Search Results (345)

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

Dye-sensitized solar cells (DSSCs) are promising alternatives for power generation due to their environmental friendliness, cost effectiveness, and strong performance under diffused light. Conversely, their low spectral response in the ultraviolet (UV) region significantly obliterates their overall performance. The so-called luminescent down-shifting (LDS) presents a practical solution by converting high-energy UV photons into visible light that can be efficiently absorbed by sensitizer dyes. Herein, a conventional solid-state technique was applied for the synthesis of an LDS, europium (II)-doped barium orthosilicate (BaSiO₃:Eu²⁺) material. The material exhibited strong UV absorption, with prominent peaks near 400 nm and within the 200–300 nm range, despite a weaker response in the visible region. The estimated optical bandgap was 3.47 eV, making it well-suited for UV absorbers. Analysis of the energy transfer mechanism from the LDS material to the N719 dye sensitizer depicted a strong spectral overlap of $2\times {10}^{10}{\mathrm{M}}^{-1}{\mathrm{c}\mathrm{m}}^{-1}{\mathrm{n}\mathrm{m}}^4$, suggesting efficient energy transfer from the donor to the acceptor. The estimated Förster distance was approximately $6.83\mathrm{n}\mathrm{m}$, which matches the absorption profile of the dye-sensitizer. Our findings demonstrate the potential of BaSiO₃:Eu²⁺ as an effective LDS material for enhancing UV light absorption and improving DSSC performance through increased spectral utilization and reduced UV-induced degradation. Full article

Energy consumption has emerged as a critical design constraint in deploying high-performance neural networks, especially on edge devices with limited power resources. In this paper, a comparative study is conducted for two prevalent deep learning paradigms—convolutional neural networks (CNNs), exemplified by ResNet18, and transformer-based large language models (LLMs), represented by GPT3-small, Llama-7B, and GPT3-175B. By analyzing how the scaling of memory energy versus computing energy affects the energy consumption of neural networks with different batch sizes (1, 4, 8, 16), it is shown that ResNet18 transitions from a memory energy-limited regime at low batch sizes to a computing energy-limited regime at higher batch sizes due to its extensive convolution operations. On the other hand, GPT-like models remain predominantly memory-bound, with large parameter tensors and frequent key–value (KV) cache lookups accounting for most of the total energy usage. Our results reveal that reducing weight-memory energy is particularly effective in transformer architectures, while improving multiply–accumulate (MAC) efficiency significantly benefits CNNs at higher workloads. We further highlight near-memory and in-memory computing approaches as promising strategies to lower data-transfer costs and enhance power efficiency in large-scale deployments. These findings offer actionable insights for architects and system designers aiming to optimize artificial intelligence (AI) performance under stringent energy budgets on battery-powered edge devices. Full article

Two-dimensional (2D) material-based resistive random-access memory (RRAM) has emerged as a promising solution for neuromorphic computing and computing-in-memory architectures. Compared to conventional metal-oxide-based RRAM, the novel 2D material-based RRAM devices demonstrate lower power consumption, higher integration density, and reduced performance variability, benefiting from their atomic-scale thickness and ultra-flat surfaces. Remarkably, 2D layered metal oxides retain these advantages while preserving the merits of traditional metal oxides, including their low cost and high environmental stability. Through a multi-step dry transfer process, we fabricated a Pd-MoO₃-Ag RRAM device featuring 2D α-MoO₃ as the resistive switching layer, with Pd and Ag serving as inert and active electrodes, respectively. Resistive switching tests revealed an excellent operational stability, low write voltage (~0.5 V), high switching ratio (>10⁶), and multi-bit storage capability (≥3 bits). Nevertheless, the device exhibited a limited retention time (~2000 s). To overcome this limitation, we developed a Gr-MoO₃-Ag heterostructure by substituting the Pd electrode with graphene (Gr). This modification achieved a fivefold improvement in the retention time (>10⁴ s). These findings demonstrate that by controlling the type and thickness of 2D materials and resistive switching layers, RRAM devices with both high On/Off ratios and long-term data retention may be developed. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

