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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unveiling the Role of In Situ Al 2 O 3 Passivation in Molecular-Ink-Processed CuIn(S,Se) 2 Photovoltaics

Here, we report on the optimization of in situ passivation of ink-based CuIn(S,Se) 2 thin-film solar cells via controlled incorporation of Al 2 O 3 in CuIn(S,Se) 2 films by the addition of Al(NO 3 ) 3 to the molecular ink precursor. For this purpose, the Al/(Al + In) (AAI) metal ratio was varied from 0.05 to 0.30. We observe that the efficiency of the cells made of Al 2 O 3 -incorporated CuIn(S,Se) 2 is consistently higher than those without Al 2 O 3 , especially due to an improvement in open-circuit voltage (V OC ) and fill factor (FF), for all tested AAI ratios. With an AAI of 0.05, a maximum efficiency of 11.2% and an average efficiency of 8.5% (measured across 18 cells) was achieved, compared to 8.5% maximum efficiency and 6.5% average efficiency for Al-free CuIn(S,Se) 2 . Furthermore, we find that cells made of Al 2 O 3 -incorporated CuIn(S,Se) 2 with an AAI of 0.2 show a narrow distribution in the photovoltaic performance, indicating higher reproducibility and higher FF. Energy-dispersive X-ray spectroscopy shows that, at AAI = 0.2, Al 2 O 3 is distributed more homogeneously at the surface of the Al 2 O 3 -incorporated CISSe. Capacitance-voltage measurements reveal a reduced defect density by incorporation of Al 2 O 3 , which could be partly responsible for the higher V OC . Furthermore, using detailed surface analysis with various X-ray and electron spectroscopy methods, we derive chemical and electronic structure information from the surface. With ultraviolet photoelectron (UPS) and inverse photoemission spectroscopies (IPES), the electronic band gap of the CuIn(S,Se) 2 thin-film surface is found to increase from 1.22 to 1.88 eV (+-0.12 eV) with Al 2 O 3 incorporation. This is accompanied by a clear reduction of the conduction band spike at the CdS/CISSe interface due to Al 2 O 3 addition, as derived by both UPS and IPES as well as temperature-dependent V OC measurements.

14 SOLAR ENERGY↗
DOE OSTI2020

Universal and versatile morphology engineering via hot fluorous solvent soaking for organic bulk heterojunction

After explosive growth of efficiency in organic solar cells (OSCs), achieving ideal morphology of bulk heterojunction remains crucial and challenging for advancing OSCs into consumer market. Herein, by utilizing the amphiphobic nature and temperature-dependent miscibility of fluorous solvent, hot fluorous solvent soaking method is developed to optimize the morphology with various donor/acceptor combinations including polymer/small-molecule, all-polymer and all-small-molecule systems. By immersing blend film into hot fluorous solvent which is utilized as liquid medium with better thermal conductivity, the molecular reorganization is accelerated. Furthermore, fluorous solvent can be miscible with the residue of chloroform and chloronaphthalene above upper critical solution temperature. This mixed solvent diffuses around inside the active layer and selectively promotes molecular reorganization, leading to optimized morphology. Compared to widely-used thermal annealing, this approach processed under mild conditions achieves superior photovoltaic performance, indicating the practicality and universality for morphological optimization in OSCs as well as other optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2021

An industrial perspective on catalysts for low-temperature CO 2 electrolysis

Electrochemical conversion of CO 2 to useful products at temperatures below 100 °C are nearing the commercial scale. Pilot units for CO 2 conversion to CO are already in testing. Units to convert CO 2 to formic acid are projected to reach pilot scale in the next year. Further, several investigators are starting to observe industrially relevant rates of the electrochemical conversion of CO 2 conversion to ethanol and ethylene with the needed hydrogen coming from water. In each case, Faradaic efficiencies of 80% or more and currents above 200 mA/cm –2 can be reproducibly achieved. In this study we describe the key advances in nano catalysts that lead to the impressive performance, indicate where additional work is needed and provide benchmarks that others can use to compare their results.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Equity‐aware power distribution system restoration

