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At least 685 records · Page 38

Thin-Layer Composite Unimorph Ferroelectric Driver Sensor Properties

Tests were conducted on 13 different configurations of a new class of piezoelectric devices called THUNDER (thin layer composite unimorph ferroelectric driver and sensor). These configurations consisted of a combination of 1, 3, 5, 7, and 9 layers of 25.4 micron thick aluminium as a backing material, with and without a top layer of 25.4 micrometer aluminum. All of these configurations used the same piezoelectric ceramic wafer (PZT-5A) with dimensions of 5.08 x 3.81 x 0.018 cm. The above configurations were tested at two stages of the manufacturing process: before and after repoling. The parameters measured included frequency, driving voltage. displacement, capacitance, and radius of curvature. An optic sensor recorded the displacement at a fixed voltage(100 - 400 V peak to peak) over a predetermined frequency range (1 - 1000 Hz). These displacement measurements were performed using a computer that controlled the process of activating and measuring the displacement of the device. A parameter alpha was defined which can be used to predict the which configuration will produce the most displacement for a free standing device.

Mossi, Karla M.↗

Beneficial Integration of Energy Storage and Load Management with Photovoltaic (PV)

In recent years, a number of industry activities have aimed at addressing the integration challenges posed by the variability and uncertainty of higher penetration of renewable generation sources, like solar photovoltaic (PV) – one of the key objectives of the Sustainable and Holistic Integration of Energy Storage and Solar PV (SHINES) program launched by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). This EPRI led Beneficial Integration of Energy Storge, and Load Management with PV project aimed to design, develop, and demonstrate end-to-end distributed energy resource (DER) integration solution to build on these activities. EPRI led project team designed and implemented a local controller that uses model predictive control (MPC) algorithm to optimally manage DERs on site by planning for a receding horizon while executing the control settings for the first step of its plan. The team has also developed a system controller to interface with the local controller to demonstrate the hierarchical control and how it can leverage DER capabilities to address challenges like over voltage and thermal limit violations which typically limits the DER hosting capacity of distribution feeders. Team has demonstrated how the local controller with optimization algorithm can effectively manage controllable loads like HVAC, water heater, and pool pumps to allow for greater integration of PV with relatively smaller energy storage system requirements. Optimal utilization of the load control can also reduce the depth of discharge of batteries to meet grid export/import limit from behind-the-meter (BTM) DERs. Proper utilization of DER capabilities via local control intelligence, like the one developed and demonstrated in this project can help the industry to address integration challenges of higher penetration of solar PV in economically efficient manner. This can help to accelerate deployment of clean renewable energy systems at lower societal cost.

14 SOLAR ENERGY↗

Collective dynamics and long-range order in thermal neuristor networks

Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Science & Technology - Other Topics↗

Magnetoelectric properties at the Co/AlN(0001) interface

AlN nitride is a large-band-gap polar material that has gained interest due to its ability to become ferroelectric and its compatibility with Si- and GaN-based technologies. This compound could, therefore, be a good candidate to design new artificial multiferroics if combined with a ferromagnetic electrode. For this work, we performed first-principles calculations to investigate the set up of the magnetoelectric coupling at the Co/AlN(0001) interface. Our results describe a complex interfacial atomic structure with variable local magnetic properties as a function of the atom alignment. We predict an average variation of interface spin magnetization, when the polarization is reversed, of 3.83 𝜇B nm −2 , close to the values already reported in the literature at metal/oxide interfaces. This confirms the potential of this AlN-based ferroelectric compound to be used in future voltage-controlled spintronic devices.

36 MATERIALS SCIENCE↗

Novel nitrogen-based organosulfur electrodes for advanced intermediate temperature batteries

Advanced secondary batteries operating at intermediate temperatures (100 to 200 C) have attracted considerable interest due to their inherent advantages (reduced corrosion and safety risks) over higher temperature systems. Current work in this laboratory has involved research on a class of intermediate temperature Na/beta double prime- alumina/RSSR batteries conceptually similar to Na/S cells, but operating within a temperature range of 100 to 150 C, and having an organosulfur rather than inorganic sulfur positive electrode. The organosulfur electrodes are based on the reversible, two electron eduction of organodisulfides to the corresponding thiolate anions, RSSR + 2 electrons yield 2RS(-), where R is an organic moiety. Among the advantages of such a generic redox couple for battery research is the ability to tailor the physical, chemical, and electrochemical properties of the RSSR molecule through choice of the organic moiety. The viscosity, liquidus range, dielectric constant, equivalent weight, and redox potential can in fact be verified in a largely predictable manner. The current work concerns the use of multiple nitrogen organosulfur molecules, chosen for application in Na/RSSR cells for their expected oxidizing character. In fact, a Na/RSSR cell containing one of these materials, the sodium salt of 5-mercapto 1-methyltetrazole, yielded the highest open circuit voltage obtained yet in the laboratory; 3.0 volts in the charged state and 2.6 volts at 100 percent discharge. Accordingly, the cycling behavior of a series of multiple nitrogen organodisulfides as well as polymeric organodisulfides are presented in this manuscript.

