Search Results (510)

Online monitoring technology for transformers is a crucial safeguard for power supply, and diagnosing dissolved gases in 10 kV distribution transformer oil is considered an effective criterion for transformer fault detection. Using density functional theory, this paper simulated the adsorption process of five dissolved gases in a 10 kV distribution transformer on Os-modified C₃N nanosheets, and by calculating the band structure, differential charge density, density of states, and work function, the related sensing and adsorption mechanisms were revealed. The results indicate that Os modification significantly enhances the gas-sensing response of C₃N nanosheets, particularly for capturing C₂H₂ and CO, which is primarily attributed to the d-orbital electrons of the doped metal. The adsorption capability of Os-modified C₃N nanosheets of dissolved gases follows the order C₂H₂ > CO > H₂ > CO₂ > CH₄, with the adsorption type being physico-chemical adsorption, and these findings provide a theoretical foundation for developing high-sensitivity gas sensors for detecting dissolved gases in a 10 kV distribution transformer. Full article

Gas station canopy grid structures develop local damage during service life, necessitating regular inspection and maintenance to prevent structural collapse. However, conventional field inspection remains inefficient and highly dependent on manual operation. This paper proposes a time domain response sensitivity methodology for damage assessment of structural members in gas station canopy grid structures. The proposed methodology advances time-domain sensitivity analysis to handle spatially complex grid structures with dense spectral characteristics, while proposing a calculation method for implementing intelligent sensing technology in field inspections that enables automated damage localization in practical canopy structures. Through analyzing time domain response sensitivity matrix, an optimal sensor placement method for spatial grid structures is presented. A double-layer spatial grid structure model is constructed to validate the time domain response sensitivity damage identification method and the optimal sensor placement method based on sensitivity analysis. The results show that the time domain response sensitivity damage identification method identifies the member damage in gas station canopy grid structural numerical model with satisfactory accuracy and efficiency, the optimal sensor placement methodology is suitable for damage identification of structural members. Full article

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence. Full article

Triboelectric nanogenerators (TENGs) have emerged as a promising technology for harvesting mechanical energy via contact electrification (CE) at diverse interfaces, including solid–liquid, liquid–liquid, and gas–liquid phases. This review systematically explores fluid-based TENGs (Flu-TENGs), introducing a foundational and novel classification framework based on direct versus indirect charge transfer to the charge-collecting electrode (CCE). This framework addresses a critical gap by providing the first unified analysis of charge transfer mechanisms across all major fluid interfaces, establishing a clear design principle for future device engineering. We comprehensively compare the underlying mechanisms and performance outcomes, revealing that direct charge transfer consistently delivers superior energy conversion—with specific studies achieving up to 11-fold higher current and 8.8-fold higher voltage in solid–liquid TENGs (SL-TENGs), 60-fold current and 3-fold voltage gains in liquid–liquid TENGs (LL-TENGs), and 34-fold current and 10-fold voltage enhancements in gas–liquid TENGs (GL-TENGs). Indirect mechanisms, relying on electrostatic induction, provide stable Alternating Current (AC) output ideal for low-power, long-term applications such as environmental sensors and wearable bioelectronics, while direct mechanisms enable high-efficiency Direct Current (DC) output suitable for energy-intensive systems including soft actuators and biomedical micro-pumps. This review highlights a paradigm shift in Flu-TENG design, where the deliberate selection of charge transfer pathways based on this framework can optimize energy harvesting and device performance across a broad spectrum of next-generation sensing, actuation, and micro-power systems. By bridging fundamental charge dynamics with application-driven engineering, this work provides actionable insights for advancing sustainable energy solutions and expanding the practical impact of TENG technology. Full article

