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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 OSTI2021

Nanoporous Iridium Nanosheets for Polymer Electrolyte Membrane Electrolysis

The growth of the hydrogen economy is predicated on advancements in electrochemical energy technologies, with water electrolysis as a key component to the technological portfolio. Much of the focus on anode catalyst development for polymer electrolyte membrane water electrolyzers (PEMWE) is centered on activity as controlled by compositional and morphological impacts on reactant/intermediate/product adsorption. However, the effectiveness of this strategy is found to be limited upon integration of these materials into PEMWE membrane electrode assemblies (MEA). Regardless of catalyst activity, the combination of electrode inhomogeneity, ionomer integration, and high density of oxide-oxide interfaces yields significant performance losses associated with poor catalytic electrode conductivity. Here many of these limitations are addressed through the development of a unique catalyst morphology composed of nanoporous Ir nanosheets (npIr(x)-NS) that exhibit high catalytic activity for the anodic oxygen evolution reaction and superior electrode electronic conductivity in comparison to a commercial IrO2 nanoparticle catalyst. The utility of the npIr(x)-NS is demonstrated through incorporation into PEMWE MEAs where their performance exceeds that of commercial catalyst coated membranes at loadings as low as 0.06 mg(Ir) cm(-2) while exhibiting a negligible loss in performance following 50 000 accelerated stress test cycles.

Polymer Electrolyte Membrane Electrolysis↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Coordinated Optimal Control of PV Inverters and HVAC Loads in Distribution Systems

The increasing integration of distributed energy resources (DERs), such as photovoltaics (PVs) and smart buildings into distribution systems complicate power system operation and controls. This paper proposes a coordinated optimal control strategy for PV inverters and Heating, ventilation, and air conditioning (HVAC) loads in smart buildings to minimize the total network loss in a distribution system. For the HVAC units, we enforce minimum on and off time constraints to avoid frequent switching that can degrade the unit. The proposed control will dispatch optimal control signals of active and reactive power to PV inverters and on/off commands to HVAC units while maintaining the nodal voltage within a secure range and the temperature of HVAC units at a comfort level. The simulation results on a modified IEEE 33-node distribution system demonstrate that the proposed coordinated control scheme can reduce the network loss.

Pani, Naveen↗
DOE OSTI2025

Cryogenic thermal modeling of microwave high density signaling

Superconducting quantum computers require microwave control lines running from room temperature to the mixing chamber of a dilution refrigerator. Adding more lines without preliminary thermal modeling to make predictions risks overwhelming the cooling power at each thermal stage. In this paper, we investigate the thermal load of SC-086/50-SCN-CN semi-rigid coaxial cable, which is commonly used for the control and readout lines of a superconducting quantum computer, as we increase the number of lines to a quantum processor. We investigate the makeup of the coaxial cables, verify the materials and dimensions, and experimentally measure the total thermal conductivity of a single cable as a function of the temperature from cryogenic to room temperature values. We also measure the cryogenic DC electrical resistance of the inner conductor as a function of temperature, allowing for the calculation of active thermal loads due to Ohmic heating. Fitting this data produces a numerical thermal conductivity function used to calculate the static heat loads due to thermal transfer within the wires resulting from a temperature gradient. The resistivity data is used to calculate active heat loads, and we use these fits in a cryogenic model of a superconducting quantum processor in a typical Bluefors XLD1000-SL dilution refrigerator, investigating how the thermal load increases with processor sizes ranging from 100 to 225 qubits. We conclude that the theoretical upper limit of the described architecture is approximately 200 qubits. However, including an engineering margin in the cooling power and the available space for microwave readout circuitry at the mixing chamber, the practical limit is approximately 140 qubits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Carbonate Minerals and Dissimilatory Iron-Reducing Organisms Trigger Synergistic Abiotic and Biotic Chain Reactions under Elevated CO 2 Concentration

