Search Results (128)

The development of multifunctional, mechanically robust, and sustainable hydrogels from renewable biomaterials has attracted increasing attention for advanced biomedical applications; however, achieving an optimal balance between mechanical stability, biofunctionality, and infection control remains challenging. In this work, collagen (COL) extracted from raw trimming wastes from a tannery is used to fabricate COL/PAA/Fe composite hydrogels via the ammonium persulfate (APS)-initiated polymerization of acrylic acid (AA) coupled with Fe³⁺-mediated coordination cross-linking. The resulting hydrogel network is stabilized by synergistic COL-poly(acrylic acid) (PAA) hydrogen bonding and dynamic Fe³⁺–carboxylate coordination, imparting enhanced mechanical strength and elasticity. The optimized hydrogel exhibited maximum tensile and compressive strengths of ~0.176 MPa at 751% elongation and ~1.945 MPa at a strain of 80%, respectively. In addition, a high ionic conductivity of 4.11 S·m⁻¹ is achieved, enabling structural integrity under deformation and suitability for flexible electronic interfaces. The prepared hydrogel also displayed rapid autonomous self-healing behavior and substantial antibacterial properties against both Gram-positive and Gram-negative bacteria. Overall, COL is employed herein as a sustainable precursor, highlighting an eco-conscious approach to biomaterial design. This work presents a versatile strategy for producing mechanically stable and biofunctional hydrogels with strong potential for wound dressing, tissue engineering, and injectable biomedical applications. Full article

Hydrogels have attracted significant interest in multifunctional applications. Among them, self-healing hydrogel stands out for its ability to autonomously repair damage through reversible interactions, yet achieving both rapid self-healing and superior mechanical strength remains challenging. In this study, we report the fabrication of a dual cross-linked hydrogel (PAA-Alg-B) prepared via free radical polymerization of acrylic acid and alginic acid, employing N,N′-methylenebisacrylamide, or vinyl-modified nanocellulose as primary cross-linker, with Fe³⁺ or borax serving as an additional dynamic cross-linker. The resulting borax based hydrogel (PAA-Alg-B) exhibits remarkable fast self-healing efficiency enabled by reversible borate ester bonds and hydrogen bonding. It demonstrates tunable mechanical strength with toughness of 137 kJ/m³ and elongation at break up to 1117%, alongside exceptional swelling capacity (448 g/g). The adsorption studies reveal high removal efficiencies for heavy metals, with maximum capacities of 87.57 mg/g (Cr³⁺), 114.02 mg/g (Ni²⁺), and 99.42 mg/g (Cu²⁺), governed by chemisorption kinetics. The PAA-Alg-B can also be used as a promising solid-state electrolyte and separator for flexible supercapacitors. Protonic modulation via H₂SO₄ soaking significantly enhances ionic conductivity, electrochemical performance, and cycling stability. These findings highlight the potential of natural polymer-based, mechanically robust, self-healing hydrogels for sustainable wastewater treatment and advanced energy storage applications. Full article

While self-healing concrete shows promise for infrastructure repair, its effectiveness is significantly compromised in low-temperature environments because of slowed reaction kinetics and the embrittlement of capsule shells. To address this limitation, novel composite microcapsules featuring an ethyl cellulose shell and a dual-core comprising expansive cement and epoxy resin were developed. These microcapsules were fabricated using a physical spheronization-coating method and subsequently incorporated into cement mortar. Response surface methodology was employed to identify the optimal system, which balances self-healing performance with the retention of mechanical properties: a microcapsule content of 3% (by mass of cement) and a particle size range of 1.4 to 1.7 mm. Under conditions of −20 °C, the optimal formulation achieved a crack surface healing ratio of up to 44.1% and a compressive strength recovery of up to 6.0%. Microstructural and spectroscopic analyses (SEM-EDS, XRD) revealed a synergistic healing mechanism. This mechanism involves the formation of calcium carbonate, C–S–H gel, and anorthite, all cohesively bonded within a polymerized epoxy network. This work establishes a functional material strategy for enabling autonomous crack repair in concrete structures subjected to cold climates. In such environments, even marginal strength recovery, when coupled with effective crack sealing, can significantly enhance structural durability. Full article