Abstract The efficient, reliable, and resilient supply of electricity has become essential for social and economic well‐being of the modern society. However, more frequent occurrence of extreme weather events has exposed inequity in the planning and operation practices of power distribution systems, evidenced in higher vulnerability and longer power interruptions for some parts of the grid as compared to others. This paper proposes an equity‐aware power distribution system restoration model in an effort to ensure a more equitable yet resilient power distribution operation after outages. To this end, the proposed equity‐aware distribution system restoration model balances the efficiency of the restoration operation and the equitable allocation of distributed energy resources among affected customers after an outage, while prioritizing the critical infrastructure (e.g. hospitals). The results demonstrate the effectiveness of the proposed framework to ensure a more equitable restoration process as measured by the proposed fairness and restoration performance indices.

Engineering↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗
DOE OSTI2023

Investigating boosted decision trees as a guide for inertial confinement fusion design

Inertial confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modeling and exploration of the experimental parameter space were essential to deepening our understanding of the mechanisms that degrade and amplify the neutron yield. The growing prevalence of machine learning in fusion studies opens a new avenue for investigation. Here in this paper, we have applied the Gradient-Boosted Decision Tree machine-learning architecture to further explore the parameter space and find correlations with the neutron yield, a key performance indicator. We find reasonable agreement between the measured and predicted yield, with a mean absolute percentage error on a randomly assigned test set of 35.5%. This model finds the characteristics of the laser pulse to be the most influential in prediction, as well as the hohlraum laser entrance hole diameter and an enhanced capsule fabrication technique. We used the trained model to scan over the design space of experiments from three different campaigns to evaluate the potential of this technique to provide design changes that could improve the resulting neutron yield. While these data-driven model cannot predict ignition without examples of ignited shots in the training set, it can be used to indicate that an unseen shot design will at least be in the upper range of previously observed neutron yields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

The number of catalytic cycles in an enzyme’s lifetime and why it matters to metabolic engineering

Metabolic engineering uses enzymes as parts to build biosystems for specified tasks. Although a part’s working life and failure modes are key engineering performance indicators, this is not yet so in metabolic engineering because it is not known how long enzymes remain functional in vivo or whether cumulative deterioration (wear-out), sudden random failure, or other causes drive replacement. Consequently, enzymes cannot be engineered to extend life and cut the high energy costs of replacement. Guided by catalyst engineering, we adopted catalytic cycles until replacement (CCR) as a metric for enzyme functional life span in vivo. CCR is the number of catalytic cycles that an enzyme mediates in vivo before failure or replacement, i.e., metabolic flux rate/protein turnover rate. We used estimated fluxes and measured protein turnover rates to calculate CCRs for ~100–200 enzymes each from Lactococcus lactis, yeast, and Arabidopsis. CCRs in these organisms had similar ranges (<10 3 to >10 7 ) but different median values (3–4 × 10 4 in L. lactis and yeast versus 4 × 10 5 in Arabidopsis). In all organisms, enzymes whose substrates, products, or mechanisms can attack reactive amino acid residues had significantly lower median CCR values than other enzymes. Taken with literature on mechanism-based inactivation, the latter finding supports the proposal that 1) random active-site damage by reaction chemistry is an important cause of enzyme failure, and 2) reactive noncatalytic residues in the active-site region are likely contributors to damage susceptibility. Enzyme engineering to raise CCRs and lower replacement costs may thus be both beneficial and feasible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A high-fidelity building performance simulation test bed for the development and evaluation of advanced controls

We present an open-source building performance simulation test bed, the Advanced Controls Test Bed (ACTB), that interfaces high-fidelity Spawn of EnergyPlus building models, with advanced controllers implemented in Python. Additionally, the ACTB leverages the Building Optimization Testing and Alfalfa platforms for managing simulations, providing an external clock, a representational state transfer (REST) application programming interface (API), and key performance indicators for evaluating the effectiveness of control strategies. The REST API allows the development of external controllers programmed in languages such as Python, which provides flexibility and a rich choice of scientific libraries for designing control sequences. We present three test cases based on the U.S. Department of Energy's Reference Small Office Building to demonstrate the ACTB's capabilities: (a) rule-based controls compliant with ASHRAE Guideline 36 control sequences; (b) an economic model predictive control implemented using do-mpc; and (c) a deep Q-network reinforcement learning agent implemented using OpenAI Gym.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Advanced co-simulation framework for assessing the interplay between occupant behaviors and demand flexibility in commercial buildings

