Search Results (528)

A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A bottleneck in current measurement systems is the readout electronics, which are usually based on optically detected magnetic resonance (ODMR). The idea is to have a hardware that tracks up to four resonances simultaneously for the detection of the three-axis magnetic field components and the temperature. Normally, expensive scientific instruments are used for the measurement setup. In this work, we present an electronic device that is based on a Zynq 7010 FPGA (Red Pitaya) with an add-on board, which has been developed to control the excitation laser, the generation of the microwaves, and interfacing the photodiode, and which provides additional fast digital outputs. The T1 measurement was chosen to demonstrate the ability to read out the spin of the system. Full article

The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system. Full article

The commercialization of proton-conducting fuel cells (PCFCs) is hindered by the limited electroactivity and durability of cathodes at intermediate temperatures ranging from 400 to 700 °C, a challenge exacerbated by an insufficient understanding of high-entropy perovskite (HEP) materials for oxygen reduction reaction (ORR) optimization. This study introduces an yttrium-doped HEP to address these limitations. A comparative analysis of Ce_0.2−xY_xBa_0.2Sr_0.2La_0.2Ca_0.2CoO_3−δ (x = 0, 0.2; designated as CBSLCC and YBSLCC) revealed that yttrium doping enhanced the ORR activity, reduced the thermal expansion coefficient (19.9 × 10⁻⁶ K⁻¹, 30–900 °C), and improved the thermomechanical compatibility with the BaZr_0.1Ce_0.7Y_0.1Yb_0.1O_3−δ electrolytes. Electrochemical testing demonstrated a peak power density equal to 586 mW cm⁻² at 700 °C, with a polarization resistance equaling 0.3 Ω cm². Yttrium-induced lattice distortion promotes proton adsorption while suppressing detrimental Co spin-state transitions. These findings advance the development of durable, high-efficiency PCFC cathodes, offering immediate applications in clean energy systems, particularly for distributed power generation. Full article

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article

Purpose: To assess the performance of 2D T2-weighted (w) Turbo Spin Echo (TSE) MRI reconstructed with a deep learning (DL)-powered super resolution reconstruction (SRR) algorithm combining compressed sensing (CS) denoising and resolution upscaling for high-resolution hippocampal imaging in patients with (epileptic) seizures and suspected hippocampal pathology. Methods: A 2D T2w TSE coronal hippocampal sequence with compressed sense (CS) factor 1 (scan time 270 s) and a CS-accelerated sequence with a CS factor of 3 (scan time 103 s) were acquired in 28 patients. Reconstructions using the SRR algorithm (CS 1-SSR-s and CS 3-SSR-s) were additionally obtained in real time. Two readers graded the images twice, based on several metrics (image quality; artifacts; visualization of anatomical details of the internal hippocampal architecture (HIA); visibility of dentate gyrus/pes hippocampi/fornix/mammillary bodies; delineation of gray and white matter). Results: Inter-readout agreement was almost perfect (Krippendorff’s alpha coefficient = 0.933). Compared to the CS 1 sequence, the CS 3 sequence significantly underperformed in all 11 metrics (p < 0.001-p = 0.04), while the CS 1-SRR-s sequence outperformed in terms of overall image quality and visualization of the left HIA and right pes hippocampi (p < 0.001-p < 0.04) but underperformed in terms of presence of artifacts (p < 0.01). Lastly, relative to the CS 1 sequence, the CS 3-SRR-s sequence was graded worse in terms of presence of artifacts (p < 0.003) but with improved visualization of the right pes hippocampi (p = 0.02). Conclusion: DL-powered SSR demonstrates its capacity to enhance imaging performance by introducing flexibility in T2w hippocampal imaging; it either improves image quality for non-accelerated imaging or preserves acceptable quality in accelerated imaging, with the additional benefit of a reduced scan time. Full article

