Search Results (316)

Cement-based batteries (CBBs) are an emerging category of multifunctional materials that combine structural load-bearing capacity with integrated electrochemical energy storage, enabling the development of self-powered infrastructure. Although previous reviews have explored selected aspects of CBB technology, a comprehensive synthesis encompassing system architectures, material strategies, and performance metrics remains insufficient. In this review, CBB systems are categorized into two representative configurations: probe-type galvanic cells and layered monolithic structures. Their structural characteristics and electrochemical behaviors are critically compared. Strategies to enhance performance include improving ionic conductivity through alkaline pore solutions, facilitating electron transport using carbon-based conductive networks, and incorporating redox-active materials such as zinc–manganese dioxide and nickel–iron couples. Early CBB prototypes demonstrated limited energy densities due to high internal resistance and inefficient utilization of active components. Recent advancements in electrode architecture, including nickel-coated carbon fiber meshes and three-dimensional nickel foam scaffolds, have achieved stable rechargeability across multiple cycles with energy densities surpassing 11 Wh/m². These findings demonstrate the practical potential of CBBs for both energy storage and additional functionalities, such as strain sensing enabled by conductive cement matrices. This review establishes a critical basis for future development of CBBs as multifunctional structural components in infrastructure applications. Full article

The development of high-performance adsorbent materials is crucial for any sorption-based energy conversion process. In such a context, composite sorbent materials, although promising in terms of performance and stability, are often challenging to shape into complex geometries. Additive manufacturing, also known as 3D printing, has emerged as a powerful technique for fabricating intricate structures with tailored properties. In this paper, an innovative three-dimensional structure, constituted by zeolite as filler and sulfonated polyether ether ketone as matrix, was obtained using additive manufacturing technology, which is mainly suitable for sorption-based energy conversion processes. The lattice structure was tailored in order to optimize the synthesis procedure and material stability. The complex three-dimensional lattice structure was obtained without a metal or plastic reinforcement support. The composite structure was evaluated to assess its structural integrity using morphological analysis. Furthermore, the adsorption/desorption capacity was evaluated using water-vapor adsorption isobars at 11 mbar at equilibrium in the temperature range 30–120 °C, confirming good adsorption/desorption capacity. Full article

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO₂, CO, H₂S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies. Full article

MXenes, a family of two-dimensional carbide and nitride nanomaterials, have demonstrated significant promise across various technological domains, particularly in energy storage applications. This review critically examines scalable synthesis techniques for MXenes and their potential integration into next-generation rechargeable battery systems. We highlight both top-down and emerging bottom-up approaches, exploring their respective efficiencies, environmental impacts, and industrial feasibility. The paper further discusses the electrochemical behavior of MXenes in lithium-ion, sodium-ion, and aluminum-ion batteries, as well as their multifunctional roles in solid-state batteries—including as electrodes, additives, and solid electrolytes. Special emphasis is placed on surface functionalization, interlayer engineering, and ion transport properties. We also compare MXenes with conventional graphite anodes, analyzing their gravimetric and volumetric performance potential. Finally, challenges such as diffusion kinetics, power density limitations, and scalability are addressed, providing a comprehensive outlook on the future of MXenes in sustainable energy storage technologies. Full article

In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of Automotive Engineers Levels 4 and 5, into focus as promising solutions for mitigating road congestion and reducing greenhouse gas emissions. However, the extent to which Autonomous Vehicles can fulfill this potential depends largely on user acceptance, patterns of use, and their integration within broader green energy and sustainability policies. The present paper aims to develop an integrated conceptual model that links behavioral determinants to environmental outcomes, assessing how individuals’ intention to adopt private autonomous vehicles can contribute to sustainable urban mobility. The model integrates five psychosocial determinants—perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control—with contextual variables such as energy source, infrastructure availability, and public policy. These components interact to predict users’ intention to adopt AVs and their perceived contribution to urban sustainability. Methodologically, the study builds on a narrative synthesis of the literature and proposes a framework applicable to empirical validation through structural equation modeling (SEM). The model draws on established frameworks such as Technology Acceptance Model (TAM), Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology, incorporating constructs including perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control, constructs later to be examined in relation to key contextual variables, including the energy source powering Autonomous Vehicles—such as electricity from mixed or renewable grids, hydrogen, or hybrid systems—and the broader policy environment (regulatory frameworks, infrastructure investment, fiscal incentives, and alignment with climate and mobility strategies and others). The research provides relevant directions for public policy and behavioral interventions in support of the development of clean and smart urban transport in the age of automation. Full article

