Search Results (72)

Electrocardiogram (ECG) monitoring on low-power edge devices requires models that balance accuracy, latency, and energy consumption. This study evaluates abrupt change detection in ECG using spiking neural networks (SNNs) trained on spike-encoded signals that preserve salient cardiac dynamics. This study used 4910 ECG segments from 290 subjects (PTB Diagnostic Database; 2.5-s windows at 1 kHz), providing context for the reported results. Under a unified architecture, preprocessing pipeline, and training schedule, we compare two representative neuron models—leaky integrate-and-fire (LIF) and adaptive exponential integrate-and-fire (AdEx). We report balanced accuracy, sensitivity, inference latency, and an energy proxy based on spike-event counts, and we examine robustness to input noise and temporal distortions. Across operating points, AdEx yields the highest overall accuracy and sensitivity, whereas LIF achieves the lowest energy cost and shortest latency, favoring deployment on resource-constrained hardware. Both SNN variants substantially reduce computational events—hence estimated energy—relative to conventional artificial neural network baselines, supporting their suitability for real-time, on-device diagnostics. These findings provide practical guidance for selecting neuron dynamics and decision thresholds to meet target accuracy–sensitivity trade-offs under energy and latency budgets. Overall, combining spike-encoded ECG with appropriately chosen SNN dynamics enables reliable abrupt change detection with notable efficiency gains, offering a path toward scalable edge-level cardiovascular monitoring. While lightweight CNNs and shallow transformers are important references, to keep the scope focused on SNN design choices and policy-aware thresholding for edge constraints, we refrain from reporting additional ANN numbers here. A seed-controlled head-to-head benchmark is reserved for future work. Full article

Spiking neural networks (SNNs) provide a biologically inspired, event-driven alternative to artificial neural networks (ANNs), potentially delivering competitive accuracy at substantially lower energy. This tutorial-study offers a unified, practice-oriented assessment that combines critical review and standardized experiments. We benchmark a shallow fully connected network (FCN) on MNIST and a deeper VGG7 architecture on CIFAR-10 across multiple neuron models (leaky integrate-and-fire (LIF), sigma–delta, etc.) and input encodings (direct, rate, temporal, etc.), using supervised surrogate-gradient training implemented in Intel Lava, SLAYER, SpikingJelly, Norse, and PyTorch. Empirically, we observe a consistent but tunable trade-off between accuracy and energy. On MNIST, sigma–delta neurons with rate or sigma–delta encodings achieve 98.1% accuracy (ANN baseline: 98.23%). On CIFAR-10, sigma–delta neurons with direct input reach 83.0% accuracy at just two time steps (ANN baseline: 83.6%). A GPU-based operation-count energy proxy indicates that many SNN configurations operate below the ANN energy baseline; some frugal codes minimize energy at the cost of accuracy, whereas accuracy-oriented settings (e.g., sigma–delta with direct or rate coding) narrow the performance gap while remaining energy-conscious—yielding up to threefold efficiency compared with matched ANNs in our setup. Thresholds and the number of time steps are decisive factors: intermediate thresholds and the minimal time window that still meets accuracy targets typically maximize efficiency per joule. We distill actionable design rules—choose the neuron–encoding pair according to the application goal (accuracy-critical vs. energy-constrained) and co-tune thresholds and time steps. Finally, we outline how event-driven neuromorphic hardware can amplify these savings through sparse, local, asynchronous computation, providing a practical playbook for embedded, real-time, and sustainable AI deployments. Full article

4LiF-MM’₂O₄ composites were synthesized via the mechanical milling of LiF and MM’₂O₄ (M = Mn, Mg, Zn; M’ = Mn Fe) for 72 h. In the obtained composites, the XRD peak broadened because of the milling, and the composites possessed a rock-salt-type structure. During charge–discharge measurements at 0.1 C, composites with spinel materials containing Mg showed particularly high discharge capacities; the discharge capacity of 4LiF-MMn₂O₄ and 4LiF-MFe₂O₄ was 310 mAh/g and 309 mAh/g, respectively. The discharge voltage was approximately 3.2 V for 4LiF-MgMn₂O₄ and approximately 2.8 V for 4LiF-MgFe₂O₄, and 4LiF-MgMn₂O₄ composites had the highest energy densities, exceeding 1000 Wh/kg. During cycle characteristic measurements with a cutoff voltage of 4.8 V and 4.4 V, the initial capacity retentions at the 100th cycle were 11% and 79%, respectively. The Coulombic efficiency was also better at a cutoff voltage of 4.4 V than that of 4.8 V, indicating that electrolyte decomposition has a significant influence on the cycle characteristics. Additionally, composites were synthesized via mechanical milling using various molar ratios of LiF and MgMn₂O₄. In xLiF-MgMn₂O₄ (x ≥ 3), the discharge potential was approximately 3.2 V, and the discharge capacity was higher than 250 mAh/g. The highest discharge capacity was observed for 4LiF-MgMn₂O₄ among xLiF-MgMn₂O₄ (x ≥ 3) composites. Full article

