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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Operational stability of mixed Sn–Pb perovskite solar cells: Mechanisms, mitigation strategies, and perspectives

Mixed Sn–Pb perovskites provide the 1.2–1.3 eV narrow-bandgap absorber needed for high efficiency of all perovskite tandems, but their deployment is limited by operational instability under light. This review synthesizes mechanistic origins and recent mitigation strategies for mixed Sn–Pb perovskite solar cells. The oxidation of Sn 2+ to Sn 4+ , often driven by iodine formation under light and bias, is the primary failure pathway; it creates Sn vacancies, self-doping, and nonradiative loss. Surface/grain-boundary defects, halide migration, reactive oxygen species, and interfacial redox at charge-transport layers, along with hole accumulation from poor band alignment, further accelerate Sn–Pb perovskite degradation. Here we survey recent stability advances across additive chemistry, surface and grain-boundary passivation, buried-interface redesign with modified or alternative hole transport layers, and solvent systems that preserve Sn 2+ and correct Sn–Pb speciation for scalable coating. Together, these recent advances have enabled devices to retain 80%–90% output for hundreds to over a thousand hours. Lastly, we provide our perspectives on further improving the operational stability of Sn–Pb perovskite and solar cells.

14 SOLAR ENERGY↗
DOE OSTI2025

Direct interpolative construction of the discrete Fourier transform as a matrix product operator

The quantum Fourier transform (QFT), which can be viewed as a reindexing of the discrete Fourier transform (DFT), has been shown to be compressible as a low-rank matrix product operator (MPO) or quantized tensor train (QTT) operator. However, the original proof of this fact does not furnish a construction of the MPO with a guaranteed error bound. Meanwhile, the existing practical construction of this MPO, based on the compression of a quantum circuit, is not as efficient as possible. We present a simple closed-form construction of the QFT MPO using the interpolative decomposition, with guaranteed near-optimal compression error for a given rank. This construction can speed up the application of the QFT and the DFT, respectively, in quantum circuit simulations and QTT applications. We also connect our interpolative construction to the approximate quantum Fourier transform (AQFT) by demonstrating that the AQFT can be viewed as an MPO constructed using a different interpolation scheme.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Power generation-cooling water Nexus: Impacts of cooling water shortage on power system operation - a simulation case study in Illinois, U.S

Cooling water shortage, frequently attributed to drought and heat waves, poses a significant threat to the operations of thermoelectric power plants and further poses a challenge for the entire power system and environmental stakeholders. Recognizing the critical nexus between power generation and cooling water availability and the potential ability of power generations to adjust generation schedules during cooling water shortages, this paper introduces a security-constrained unit commitment and economic dispatch model considering water-energy nexus. In specific, the model is augmented with a unit-level cooling water requirement (CWR) model and multi-level cooling water availability (CWA) constraints. The unit-level CWR model quantifies the cooling water withdrawal per MWh of power generation, taking into account factors such as thermoelectric generation technologies, cooling system technologies, and environmental parameters. The multi-level CWA constraints incorporate pump-level, plant-level, watershed-level, and forced minimum power constraints, utilizing data derived from actual-based cooling water shortage scenarios. Using a simulation case study in Illinois, United States, this research examines the reliability, economic, and environmental implications of cooling water shortages on power system operations. The results show that Illinois may experience 10-15% daily load curtailment and severe congestion between certain regions from the east to central during cooling water shortages, while once-through and wet-tower units experience a 52% and 17% reduction in power generation. In conclusion, overall cooling water withdrawal decreases by 24-38% as severity intensifies.

Cooling water shortage↗
DOE OSTI2026

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Material Recovery Facilities (MRFs) in the United States: Operations, revenue, and the impact of scale