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies. Full article

High-temperature fuel cells and electrolyzers, particularly molten carbonate fuel cells (MCFCs) and Molten Carbonate Electrolyzers (MCEs), are expected to play a critical role in clean power generation, hydrogen production, and integrated CO₂ separation. Unfortunately, despite their potential, these technologies have not yet reached full commercialization. The main reason for this is material degradation. In particular, the corrosion of metallic components continues to be a leading cause of performance loss and system failure. This review provides a comprehensive assessment of degradation mechanisms in MCFC and MCE systems. It examines key metallic components, such as current collectors and bipolar plates, focusing on the performance of commonly used materials, including stainless steels and advanced alloys, under prolonged exposure to corrosive environments. To address degradation issues, this review evaluates current mitigation strategies and discusses material selection, protective coatings application, and the optimization of operational parameters. Advances in alloy development, coatings, surface treatments, and process controls have been compared in terms of effectiveness, scalability, and long-term stability. The review concludes with a synthesis of current best practices and future directions, emphasizing the need for integrated, multi-functional solutions to achieve the lifetimes required for full commercialization. By linking materials science, electrochemistry, and systems engineering, this review offers directions for the development of corrosion-resistant MCFC and MCE technologies in support of a hydrogen-based, carbon-neutral energy future. Full article

Recent advances in technologies such as blockchain, the Internet of Things (IoT), Cyber–Physical Systems (CPSs), and the Industrial Internet of Things (IIoT) have driven the digitalization and intelligent transformation of modern industries. However, embedded control devices within power system communication infrastructures have become increasingly susceptible to cyber threats due to escalating software complexity and extensive network exposure. We have seen that symmetric conventional patching techniques—both static and dynamic—often fail to satisfy the stringent requirements of real-time responsiveness and computational efficiency in resource-constrained environments of all kinds of power grids. To address this limitation, we have proposed a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method has integrated binary-level vulnerability modeling, execution-trace-driven fault localization, and lightweight patch synthesis, enabling dynamic, in-place code redirection without disrupting ongoing operations. By constructing a system-level instruction flow model, the framework has leveraged on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture have revealed that the proposed approach has reduced patch latency by 92% over static techniques, which are symmetrical in a working way, while incurring less than 3% CPU overhead. This work has offered a scalable and real-time model-driven defense strategy that has enhanced the cyber–physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability. Full article

The accelerating global demand for sustainable and efficient energy storage has driven substantial interest in supercapacitor technology due to its superior power density, fast charge–discharge capability, and long cycle life. However, the low energy density of supercapacitors remains a key bottleneck, limiting their broader application. This review provides a comprehensive and focused overview of the latest breakthroughs in supercapacitor research, emphasizing strategies to overcome this limitation through advanced material engineering and device design. We explore cutting-edge developments in electrode materials, including carbon-based nanostructures, metal oxides, redox-active polymers, and emerging frameworks such as metal–organic frameworks (MOFs) and covalent organic frameworks (COFs). These materials offer high surface area, tunable porosity, and enhanced conductivity, which collectively improve the electrochemical performance. Additionally, recent advances in electrolyte systems—ranging from aqueous to ionic liquids and organic electrolytes—are critically assessed for their role in expanding the operating voltage window and enhancing device stability. The review also highlights innovations in device architectures, such as hybrid, asymmetric, and flexible supercapacitor configurations, that contribute to the simultaneous improvement of energy and power densities. We identify persistent challenges in scaling up nanomaterial synthesis, maintaining long-term operational stability, and integrating materials into practical energy systems. By synthesizing these state-of-the-art advancements, this review outlines a roadmap for next-generation supercapacitors and presents novel perspectives on the synergistic integration of materials, electrolytes, and device engineering. These insights aim to guide future research toward realizing high-energy, high-efficiency, and scalable supercapacitor systems suitable for applications in electric vehicles, renewable energy storage, and next-generation portable electronics. Full article

Fault detection and diagnosis (FDD) in power plant systems is a rapidly evolving field driven by the increasing complexity of industrial infrastructure and the demand for reliability, safety, and predictive maintenance. This review presents a structured and data-driven synthesis of 185 peer-reviewed articles, sourced from journals indexed in MDPI and Elsevier, as well as through the Google Scholar search engine, published between 2019 and 2025. The study systematically classifies these articles by plant type, sensor technology, algorithm category, and diagnostic pipeline (detection, localization, resolution). The analysis reveals a significant transition from traditional statistical methods to machine learning (ML) and deep learning (DL) models, with over 70% of recent studies employing AI-driven approaches. However, only 30.3% of the articles addressed the full diagnostic pipeline and merely 17.3% targeted system-level faults. Most research remains component-focused and lacks real-world validation or interpretability. A novel taxonomy of diagnostic configurations, mapping system types, sensor use, algorithmic strategy, and functional depth is proposed. In addition, a methodological checklist is introduced to evaluate the completeness and operational readiness of FDD studies. Key findings are summarized in a comparative matrix, highlighting trends, gaps, and inconsistencies across publication sources. This review identifies critical research gaps—including the underuse of hybrid models, lack of benchmark datasets, and limited integration between detection and control layers—and offers concrete recommendations for future research. Combining a thematic and quantitative approach, this article aims to support researchers, engineers, and decision-makers in developing more robust, scalable, and transparent diagnostic systems for power generation infrastructure. Full article