In scaled laser-driven magnetized liner inertial fusion (MagLIF), externally applied magnetic fields improve energy coupling by suppressing electron thermal conduction, enhancing Joule heating, and increasing $\alpha$-particle energy deposition. However, confinement can be significantly degraded by magnetic flux transport, dominated by resistive diffusion, and more critically, the Nernst effect. One-dimensional magnetohydrodynamic simulations demonstrate that increasing the applied field generally enhances neutron yield, but when the Nernst effect is included, the benefit of stronger magnetization diminishes. Stagnation is achieved at 2.72 ns, yielding a peak temperature of 2.17 keV and a neutron production of $1.2\times {10}^{12}$. When the Nernst effect is taken into account, the neutron yield decreases by 57.3% compared with the case without it under an initial magnetic field of 10 T. During the implosion, the magnetic field in the fuel gradually diffuses outward into the outer liner. By stagnation, the magnetic flux of fuel has decreased by 33.8%. Based on the characteristics of the Nernst effect, an optimized initial magnetic field of approximately 6 T is identified, which yields an about 2.5 times higher neutron yield than the unmagnetized case. These findings emphasize the key role of magnetic–energy coupling in target performance and provide guidance for the design and scaling of magnetized targets. Full article

Buildings consume 40% of global energy, over half of which is used for cooling and heating. Tungsten bronze (M_xWO₃) holds promise for smart windows due to its ability to block near-infrared (NIR) heat radiation while maintaining visible light transmittance. However, conventional high-temperature synthesis is energy intensive. Here, we develop a low-temperature hydrothermal method (170 °C) to prepare Li and Cs co-doped tungsten oxide using WCl₆, LiF, and CsOH·H₂O as precursors, with acetic acid as a crystallographic modulator. The material exhibits a hexagonal structure (P6₃/mcm) and Li⁺-induced lattice expansion (0.34 nm spacing). Combined XPS and ICP-OES analyses confirm the chemical composition as Cs_0.31Li_0.09WO₃ and reveal a positive correlation between the W⁵⁺ content (15.76%) and oxygen vacancy concentration, which is identified as the key factor enhancing the NIR absorption. The material demonstrates excellent visible light transmission and NIR shielding properties. Our work provides a more energy-efficient and sustainable pathway for the production of smart window materials. Full article

The operational failure of lithium-ion batteries under extreme temperatures (−40~60 °C) stems primarily from electrolyte limitations. While prior efforts improved either low-temperature or high-temperature performance independently, holistic electrolyte design with practical validation remains elusive. Herein, we develop an all-climate electrolyte (ACE) through synergistic coordination of solvent, Li salt, and additive, achieving low viscosity (<10 mPa·s at −40 °C) and high ionic conductivity (7.0 mS cm⁻¹ at −40 °C). Raman and NMR spectra reveal MA and EC co-occupying Li⁺ solvation sheath while EMC acts as a diluent, enabling rapid ion transport. Consequently, LiFePO₄ (LFP)|graphite (Gr) cell delivers unprecedented cyclability: zero capacity decay over 500 cycles at 0 °C, stable operation across −40~60 °C, and 94.1% retention after 100 cycles at 45 °C in Ah-level pouch cells. XPS and SEM analysis demonstrate lithium difluorophosphate (LiDFP) and lithium bis(fluorosulfonyl)imide (LiFSI) collaboratively remodel SEI/CEI interphases, enriching them with LiF, Li₃PO₄, and Li₂SO₄. This inorganic-dominant architecture enhances interfacial Li⁺ kinetics and all-climate stability compared to the baseline electrolyte. Our tripartite electrolyte strategy provides a material-agnostic solution for all-climate energy storage. Full article

Lithium fluoride (LiF) crystals are widely employed both as optical windows transparent in the ultraviolet spectral region and as efficient personal dosimeters, with their application scope recently expanding into lithium-ion technologies. Moreover, as an alkali halide crystal (AHC), LiF serves as a model system for studying and simulating radiation effects in solids. This work identifies radiation-induced defects formed in lithium fluoride upon irradiation with swift heavy ion beams (N, O, Kr, U) and intense pulsed electron beams, investigates their thermal stability, and performs computer modeling of annealing processes. The theoretical analysis of existing experimental kinetics for $F$-centers induced by electron and heavy ion irradiation reveals considerable differences in the activation energies for interstitial migration. A strong correlation between the activation energy $E_a$ and the pre-exponential factor $X$($E_a$) is observed; notably, $X$($E_a$) is no longer constant but closely matches the potential function Ea. Indeed, with increasing irradiation dose, both the migration energy $E_a$ and pre-exponential factor $X$ decrease simultaneously, leading to an effective increase in the defect diffusion rate. Full article

Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors—including oscillatory, leaky integrate-and-fire (LIF), Hodgkin–Huxley (H-H), and stochastic dynamics—and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation. We review memristor-based neuron circuits regarding architectures, materials, and key performance metrics. At the system level, we summarize bio-inspired neuromorphic platforms integrating TSM neurons with visual, tactile, thermal, and olfactory sensors, achieving real-time edge computation with high accuracy and low power. Finally, we critically examine key challenges—such as stochastic switching origins, device variability, and endurance limits—and propose future directions toward reconfigurable, robust, and scalable memristive neuromorphic architectures. Full article

This paper proposes Linguistic Intuitionistic Fuzzy (LIF) aggregation operators, LIF-energies, LIF-correlation, and LIF-correlation coefficients. Supporting theorems are also proven for the proposed functions, which are utilized in the Linguistic Intuitionistic Fuzzy–Vlse Kriterijumska Optimizacija Kompromisno Resenje (LIF-VIKOR) method within Decision Support Systems (DSS). Additionally, numerical examples are presented to validate the method. The sensitivity analysis of weighting vectors is conducted, and the consistency of final rankings affirms the robustness of the proposed approaches. Arithmetic operations, specifically subtraction and division, are applied to LIF numbers (LIFNs) within the LIF-VIKOR algorithm. Furthermore, a function called the Linguistic Median Membership (LMM) function is introduced to convert LIFN values into crisp numbers. In the LIF-VIKOR algorithm, the proposed correlation coefficient is used for ranking alternatives, while the entropy method is applied to compute weights. Sensitivity analysis is performed to ensure the consistency of the proposed method. Finally, an Artificial Neural Network (ANN) is integrated into the VIKOR algorithm to enhance computational efficiency, reducing the time and manpower required to solve the model. Full article

Li/graphite fluoride (Li/CF_x) batteries display the highest energy densities among those of commercially available primary Li batteries but fail to satisfy the high-performance requirements of advanced applications. To address this drawback, two liquid organic dinitrates, namely, 1,4-butanediol dinitrate (BDE) and 2,2,3,3-tetrafluoro-1,4-butanediol dinitrate (TBD), were employed as high-energy energetic materials, and they were highly compatible with the electrolytes of Li/CF_x batteries. The use of Super P electrodes confirmed that the reduction reaction mechanisms of both nitrate ester-based compounds delivered considerable specific capacities, associated with discharge potentials matching that of the Li/CF_x battery. When considering the combined mass of the electrolyte and cathode as the active material, the overall energy densities of the Li/CF_x batteries increased by 25.3% (TBD) and 20.8% (BDE), reaching 1005.50 and 969.1 Wh/kg, respectively. The superior performance of TBD was due to the synergistic effects of the high electronegativities and levels of steric hindrance of the F atoms. Moreover, the nanocrystal LiF particles generated by TBD induced crack formation within the fluorinated graphite, increasing the lithium-ion accessible surface area and enhancing its utilization efficiency. These combined factors enhanced the reactivity of TBD and facilitated its involvement in electrochemical reactions, thus improving the capacity of the battery. The developed strategy enables the facile, cost-effective enhancement of the capacities of Li/CF_x batteries, paving the way for their practical use in energy-demanding devices. Full article

Prelithiation has been widely accepted as one of the most promising strategies to compensate for the loss of active substance and to improve the initial Coulombic efficiency in silicon-based anodes for advanced high-energy-density batteries. But because of their unstable solid electrolyte interface (SEI) layer and low initial Coulombic efficiency, they expand in volume during prelithiation and react with moisture, which makes commercialization a difficult process. Herein, we have developed a strategy using lithium bis(fluorosulfonyl)imide (LiFSI) treatment to eliminate redundant lithium and generate LiF-based inorganic compounds on the surface of the prelithiated electrode. Such method not only reduces the reactiveness of the prelithiated anode but also enhances the ionic conductivity of the SEI. The rich LiF surface works as an artificial SEI, and according to electrochemical evaluation, the initial Coulombic efficiency of the prelithiated silicon anode treated with LiFSI can reach 92.9%. This technique not only increases the battery’s energy density but also its cycle stability, resulting in superior capacity retention and a longer cycling life. Full article

