An organic device with volatility on demand
We report ion trapping in crystalline domains of electrochemical transistors can be used to create a device capable of both volatile and non-volatile operation.
36 MATERIALS SCIENCE↗
SEARCH · Engineering Papers
Results for “device operation”
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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Correlation of Surface Acoustic Wave (SAW) force myography sensor output with elbow joint torque
Accurate assessment of skeletal muscle forces and net joint torque is essential for preventing fatigue-related injuries, optimizing physical training, and monitoring disease progression in neuromuscular conditions. However, existing joint torque evaluation techniques are hindered by limited portability and high operational costs, confining their use to controlled laboratory or clinical settings. Despite substantial advances in wearable joint torque estimation systems, ongoing challenges such as power constraints, bulky wired setups, and susceptibility to environmental or motion artifacts underscore the urgent need for truly batteryless, wireless solutions deployable in real-world settings. This paper proposes a novel surface acoustic wave (SAW)-based force myography (FMG) system for noninvasive measurement of joint torque, validated against a gold-standard electromechanical dynamometer. The approach uses a single SAW sensor embedded in an armband to detect volumetric biceps brachii changes, with a second-order polynomial mapping sensor output and elbow angle to torque. Seven participants were tested in both isometric (15°–90°) and isokinetic (10°/s and 20°/s) supinated elbow flexion tasks. Under isometric conditions, subject-specific calibration achieved a normalized root-mean-square error (NRMSE) of 13.6% ± 6.0% and R 2 = 0.834 ± 0.180, while a group-level model yielded 14.4% ± 6.8% and 0.808 ± 0.208, respectively. For isokinetic trials, the group model produced an NRMSE of 24.1% ± 6.6% at 10°/s and 24.9% ± 08.9% at 20°/s, highlighting the feasibility of using a single-sensor SAW-FMG setup across different speeds. Because SAW devices support wireless, battery-free operation, the proposed system offers a pathway to portable, real-time monitoring for sports medicine, rehabilitation, and clinical diagnostics.
36 MATERIALS SCIENCE↗
A polarimetry-based field-deployable non-interruptive mirror soiling detection method
The soiling level of heliostat mirrors in Concentrated Solar Power (CSP) fields is one of the key factors that significantly influences optical efficiency. State-of-the-art methods of monitoring heliostats soiling levels still face various challenges, including slow speed, labor-intensive operations, resolution and accuracy constraints or interruptions to solar field operations. Here, we present a rapid, cost-effective, and non-intrusive method for mirror soiling detection based on polarimetric imaging, referred to as Polarimetric Imaging-based Mirror Soiling (PIMS). The compact PIMS device is designed for integration with unmanned aerial vehicles (UAVs), enabling rapid, large-area assessments of heliostat mirrors for efficient soiling detection. Our method utilizes the correlation between the Degree of Linear Polarization (DoLP) and surface soiling level based on Mie scattering theory and Monte Carlo simulations. Field deployment of the PIMS method requires minimal device installation, and its UAV-based operation allows for soiling detection without interrupting plant activities. The PIMS method holds the potential for mirror soiling detection across various concentrated solar power (CSP) plants and can be further adapted for other types of solar fields, such as parabolic trough systems.
CSP Field↗
Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing
Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, in this work, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.
42 ENGINEERING↗
Electrochemical ion insertion from the atomic to the device scale
Electrochemical ion insertion involves coupled ion–electron transfer reactions, transport of guest species and redox of the host. The hosts are typically anisotropic solids with 2D conduction planes but can also be materials with 1D or isotropic transport pathways. These insertion compounds have traditionally been studied in the context of energy storage but also find extensive applications in electrocatalysis, optoelectronics and computing. Recent developments in operando, ultrafast and high-resolution characterization methods, as well as accurate theoretical simulation methods, have led to a renaissance in the understanding of ion-insertion compounds. In this Review, we present a unified framework for understanding insertion compounds across timescales and length scales ranging from atomic to device levels. Using graphite, transition metal dichalcogenides, layered oxides, oxyhydroxides and olivines as examples, we explore commonalities in these materials in terms of point defects, interfacial reactions and phase transformations. We illustrate similarities in the operating principles of various ion-insertion devices, ranging from batteries and electrocatalysts to electrochromics and thermal transistors, with the goal of unifying research across disciplinary boundaries.