With buildings contributing significantly to electricity usage, enabling demand flexibility becomes a challenge, especially when accounting for occupant comfort. This study introduces an innovative co-simulation framework integrating multiple models: heating, ventilation, and air conditioning (HVAC) system, building zone load, indoor airflow, supervisory control, and occupant comfort and behavior. Uniquely, this framework allows for a comprehensive and dynamic analysis of building systems and occupant interactions in demand response events. Using this framework, we conducted a case study using a typical small office building model. Specifically, we focused on three areas: (1) the impact of indoor airflow modeling on energy use, occupant comfort, and behaviors forecasting, (2) the impact of occupant behaviors on demand flexibility, and (3) occupant comfort and behaviors under demand response events. Key performance indicators such as energy use, flexibility factor, durations of occupant discomfort and occupant behaviors were analyzed. Our findings indicated variations in energy usage and occupant comfort within demand flexibility events, marked by uncertainty boundaries, with variability in demand shedding up to 57.9%. Here, we concluded that this framework is suitable for analyzing typical commercial buildings and their HVAC systems in terms of demand flexibility potential under the impact of occupant behaviors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Overcoming time and complexity limitations in molecular dynamics investigations of equilibrium melting

Abstract A hybrid Monte-Carlo molecular-dynamics method for determining solidus and liquidus compositions in multicomponent systems is presented that overcomes both the time limitations in conventional molecular dynamics that prevent the evolution of distinct solid and liquid compositions via diffusion and the complexity challenge that prevents use of thermodynamic assessment in systems of many components. This hybrid method is validated in the Cu–Ni system against an independent assessment of solidus and liquidus compositions based on the regular solution model. Strategies for efficient mapping of different phase diagrams, based on the thermodynamic parameter T 0 , the temperature at which two phases of the composition X 0 have equal free energies, are presented and then demonstrated for the copper-nickel fully miscible system and the gold–silicon eutectic system. A calculation of the solidus and liquidus sampled during the equilibrium melting of equiatomic CrMnFeCoNi is performed, indicating that this method has potential to be extended to the study of many component alloys.

Au-Si↗
DOE OSTI2022

Radiological HEPA Filter 10-year Lifetime Evaluation in Research Facilities

High-efficiency particulate air (HEPA) filters are widely employed by nuclear facilities to remove radiological particulate matter from their effluent exhaust streams. The purpose of this study is to evaluate the relationships between the 10-year HEPA filter lifetime deployment and its other performance indicators. This 10-year-long endeavor to collect and analyze data regarding the service life of HEPA filters at the Pacific Northwest National Laboratory began in 2010. A set of HEPA filters were selected and have been surveyed and analyzed at least annually to verify compliance with permit conditions. The study suggests the frequency of filter replacement should be based on the actual operational requirements, such as fume hood face velocity and/or efficiency test results, instead of on the prescribed filter “age limit” of 10 years from the date of manufacture (e.g., birth date) when operating under dry conditions. The study has now been completed, and over the past decade all the HEPA filters have been replaced, due to either technical issues as listed in this report or the previously recommended filter “age limit” of 10 years as prescribed by the oversight bodies. Experimentally determined failure rates are also determined from the data set and can be used to estimate the chances of HEPA filters surviving 15, 20, or even 30 years.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Boosting the External Quantum Efficiency of AlGaN-Based Metal–Semiconductor–Metal Ultraviolet Photodiodes by Electrode Geometry Variation

Fast aluminum-gallium-nitride–based metal–semiconductor–metal ultraviolet photodiodes were successfully designed, fabricated, and characterized using conventional photolithography techniques. Various electrode geometries were fabricated to investigate the influence of metal contact shapes on the device performance indices with emphasis on the response speed and bias-voltage–independent efficiency. Furthermore, based on the independently measured Hall mobility and electric field, we evaluated the carrier transit time of the devices as 1.31 ps with bias-voltage–independent external quantum efficiency of 1198% in photoconductive mode at 19.5 V and 70% at 60 V for n -doped and intrinsic devices, respectively.