The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy. Full article

This work comparably investigates the gas sensing potential of noble metal (Pd and Ru)-doped GeS₂ monolayers upon three C₄F₇N decomposed species (FCN, CF₃CN, and C₂F₄) using the first-principles theory, for operation status evaluation in C₄F₇N-insulated devices. The Pd- and Ru-doping effects on the pristine GeS₂ monolayer are analyzed, followed by the adsorption mechanism and sensing performance of two doped monolayers. Our results demonstrate that while Ru doping induces stronger surface interactions with the GeS₂ substrate and consequently exhibits superior adsorption strengths upon the three gases, the Pd-doped monolayer shows remarkable advantages in charge transfer capability that leads to exceptional room-temperature sensitivity responses of −99.6% (FCN), −95.0% (CF₃CN), and −88.0% (C₂F₄), thus significantly outperforming the Ru-doped system. Combined with the instantaneous recovery for gas desorption, the Pd-GeS₂ monolayer holds significance as an ideal room-temperature sensor to monitor the operation status of C₄F₇N-insulated devices in power systems. This research provides promising insights into the application of GeS₂-based materials for gas sensing in power systems and emphasizes the importance of dopant selection in designing high-performance gas sensing materials, especially for developing advanced electrical equipment monitoring technologies. Full article

Agriculture stands at a pivotal juncture in the twenty-first century, confronting the converging crises of climate change, biodiversity loss and rising food demand, even as it is increasingly recognised as part of the solution. This paper assesses the transformative potential of integrating three emerging paradigms—digital agriculture, regenerative agriculture and decommoditised agriculture—into a unified approach capable of delivering productivity, ecological restoration and economic viability. Digital agriculture deploys artificial intelligence, Internet of Things (IoT) networks and remote sensing to optimise inputs and sharpen decision-making. Regenerative agriculture seeks to rebuild soil function, enhance biodiversity and restore ecosystem processes through holistic, adaptive management. Decommoditised agriculture reorients value chains from bulk markets towards quality-differentiated systems that privilege direct producer–consumer relationships, value-added processing and regional market development, enabling price premiums and community resilience. We examine their convergence through the “3N” lens—net-zero greenhouse gas emissions, nature-positive outcomes and nutrition-balanced food systems. Integration creates clear complementarities: digital tools monitor, verify and optimise regenerative practices; regenerative systems provide the ecological foundation for sustainable intensification; and decommoditised models supply economic incentives that reward stewardship and nutritional quality. Persistent barriers include the digital divide, data governance, technical complexity and fragmented policy settings. Realising the benefits will require technology democratisation, interdisciplinary research, enabling regulation and farmer-centred innovation processes. We conclude that converging digital, regenerative and decommoditised approaches offers a credible and necessary pathway to resilient, sustainable and equitable agri-food systems. Full article

Obtaining accurate information on stratigraphic conditions and drilling status is necessary to ensure the safety of the drilling process and to guarantee the production of oil and gas. Sensing while drilling and intelligent monitoring technology, which employ multiple sensors and involve the use of intelligent algorithms, can be used to collect downhole information in situ to ensure safe, reliable, and efficient drilling and mining operations. These approaches are characterized by effective sensing and comprehensive utilization of drilling information through the integration of multi-sensor signals and intelligent algorithms, a core component of machine learning. The article summarizes the current research status of domestic and international sensing while drilling and intelligent monitoring technology using systematically collected relevant information. Specifically, first, the drilling-sensing methods used for in situ acquisition of downhole information, including fiber-optic sensing, electronic-nose sensing, drilling engineering-parameter sensing, drilling mud-parameter sensing, drilling acoustic logging, drilling electromagnetic wave logging, and drilling seismic logging, are described. Next, the basic composition and development direction of each sensing technology are analyzed. Subsequently, the application of intelligent monitoring technology based on machine learning in various aspects of drilling- and mining-status identification, including bit wear monitoring, stuck drill real-time monitoring, well surge real-time monitoring, and real-time monitoring of oil and gas output, is introduced. Finally, the potential applications of sensing while drilling and intelligent monitoring technology in deep-earth, deep-sea, and deep-space contexts are discussed, and the challenges, constraints, and development trends are summarized. Full article