Despite spiking neural networks (SNNs) inherently exceling at processing time series due to their rich spatio-temporal information and efficient event-driven computing, the challenge of extracting complex correlations between variables in multivariate time series (MTS) remains to be addressed. This paper proposes a reinforced, event-driven, and attention-based convolution SNN model (REAT-CSNN) with three novel features. First, a joint Gramian Angular Field and Rate (GAFR) coding scheme is proposed to convert MTS into spike images, preserving the inherent features in MTS, such as the temporal patterns and spatio-temporal correlations between time series. Second, an advanced LIF-pooling strategy is developed, which is then theoretically and empirically proved to be effective in preserving more features from the regions of interest in spike images than average-pooling strategies. Third, a convolutional block attention mechanism (CBAM) is redesigned to support spike-based input, enhancing event-driven characteristics in weighting operations while maintaining outstanding capability to capture the information encoded in spike images. Experiments on multiple MTS data sets, such as stocks and PM2.5 data sets, demonstrate that our model rivals, and even surpasses, some CNN- and RNN-based techniques, with up to 3% better performance, while consuming significantly less energy. Full article

A Si/SiC/SiO₂ (53/44/3 wt.%) composite is evaluated as an anode material for Li-ion batteries. This material, a result of the high-energy ball-milling of a by-product of the carbothermal reduction of silica (Si slag), is predominantly made up of micrometric particles of amorphous or short-range order Si in which submicrometric SiC inclusions are dispersed. Its capacity is 860 mAh g⁻¹ (1.7 mAh cm⁻²) after 200 cycles in half-cell configuration and 1.6 mAh cm⁻² after 70 cycles in full-cell. The SiC component is not electroactive for lithiation but plays a key role in the electrode stability by preventing the formation of the c-Li₁₅S_i4 phase, known to accelerate electrode degradation. It is shown that capacity decay with cycling mainly originates from solid electrolyte interphase (SEI) growth rather than particle disconnections. Complementary wide angle X-ray scattering (WAXS) analyses confirm the SEI grows alongside cycling and allows for the highlighting of its major components, namely, Li₂CO₃ and LiF. The morphological evolution of the electrode upon cycling is studied by electrochemical dilatometry, operando optical microscopy, and focused ion beam (FIB) and broad ion beam (BIB) scanning electron microscopy (SEM). No particle cracking is observed. However, reconstructed 3D imaging of the electrodes before and after 10 and 200 cycles clearly shows that the particles progressively evolve a dendritic structure. The SEI grows on and within the particles and induces a significant decrease in the electrode’s porosity and an increase in its thickness. Full article

Rare-earth molten-salt electrolysis slag contains a substantial quantity of rare-earth elements, rendering it a valuable secondary resource for rare-earth recovery. To achieve the efficient recovery of praseodymium (Pr), neodymium (Nd), lithium (Li), and fluorine (F) from rare-earth molten-salt electrolysis slag, this paper proposes an atmospheric alkaline leaching method. The leaching efficiency of Nd, Pr, F (95.02%), and Li (95.87%) can be reached at a NaOH concentration of 80%, a reaction temperature of 180 °C, a reaction time of 2 h, and an alkali to slag ratio of 3:1. Leaching efficiency kinetic analysis shows that the leaching processes of fluorine and lithium are both controlled by interfacial chemical reactions, with apparent activation energies of 59.06 kJ/mol and 57.33 kJ/mol, respectively. The mineral phase transformation and morphological analysis were studied by X-ray diffractometer and scanning electron microscope. The results indicated that rare-earth fluoride (REF₃) reacts with sodium hydroxide to form rare-earth hydroxide (RE(OH)₃) and soluble sodium fluoride (NaF), while LiF is converted into LiOH and enters the liquid phase. High-efficiency separation was achieved by washing with water, avoiding high-temperature energy consumption and the problem of fluorine-containing waste gas. Full article

The morphological, structural, and thermoluminescence (TL) properties of LiF:SiO₅ doped with Ce³⁺ solid powder phosphor were systematically analyzed. X-ray diffraction (XRD) confirmed the crystalline nature of single-phase LiF:SiO₅:Ce³⁺ nanoparticles (NPs), with crystalline size (D) determined using the Williamson–Hall (W–H) and Scherrer methods. Ce³⁺ doping induced structural modifications, reflected in variations of full width at half maximum (FWHM), strain, and stress values. The TL glow curve revealed two distinct peaks at approximately 64 °C and 134 °C, shedding light on the electron capture and release mechanisms following beta irradiation. A dose-dependent study demonstrated that TL intensity increased proportionally with radiation exposure, showing a superlinearity relationship up to 6 Gy. Additionally, investigations into different heating rates indicated only a slight shift in peak of the temperature, confirming the thermal stability of the materials. This study provides valuable insights into the TL behavior of LiF:SiO₅:Ce³⁺, making it a promising candidate for radiation dosimetry and luminescence applications. Full article