cs + , CH 3 NH 3 + , HC(NH 2 ) 2 + ], M = [Pb, Sn, Ge], X = [I – , Br – , Cl – ]) have rapidly emerged as candidates to improve the structural stability and device lifetime of 3D perovskite semiconductor devices under operating conditions. The addition of the large A' cation to the traditional AMX 3 structure introduces several synthetic degrees of freedom and breaks M–X bonds, giving rise to peculiar critical phase behavior in the phase space of these complex materials. In this work, we propose a thermodynamic model parametrized by first-principles calculations to generate the phase diagram of 2D and quasi-2D perovskites (q-2DPKs) based on the mechanics and electrostatics of the interface between the A' cations and the metal halide octahedral network. Focusing on the most commonly studied methylammonium lead iodide system where A' is n-butylammonium (BA; CH 3 (CH 2 ) 3 NH 3 + ), we find that the apparent difficulty in synthesizing phase-pure samples with a stoichiometric index N > 5 can be attributed to the energetic competition between repulsion of opposing interfacial dipole layers and mechanical relaxation induced by interfacial stress. Our model shows quantitative agreement with experimental observations of the maximum phase-pure stoichiometric index (N crit ) and explains the nonmonotonic evolution of the lattice parameters with increasing stoichiometric index (N). This model is generalizable to the entire family of q-2DPKs and can guide the design of photovoltaic and optical materials that combine the structural stability of the q-2DPKs while retaining the charge carrier properties of their 3D counterparts.

14 SOLAR ENERGY↗
DOE OSTI2020

Family of Robust and Strongly Luminescent CuI-Based Hybrid Networks Made of Ionic and Dative Bonds

The CuI-derived inorganic–organic hybrid compounds are considered as promising phosphors for the lighting industry. Herein, exploiting N-monoalkylated hexaminium salts, [R-HMTA]X (R = Me, Et, Pr, and propargyl; X = Cl and I), as multibridging ligands, we have designed and synthesized a unique class of one-dimensional and two-dimensional hybrid CuI-materials. The reactions of these salts with CuI give rise to All-in-One (AIO) type compounds combining ionic and dative bonds between inorganic and organic components. The latter is formed by structurally unique inorganic [Cu x I y ] (y–x)– clusters, chains, or sheets interconnected through [R-HMTA] + cations via multiple Cu–N bonds. The so-designed compounds at ambient temperature exhibit tunable luminescence spanning from deep blue to red color (λ em = 430–625 nm) with microsecond lifetimes and the quantum efficiency of up to 78%. Remarkably, the AIO materials feature nontrivial excitation- (ED) and temperature-dependent (TD) luminescence, allowing their emission color to be finely adjusted from deep blue to red through changing the excitation wavelength and/or temperature. Based on the TD emission spectroscopy and theoretical calculations, a possible mechanism of the luminescence has been proposed. In conclusion, the very interesting luminescence characteristics coupled with good thermal and photostability render these AIO hybrid materials possible candidates for applications in energy-efficient lighting devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fluorinated polythiophenes with ester side chains for boosting the V OC and efficiency in non-fullerene polymer solar cells

As the power conversion efficiency (PCE) of organic photovoltaics (OPVs) approximates the 19 % threshold, wide bandgap (WBG) polythiophenes (PTs) have gained increasing attention due to their simple chemical structures and ease of synthesis, making them promising candidates for large-scale production. However, the benchmark polymer poly(3-hexylthiophene) (P3HT) is limited by its high-lying HOMO energy level, which restricts the open-circuit voltage (V OC ) in solar cells. Here, in this study, we introduce a novel series of PT derivatives (PDC8-T, PDC8-3T, PDC16-3T, and PDC16-3T-2F) featuring ester side chains designed to fine-tune electronic properties through a streamlined three-step synthesis. Additionally, we incorporated a π-spacer to reduce steric hindrance and elongated alkyl side chains to improve solubility and processability. Compared to P3HT, these PT derivatives demonstrate a significant reduction in HOMO energy levels, lowering by approximately 0.3–0.4 eV. Among them, PDC16-3T-2F—with fluorine atom substitution—achieves the lowest HOMO energy level, induces a coplanar molecular conformation, and enhances polymer aggregation behavior. We evaluated these PT derivatives in inverted non-fullerene bulk-heterojunction (NFA BHJ) OPVs. The PDC8-3T device showed a relatively low PCE of 0.69 %, with a V OC of 0.76 V, a short-circuit current density (J SC ) of 3.32 mA/cm 2 , and a fill factor (FF) of 27.3 %. In contrast, the PDC16-3T-2F device achieved an impressive PCE of 7.21 %, with a V OC of 0.85 V, a J SC of 14.60 mA/cm 2 , and an FF of 58.4 %. This remarkable improvement is attributed to the fluorine substitution, which not only enhances molecular orientation but also downshifts the HOMO energy level and further boosts the V OC . Hence, these molecular design strategies have led to a fibrillar bicontinuous interpenetrating network with optimal nanoscale phase separation within the active layer.