Increasing CO 2 emission has resulted in pressing climate and environmental issues. While abiotic and biotic processes mediating the fate of CO 2 have been studied separately, their interactions and combined effects have been poorly understood. To explore this knowledge gap, an iron-reducing organism, Orenia metallireducens, was cultured under 18 conditions that systematically varied in headspace CO 2 concentrations, ferric oxide loading, and dolomite (CaMg(CO 3 ) 2 ) availability. The results showed that abiotic and biotic processes interactively mediate CO 2 acidification and sequestration through "chain reactions", with pH being the dominant variable. Specifically, dolomite alleviated CO 2 stress on microbial activity, possibly via pH control that transforms the inhibitory CO 2 to the more benign bicarbonate species. The microbial iron reduction further impacted pH via the competition between proton (H + ) consumption during iron reduction and H + generation from oxidization of the organic substrate. Under Fe(III)-rich conditions, microbial iron reduction increased pH, driving dissolved CO 2 to form bicarbonate. Spectroscopic and microscopic analyses showed enhanced formation of siderite (FeCO 3 ) under elevated CO 2 , supporting its incorporation into solids. In conclusion, the results of these CO 2 -microbe-mineral experiments provide insights into the synergistic abiotic and biotic processes that alleviate CO 2 acidification and favor its sequestration, which can be instructive for practical applications (e.g., acidification remediation, CO 2 sequestration, and modeling of carbon flux).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Design of the American Wake Experiment (AWAKEN) field campaign

The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different operational and atmospheric conditions and validate the use of different wind plant control strategies and simulation frameworks. An extensive field campaign like AWAKEN requires proper coordination and long-term planning to be successful. In this paper, we review the major activities planned during AWAKEN to provide information for current and future project partners. Specifically, we provide information about the project sites, their planned instruments, and how these will relate to the scientific objectives of the overall AWAKEN project.

17 WIND ENERGY↗
DOE OSTI2020

Role of the metal-support interface in the hydrodeoxygenation reaction of phenol

In this paper, the effect of interfacial sites between Pd particles and Nb 2 O 5 species is investigated by testing a series of Pd-Nb 2 O 5 /SiO 2 catalysts with different niobium loadings for the HDO reaction of phenol in the gas phase. Important differences in the selectivity to deoxygenated product were observed depending on the presence of niobium oxide close to Pd particles, which reveals the key role of the type of active phase in the control of reaction steps. It was found that Pd/SiO 2 catalyst promotes hydrogenation pathways, producing cyclohexanone as the major product. For Pd-Nb 2 O 5 /SiO 2 catalyst containing a Nb/Pd molar ratio of 0.5, a sharp increase in the selectivity to benzene is observed (7.5-fold). Increasing the Nb/Pd molar ratio, the formation of benzene is enhanced. The results showed that the Pd-Nb 2 O 5 interface, composed by an oxophilic oxide in the perimeter of the metal particle, is responsible for the activation of the Csingle bondO bond, promoting the deoxygenation reaction.

09 BIOMASS FUELS↗
DOE OSTI2023

Facet-dependent strong metal-support interactions control the C–O bond activation

Reducible metal oxides are selective and effective for C–O bond scission of hydrodeoxygenation reactions via the reverse Mars-van Krevelen mechanism. Creating efficient redox centers while minimizing metal surfaces leads to highly selective catalysts. Single noble metal atoms activate the surface M–O bond, but the catalyst activity is limited due to low loading. Here, we report that the strong metal-support interaction between Ir and CeO 2 is facet sensitive, and certain facets regulate the C–O bond cleavage. At 300°C reduction, Ir is mostly encapsulated on an octahedron by (111) facets but remains exposed by (110) facets. The former is selective, whereas the latter is not. In conclusion, density functional theory indicates that Ir encapsulation is favored on (111) under reaction conditions, and oxygen vacancies more readily form on encapsulated Ir than on pristine ceria.