Visco, S. J.↗

Discharge Chamber Performance of the NEXIS Ion Thruster

The Nuclear-Electric Xenon Ion System (NEXIS) thruster was designed to produce greater than or equal to 70% efficiency at ISPs in excess of 6500 sec and total power levels in excess of 15 kW. In order to achieve this performance, the thruster requires a large area plasma generator capable of high propellant utilimtion efficiency and low discharge loss while producing a very flat, uniform beam profile. Fortunately, larger thrusters can be made more uniform and efficient due to the higher volume to surface ratio, provided that the magnetic cusp confinement is designed properly and the thruster length to diameter ratio is adequate. This paper describes the discharge chamber performance of the NEXIS Laboratory Model (LM) thruster. The LM discharge chamber is 65 cm in diameter at the grid plane and uses 6 ring-cusps to provide magnetic confinement of the plasma. The thruster was tested with flat carbon-carbon composite grids with the hole pattern masked to 57 cm in diameter and a conventional Type-B "1/2" diameter hollow cathode. During the preliminary "discharge only" tests, the LM thruster demonstrated profile factors of 0.84 and a discharge loss of about 160 eV/ion at 25 V discharge voltage and over 90% propellant utilization efficiency in simulated beam extraction experiments at 3.9 A of beam current. Analysis of the data from these tests used the discharge-only model developed by Brophy. Subsequent beam extraction experiments validated the key variables used in the model to predict the performance from the discharge-only data, and demonstrated 3.9 A of beam current at over 90% propellant utilization efficiency with a flatness parameter of better than 0.8 and a discharge loss of about 185 eV/ion. The slightly higher discharge loss measured during beam extractions was found to be due to a lower screen transparency in the as-manufactured LM grid set. Plasma measurements with a scanning probe internal to the thruster near the screen grid showed plasma densities over l x 10(exp 11) per cubic centimeter and electron temperatures of 3.5 to 5.5 eV depending on the operation parameters. The performance of the NEXIS discharge chamber contributed to the over 78% thruster efficiency measured during beam extraction at 7500 sec ISP and 25 kW of power, and over 81% thruster efficiency measured at 8500 sec ISP.

plasma generation↗

Development of a Low Inductance Linear Alternator for Stirling Power Convertors

The free-piston Stirling power convertor is a promising technology for high efficiency heat-to-electricity power conversion in space. Stirling power convertors typically utilize linear alternators for converting mechanical motion into electricity. The linear alternator is one of the heaviest components of modern Stirling power convertors. In addition, state-of-art Stirling linear alternators usually require the use of tuning capacitors or active power factor correction controllers to maximize convertor output power. The linear alternator to be discussed in this paper, eliminates the need for tuning capacitors and delivers electrical power output in which current is inherently in phase with voltage. No power factor correction is needed. In addition, the linear alternator concept requires very little iron, so core loss has been virtually eliminated. This concept is a unique moving coil design where the magnetic flux path is defined by the magnets themselves. This paper presents computational predictions for two different low inductance alternator configurations, and compares the predictions with experimental data for one of the configurations that has been built and is currently being tested.

Stirling↗

Development of a Low-Inductance Linear Alternator for Stirling Power Convertors

The free-piston Stirling power convertor is a promising technology for high-efficiency heat-to-electricity power conversion in space. Stirling power convertors typically utilize linear alternators for converting mechanical motion into electricity. The linear alternator is one of the heaviest components of modern Stirling power convertors. In addition, state-of-the-art Stirling linear alternators usually require the use of tuning capacitors or active power factor correction controllers to maximize convertor output power. The linear alternator to be discussed in this paper eliminates the need for tuning capacitors and delivers electrical power output in which current is inherently in phase with voltage. No power factor correction is needed. In addition, the linear alternator concept requires very little iron, so core loss has been virtually eliminated. This concept is a unique moving coil design where the magnetic flux path is defined by the magnets themselves. This paper presents computational predictions for two different low inductance alternator configurations. Additionally, one of the configurations was built and tested at GRC, and the experimental data is compared with the predictions.