An analysis was conducted using nationwide survey data to evaluate how material recovery facilities (MRFs) operations vary regionally and with scale. The survey characterized materials, processes, and energy use involved with operations, and revenue for recyclables. This is the first nationwide analysis of MRFs in the US that accounts for mass processed, energy consumed, and revenue. Of a population of 521 MRFs, 48 responses representing MRFs from five US regions were received and analyzed (9.2 % response rate). Responses were analyzed by size according to yearly mass of inbound materials (small: <1,000 Mg/year, medium: 1,000–10,000 Mg/year, and large: >10,000 Mg/year). Most MRFs identify as single-stream; source from residences; utilize tipping floors, picking lines, baling and warehousing; and are powered by electricity. Most revenue and inbound mass (>50%) came from fiber (cardboard and paper). Glass had little revenue, and plastics were difficult to transition to market. Percent residue ranged from 1-39%, averaged <20%, and increased as the mass of inbound material increased. Large MRFs reported more sources of material, employed advanced sorting technology, had greater plastics revenue (33% versus 5% for small MRFs), and had more market access for plastics compared to small MRFs. Large MRFs had two orders of magnitude less annual electricity consumption per Mg recyclables than small MRFs (5–90 kWh/Mg versus ∼300–550 kWh/Mg). Results demonstrate environmental and economic benefits of larger-scale MRFs, which could be implemented more broadly in the US through regional hub-and-spoke arrangements for collecting and processing recyclables, lowering energy consumption and increasing revenue for recyclables.

Hub-and-Spoke↗
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Identifying Adversarial Cyber-Activity in Operational Technology Environments Using Bayesian Networks

Critical infrastructure and other operational technology (OT) environments face increasing cybersecurity risks from adversarial behavior. This paper describes the development of a risk model using a Bayesian network to enhance the comprehension of observable cyber events caused by malicious activity in OT environments. The core of the Bayesian network is a process model that describes the stages of adversary behavior. The remainder of the model is based on the MITRE ATT&CK® for Industrial Control Systems (ICS) taxonomy, which includes tactics and techniques that may be used by the adversary. The observables provide evidence for adversary behavior through the intermediary technique and tactic nodes. One challenge in constructing this model is a lack of open-source data from cyber-attacks on OT systems. This paper discusses learning from limited data, the elicitation of expert opinion to construct the conditional probability tables when data is scarce, and the refinement of the most difficult conditional probabilities tables using several forms of sensitivity analyses. Finally, the Bayesian network is demonstrated using two historical case studies: the DarkSide ransomware attack on the Colonial Pipeline and the destructive cyberattack targeting the ThyssenKrupp blast furnace. Index Terms—Cybersecurity, industrial control systems, operational technology

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗
DOE OSTI2025

A Performance Portable, Fully Implicit Landau Collision Operator with Batched Linear Solvers

Modern accelerators use hierarchical parallel programming models that enable massive multithreading within a processing element (PE), with multiple PEs per device driven by traditional processes. Batching is a technique for exposing PE-level parallelism in algorithms that have traditionally run on MPI processes or multiple threads within a single process. Opportunities for batching arise in, for example, kinetic discretizations of magnetized plasmas where collisions are advanced in velocity space at each spatial point independently. This paper builds on previous work on a high-performance, fully nonlinear, Landau collision operator by batching the linear solver, as well as batching the spatial point problems and adding new support for multiple grids for multiscale, multispecies problems. An anisotropic relaxation verification test that agrees well with previously published results and analytical models is presented. The performance results from NVIDIA A100 and AMD MI250X nodes are presented with hardware utilization analysis for each architecture. Finally, the entire implicit Landau operator time advance is implemented in Kokkos for performance portability, running entirely on the device and is available in the PETSc numerical library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Light Water Reactor Sustainability Program: Use of Time Distributions to Predict Operator Procedure Performance in Dynamic Human Reliability Analysis

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) framework affords software capable of conducting human reliability analysis (HRA) using a dynamic approach built around operating procedures (OPs) from nuclear power plants (NPPs). Previous HUNTER reports document the development of this software tool, the coupling of HUNTER to the simulator code, the collection of operator performance data by using simulators to calibrate HUNTER models, and linking HUNTER to probabilistic risk assessment (PRA) software. The present report largely addresses two topics. The first is a new function in HUNTER called the HUNTER Procedure Performance Predictor (P3). HUNTER P3 uses HUNTER’s built in Monte Carlo tools featuring human performance variability to identify potential error traps in procedures. The second topic is time distribution analysis to generate time inputs for dynamic HRA. The current analysis was performed to investigate time distributions for task primitives, which are the minimum task unit of analysis used in dynamic HRA modeling. Using the time distribution data, the elapsed time for human actions in an extended loss of AC power (ELAP) scenario is then investigated. Time data and prediction are essential for modeling procedure performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Hydrogen Applications for Energy Transition in Port and Airport Operations at the Port Authority of New York and New Jersey