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

Atomic Layers of B2 CuPd on Cu Nanocubes as Catalysts for Selective Hydrogenation

The search for highly active and selective catalysts with high precious metal atom utilization efficiency has attracted increasing interest in both the fundamental synthesis of materials and important industrial reactions. Here, in this work, we report the synthesis of Pd–Cu nanocubes with a Cu core and an ordered B2 intermetallic CuPd shell with controllable atomic layers on the surface (denoted as Cu/B2 CuPd), which can efficiently and robustly catalyze the selective hydrogenation of acetylene (C 2 H 2 ) to ethylene (C 2 H 4 ) under mild conditions. The optimized Cu/B2 CuPd with a Pd loading of 9.5 at. % exhibited outstanding performance in the C 2 H 2 semi-hydrogenation with 100% C 2 H 2 conversion and 95.2% C 2 H 4 selectivity at 90 °C. We attributed this outstanding performance to the core/shell structure with a high surface density of active Pd sites isolated by Cu in the B2 intermetallic matrix, representing a structural motif of single-atom alloys (SAAs) on the surface. The combined experimental and computational studies further revealed that the electronic states of Pd and Cu are modulated by SAAs from the synergistic effect between Pd and Cu, leading to enhanced performance compared with pristine Pd and Cu catalysts. This study provides a new synthetic methodology for making single-atom catalysts with high precious metal atom utilization efficiency, enabling simultaneous tuning of both geometric and electronic structures of Pd active sites for enhanced catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Local Communication in Populations of Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) have shown great potential for demand response (DR) in electric grid operations. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of a small number of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a linearized model of the aggregate system and the results of that analysis compared to simulation results. The proposed modification of thermostat behavior results in fourfold reduction in the post-DR peak while suppressing ensuing oscillations at the expense of a modest increase in compressor cycling. Further, the linearized model also provides insight into the aggregate behavior of the population.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Multi-objective automatic discovery of optimized metamaterials for varying velocity impact protection

Mechanical metamaterials have demonstrated exceptional impact performance while remaining lightweight. Impact resistance has traditionally been investigated using quasi-static simulations, often with the assumption that performance will translate to high-velocity impact scenarios. However, critical crash protection parameters—such as peak stress and absorbed energy—are highly sensitive to impact velocity, leading to inconsistent performance under dynamic loading. To address this, we introduce a strain-rate-aware, active deep learning framework that enables multi-objective optimization of impact protection metrics across a wide range of impact velocities. Our framework captures the strain-rate sensitivity of architected lattices by learning to control spatial gradation in cellular metamaterials, resulting in over 200 % enhancement in impact protection relative to state-of-the-art designs such as Voronoi and re-entrant lattices. We demonstrate its practical utility by designing next-generation lattice structures for automotive bumper systems that satisfy multiple, velocity-specific safety criteria—capabilities beyond those of conventional designs. More than just a predictive tool, this framework marks the first step towards enabling adaptable impact-resistant structures across dynamic regimes.

Deep learning↗
DOE OSTI2020

Quantitative Cu Counting Methodologies for Cu/SSZ-13 Selective Catalytic Reduction Catalysts by Electron Paramagnetic Resonance Spectroscopy

Here, two Cu/SSZ-13 selective catalytic reduction (SCR) catalysts with distinct Si/Al ratios and isolated Z2Cu and ZCuOH distributions are prepared for in situ electron paramagnetic resonance (EPR) spectroscopic studies. These in situ studies include dehydration, titration of dehydrated samples with NO+O 2 and NH 3 , titration of NH 3 saturated samples with NO+O 2 , and finally steady-state standard NH 3 -SCR reaction. During dehydration, EPR active hydrated ZCuOH loses H 2 O ligands and becomes EPR silent due to a pseudo Jahn-Teller effect; a portion of ZCuOH also undergoes autoreduction to ZCu(I) species, a process that also induces EPR invisibility. During NO+O 2 treatment of dehydrated samples, ZCu(I) species are oxidized to Cu(II)-NO 3 - species, regaining EPR visibility. During NH3 titration, EPR silent dehydrated ZCuOH can also regain EPR visibility by coordinating with NH 3 ligands. During NO+O 2 titration of NH 3 -saturated samples, EPR active Cu contents first decrease due to Cu(II) reduction to Cu(I), and then increase due to Cu(II)-NO 3 - species formation. However, the Cu(II)-NO 3 - formation chemistry is substantially slower for the Si/Al = 36 catalyst. In steady-state SCR studies, the EPR active content decreases with increasing temperature in the kinetically controlled low-temperature regime and becomes largely invariant in mass-transfer limited regime. Importantly, Cu sites in the SCR more active Si/Al = 6 catalyst display substantially higher EPR visibility than the SCR less active Si/Al = 36 catalyst at any reaction temperatures tested. The higher Cu loading for the former catalyst is believed to be key for this difference.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Ubiquitous Power Electronics in Future Power Systems: Recommendations to fully utilize fast control capabilities