Stirling↗

Degree Of Diminution In Vagal-Cardiac Activity Predicts Sudden Death In Familial Dysautonomia When Resting Tachycardia Is Absent

Patients with familial dysautonomia (FD) have an increased risk of sudden death, but sensitive and specific predictors of sudden death in FD are lacking. Methods. We recorded 10-min resting high-fidelity 12-lead ECGs in 14 FD patients and in 14 age/gender-matched healthy subjects and studied 25+ different heart rate variability (HRV) indices for their ability to predict sudden death in the FD patients. Indices studied included those from 4 "nonlinear" HRV techniques (detrended fluctuation analysis, approximate entropy, correlation dimension, and PoincarC analyses). The predictive value of PR, QRS, QTc and JTc intervals, QT dispersion (QTd), beat-to-beat QT and PR interval variability indices (QTVI and PRVI) and 12- lead high frequency QRS ECG (150-250 Hz) were also studied. FD patients and controls (C) differed (Pless than 0.0l) with respect to 20+ of the HRV indices (FD less than C) and with respect to QTVI and PRVI (FDBC) and HF QRS- related root mean squared voltages (FDBC) and reduced amplitude zone counts (FD less than C). They differed less with respect to PR intervals (FD less than C) and JTc intervals (FD greater than C) (P less than 0.05 for both) and did not differ at all with respect to QRS and QTc intervals and to QTd. Within 12 months after study, 2 of the 14 patients succumbed to sudden cardiac arrest. The best predictor of sudden death was the degree of diminution in HRV vagal-cardiac (parasympathetic) parameters such as RMSSD, the SDl of Poincare plots, and HF spectral power. Excluding the two FD patients who had resting tachycardia (HR greater than 100, which confounds traditional HRV analyses), the following criteria were independently 100% sensitive and 100% specific for predicting sudden death in the remaining 12 FD patients during spontaneous breathing: RMSSD less than 13 ms and/or PoincarC SD1 less than 9 ms. In FD patients without supine tachycardia, the degree of diminution in parasympathetic HRV parameters (by high-fidelity ECG) predicts incipient death.

Schlegel, T. T.↗

Atomic-Scale Structural Mapping of Active Sites in Monolayer PGM-Free Catalysts by Low-Voltage 4D-STEM

Two-dimensional (2D) materials have attracted a large amount of attention in both basic and applied fields, and scanning transmission electron microscopy (STEM) is often uniquely well-suited for characterizing the atomic-scale structure of these materials [1-4]. As a result, STEM is poised to significantly impact progress on platinum group metal (PGM)-free catalysts, which are currently under intense development to enable low-cost, commercially viable hydrogen fuel cells [5]. While recent advancements have resulted in fuel cell performance comparable to Pt catalysts by some measures [6], cell durability remains a significant challenge, limiting practical applications [7]. Catalytically active sites in PGM-free materials are proposed to be FeN4 complexes embedded in a graphene lattice (Fig. 1b) within layered or other larger materials, but this is still under debate largely due to the range of potential actives sites predicted by computational methods and lack of methods for directly validating these models [5]. Fundamental insights into the atomic structure and resulting degradation pathways of proposed active sites are therefore needed to fully understand and control cell performance and durability [6].2D materials typically make ideal samples for STEM, but those within PGM-free catalysts present additional challenges since these materials are often defect-rich, with a high density of edges, dopant atoms, etc., which significantly increase susceptibility to beam damage at standard operating voltages. This makes analysis of potential FeN4 active sites particularly challenging, since a large proportion of Fe exists at edge sites where beam-induced atomic displacements can prohibit high-resolution structural characterization [6]. Conventional dark-field imaging compounds this problem by producing less signal for a given dose and being less sensitive to light elements than dose-efficient phase contrast imaging techniques such as those enabled by four-dimensional (4D)-STEM [8-10] (Fig. 1a). Consequently, active site structural analysis is often left to methods such as low-resolution imaging combined with quantum chemical calculations [6], which hinders accurate determination of reaction and degradation mechanisms.Here, we demonstrate direct atomic-scale structural mapping of FeN4 sites by performing low-voltage 4D-STEM on a model PGM-free catalyst system with many exposed monolayer regions. To accomplish this, we pair a 30 keV aberration-corrected probe with a fast pixelated detector that has optimal performance at low beam voltages [11]. This enables us to simultaneously image light and heavy elements with high signal-to-noise by center-of-mass analysis (Fig. 1c) while minimizing beam-induced atomic displacements at sensitive sites. The monolayer nature of these materials additionally allows for experimental validation by direct comparison with multislice simulations [12] of model structures (Fig. 1d-e). This work demonstrates how low-voltage 4D-STEM will provide new insights into the atomic-scale structure and degradation mechanisms of active sites in PGM-free catalysts, facilitating the development of low-cost hydrogen fuel cells and other energy conversion technologies in the future [13].