The increasing demand for sustainable energy and clean water has prompted the exploration of alternative solutions to reduce reliance on fossil fuels. In this context, hydrogen production through water electrolysis powered by solar energy presents a promising pathway toward a zero-carbon footprint. This study investigates the potential of copper slag, an abundant industrial waste, as a low-cost electrocatalyst for the hydrogen evolution reaction (HER) in contact with saline water such as 0.5 M NaCl and seawater, comparing the electrochemical response when in contact with geothermal water from El Tatio (Atacama Desert). The physicochemical characterisation of copper slag was performed using XRD, Raman, and SEM-EDS to determine its surface properties. Electrochemical evaluations were conducted in 0.5 M NaCl and natural seawater using polarisation techniques to assess the corrosion behaviour and catalytic efficiency of the copper slag electrodes. The results indicate that copper slag exhibits high stability and promising HER kinetics, particularly in seawater, where its mesoporous structure facilitates efficient charge transfer processes. The key novelty of this manuscript lies in the direct revalorisation of untreated copper slag as a functional electrode for HER in real seawater and geothermal water, avoiding the use of expensive noble metals and aligning with circular economy principles. This innovative combination of recycled material and natural saline electrolyte enhances both the technical and economic viability of electrolysis, while reducing environmental impact and promoting green hydrogen production in coastal regions with high solar potential. This research contributes to the value of industrial waste, offering a viable pathway for advancing sustainable hydrogen technologies in real-world environments. Full article

Resistive gas sensors have attracted significant attention due to their simple architecture, low cost, and ease of integration, with widespread applications in environmental monitoring, industrial safety, and healthcare diagnostics. This review provides a comprehensive overview of recent advances in resistive gas sensors, focusing on their fundamental working mechanisms, sensing material design, device architecture optimization, and intelligent system integration. These sensors primarily operate based on changes in electrical resistance induced by interactions between gas molecules and sensing materials, including physical adsorption, charge transfer, and surface redox reactions. In terms of materials, metal oxide semiconductors, conductive polymers, carbon-based nanomaterials, and their composites have demonstrated enhanced sensitivity and selectivity through strategies such as doping, surface functionalization, and heterojunction engineering, while also enabling reduced operating temperatures. Device-level innovations—such as microheater integration, self-heated nanowires, and multi-sensor arrays—have further improved response speed and energy efficiency. Moreover, the incorporation of artificial intelligence (AI) and Internet of Things (IoT) technologies has significantly advanced signal processing, pattern recognition, and long-term operational stability. Machine learning (ML) algorithms have enabled intelligent design of novel sensing materials, optimized multi-gas identification, and enhanced data reliability in complex environments. These synergistic developments are driving resistive gas sensors toward low-power, highly integrated, and multifunctional platforms, particularly in emerging applications such as wearable electronics, breath diagnostics, and smart city infrastructure. This review concludes with a perspective on future research directions, emphasizing the importance of improving material stability, interference resistance, standardized fabrication, and intelligent system integration for large-scale practical deployment. Full article

This study investigates the potential of low-cost, naturally available porous materials (PoMs), gravel, marble, flint, and sandstone, as thermal management for photovoltaic (PV) panels. Experiments were conducted in a controlled environment at a solar energy laboratory, where variables such as solar irradiance, ambient temperature, air velocity, and water flow were carefully regulated. A solar simulator delivering a constant irradiance of 1250 W/m² was used to replicate solar conditions throughout each 3 h trial. The test setup involved polycrystalline PV panels (30 W rated) fitted with cooling channels filled with PoMs of varying porosities (0.35–0.48), evaluated across water flow rates ranging from 1 to 4 L/min. Experimental results showed that PoM cooling significantly outperformed both water-only and passive cooling. Among all the materials tested, sandstone with a porosity of 0.35 and a flow rate of 2.0 L/min demonstrated the highest cooling performance, reducing the panel surface temperature by 58.08% (from 87.7 °C to 36.77 °C), enhancing electrical efficiency by 57.87% (from 4.13% to 6.52%), and increasing power output by 57.81% (from 12.42 W to 19.6 W) compared to the uncooled panel. The enhanced heat transfer (HT) was attributed to improved conductive and convective interactions facilitated by lower porosity and optimal fluid velocity. Furthermore, the cooling system improved I–V characteristics by stabilizing short-circuit current and enhancing open-circuit voltage. Comparative analysis revealed material-dependent efficacy—sandstone > flint > marble > gravel—attributed to thermal conductivity gradients (sandstone: 5 W/m·K vs. gravel: 1.19 W/m·K). The configuration with 0.35 porosity and a 2.0 L/min flow rate proved to be the most effective, offering an optimal balance between thermal performance and resource usage, with an 8–10% efficiency gain over standard water cooling. This study highlights 2.0 L/min as the ideal flow rate, as higher rates lead to increased water usage without significant cooling improvements. Additionally, lower porosity (0.35) enhances convective heat transfer, contributing to improved thermal performance while maintaining energy efficiency. Full article