Due to its unique benefits over conventional subtractive manufacturing, additive manufacturing methods continue to attract interest in both academia and industry. One such method is called Cold Spray Additive Manufacturing (CSAM), a solid-state coating deposition technology to manufacture repair metallic components using a gas jet and powder particles. CSAM offers low heat input, stable phases, suitability for heat-sensitive substrates, and high deposition rates. However, persistent challenges include porosity control, geometric accuracy near edges and concavities, anisotropy, and cost sensitivities linked to gas selection and nozzle wear. Interdisciplinary research across manufacturing science, materials characterisation, robotics, control, artificial intelligence (AI), and machine learning (ML) is deployed to overcome these issues. ML supports quality prediction, inverse parameter design, in situ monitoring, and surrogate models that couple process physics with data. To demonstrate the impact of AI and ML on CSAM, this study presents a systematic literature review to identify, evaluate, and analyse published studies in this domain. The most relevant studies in the literature are analysed using keyword co-occurrence and clustering. Four themes were identified: design for CSAM, material analytics, real-time monitoring and defect analytics, and deposition and AI-enabled optimisation. Based on this synthesis, core challenges are identified as small and varied datasets, transfer and identifiability limits, and fragmented sensing. Main opportunities are outlined as physics-based surrogates, active learning, uncertainty-aware inversion, and cloud-edge control for reliable and adaptable ML use in CSAM. By systematically mapping the current landscape, this work provides a critical roadmap for researchers to target the most significant challenges and opportunities in applying AI/ML to industrialise CSAM. Full article

Intelligent gas sensors are indispensable devices widely used in modern society for environmental monitoring, healthcare, the food industry, and public safety. Recent advancements in wireless communication, cloud storage, computing technologies, and artificial intelligence algorithms have significantly enhanced the intelligence level and performance requirements of these sensors. Particularly in the Internet of Things (IoT) environment, flexible and wearable gas sensors are playing an increasingly important role due to their convenience and real-time monitoring capabilities. This review systematically summarizes the latest progress in intelligent gas sensors, covering conceptual frameworks, working principles, and applications across various fields, as well as the construction of IoT networks using sensor arrays. It provides a comprehensive assessment of recent advancements in intelligent gas sensing technologies, highlighting innovations in device architecture, functional mechanisms, and performance in diverse application environments. Special emphasis is placed on transformative developments in flexible and wearable sensor platforms and the enhanced intelligence achieved through the integration of advanced computational algorithms and machine learning techniques. Finally, a summary and future prospects are presented. Despite significant progress, intelligent gas sensors still face challenges related to sensing accuracy, stability, and cost in future applications. Full article

The increasing demand for reliable, sensitive, and cost-effective gas sensors drives ongoing research in this field. Ideal gas sensors must demonstrate high sensitivity and selectivity, stability, rapid response and recovery times, energy efficiency, and affordability. One-dimensional (1D) metal oxide semiconductors (MOSs) are prominent candidates due to their excellent sensing properties and straightforward fabrication processes. The sensing efficacy of 1D MOSs is heavily dependent on their surface area and porosity, which influence gas interaction and detection efficiency. Polymeric templates serve as effective tools for enhancing these properties by enabling the creation of uniform, porous nanostructures with high surface area, thereby improving gas adsorption, sensitivity, and dynamic response characteristics. This review systematically examines the role of polymeric templates in the construction of 1D MOSs for gas sensing applications. It discusses critical factors influencing polymer template selection and how this choice affects key microstructural parameters, such as grain size, pore distribution, and defect density, essential to sensor performance. The recent literature highlights the mechanisms through which polymer templates facilitate the fine-tuning of nanostructures. Future research directions include exploring novel polymer architectures, developing scalable synthesis methods, and integrating these sensors with emerging technologies. Full article

Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are limited by high costs and sparse deployment. This paper presents the development of a real-time, low-cost air quality forecasting system that integrates IoT-based sensing units with predictive machine learning algorithms. The proposed system employs low-cost gas sensors and microcontroller-based hardware to monitor pollutants such as particulate matter, carbon monoxide, carbon dioxide and volatile organic compounds. A fully functional prototype device was designed and manufactured using Fused Deposition Modeling (FDM) with modular and scalable features. The data acquisition pipeline includes on-device adjustment, local smoothing, and cloud transfer for real-time storage and visualization. Advanced feature engineering and a multi-model training strategy were used to generate accurate short-term forecasts. Among the models tested, the GRU-based deep learning model yielded the highest performance, achieving $R^2$ values above 0.93 and maintaining latency below 130 ms, suitable for real-time use. The system also achieved over 91% accuracy in health-based AQI category predictions and demonstrated stable performance without sensor saturation under high-pollution conditions. This study demonstrates that combining embedded hardware, real-time analytics, and ML-driven forecasting enables robust and scalable air quality management solutions, contributing directly to sustainable development goals through enhanced environmental monitoring and public health responsiveness. Full article