The Port Authority of New York and New Jersey (PANYNJ) is focused on achieving meaningful reductions in emissions as part of its environmental sustainability efforts. To reach its 2030 target for reducing Scope 1 and Scope 2 carbon dioxide equivalent (CO2e) emissions and its goal of net-zero emissions by 2050, PANYNJ is exploring a range of energy solutions, including hydrogen technologies. This report evaluates the potential role of hydrogen in reducing emissions across key operational areas: vehicles, equipment, stationary power, aviation propulsion, and marine propulsion. It examines hydrogen's technical feasibility, infrastructure needs, and economic implications within PANYNJ's operational context. The findings aim to inform decisions as PANYNJ transitions to cleaner energy sources and reduces environmental impacts. Based on the existing literature and stakeholders' feedback, the report outlines both opportunities and challenges associated with hydrogen integration, providing insights to guide PANYNJ's future sustainability initiatives.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

NRC Reactor Operating Experience Analysis and Trend Summary: 2024 Update

This report presents a summary of the Nuclear Regulatory Commission’s (NRC’s) reactor operating experience analyses with data through 2024 as well as the reliability and frequency trends identified in the 2024 update reports for the component performance studies, loss-of-offsite power analysis, initiating events analysis, and system studies provided on the NRC Reactor Operating Experience Results and Databases website (https://nrcoe.inl.gov/).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Costs of Exposure to Industrial Livestock Operations

Concentrated animal feeding operations, particularly hog and poultry farms, have expanded rapidly in North Carolina in recent decades. The air pollution and water contamination they generate cause many environmental and health problems for local communities. Using the universe of farm characteristics and housing transaction data in North Carolina, we recover hedonic estimates of property value impacts from exposure to these industrial livestock operations. Furthermore, our results show large and significant negative impacts on nearby home values, particularly when those properties depend on private wells.

Concentrated Animal Feeding Operation↗
DOE OSTI2024

Impact of Hydrological Data on Power System Operational Studies: Preprint

Hydropower is expected to play an important role in maintaining grid reliability and flexibility as the share of of variable renewable energy increases. While the current hydropower operational models have been studied and used widely, they haven't been updated for decades to meet new performance standards. For example, current steady state and dynamic models often neglect hydrological conditions, which may lead to unrealistic expectations when relying on hydropower for energy and ancillary services. To study this impact, a multi-timescale hydrological model was created by leveraging the National Renewable Energy Laboratory-developed Multi-timescale Integrated Dynamics and Scheduling (MIDAS) tool. Using MIDAS, we compare the impact of considering hydrological conditions in a day-ahead unit commitment (DAUC) schedule on the reduced 240-bus Western Interconnect (WI) test system under winter and summer case studies. We show that neglecting current hydrological conditions of hydropower plants in power system models can lead to an overestimation of hydropower capabilities, which could lead to power balancing issues. For example, power system DAUC simulation results of our reduced test system show that in the case where hydrological conditions are not considered in the model, an approximate 31% overestimation of hydropower capabilities occurs in the summer case and approximately 60% occurs in the winter case compared to what is available. Additionally, results show an underestimation of WI day-ahead power system generation costs by approximately $54M - $80M in the weekly summer scenario and $116M - $126M in the weekly winter scenario. This analysis helps to underscore the importance of considering hydrological data in power system operational studies.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
DOE OSTI2024

Improved Operational Stability of Blue Phosphorescent OLEDs by Functionalizing Phenyl‐Carbene Groups of Tetradentate Pt(II) Complexes

Abstract Stable and efficient deep‐blue organic light‐emitting diodes (OLEDs) are in high demand for display and lighting applications but are rarely reported due to their poor operational lifetimes. Herein, the study designs and synthesizes two novel N ‐heterocyclic carbene (NHC)‐based tetradentate Pt(II) complexes PtON5‐dtb and PtON5N‐dtb, and thoroughly investigate their electrochemical and photophysical properties. Functionalization of the NHC moieties can increase the metal‐to‐ligand charge transfer ( 1/3 MLCT) characters in their lowest triplet excited‐states, resulting in significantly shortened photoluminescent lifetimes and remarkably improved device performance. A deep blue OLED employing PtON5N‐dtb as an emitter exhibits a narrow spectral bandwidth with a full‐width at half maximum (FWHM) of 30 nm and a CIE y value of 0.17 and demonstrates a maximum external quantum efficiency (EQE) of 20.4% with a small efficiency roll‐off, which maintains a high EQE of 18.5% at 1000 cd m −2 . Moreover, the deep blue OLED also realizes a long‐measured operational lifetime LT 90 (time to 90% of the initial luminance) of 71 hours with an initial brightness of 1134 cd m −2 , corresponding to an estimated device lifetime LT 90 of 85 h at 1000 cd m −2 . This represented an eightfold lifetime improvement for PtON5N‐dtb‐based deep blue OLED compared to PtON7‐dtb in the same device setting.