Direct interface circuits (DICs) connect resistive, capacitive, and inductive sensors directly to microcontrollers or FPGAs, eliminating analog conditioning stages and offering compact, low-cost, and low-power instrumentation. This systematic review qualitatively synthesizes research up to March 2025 on DIC operation principles, performance metrics, and application domains. Following PRISMA guidelines, 90 studies from IEEE Xplore, ScienceDirect, MDPI, SpringerLink, Scopus, and Google Scholar were selected based on predefined inclusion criteria. Most studies focused on RC-based circuits (53%), followed by RL-based (5%) and charge transfer capacitive interfaces (5%). RC-DICs demonstrated accuracies below 0.01% using adaptive calibration; RL-DICs achieved resolutions of 10–12 bits with higher cycle requirements, while charge transfer interfaces presented systematic errors up to ±5% due to parasitic capacitances. Environmental monitoring, biomedical sensing, liquid-level control, and vehicular detection were frequent application fields. Due to methodological heterogeneity, findings were synthesized qualitatively without quantitative meta-analysis or formal bias assessments. Future research directions include enhanced noise immunity, simplified calibration, and robust parasitic effect compensation. Full article

This study presents the development, simulation, and hardware implementation of a 48 V, 1 kW mid-range wireless power transfer (WPT) platform for autonomous guided vehicle (AGV) charging in industrial applications. The system uses an LCC-S compensation topology, selected for its ability to maintain a constant output voltage and deliver high efficiency even under load variations at a typical coil distance of 15 cm. It can also operate at different distances by adjusting the compensator circuit. A proportional–integral (PI) controller is implemented for current regulation, offering a practical, low-cost solution well suited to industrial embedded systems. Compared to advanced control strategies, the PI controller provides sufficient accuracy with minimal computational demand, enabling reliable operation in real-world environments. Current adjustment can be dynamically carried out in response to real-time changes and continuously monitored based on the AGV battery’s state of charge (SOC). Simulation and experimental results validate the system’s performance, achieving over 80% efficiency and demonstrating its feasibility for scalable, robust AGV charging in Industry 4.0 Manufacturing Settings. Full article

The dynamics of carbon transfer and shifting consumer preferences toward low-carbon products significantly influence firms’ strategic choices and accelerate their transition to greener practices. This study models a secondary supply chain involving a supplier, a high-carbon manufacturer, and a low-carbon manufacturer, analyzing equilibrium outcomes for pricing and profit under two power structures: one where the high-carbon manufacturer holds greater influence, and another where both manufacturers have equal power. Numerical simulations are used to examine how carbon transfer and consumer preferences shape pricing, profitability, and strategic responses across the supply chain. The results show that high-carbon manufacturers with greater market power raise prices to offset the cost of carbon, while those with equal power are more constrained by competition and have to track market dynamics in pricing. Low-carbon manufacturers, more sensitive to consumer preferences, benefit from rising demand, gaining pricing power and sales, while high-carbon manufacturers need to raise prices initially and then gradually reduce them. Although increased carbon transfers offer high-carbon manufacturers greater strategic flexibility, they raise supplier costs and prices for high-carbon products, with limited effect on low-carbon manufacturers. Full article

Triboelectric nanogenerators (TENGs) are pivotal for powering small electronic devices by converting mechanical energy into electrical energy. However, the wear resistance of TENG friction layers remains a critical barrier to their long-term performance. This study introduces a hybrid material combining fluorinated ethylene vinyl ether (FEVE) and three-dimensional hierarchical porous graphene (3D HPG) to address these challenges. FEVE was selected for its low friction coefficient and excellent wear resistance, while 3D HPG enhances charge generation and transfer efficiency. The incorporation of 3D HPG into FEVE significantly improves both triboelectric output and durability, achieving a charge density of 140 μC/m², surpassing conventional copper-based TENGs (50–120 μC/m²). The hybrid material demonstrates minimal performance degradation over 10⁵ sliding cycles, highlighting its potential for durable, low-cost, and high-efficiency TENGs in wearable and portable electronics. Full article