14 SOLAR ENERGY↗
DOE OSTI2023

Enhancing the Electrode Gravimetric Capacity of Li 1.2 Mn 0.4 Ti 0.4 O 2 Cathode Using Interfacial Carbon Deposition and Carbon Nanotube-Mediated Electrical Percolation

Mn-based cation disordered rocksalt oxides (Mn-DRX) are emerging as promising cathode materials for next-generation Li-ion batteries due to their high specific capacities and cobalt and nickel free characteristic. However, to reach the theoretical capacity, solid-state method synthesized Mn-DRX materials require activation via post-synthetic ball milling, typically incorporating more than 20 wt.% conductive carbon that adversely reduces the electrode level gravimetric capacity. To solve this issue, we firstly deposit amorphous carbon on the surface of the Li 1.2 Mn 0.4 Ti 0.4 O 2 (LMTO) particles to increase the electrical conductivity by a five order of magnitude. Although the cathode material gravimetric first charge capacity reaches 180 mAh/g, its highly irreversible behavior leads to a 70 mAh/g first discharge capacity. Subsequently, to ensure a good electrical percolation network, the LMTO material is ball milled with multi-wall carbon nanotube (CNT) to obtain a 78.7 wt.% LMTO active material loading in the cathode electrode (LMTO-CNT). As a result, a 210 mAh/g cathode electrode gravimetric first charge and 165 mAh/g first discharge capacity are obtained, compared to the respective capacity values of 222 mAh/g and 155 mAh/g for the LMTO ball milled with 20 wt.% SuperP C65 electrode (LMTO-SP). After 50 cycles, the LMTO-CNT delivers a 121 mAh/g electrode gravimetric discharge capacity, largely outperforming the 44 mAh/g value of the LMTO-SP. In conclusion, our study demonstrates that while ball milling is necessary to achieve the theoretical capacity of LMTO, a careful selection of the additive, such as CNT, effectively reduces the required carbon quantity to achieve a higher electrode gravimetric discharge capacity.

25 ENERGY STORAGE↗
DOE OSTI2023

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Abstract Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Materials Science↗
DOE OSTI2026

Point defects in semiconductors: Friends and foes for quantum technologies

Point defects in semiconductors are both a curse and a blessing in microelectronics: they enable the control of electrical conductivity through doping, yet can also act as trapping and recombination centers that degrade device performance. In quantum information science, defects play a similarly dual role. They can be harnessed as spin–photon interfaces enabling the coupling of electronic and nuclear spins to light and the creation of distributed entanglement for quantum networks or used as atomistic scale sensors for quantum sensing. At the same time, defects are a major source of decoherence for superconducting qubits, one of the leading quantum computing platforms. This article discusses how a deeper materials-level understanding of defects can guide the design of improved quantum devices for communication, sensing, and computation.

Zhu, Yizhi [Rice University, Houston, TX (United S↗
DOE OSTI2023

Demonstration of acoustic monitoring for structural health of microreactors: Through use of neural networks and resonant ultrasound spectroscopy

Nuclear microreactors prioritize modularity and portability and are intended to be a cost-effective technology for non-conventional nuclear markets. As such, the development of microreactors into a safe and feasible solution for energy security applications will necessitate the development of non-destructive technologies to monitor the integrity of inaccessible reactors components during operation. This demonstration applies linear and nonlinear acoustic techniques, in combination with machine learning, to detect and classify mechanical changes (stress and damage) in a test article which are broadly representative of potential operating challenges within a functioning microreactor. All necessary data has been collected for this demonstration, with minor experimental issues identified that can be addressed in follow-on work. Motivated by the expected conditions within a functioning microreactor, we have demonstrated our monitoring techniques on a core-block-like test article using an unstructured excitation source that approximates the noisy acoustic environment expected during reactor operation. At all stress states mechanically applied to the test article, a machine learning model using an artificial neural network was able to classify with 100% accuracy whether a 3D laser vibrometry point measurement was made on an intact or artificially defective test article. Further, model predictions about whether the defect interface was rough or smooth were 95% accurate, indicating the ability of acoustic techniques to recover defect characteristics. Resonant ultrasound spectroscopy (RUS) was also applied to the dataset to provide further quantitative insights about material properties. RUS analysis was ultimately hampered by several minor experimental and data issues, limiting results to certain cases for this demonstration. Last, analysis using nonlinear RUS exhibited sensitivity to changing levels of applied stresses for each intact and defective state. As presented in this demonstration, acoustic monitoring exhibits sensitivity to stress changes, which are of concern due to high thermal gradients expected during startup and operation. Further, our techniques distinguish between measurements made on intact and damaged test articles.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Similarity-based scaling networks for capacitive radio frequency discharge plasmas