42 ENGINEERING↗
DOE OSTI2021

Field-Array Benchmark of Commercial Bifacial PV Technologies with Publicly Available Data

We present results for a 75-kW field array deployed with rows of 5 different commercially available bifacial technologies. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed in the 10-row field. High accuracy string-level DC monitoring and module-level measurements have been recorded for six months. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. A custom-module to measure shading loss from the torque-tube was installed. One month data shows up to 6% irradiance non-uniformity. A fixed-tilt test-site with 3 years of bifacial data is also presented, with a bifacial gain of 3%.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

User-Centric Communication With Aerial Network for 6G: A Reinforcement Learning Approach

Meeting the diverse needs of user verticals requires innovative cellular architectures that can offer additional degrees of freedom to provide on-demand services. The terrestrial user-centric radio access network (UC-RAN) stands out as an excellent choice for this purpose. However, a drawback of UC-RAN is its tendency to prioritize high-priority verticals, often resulting in a subpar quality of experience for low-priority verticals. This issue is particularly exacerbated in hotspot areas. Here, to address this problem, we introduce an aerial network integrated with terrestrial UC-RAN to provide coverage to users which are not served by the terrestrial network. Furthermore, we analyze the impact of key configuration and optimization parameters (COPs), such as location, transmit power, altitude, and beamwidth of aerial base stations (ABSs) on system key performance indicators (KPIs), such as coverage, latency satisfaction, average spectral efficiency, and energy efficiency. We formulate a robust multiobjective function to maximize these KPIs without biasing toward any specific KPI(s). Finally, we propose a deep reinforcement learning optimization framework based on the state-of-the-art soft actor-critic algorithm to control ABS COPs and optimize system KPIs. Experimental evaluations demonstrate that the proposed optimization framework can converge to near-optimal solutions derived from the pseudo brute force in a few thousand epochs.

6G↗
DOE OSTI2022

Real-Time Hardware-in-The-Loop Implementation of Protection and Self-Healing of Microgrids

The design of a reliable protection scheme for microgrids often requires communication between protective devices and microgrid controllers. The authors have developed such a communication assisted scalable protection scheme with a self healing feature to protect a microgrid with 100% Inverter Based Resources (IBRs). The communication assisted scheme must be validated in real time with cyber physical co-simulation for successful demonstration. In this regard, the paper presents a co-simulation platform between a simulated power system model using RTDS and physical protective devices. The primary protection of the scheme is programmed in SEL 421-7 relay and backup protection is programmed in MATLAB on a generic computer acting as a microgrid controller. The IEC 61850 models are used to communicate between SEL-421-7 relay and RTDS, whereas TCP/IP communication connects the microgrid controller to RTDS. The paper’s focus is to demonstrate the co- simulation platform with communication links established using both protocols and validate the proposed scheme in real-time on the IEEE 123 node distribution feeder. The paper explains the configuration of the IEC 61850 and TCP/IP communications as the interface requires proper hardware and software setup. Furthermore, the real time performance indicates the Hardware In the Loop (HIL) framework as a competent testing environment for the developed protection scheme for microgrids.

42 ENGINEERING↗
DOE OSTI2025

Time Resolution Characterization of 4H-SiC LGADs with a 90 Sr Source

This work presents timing measurements of 4H-SiC Low Gain Avalanche Detectors (4H-SiC LGADs) using beta particles from a 90Sr source. The 4H-SiC LGADs exhibit fast signal responses, and a time resolution of 61 ps was achieved, comparable to that of standard Si LGADs. The present limitation in the time resolution of 4H-SiC LGADs appears to stem from limited charge generation. Nevertheless, their higher voltage tolerance and faster carrier drift suggest that, with increased charge collection, their timing performance could approach or even surpass that of Si LGADs. Furthermore, these results demonstrate the strong potential of 4H-SiC LGADs as a robust platform for precision timing in future 4D tracking detectors, while also highlighting that signal charge is the dominant factor currently limiting their performance, indicating that further optimization of gain and drift structures will be essential for future development.

43 PARTICLE ACCELERATORS↗