Poly[bis(4-phenyl) (2,5,6-trimethylphenyl) amine (PTAA), as a hole transfer material, has been widely used in perovskite solar cells (PSCs). However, the optimal solvent for preparing the PTAA solution and coating the PTAA layer is still uncertain. In this work, we investigated three types of organic solvents (toluene, chlorobenzene and dichlorobenzene) for processing PTAA layers as the hole transport layer in PSCs. Based on the experimental verification and molecular dynamics simulation results, all the evidence indicated that toluene performs best among the three candidates. This is attributed to the significant polarity difference between toluene and PTAA, which leads to the formation of a uniform surface morphology characterized by granular protuberances after spin coating. The contact area of the hole transfer layer with the surface aggregation is increased in reference to the rough surface, and the hydrophilicity of the PTAA layer is also increased. The improvement of these two aspects are conducive to the effective interfacial charge transfer. This leads to the generation of more photocurrent. The PSCs employing toluene-processed PTAA exhibit an average power conversion efficiency (PCE) of 19.1%, which is higher than that of PSCs using chlorobenzene- and dichlorobenzene-processed PTAA (17.3–17.9%). This work provides a direct optimization strategy for researchers aiming to fabricate PSCs based on PTAA as a hole transport layer and lays a solid foundation for the development of high-efficiency inverted PSCs. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

This work reports the fabrication and optimization of nylon/multi-walled carbon nanotube (MWCNT) nanocomposite-based triboelectric nanogenerators (TENGs) using a spin coating method. By carefully tuning the MWCNT concentration, the device achieved a substantial enhancement in electrical output, with open-circuit voltage and short-circuit current peaking at 29.7 V and 3.0 μA, respectively, at 0.05 wt% MWCNT loading on the surface of nylon. The corresponding power density reached approximately 13.9 mW/m², representing a significant improvement over pure nylon-based TENGs. The enhanced performance is attributed to improved charge trapping and dielectric properties due to well-dispersed MWCNTs on the surface of nylon, while excessive loading caused agglomeration, reducing efficiency. This lightweight, flexible nanocomposite TENG offers a promising solution for efficient, sustainable energy harvesting in wearable electronics and self-powered sensor systems, highlighting its potential for practical energy applications. Full article

Polytetrafluoroethylene (PTFE) has been widely used as a high-frequency dielectric substrate due to its excellent dielectric properties and thermal stability. However, with its low intrinsic thermal conductivity, PTFE falls short in meeting the escalating heat dissipation demands of high-power density, high-frequency communication systems. Although the thermal conductivity of PTFE composites can be effectively improved by the high thermal conductivity fillers, it is always accompanied by a decline in dielectric properties. Molecular chain ordering is regarded as an effective strategy to improve the intrinsic thermal conductivity of polymers while maintaining dielectric properties. Unfortunately, the conventional preparation methods for ordered molecular chains, such as electrostatic spinning and uniaxial stretching, are not applicable to the preparation of PTFE substrates. In this work, fluorinated graphite (FGi) is employed to induce the in-plane orientation of PTFE molecular chains. As a result, the PTFE composite with 0.5 wt% FGi loading exhibits an in-plane thermal conductivity of 1.21 W·m⁻¹·K⁻¹, six times higher than the in-plane thermal conductivity of pure PTFE. In addition, this composite exhibits a superior dielectric constant of 2.06 and dielectric loss of 0.0021 at 40 GHz. This work introduces a facile method to achieve improved thermal conductivity of PTFE while maintaining its excellent dielectric properties. Full article

This paper presents a novel transposed MRAM architecture (WinEdge) specifically optimized for Winograd convolution acceleration in edge computing devices. Leveraging Magnetic Tunnel Junctions (MTJs) with Spin Hall Effect (SHE)-assisted Spin-Transfer Torque (STT) writing, the proposed design enables a single SHE current to simultaneously write data to four MTJs, substantially reducing power consumption. Additionally, the integration of stacked MTJs significantly improves storage density. The proposed WinEdge efficiently supports both standard and transposed data access modes regardless of bit-width, achieving up to 36% lower power, 47% reduced energy consumption, and 28% faster processing speed compared to existing designs. Simulations conducted in 45 nm CMOS technology validate its superiority over conventional SRAM-based solutions for convolutional neural network (CNN) acceleration in resource-constrained edge environments. Full article