Conservation agriculture (CA) is widely recognized as the cornerstone of sustainable agriculture. It prioritizes minimizing soil disturbance, maintaining permanent soil cover, and diversifying crop species to restore soil health and ecosystem resilience. This review synthesizes the effects of CA on the soil’s physical–chemical and biological properties. It demonstrates its effectiveness in improving soil structure, enhancing organic carbon sequestration, promoting microbial activity, increasing water-use efficiency, and reducing erosion and nutrient losses. The paper then highlights the broad environmental, economic, and social benefits of CA. These include biodiversity conservation, reduced greenhouse gas emissions, improved yields, and increased food system resilience. The review explores the synergistic role of technological innovations such as precision agriculture, remote sensing, and digital tools in scaling CA for higher productivity and sustainability. The review then examines how socioeconomic conditions, institutional frameworks, and policy interventions shape CA adoption and impact. Despite its growing adoption, CA’s successful implementation will require strategies adapted for local needs, capacity-building, and supportive, inclusive policies. Finally, the review identifies key CA research gaps and future directions. This provides a comprehensive foundation to advance CA as a climate-smart, resilient, and sustainable pathway to ensure global food security and environmental stewardship. Full article

Polymer gel-based triboelectric nanogenerators (TENGs) have emerged as versatile platforms for self-powered sensing due to their inherent softness, stretchability, and tunable conductivity. This review comprehensively explores the roles of polymer gels in TENG architecture, including their function as triboelectric layers, electrodes, and conductive matrices. We analyze four operational modes—vertical contact-separation, lateral-sliding, single-electrode, and freestanding configurations—alongside key performance metrics. Recent studies have reported output voltages of up to 545 V, short-circuit currents of 48.7 μA, and power densities exceeding 120 mW/m², demonstrating the high efficiency of gel-based TENGs. Gel materials are classified by network structure (single-, double-, and multi-network), matrix composition (hydrogels, aerogels, and ionic gels), and dielectric medium. Strategies to enhance conductivity using ionic salts, conductive polymers, and nanomaterials are discussed in relation to triboelectric output and sensing sensitivity. Morphological features such as surface roughness, porosity, and micro/nano-patterning are examined for their impact on charge generation. Application-focused sections detail the integration of gel-based TENGs in health monitoring (e.g., sweat, glucose, respiratory, and tremor sensing), environmental sensing (e.g., humidity, fire, marine, and gas detection), and tactile interfaces (e.g., e-skin and wearable electronics). Finally, we address current challenges, including mechanical durability, dehydration, and system integration, and outline future directions involving self-healing gels, hybrid architectures, and AI-assisted sensing. This review expands the subject area by synthesizing recent advances and offering a strategic roadmap for developing intelligent, sustainable, and multifunctional TENG-based sensing technologies. Full article

Chemiresistive gas sensors are extensively employed in environmental monitoring, disease diagnostics, and industrial safety due to their high sensitivity, low cost, and miniaturization. However, the high cross-sensitivity and poor selectivity of gas sensors limit their practical applications in complex environmental detection. In particular, the mechanisms underlying the selective response of certain chemiresistive materials to specific gases are not yet fully understood. In this review, we systematically discuss material design strategies and system integration techniques for enhancing the selectivity and sensitivity of gas sensors. The focus of material design primarily on the modification and optimization of advanced functional materials, including semiconductor metal oxides (SMOs), metallic/alloy systems, conjugated polymers (CPs), and two-dimensional nanomaterials. This study offers a comprehensive investigation into the underlying mechanisms for enhancing the gas sensing performance through oxygen vacancy modulation, single-atom catalysis, and heterojunction engineering. Furthermore, we explore the potential of emerging technologies, such as bionics and artificial intelligence, to synergistically integrate with functional sensitive materials, thereby achieving a significant enhancement in the selectivity of gas sensors. This review concludes by offering recommendations aimed at improving the selectivity of gas sensors, along with suggesting potential avenues for future research and development. Full article