Power electronics is becoming ubiquitous in power systems because the rapid growth of variable renewable generation such as wind and solar, responsive load, electric vehicles, energy storage, as well as DC transmission. As a result, a high percentage of electricity will be generated, transmitted or consumed by power electronics in the future power system. Such a high penetration of power electronics is changing power system operation landscapes and dynamic characteristics. Two major kinds of impact are reduced system mechanical inertia by inverter-based resources displacing conventional generation and active participation of many distributed energy resources through inverters. This has caused significant reliability challenges for power system operation. The mitigation approach so far has mostly focused on accommodating power electronics inverters as passive devices or creating synthetic inertias to make inverter-based generation behaving as conventional generators. However, the high-speed control capabilities of power electronics present new opportunities for achieving an optimal performance beyond conventional power systems. A lower-inertia system can be more responsive for improving reliability, and distributed small resources can be more adaptive and scalable for better flexibility and resilience. Power electronic inverters are capable of multiple fast control functions. To fully utilized the ubiquitous power electronics in power systems requires technology advancements in the following areas: data and communications to capture the new dynamic behaviors introduced by power electronics; modeling and simulation to understand the behaviors of power electronics and its interactions with other components in the context of power systems; control and optimization methods for fully utilizing the capabilities of power electronics for a new paradigm of performance beyond the traditional inertia-heavy system; and significant upgrades in power electronics hardware to cater to the new control capabilities. If properly controlled and optimized, power electronics can help transform the power system to be responsive, adaptive, and scalable.

Huang, Zhenyu↗
DOE OSTI2023

SOLAr Critical Infrastructure Energization (SOLACE): Leveraging Distributed Energy Resources to Provide Local Power

This document is a technical report based on a large-scale DOE-funded project conducted between 2019 and 2022. The project, called SOLAr Critical infrastructure Energization (SOLACE), is aimed at leveraging distributed energy resources (DERs) to provide local power to communities, feeders, or other regions. The project included a particular focus on high-value critical loads such as municipal water supply, telecommunication hubs, and disaster shelters. The key developments of the project were: (1) A comprehensive pre-event power system analysis methodology that identifies and characterizes the viable local power options. (2) A DMS-based example control system for activating and operating the local power solution during a time of crisis. (3) Grid-forming inverter technology to enable isolated local power operation and black-starting. (4) Cyber-security considerations for isolated systems when wider-area communication may be offline. This report describes the overall process that identifies and assesses the viable DER-based local power solutions for a given facility, region, or community. The report documents the process, including the key analysis steps, tools required, data requirements, and recommended pass/fail criteria for each step. It also provides a sample implementation of this process through a test case. Finally, it provides details of how a viable pre-event plan can be selected and activated at go-time when an event has occurred.

14 SOLAR ENERGY↗
DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗
DOE OSTI2026

EVs@Scale: NextGen Profiles EVSE Characterization 2025

As part of the U.S. DOE EVs@Scale consortium, the Next-Generation Profiles (NextGen Profiles [NGP]) project presents analysis and results from the characterization of high-power conductive and wireless charging infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. Electric Vehicle Service Equipment (EVSE) characterization involves testing over a wide range of DC charging currents and voltages during nominal and off-nominal conditions. This testing allows for a better understanding of the impact that high-power charging will have on the electric grid. A common set of standard test plans, procedures, and data requirements were applied to the characterization in this document with minor updates and improvements. This report covers all conductive characterization activities performed between October 2024 and September 2025 on the Delta Electronics 350KW Electric Vehicle Charging System, consisting of power cabinet model EIDN-U350KTA01 and dispenser model EIDD-U350SSUUAEG-350.Key Findings include: Output regulation, Efficiency and power factor, Load management, Grid Resilience, Smart Charge Management (SCM) performance, Thermal control system performance, Multi-port simultaneous charging performance, and Selected performance comparisons with other EVSEs characterized in the NextGen Profiles project. Hot and cold temperature testing was not conducted on the Delta 350KW due to laboratory limitations. Future research could include continued testing the Delta hardware under off-nominal temperature conditions including multi-port/multi-session simultaneous charge testing, in addition to collecting data on other high-power conductive chargers to augment.