Aiming to obtain insight into the dynamic properties of ionogels, ¹H NMR relaxation experiments were performed for an ionogel composed of 1-butyl-3-methyl-imidazolium chloride [BMIM][Cl] and propylene carbonate. The experiments were conducted in the frequency range of 10 kHz to 20 MHz, spanning the temperature range of 273 K to 338 K. The data were analyzed in term s of a relaxation model including two relaxation contributions—one of them associated with anisotropic (two-dimensional) translation diffusion, the second one representing a power law dependence of spin-lattice relaxation rates on the resonance frequency. The power law relaxation term (characterized by a very low power law factor of about 0.1) was attributed to the collective dynamics of the partially immobilized propylene carbonate matrix, while the relaxation contribution associated with anisotropic translation diffusion was attributed to the movement of BMIM cations in the matrix; the translation diffusion coefficient was estimated as varying in the range of 10⁻¹³ m²/s–10⁻¹² m²/s. Moreover, other parameters were determined as a result of the analysis, such as the residence lifetime on the matrix surfaces. Subsequently, the temperature dependencies of the determined parameters were assessed. Full article

This paper presents a novel approach to economic dispatch (ED) optimization in power systems through the application of Proximal Policy Optimization (PPO), an advanced reinforcement learning algorithm. The economic dispatch problem, a fundamental challenge in power system operations, involves optimizing the generation output of multiple units to minimize operational costs while satisfying load demands and technical constraints. Traditional methods often struggle with the non-linear, non-convex nature of modern ED problems, especially with increasing penetration of renewable energy sources. Our PPO-based methodology transforms the ED problem into a reinforcement learning framework where an agent learns optimal generator scheduling policies through continuous interaction with a simulated power system environment. The proposed approach is validated on a 15-generator test system with varying load demands and operational constraints. Experimental results demonstrate that the PPO algorithm achieves superior performance compared to conventional techniques, with cost reductions of up to 7.3% and enhanced convergence stability. The algorithm successfully handles complex constraints including generator limits, ramp rates, and spinning reserve requirements, while maintaining power balance with negligible error margins. Furthermore, the computational efficiency of the PPO approach allows for real-time adjustments to rapidly changing system conditions, making it particularly suitable for modern power grids with high renewable energy penetration. Full article

Atomic magnetometers based on the spin-exchange relaxation-free (SERF) regime have broad applications in bio-magnetic measurement due to their high sensitivity and miniaturized size. In this paper, we propose a SERF-based magnetometer using 1 × 2 polarization-maintaining fiber (PMF) with single-beam parameter optimization. The impacts of temperature, pumping laser power, and modulation amplitude on the magnetometer’s response signal at the SERF regime are examined. Moreover, through the simulation of zero-field resonance, the compensation accuracy is optimized. To further improve the compensation stability and accuracy, a novel finite state machine (FSM)-assisted iterative optimization magnetic field compensation algorithm is proposed. A pT-level compensation resolution with an error below 1.6% is achieved, which lays the foundation for the subsequent application of biomagnetic measurement arrays. Full article

This study reports a gate-tunable three-terminal optoelectronic synaptic device based on an Al/ZnO nanoparticles (NPs)/SiO₂/Si structure for neuromorphic in-sensor memory applications. The ZnO NP film, fabricated via spin coating, exhibited strong UV-induced excitatory post-synaptic current (EPSC) responses that were modulated by gate voltage through charge injection across the SiO₂ dielectric rather than by conventional field effect. Optical stimulation enabled short-term synaptic plasticity, with paired-pulse facilitation (PPF) values reaching 185% at a gate voltage of −5.0 V and decreasing to 180% at +5.0 V, confirming gate-dependent modulation of synaptic weight. Repeated stimulation enhanced learning efficiency and memory retention, as demonstrated by reduced pulse numbers for relearning and slower EPSC decay. Wickelgren’s power law analysis further revealed a decrease in the forgetting rate under negative gate bias, indicating improved long-term memory characteristics. A 3 × 3 synaptic device array visualized visual memory formation through EPSC-based color mapping, with darker intensities and slower fading observed under −5.0 V bias. These results highlight the critical role of gate-voltage-induced charge injection through the SiO₂ dielectric in controlling optical potentiation and electrical depression, establishing ZnO NP-based optoelectronic synaptic devices as promising platforms for energy-efficient, light-driven neuromorphic computing. Full article