High-power clutches operating under high-frequency engagement–disengagement cycles demand piston ring seals with exceptional leakage control and tribological reliability. Conventional architectures often experience lubrication failure and severe adhesive wear during transient pressure fluctuations. This research proposes an autonomous intelligent sealing strategy leveraging fluid pressure-induced morphological evolution. By strategically integrating periodic macroscopic structural relief features on the non-sealing surface, the sealing interface transforms into a micron-scale wavy topography in response to hydraulic loading. This structurally embedded intelligence significantly improves fluid pressure distribution, facilitating a transition toward a more favorable lubrication regime. Furthermore, a “self-healing and positional stagnation” logic is elucidated: upon pressure dissipation, the induced waviness elastically recovers to a planar state to ensure sealing integrity, while the ring maintains its axial position due to the predominant frictional resistance of the secondary seal. This synergistic mechanism effectively precludes deleterious dry friction during the clutch disengagement phase. High-fidelity numerical investigations, benchmarked against established experimental data, identify the rectangular groove configuration as the optimal geometry for maximizing waviness amplitude (≈1.5 µm). This research provides a robust framework for developing responsive, zero-wear intelligent seals in advanced power transmissions. Full article

Reciprocating compressor air volume control systems have been extensively investigated, with a primary objective of reducing energy consumption and associated carbon footprints. As a multi-actuator system, failures in this energy-efficient configuration can trigger severe operational disruptions with cascading consequences. To address this, we initially constructed numerical models of the multi-actuator energy-efficient system to decode the variational patterns of compressor dynamic pressure pulsations and connecting-rod small-end bush tribological behaviors under partial actuator fault conditions, thereby establishing foundational data for fault degradation stratification. Building upon this, we propose a Prognostics and Health Management (PHM) algorithm using fault degradation analysis, thereby materializing self-recovery functionality in response to various fault conditions. Experimental validation demonstrates that the self-recovery algorithm successfully contained deterioration propagation through proactive intervention. The system achieved autonomous healing within 8 s (mild faults) and 13 s (moderate faults), constraining discharge fluctuations and vibration amplitude within allowable thresholds. This study establishes a solution framework for preserving multi-actuator energy-efficient systems’ health, accuracy, and economy. Full article

The rapid expansion of Internet of Things (IoT)-enabled smart grids has intensified the need for reliable fault detection and autonomous self-healing under non-stationary operating conditions characterized by frequent concept drift. To address the limitations of static and single-strategy adaptive models, this paper proposes H-CLAS, a novel Hybrid Continual Learning for Adaptive Self-healing framework that unifies regularization-based, memory-based, architectural, and meta-learning strategies within a single adaptive pipeline. The framework integrates convolutional neural networks (CNNs) for fault detection, graph neural networks for topology-aware fault localization, reinforcement learning for self-healing control, and a hybrid drift detection mechanism combining ADWIN and Page–Hinkley tests. Continual adaptation is achieved through the synergistic use of Elastic Weight Consolidation, memory-augmented replay, progressive neural network expansion, and Model-Agnostic Meta-Learning for rapid adaptation to emerging drifts. Extensive experiments conducted on the Smart City Air Quality and Network Intrusion Detection Dataset (NSL-KDD) demonstrate that H-CLAS achieves accuracy improvements of 12–15% over baseline methods, reduces false positives by over 50%, and enables 2–3× faster recovery after drift events. By enhancing resilience, reliability, and autonomy in critical IoT-driven infrastructures, the proposed framework contributes to improved grid stability, reduced downtime, and safer, more sustainable energy and urban monitoring systems, thereby providing significant societal and environmental benefits. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Anti-icing materials have attracted considerable research interest due to their potential applications in preventing ice accretion and growth. However, a major challenge in the field is how to enhance durability while maintaining anti-icing performance. This study proposes a facile fabrication method for anti-icing coatings with anti-aging and self-healing abilities. A three-dimensionally cross-linked block copolymer, synthesized from polydimethylsiloxane, 4-aminophenyl disulfide, and trimethylolpropane triglycidyl ether, yielded a coating with excellent anti-icing/de-icing performance, including a low ice adhesion strength (29.2 kPa) and a high icing delay time (1389 s). The introduction of 4-aminophenyl disulfide enables dynamic disulfide bond reorganization and aromatic framework formation, synergistically conferring the icephobic coating with self-repair mechanisms and an anti-aging function. The coating exhibited a rapid self-healing capability (within 4 h), which is facilitated by the dynamic exchange of its hydrogen and disulfide bonds. Furthermore, the material demonstrated outstanding durability against physical wear and ultraviolet radiation. After being subjected to a 1000-cycle abrasion test and ultraviolet aging, the coating successfully retained more than 70% of its original performance in both icing delay time and ice adhesion strength. This paper proposes a facile strategy for developing self-healing and anti-aging anti-icing coatings and proposes innovative strategies for multifunctional anti-icing coatings. Full article