25 ENERGY STORAGE↗
DOE OSTI2022

Controlled site coverage of strong metal–support interaction (SMSI) on Pd NP catalysts

Here, strong metal–support interaction catalysts have been shown to improve desired product selectivity at the cost of fractional rates due to active site coverage. The goal of this study was to determine if the active site coverage of metallic nanoparticles could be controlled to lower levels than have been previously reported in SMSI catalysts with the aim of improving the rate while maintaining high selectivity. 2Pd–XTi/SiO 2 (2 wt% Pd, X wt% Ti) strong metal–support interaction (SMSI) catalysts with Ti loadings between 0–1.0 wt% were synthesized to control Pd nanoparticle coverage. Calcination at 450 °C and reduction at 550 °C were sufficient for forming ~2 nm sized Pd particles in all catalysts. Increasing the Ti loading from 0.1 to 1.0 wt% increased the surface coverage from 40 to 85% at a fixed reduction temperature of 550 °C. The IR spectra of the SMSI catalysts were similar with a high fraction of linear bonded CO which was much higher than that of Pd nanoparticles of similar size. The SMSI overlayer could be removed by oxidation at 350 °C and re-reduction at 200 °C. EXAFS of the oxidized catalysts indicates that nearly full oxidation of the metallic nanoparticle was required to remove the SMSI overlayer. Oxidation temperatures from 30 to 300 °C partially oxidized the Pd nanoparticles and subsequent re-reduction at 200 °C partially decreases the SMSI coverage. The fractional surface coverage was determined by measuring the rate of propylene hydrogenation with and without the SMSI overlayer. Increasing the reduction temperature from 200 to 550 °C increased the SMSI coverage from 0 to 85% depending on the Ti loading and temperature. In conclusion, after reduction at 550 °C and oxidation at 350 °C, the range of coverages varied between ~10% with 0.1 wt% Ti after re-reduction at 300 °C and ~85% with 1 wt% Ti after reduction at 550 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Effects of the method of active site characterization for determining structure-sensitivity in Ag-catalyzed ethylene epoxidation

We report catalysts have been prepared on a low surface area α-Al 2 O 3 support used commercially for previous generation olefin epoxidation catalysts. Prescreening of the low surface area alumina (0.73 m 2 /g) indicated the absence of acid catalyzed isomerization of EO at an evaluation temperature of 210 °C. A 0.1 wt% Ag base material synthesized by incipient wetness impregnation of AgNO 3 was used as a base material for electroless deposition (ED) of additional Ag to increase particle sizes by controlled reduction of Ag + directly onto the preexisting Ag surface to form weight loadings between 0.3 and 5.0 wt% metal. A 12 wt% Ag/α-Al 2 O 3 using Ag 2 C 2 O 4 as the Ag precursor was also prepared to compare performance of the ED samples with a catalyst more typical of industrial formulations. Characterization by SEM, STEM, and hydrogen titration of oxygen precovered Ag characterized before and after catalytic evaluation indicated that microscopy is required to accurately represent distributions of Ag particle sizes, but H 2 titration of O-precovered Ag gives the best representation of active sites since it directly counts the number of Ag surface sites. Larger particles >100 nm are resistant to both Ag sintering and carbon foulant; TOF values were relatively insensitive to particle size with only a 2.2 × difference between the best and worst performing samples. Selectivity, which is not a function of TOF, shows the most significant structure sensitivity effect where particle sizes follow the trend 67 nm ≈ 92 nm (58% EO) < 157 nm (67% EO) < 211–542 nm (73% EO). The lower EO selectivities were also correlated with increased fouling for the smaller Ag sizes, suggesting that more strongly bound EO precursor(s) leads to combustion and CO 2 /H 2 O formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