We demonstrate similarity-based scaling networks for capacitive radio frequency (RF) plasmas, which extensively correlate discharge characteristics under varied conditions, incorporating the transition from original to similarity states. Based on fully kinetic particle-in-cell simulations, similar RF discharges in argon are demonstrated with three external control parameters (gas pressure, gap distance, and driving frequency) simultaneously tuned. A complete set of scaling pathways regarding fundamental discharge parameters is obtained, from which each plasma state finds its neighboring node with only one control parameter tuned. The results from this study provide a promising strategy for plasma multi-parameter mapping, enabling effective cross-comparisons, prediction, and manipulation of RF discharge plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Upsampling Monte Carlo reactor simulation tallies in depleted LWR assemblies fueled with LEU and HALEU using a convolutional neural network

Simulating nuclear reactor cores at the highest achievable spatial and energy resolution is critical in modeling these systems accurately. Increasing the resolution, however, can dramatically increase the memory and central processing unit time required to run simulations. A convolutional neural network was shown previously to accurately upsample tally results of simulated light water reactor assemblies fueled with fresh, low enriched uranium. Here, we show that a convolutional neural network can be used to upsample tally results in assemblies containing fresh and depleted fuel enriched from 1.6 to 19.9 atom percent. The network was trained using neutron flux tallies from simulations of light water reactor assemblies with a range of fuel and coolant temperatures and a diverse selection of geometries. Accurate predictions of flux tallies are possible even on test assemblies with geometries and burnup levels well outside the range of those present in the training and validation data. The network improves the data density by a factor of 8 over a broad range of light water reactor assemblies while incurring insignificant additional computational cost to a Monte Carlo simulation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Advanced Transmission Technologies –GETs and HPCs Session 3: HPCs and Building Actions Plans to Digital Assurance Risks

The third session of the Idaho National Laboratory’s (INL) Technical Assistance for Digital Assurance (TADA) program, held on November 11, 2025, centered on High Performance Conductors (HPCs) and the formulation of action plans to address digital assurance risks associated with Grid-Enhancing Technologies (GETs). This session convened experts from utilities, vendors, and government agencies to examine the technical, operational, and cybersecurity aspects of HPC deployment. Discussions highlighted the benefits of HPCs, such as their ability to rapidly increase transmission capacity using existing corridors, improve grid resilience, reduce system losses, and align with FERC Orders 2023 and 1920. Participants evaluated supply chain and digital assurance risks, including reliance on imported materials, limited domestic manufacturing capacity, workforce shortages, and traceability issues. The session also emphasized the importance of digital trust, integration-layer cybersecurity, and unified risk frameworks, introducing tools like intrusion detection systems, encryption, zero trust networking, and firmware integrity. Recaps of earlier workshops on Dynamic Line Ratings (DLRs), Advanced Power Flow Control (APFC), and Transmission Topology Optimization (TTO) underscored institutional barriers and integration challenges. Action plans were proposed to mitigate issues such as inconsistent cybersecurity practices, SBOM usage, supply chain visibility, operator trust, and misaligned incentives. Additionally, INL presented its supply chain risk management tools and Cyber-Informed Engineering (CIE) principles to support secure procurement and system design. The session concluded with a commitment to share key takeaways, incorporate cohort feedback into future policy development, and continue collaborative engagement through upcoming pilot activities. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Rapid Quantum Ground State Preparation via Dissipative Dynamics