Sludge-derived biochar (SDB) synthesized by the pyrolysis of sludge is gaining enormous interest as a sustainable solution to wastewater treatment and sludge disposal. Despite the proliferation of general biochar reviews, a focused synthesis on SDB-specific advances, particularly covering the recent surge in multifunctional wastewater treatment applications (2020–2025), receives little emphasis. In particular, a critical analysis of recent trends, application challenges, and future research directions for SDB is still limited. Unlike broader biochar reviews, this mini-review highlights the comparative advantages and limitations of SDB, identifies emerging integration strategies (e.g., bio-electrochemical systems, catalytic membranes), and outlines future research priorities toward enhancing the durability and environmental safety of SDB applications. Specifically, this review summarized the advances from 2020 to 2025, focusing exclusively on functional modifications, and practical applications of SDB across diverse wastewater treatment technologies involved in adsorption, catalytic oxidation, membrane integration, electrochemical processes and bio-treatment systems. Quantitative comparisons of adsorption capacities (e.g., >99% Cd2+ removal, >150 mg/g tetracycline adsorption) and catalytic degradation efficiencies are provided to illustrate recent improvements. The potential of SDB in evaluating traditional and emerging contaminant degradation among the Fenton-like, persulfate, and peracetic acid activation systems was emphasized. Integration with membrane technologies reduces fouling, while electrochemical applications, including microbial fuel cells, yield higher power densities. To improve the functionality of SDB-based systems in targeting contamination removal, modification strategies, i.e., thermal activation, heteroatom doping (N, S, P), and metal loading, played crucial roles. Emerging trends highlight hybrid systems and persistent free radicals for non-radical pathways. Despite progress, critical challenges persist in scalability, long-term stability, lifecycle assessments, and scale-up implementation. The targeted synthesis of this review offers valuable insights to guide the development and practical deployment of SDB in sustainable wastewater management. Full article

Ammonia is gaining increasing attention as a zero-carbon fuel and hydrogen carrier, offering high energy density, mature liquefaction infrastructure, and strong compatibility with existing energy systems. This review presents a comprehensive summary of the recent advances in ammonia-based clean energy systems. It covers the fuel’s physicochemical properties, green synthesis pathways, storage and transport technologies, combustion behavior, NO_X formation mechanisms, emission control strategies, and safety considerations. Co-firing approaches with hydrogen, methane, coal, and DME are evaluated to address ammonia’s low reactivity and narrow flammability limits. This paper further reviews engineering applications across power generation, maritime propulsion, and long-duration energy storage, drawing insights from current demonstration projects. Key technical barriers—including ignition delay, NO_X emissions, ammonia slip, and economic feasibility—are critically examined. Finally, future development trends are discussed, highlighting the importance of integrated system design, low-NO_X combustor development, solid-state storage materials, and supportive policy frameworks. Ammonia is expected to serve as a strategic energy vector bridging green hydrogen production with zero-carbon end-use, facilitating the transition to a sustainable, secure, and flexible energy future. Full article

Deep learning has made remarkable progress in the field of crack segmentation, particularly in handling large-scale datasets and complex images, owing to the substantial computational power currently available. However, existing methods still face significant challenges when processing images with low contrast, fine cracks, or strong noise interference. This paper introduces a novel semantic diffusion model capable of generating synthetic crack images from segmentation masks. The proposed model outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating enhanced crack segmentation performance in complex backgrounds and addressing a critical challenge in engineering crack detection. Additionally, a new crack width calculation method is proposed, which further optimizes the measurement accuracy of crack width by leveraging the medial axis of the segmentation mask, thereby improving the model’s ability to describe crack morphology. To comprehensively evaluate the model’s performance, the dataset was categorized, and a detailed analysis of crack width errors was conducted for different regions. Specifically, the median and interquartile range (IQR) of width errors were calculated for four distinct regions: the central wall, corner edges, oblique intersections, and wall and column surfaces. Experimental results demonstrate that the proposed model excels in all regions, particularly in complex areas such as corner edges and oblique intersections, where the error is significantly lower than that of existing methods. These innovations collectively advance crack segmentation technology and provide a new solution for efficient crack detection in practical applications. Full article

Investigating valley-related physics in rare intrinsic ferromagnetic materials with high-temperature stability and viable synthesis methods is of vital importance for advancing fundamental physics and information technology. Through first-principles calculations, we forecast that monolayer TiAlTe₃ has superb structural stability, a ferromagnetic coupling mechanism deriving from direct-exchange and superexchange interactions, and a high magnetic transition temperature. We observed spontaneous valley polarization of 103 meV in the bottom conduction band when monolayer TiAlTe₃ is magnetized toward an out-of-plane orientation. Additionally, because of its powerful valley-contrasting Berry curvature, the anomalous valley Hall effect emerges under an in-plane electric field. The cooperation of ferromagnetic coupling, a high magnetic transition temperature, and spontaneous valley polarization makes monolayer TiAlTe₃ a promising room-temperature ferrovalley material for use in nanoscale spintronics and valleytronics. Full article