Zachman, Michael↗

Breaking the passivation barrier via d-p orbital optimization for stable hydrogen production and sulfion upgrading

The development of energy-efficient hydrogen production technologies represents a critical pathway toward achieving global carbon neutrality objectives. This work provides fundamental insights into overcoming catalyst passivation challenges in sulfide oxidation reaction (SOR)-coupled hydrogen evolution reaction (HER) systems through precise orbital hybridization engineering. Our theoretical simulations reveal that sulfur-passivated ruthenium surfaces can effectively modulate d-p orbital hybridization, significantly reduce d-electron activity while stabilizing long-chain S 8 species and decreasing intermediate adsorption energies. Furthermore, metal carbides/ruthenium heterostructure (MC/Ru, M = V, Mo, W) was designed to achieve simultaneous optimization of both HER (∆G H* = −0.11 eV) and SOR (∆G RDS = 1.51 eV) via work-function-mediated interfacial electron transfer, which effectively tailors surface electronic states. Guided by theoretical predictions, we successfully synthesized a series of metal carbides/ruthenium/nitrogen-doped carbon catalysts based on a solid-phase reaction and designated as MC/Ru@NC (M = V, Mo, W). The optimized VC/Ru@NC catalyst exhibits exceptional performance in a membrane-free two-electrode system, achieving an ultralow cell voltage of 0.76 V at 200 mA cm −2 with outstanding stability over 1600 h, while maintaining 97.5 % Faradaic efficiency for hydrogen production and 73.8 % sulfur recovery efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Deciphering coulombic loss in lithium-ion batteries and beyond

Lithium-ion batteries are pivotal for modern energy storage, yet accurately predicting their lifespan remains a critical challenge. While descriptors like coulombic efficiency are widely used to assess battery longevity, the unclear physical origins of coulombic losses cause semi-quantitative correlation with capacity, complicating battery development. Here, we combine high-precision leakage current and open-circuit-voltage measurements with charge conservation principles to explore microscopic charge consumptions at electrode-electrolyte interfaces across diverse chemistries. We demonstrate that coulombic loss arises from a synergy between local charge neutrality and global charge compensation, reconciling its quantitative correlation to capacity. Contrary to conventional assumptions equating coulombic loss with irreversible capacity loss, this framework resolves systematic overestimations and paradoxical phenomena in existing chemistries. Our findings establish physics-informed criteria for accelerated lifespan evaluation and guide rational design of long-life lithium-ion batteries and beyond.

Batteries↗

Prognostics for Electronics Components of Avionics Systems

Electronics components have and increasingly critical role in avionics systems and for the development of future aircraft systems. Prognostics of such components is becoming a very important research filed as a result of the need to provide aircraft systems with system level health management. This paper reports on a prognostics application for electronics components of avionics systems, in particular, its application to the Isolated Gate Bipolar Transistor (IGBT). The remaining useful life prediction for the IGBT is based on the particle filter framework, leveraging data from an accelerated aging tests on IGBTs. The accelerated aging test provided thermal-electrical overstress by applying thermal cycling to the device. In-situ state monitoring, including measurements of the steady-state voltages and currents, electrical transients, and thermal transients are recorded and used as potential precursors of failure.

Celaya, Jose R.↗

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗

Li 8 MnO 6 : A Novel Cathode Material with Only Anionic Redox

In Li-excess transition-metal-oxide cathode materials, anionic oxygen redox can offer high capacity and high voltages, although peroxo and superoxo species may cause oxygen loss, poor cycling performance, and capacity fading. Previous work showed that undesirable formation of peroxide and superoxide bonds is controlled to some extent by Mn substitution, and the present work uses density functional calculations to examine the reasons for this by studying the anionic redox mechanism Li 8 MnO 6 . This material is obtained by substituting Mn for Sn in Li 8 SnO 6 or for Zr in Li 8 ZrO 6 , and we also compare to previous work on those materials. The calculations predict that Li 8 MnO 6 is stable at room temperature (with a band gap of 3.19 eV as calculated HSE06 and 1.82 eV as calculated with the less reliable with PBE+U), and they elucidate the chemical and structural effects involved in the inhibition of oxygen release in this cathode. Throughout the whole delithiation process, only O 2- ions are oxidized. The directional Mn-O bonds formed from unfilled 3d orbitals effectively inhibit the formation of O-O bonds, and the layered structure is maintained even after removing 3 Li per Li 8 MnO 6 formula unit. The calculated average voltage for removal of 3 Li is 3.69 V by HSE06, and the corresponding capacity is 389 mAh/g. The high voltage of oxygen anionic redox and the high capacity result in a high energy density of 1436 Wh/kg. The Li-ion diffusion barrier for the dominant interlayer diffusion path along the c-axis is 0.57 eV by PBE+U. Finally, these results help us to understand the oxygen redox mechanism in a new lithium-rich Li 8 MnO 6 cathode material and contribute to the design of high-energy-density lithium-ion-battery cathode materials with favorable electrochemical properties based on anionic oxygen redox.

Li8MnO6↗