Polymeric coatings play a pivotal role in enhancing the durability, functionality, and sustainability of structural materials exposed to harsh environmental conditions. Recent advances in artificial intelligence (AI) have transformed the development, optimization, and evaluation of these coatings by enabling data-driven material discovery, predictive performance modeling, and autonomous inspection. This review aims to provide a comprehensive overview on AI-driven polymeric coating strategies for structural applications, emphasizing the integration of machine learning, computer vision, and multi-physics simulations into traditional materials engineering frameworks. The discussion encompasses AI-assisted material selection methods for polymers, fillers, and surface modifiers; predictive models for corrosion, fatigue, and degradation; and intelligent evaluation systems using digital imaging, sensor fusion, and data analytics. Case studies highlight emerging trends such as self-healing, smart, and sustainable coatings that leverage AI to balance mechanical performance, environmental resistance, and carbon footprint. The review concludes with identifying current challenges—including data scarcity, model interpretability, and cross-domain integration—and proposes future research directions toward explainable, autonomous, and circular coating design pipelines. Full article

Self-healing concrete provides an eco-efficient approach for restoring cracks through autonomous repair, reducing maintenance demands and enhancing long-term durability. This study evaluates concrete incorporating Bacillus subtilis bacteria and sisal fibers to examine their individual and combined effects on mechanical performance and microstructural development. Bacterial cells at a concentration of 2 × 10⁸ CFU/mL were introduced with calcium lactate as a nutrient source at varying dosages, while sisal fibers were added at a volume fraction of 0.9%. Concrete mixes containing 0%, 2.5%, and 5% bacterial content were tested under fresh-water curing. Compressive, splitting tensile, and flexural strengths were assessed at multiple ages, accompanied by SEM and EDS analyses to investigate healing products and microstructural alterations. Bacteria-enhanced mixes demonstrated improved long-term compressive behavior, with B5/5/1 reaching 50.1 MPa at 56 days, while higher bacterial content slightly reduced early-age strength but benefited later performance. Incorporating sisal fibers consistently improved mechanical resistance, notably in combination with bacteria. The SB5/5/1 mix achieved 55.2 MPa at 56 days, representing a 30% gain over the control. Tensile strength was particularly influenced by fibers, with SB10/5/1 recording 6.3 MPa at 56 days (≈70% increase). Flexural strength results similarly highlighted the superior behavior of hybrid systems, where SB10/5/1 attained 9.2 MPa (+67%), reflecting enhanced self-healing efficiency even under challenging curing conditions. Full article

Artificial muscles translate the biological principles of motion into soft, adaptive, and multifunctional actuation. This review accordingly highlights research into natural actuation strategies, such as skeletal muscles, muscular hydrostats, spider silk, and plant turgor systems, to reveal the principles underlying energy conversion and deformation control. Building on these insights, polymer-based artificial muscles based on these principles, including pneumatic muscles, dielectric elastomers, and ionic electroactive systems, are described and their capabilities for efficient contraction, bending, and twisting with tunable stiffness and responsiveness are summarized. Furthermore, the abilities of carbon nanotube composites and twisted yarns to amplify nanoscale dimensional changes through hierarchical helical architectures and achieve power and work densities comparable to those of natural muscle are discussed. Finally, the integration of these actuators into soft robotic systems is explored through biomimetic locomotion and manipulation systems ranging from jellyfish-inspired swimmers to octopus-like grippers, gecko-adhesive manipulators, and beetle-inspired flapping wings. Despite rapid progress in the development of artificial muscles, challenges remain in achieving long-term durability, energy efficiency, integrated sensing, and closed-loop control. Therefore, future research should focus on developing intelligent muscular systems that combine actuation, perception, and self-healing to advance progress toward realizing autonomous, lifelike machines that embody the organizational principles of living systems. Full article

Self-healing polymer-based coatings have emerged as a new generation of adaptive protective materials capable of restoring their structure and function after damage. This review provides a comprehensive analysis of current strategies enabling autonomous or externally triggered repair in polymeric films, including encapsulation, reversible chemistry, and microvascular network formation. Emphasis is placed on polymer–inorganic hybrid composites and vitrimeric systems, which integrate barrier protection with stimuli-responsive healing and recyclability. Comparative performance across different matrices—epoxy, polyurethane, silicone, and polyimine—is discussed in relation to corrosion protection and biomedical interfaces. The review also highlights how dynamic covalent and supramolecular interactions in hydrogels enable self-repair under physiological conditions. Recent advances demonstrate that tailoring interfacial compatibility, healing kinetics, and trigger specificity can achieve repeatable, multi-cycle recovery of both mechanical integrity and functional performance. A representative selection of published patents is also shown to illustrate recent technological advancements in the field. Finally, key challenges are identified in standardizing evaluation protocols, ensuring long-term stability, and scaling sustainable manufacturing. Collectively, these developments illustrate the growing maturity of self-healing polymer coatings as multifunctional materials bridging engineering, environmental, and biomedical applications. Full article