Li, Guijie↗
DOE OSTI2025

Extreme Enhancement-Mode Operation Accumulation Channel Hydrogen-Terminated Diamond FETs with V th < -6 V and High on-Current

In this work, a new Field Effect Transistor device concept based on hydrogen-terminated diamond (H-diamond) is demonstrated that operates in an Accumulation Channel rather than a Transfer Doping regime. The FET devices demonstrate both extreme enhancement-mode operation and high on-current with improved channel charge mobility compared to Transfer-Doped equivalents. Electron-beam evaporated Al 2 O 3 is used on H-diamond to suppress the Transfer Doping mechanism and produce an extremely high ungated channel resistance. A high-quality H-diamond surface with an unpinned Fermi level is crucially achieved, allowing for the formation of a high-density hole accumulation layer by gating the entire device channel which is encapsulated in dual-stacks of Al 2 O 3 . Completed devices with gate/channel length of 1 µm demonstrate record threshold voltage < -6 V with on-current > 80 mA mm -1 . Carrier density and mobility figures extracted by CV analysis indicate a high 2D charge density of ≈ 2 × 10 12 cm -2 and increased hole mobility of 110 cm 2 V -1 s -1 in comparison with more traditional Transfer-Doped H-diamond FETs. These results demonstrate the most negative threshold voltage yet reported for H-diamond FETs and highlight a powerful new strategy to greatly improve carrier mobility and enable enhanced high power and high frequency diamond transistor performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Probing Electrode Transformation under Dynamic Operation for Alkaline Water Electrolysis

Alkaline water electrolyzers (AWEs) play a pivotal role in the realm of large-scale hydrogen production. However, AWEs face significant challenges in electrode degradation particularly under dynamic operating conditions, induced by reverse current phenomenon during frequent startup/shutdown. Herein, this study aims to rationalize the degradation mechanisms of AWEs under these conditions. A three-electrode membrane electrode assembly (MEA) setup is first utilized to decouple polarization behaviors of anode and cathode in AWEs. Following a proposed accelerated stress testing protocol, the setup allows for tracking individual electrode performance transformations during frequent reverse current operation. Integrating operando cell studies with in situ and post-mortem characterizations, it is showed that continuous formation of highly active species, nickel (oxy)hydroxides, improves the anode performance for oxygen evolution reaction. On the contrary, irreversible oxidation of nickel to β-nickel hydroxide results in a severe degradation of cathode, leading to material dissolution, poor electrical conductivity and loss of catalytic activity for hydrogen evolution reaction. These results provide insights in nickel-based electrode transformation mechanisms for alkaline water electrolysis and indicate that cathode with higher redox reversibility can potentially improve durability of AWEs under dynamic conditions.

08 HYDROGEN↗
DOE OSTI2024

Direct air capture with amino acid solvent: Operational optimization using a crossflow air‐liquid contactor

Direct air capture (DAC) is a negative emission technology for removing CO2 from the atmosphere to maintain the CO 2 level within a reasonable range so as to address greenhouse effects. In this study, the operational optimization of lab-scale DAC has been investigated using a crossflow air-liquid contactor loaded with a three dimensionally printed Gyroid packing structure and a potassium sarcosinate solvent. The effects of various parameters, including feed air flow rate, liquid solvent flow rate, contactor geometry, and ambient temperature, are examined. The results demonstrate that the Gyroid packing design achieves comparable CO 2 capture performance to conventional packed beds but with a significantly lower pressure drop of up to 77.8%, suggesting its potential as an efficient and cost-effective solution for gas–liquid contactors in DAC. Additionally, the study explores the climate impact on CO 2 capture performance and finds that as the air temperature increases from 35 to 95°F at a fixed relative humidity of 80%, the CO 2 capture rate increased from 23.2% to 46.8% with better stability. The research highlights the importance of optimizing contactor design and operational conditions to improve the CO 2 capture rate and feasibility of DAC systems as a negative emission technology for addressing greenhouse effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