36 MATERIALS SCIENCE↗
Impact Analysis of Data Integrity Attacks on FACTS-based Wide-Area Voltage Control System
Energy management system (EMS) consists of several wide-area control applications that serve as a backbone for security, stability, and reliability of the power system. Wide-area voltage control system (WAVCS), one of the critical wide-area applications, operates in coordination with local Flexible AC Transmission System (FACTS) devices to provide voltage security and optimal management of active and reactive power resources. Since the WAVCS relies on wide-area communication and data sharing devices, possible cybersecurity vulnerabilities have to be addressed to ensure the closed-loop operation of WAVCS. In this paper, we present a methodology for performing an impact analysis of cyber-attacks in WAVCS cybersecurity. In particular, different types of data integrity attacks, such as malicious tripping, fault replay, and signal altering attacks, are considered, and detailed impact analysis is conducted in a testbed environment using the Kundur's four machine two-area system. For performing an impact analysis, the transient voltage stability of the sensitive bus voltage is studied, followed by the quantitative assessment and severity ranking using the voltage profile index. Our experimental evaluation reveals that the data integrity attacks on control signals exhibit a higher attack severity than on the measurement signals. Further, the severity of these attacks varies with nature (static or dynamic), location, and types of attacks.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Uncertainty guided online ensemble for non-stationary data streams in fusion science
Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.
AI↗
Design, characterization and indoor validation of the optical soiling detector “DUSST”
Nowadays, photovoltaic (PV) technology has reached a high level of maturity in terms of module efficiency and cost competitiveness in comparison with other energy technologies. As PV has achieved high levels of deployment, the development of devices that can help to reduce PV operation and maintenance costs has become a priority. Soiling can be cause of significant losses in certain PV plants and its detection has become essential to ensure a correct mitigation. For this reason, accurate and low-cost monitoring devices are needed. While soiling stations have been traditionally employed to measure the impact of soiling, their high cost and maintenance have led to the development of innovative low-cost optical sensors, such as the device presented in this work and named “DUSST” (Detector Unit for Soiling Spectral Transmittance). The thermal characterization of DUSST’s components and the methodology used to predict soiling transmittance losses are presented in this study. The results demonstrate that the losses can be predicted with an error lower than 1.4%. The method has been verified with an experimental campaign with naturally soiled coupons exposed outdoors in Jaén, Spain.
14 SOLAR ENERGY↗
Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors
The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid “close-in”, and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.
47 OTHER INSTRUMENTATION↗
Nanoscale Elemental Mapping of Intact Solid–Liquid Interfaces and Reactive Materials in Energy Devices Enabled by Cryo-FIB/SEM
Many modern energy devices rely on solid–liquid interfaces, highly reactive materials, or both, for their operation and performance. The difficulty of characterizing such materials means these devices often lack high-resolution characterization in an unaltered state. Here, we demonstrate how cryogenic sample preparation and transfer can extend the capabilities of FIB/SEM techniques to solid–liquid interfaces and reactive materials common to energy devices by preserving their integrity through all stages of preparation and characterization. We additionally show how cryo-FIB/SEM paired with energy dispersive X-ray spectroscopy enables nanoscale elemental mapping of cross-sections produced in these materials and discuss strategies to achieve optimal results. Finally, we consider current limitations of the technique and propose future developments that could enhance its capabilities. Our results illustrate that cryo-FIB/SEM will be a useful technique for fields where solid–liquid interfaces or reactive materials play an important role and could, thus far, not be characterized at high resolution.
25 ENERGY STORAGE↗
Noise limits for dc SQUID readout of high- Q resonators below 300 MHz
We present the limits on noise for the readout of cryogenic high-Q resonators using dc Superconducting Quantum Interference Devices (SQUIDs) below 300 MHz. This analysis uses realized first-stage SQUIDs (previously published), whose performance is well described by Tesche–Clarke (TC) theory, coupled directly to the resonators. We also present data from a prototype second-stage dc SQUID array designed to couple to this first-stage SQUID as a follow-on amplifier with high system bandwidth. This analysis is the first full consideration of dc SQUID noise performance referred to a high-Q resonator over this frequency range and is presented relative to the standard quantum limit. We include imprecision, backaction, and backaction–imprecision noise correlations from TC theory, the noise contributed by the second-stage SQUIDs, wiring, and preamplifiers, and optimizations for both on-resonance measurements and off-resonance scan sensitivity. This architecture has modern relevance due to the increased interest in axion searches and the requirements of the DMRadio-m 3 axion search, which uses dc SQUIDs in this frequency range.
Bosons↗
Controlling the status of frequency-managed electric devices
Apparatus comprise control circuitry configured to control a line frequency of a microgrid by directing a first set of one or more electrical devices coupled to the microgrid to produce a change in the line frequency through a change in operation of the first set, such that a second set of one or more electrical devices coupled to the microgrid adjusts operation in response to a detected line frequency changeably produced by the first set. Related methods and additional apparatus and methods are disclosed. Apparatus and methods are disclosed that use line frequency and/or line voltage. Apparatus and methods are also disclosed that use control history.