Geothermal energy, recognized as a sustainable and clean resource, is playing an increasingly critical role in the global shift toward low-carbon energy systems. Nevertheless, the exploitation of fractured geothermal reservoirs is often impeded by severe lost circulation during drilling, where conventional plugging materials fail under high-temperature, high-salinity, and high-pressure conditions due to inadequate mechanical strength, poor thermal resistance, and lack of self-adaptive sealing behavior. In response, self-healing materials have emerged as an innovative strategy for developing intelligent lost circulation control technologies. Herein, we report a novel self-healing gel (XFFD) synthesized via inverse emulsion polymerization using acrylamide (AM), acrylic acid (AA), p-nitroblue tetrazolium (PNBT), and modified silica nanoparticles (PAS). The resulting material exhibits exceptional thermal stability, with decomposition onset above 356 °C, as determined by thermogravimetric analysis. Rheological and mechanical assessments reveal outstanding viscoelasticity, moderate swelling capacity (4.17-fold in deionized water), and a high self-recovery efficiency of 91.15%, accompanied by a bearing strength of 3.65 MPa. Mechanistic investigations indicate that the autonomous repair capability stems from dynamic non-covalent interactions—primarily hydrogen bonding and ionic associations—enabled by amide and carboxyl groups within the polymer network. Sand bed filtration tests under simulated geothermal conditions (150 °C, 8% salinity) demonstrate that XFFD forms a robust sealing barrier with significantly shallower invasion depth compared to conventional materials such as sulfonated asphalt and calcium carbonate. This work presents an effective self-healing gel system that ensures reliable wellbore strengthening and fluid loss control in challenging high-temperature, high-salinity geothermal drilling operations. Full article

Leading-edge erosion (LEE) of wind-turbine blades, driven primarily by rain erosion, particulate erosion, and environmental ageing, remains one of the most pervasive causes of performance loss and maintenance cost in offshore and onshore wind farms. Self-healing coatings, which autonomously or semi-autonomously restore barriers and mechanical function after damage, promise a paradigm shift in blade protection by combining immediate impact resistance with in-service reparability. This review surveys the state of the art in self-healing coating technologies (intrinsic chemistries such as non-covalent interactions or dynamic covalent bonds; extrinsic systems including micro/nanocapsules and microvascular networks) and evaluates their suitability for anti-erosion, mechanical robustness, and multifunctional protection of leading edges. The outcomes of theoretical, experimental, modelling and field-oriented studies on the leading-edge protection and coating characterisation identify which self-healing concepts best meet the simultaneous requirements of toughness, adhesion, surface finish, and long-term durability of wind blade applications. Key gaps are highlighted, notably trade-offs between healing efficiency and mechanical toughness, challenges in large-area and sprayable application methods, and the need for standardised characterisation and testing of self-healing coating protocols. We propose a roadmap for targeted materials research, accelerated testing, and field trials. This review discusses recent studies to guide materials scientists and renewable-energy engineers toward promising routes to deployable, multifunctional, self-healing anti-erosion coatings, especially for wind-energy infrastructure. Full article

Despite notable advances in load forecasting and fault detection, current power system operation and maintenance (O&M) technologies remain fragmented into independent and primarily reactive modules. Load forecasting estimates future demand, whereas fault detection identifies whether abnormal conditions exist in the present state. This paper proposes a unified and proactive Cognitive Digital Twin (CDT) system. Unlike traditional data-driven approaches, the CDT integrates Large Language Models (LLMs) and Knowledge Graphs (KGs) as cognitive cores to enable deeper reasoning and context-aware decision-making. The CDT system not only mirrors the physical grid but also acts as an intelligent O&M engine capable of understanding, reasoning, predicting, and self-diagnosing. The core innovation lies in prediction-based anomaly detection. The system first estimates the expected healthy state of the grid at future time steps and then compares real-time monitoring data against these predictions to identify incipient anomalies. This enables genuine foresight rather than simple reactive detection. By orchestrating advanced analytical modules, including CNN–LSTM hybrid models and optimization algorithms, the CDT supports autonomous O&M operations with transparent and explainable decision-making. These capabilities enhance grid resilience and improve the system’s capacity for self-healing. Full article