Inspired by natural cooling processes, dissipation has become a promising approach for preparing low-energy states of quantum systems. However, the potential of dissipative protocols remains unclear beyond certain commuting Hamiltonians. This work provides significant analytical and numerical insights into the power of dissipation for preparing the ground state of noncommuting Hamiltonians. For quasi-free dissipative dynamics, including certain 1D spin systems with boundary dissipation, our results reveal a new connection between the mixing time in trace distance and the spectral properties of a non-Hermitian Hamiltonian, leading to an explicit and sharp bound on the mixing time that scales polynomially with system size. For more general spin systems, we develop a tensor network-based algorithm for constructing the Lindblad jump operator and for simulating the dynamics. Using this algorithm, we demonstrate numerically that dissipative ground state preparation protocols can achieve rapid mixing for certain 1D local Hamiltonians under bulk dissipation, with a mixing time that scales logarithmically with the system size. We then prove the rapid mixing result for certain weakly interacting spin and fermionic systems in arbitrary dimensions, extending recent results for high-temperature quantum Gibbs samplers to the zero-temperature regime. Together, these results show that dissipation can be a powerful tool for ground state preparation, with potential applications across condensed matter physics, quantum materials science, and beyond.

decoherence↗
DOE OSTI2020

Resilient U.S. Land Ports of Entry

The continued operation of Land Ports of Entry (LPOE), managed by the Customs and Border Protection (CBP) and General Services Administration% is vital to the U.S. economy and security. Border faculties are included in the Department of Homeland Security (DHS) Government Facilities Sector2, one of the 16 critical infrastructures "whose assets, systems, and networks, whether physical or virtual, are considered so vital to the United States that their incapacitation or destruction would have a debilitating effect on security, national economic security, national public health or safety, or any combination thereof.'" Specifically, disruptions to the flow of border crossing traffic, in the form of closures or increased border crossing wait times, impact the economy and security of all countries involved. This paper describes a process for analyzing and improving the resilience of U.S. Land Ports of Entry. For LPOE, the team believes that energy resilience is the primary objective due to the complete reliance on the e-manifest system and the increasing use of Multi-Energy Portals (MEPs). Emanifests are part of CPB's Automated Commercial Environment (ACE). They document several key pieces of information about cargo vehicles wishing to cross the border into the United States and are submitted before arriving at the port. Vehicles can be flagged for more invasive inspection based on the content of the e-manifest. MEPs are a non-intrusive inspection (NII) technology used to scan the contents of the cargo. Together MEPs and ACE serve an important role in aiding CBP with their mission to protect "the public from dangerous people and materials", and "enabling legitimate trade and travel.'" To analyze resilience of a port, the team would need to understand the port's current energy usage, which systems depend on energy and what backup systems exist, and any emergency operation plans that dictate how systems are operated in the event of a power outage. The team would also need to determine the design basis threats (DBTs) for the LPOE which could include natural disasters, manmade events, and accidents. The magnitudes of the DBTs are calculated and are then translated to expected impacts on the infrastructure and systems at the port. With this information gathered, existing LPOE models developed here at Sandia National Laboratories could be extended to support decisions about resilience. Current models are implemented in FlexSim, a 3rd party discrete event simulator. FlexSim provides 3-D visuals of physical layout that can reveal valuable insights, allows input to be variable (e.g. time it takes to interact with the CBP officer at primary inspection can vary) so that a whole range of possibilities can be captured in the results, and can be used to collect user-defined output metrics. Current LPOE models focus on cargo vehicle traffic, and process changes caused by the installation of new drive-through MEPs. Extending them to address resilience questions would require the addition of key pieces of information learned during the resilience analysis including critical systems, failure rates, and process changes for when failures occur. The primary output metric for current models is border crossing wait time. Additional metrics would also be added to the model to gain a more complete understanding of impacts related to resilience, for example, MEP scan rate. Once complete, the model could be used to analyze the effectiveness of mitigation strategies representing some future state.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Conjugated Polyelectrolyte-Based Complex Fluids as Aqueous Exciton Transport Networks

The ability to assemble artificial systems that mimic aspects of natural light-harvesting functions is fascinating and attractive for materials design. Given the complexity of such a system, a simple design pathway is desirable. Here, we argue that associative phase separation of oppositely charged conjugated polyelectrolytes (CPEs) can provide such a path in an environmentally benign medium: water. We find that complexation between an exciton–donor and acceptor CPE leads to formation of a complex fluid. We interrogate exciton transfer from the donor to the acceptor CPE within the complex fluid and find that transfer is highly efficient. We also find that excess molecular ions can tune the modulus of the inter-CPE complex fluid. Even at high ion concentrations, CPEs remain complexed with significantly delocalized electronic wavefunctions. In conclusion, our work lays the rational foundation for complex, tunable aqueous light-harvesting systems via the intrinsic thermodynamics of associative phase separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