Hammerstrom, Donald J.↗
Search for coherent elastic neutrino-nucleus scattering at a nuclear reactor with CONNIE 2019 data
The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is taking data at the Angra 2 nuclear reactor with the aim of detecting the coherent elastic scattering of reactor antineutrinos with silicon nuclei using charge-coupled devices (CCDs). In 2019 the experiment operated with a hardware binning applied to the readout stage, leading to lower levels of readout noise and improving the detection threshold down to 50 eV. The results of the analysis of 2019 data are reported here, corresponding to the detector array of 8 CCDs with a fiducial mass of 36.2 g and a total exposure of 2.2 kg-days. The difference between the reactor-on and reactor-off spectra shows no excess at low energies and yields upper limits at 95% confidence level for the neutrino interaction rates. In the lowest-energy range, 50 − 180 eV, the expected limit stands at 34 (39) times the standard model prediction, while the observed limit is 66 (75) times the standard model prediction with Sarkis (Chavarria) quenching factors.[graphic not available: see fulltext]
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Optimizing inference of segmentation on high-resolution images in MLExchange
MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.
Lu, Shizhao↗
Self-aligned concentrating immersion-lens arrays for patterning and efficiency recovery in scaffold-reinforced perovskite solar cells
In contrast to existing silicon, CIGS, and multi-junction cells that exhibit remarkable mechanical durability, perovskite solar cells have been shown to delaminate when subjected to the mechanical loads that occur during processing and field exposures, limiting their potential as a reliable solar technology. Mechanical reinforcement is thus essential for durable and reliable perovskite technologies. One current strategy to overcome the thermomechanical fragility of perovskite solar cells is to extrinsically shield them by introducing reinforcing scaffolds which partition the cell into many distinct microcells, but improvements in mechanical stability coincide with a reduced device efficiency due to parasitic absorption by the scaffold. We address this reduced efficiency by integrating concentrating immersion-lens arrays (CILAs) into scaffold-reinforced solar cells. These scaffolds are lithographically patterned by a maskless process, in which ultraviolet light is used to pattern the scaffolds through the CILAs, ensuring self-alignment and optical contact with the microcells. Perovskite devices are deposited into the microcells, and during operation, light is concentrated into the microcells and away from the insulating scaffolds, resulting in mechanically resilient solar cells with efficiencies comparable to planar devices. The CILAs also exhibit passive tracking of incident light—verified experimentally and with ray-tracing simulations—which is critical for optimal power output as the sun moves across the sky during the day. The simplicity of the fabrication process and the efficiency of the resulting scaffolded devices show that a lens-integrated scaffold-reinforced structure is a potential pathway to high-performance, robust, commercially viable perovskite solar cells.
14 SOLAR ENERGY↗
A Review: Indirect Optimal Control of Wave Energy Converters
Wave energy conversion has been the subject of interest in the past several years. While there are several concepts for converting wave power into electric power, the cost of the electric power harvested from ocean waves remains high. One of the main challenges, though it receives less attention, is the control of the wave energy converter (WEC). This paper presents a treatment for the WEC control problem within the context of optimal control theory. The result is systematic development for an explicit expression for a control that maximizes the harvested energy while meeting operational constraints such as the maximum device stroke and the maximum control force. The control presented here can also be adjusted to meet device design constraints such as a limitation on the amount of reactive power available from the power take-off (PTO) unit; this feature enables a control co-design for the PTO unit. Numerical simulations are presented in this paper for demonstration.
control design↗
Observation of Potential-Induced Hydration on the Surface of Ceramic Proton Conductors Using In Situ Near-Ambient Pressure X-ray Photoelectron Spectroscopy
Interactions of ceramic proton conductors with the environment under operating conditions play an essential role on material properties and device performance. It remains unclear how the chemical environment of material, as modulated by the operating condition, affects the proton conductivity. Combining near-ambient pressure X-ray photoelectron spectroscopy and impedance spectroscopy, we investigate the chemical environment changes of oxygen and the conductivity of BaZr 0.9 Y 0.1 O 3-δ under operating condition. Changes in O 1s core level spectra indicate that adding water vapor pressure increases both hydroxyl groups and active proton sites at undercoordinated oxygen. Applying external potential further promotes this hydration effect, in particular, by increasing the amount of undercoordinated oxygen. The enhanced hydration is accompanied by improved proton conductivity. Here, this work highlights the effects of undercoordinated oxygen for improving the proton conductivity in ceramics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-Voltage Phosphate Cathodes for Rechargeable Ca-Ion Batteries
Calcium-ion batteries (CIBs) are under investigation as next-generation energy storage devices due to their theoretically high operating potentials and lower costs tied to the high natural abundance of calcium. However, the development of CIBs has been limited by the lack of available positive electrode materials. In this study, for the first time, we report two functional polyanionic phosphate materials as high-voltage cathodes for CIBs at room temperature. NaV 2 (PO 4 ) 3 electrodes were found to reversibly intercalate 0.6 mol of Ca 2+ (81 mA h g –1 ) near 3.2 V (vs Ca 2+ /Ca) with stable cycling performance at a current density of 3.5 mA g –1 . The olivine framework material FePO 4 reversibly intercalates 0.2 mol of Ca 2+ (72 mA h g –1 ) near 2.9 V (vs Ca 2+ /Ca) at a current density of 7.5 mA g –1 in the first cycle. Structural, electronic, and compositional changes are consistent with reversible Ca 2+ intercalation into these two materials.
25 ENERGY STORAGE↗