Search Results (35)

Liquid metals (LMs), with their unique combination of high electrical conductivity and mechanical deformability, have emerged as promising materials for stretchable electronics and biointerfaces. However, the practical application of bulk LMs in wearable sensors has been hindered by processing challenges and low stability. To overcome these limitations, liquid metal particles (LMPs) encapsulated by native oxide shells have gained attention as versatile and stable fillers for stretchable and conductive composites. Recent advances have focused on the development of LM-based hybrid composites that combine LMPs with metal, carbon, or polymeric fillers. These systems offer enhanced electrical and mechanical properties and can form conductive networks without the need for additional sintering processes. They also impart composites with multiple functions such as self-healing, electromagnetic interference shielding, and recyclability. Hence, the present review summarizes the fabrication methods and functional properties of LM-based composites, with a particular focus on their applications in wearable sensing. In addition, recent developments in the use of LM composites for physical motion monitoring (e.g., strain and pressure sensing) and electrophysiological signal recording (e.g., EMG and ECG) are presented, and the key challenges and opportunities for next-generation wearable platforms are discussed. Full article

Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance and complex temporal dependencies. To address this, we propose a novel hybrid framework combining Attention-Augmented Convolutional Neural Networks (AACNN) with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing the minority class. The model captures spatial features and long-range temporal patterns and learns effectively from imbalanced data streams. The novelty lies in the integration of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture. Model evaluation is based on multiple performance metrics, including accuracy, F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with faster convergence and improved generalization across both datasets. Full article

Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability. Full article

Spinal cord injuries (SCIs) present a major clinical challenge, often resulting in permanent loss of function and limited treatment options. Traditional approaches, including surgery, drugs, and rehabilitation, have had modest success in restoring neural connectivity due to the complex pathophysiology of SCI. In recent years, bioactive hydrogels have gained attention as a versatile platform for neural repair. Their ability to mimic the extracellular matrix, deliver therapeutic agents, and support cell survival makes them promising tools in regenerative medicine. This narrative review highlights the latest advances in hydrogel-based therapies for SCI, with a focus on innovations such as self-healing, conductive, and anti-inflammatory hydrogels. We also explore hybrid approaches that integrate nanomaterials, stem cells, and bioelectronics to address both primary and secondary injury mechanisms. While various hydrogel systems have been investigated, we place particular emphasis on gelatin-based hydrogels, especially gelatin methacryloyl (GelMA), due to their emerging clinical relevance. GelMA stands out for its bioactivity, tunable mechanics, and compatibility with 3D printing, making it a strong candidate for personalized therapies and scalable production. Unlike previous reviews that broadly summarize hydrogel use, this work specifically contextualizes gelatin-based platforms within the wider landscape of SCI repair, underscoring their translational potential. We also address current challenges, such as immune response, long-term integration, and clinical validation, and suggest future directions for bridging the gap from bench to bedside. Full article

Enzymes, as nature’s precision biocatalysts, hold transformative potential across industrial, environmental, and biomedical sectors. However, their instability, solvent sensitivity, and limited reusability in their free form necessitate advanced immobilization strategies to enhance their robustness and scalability. This review critically examines cutting-edge advancements in enzyme immobilization, focusing on the integration of artificial intelligence (AI), novel nanomaterials, and dynamic carrier systems to overcome the traditional limitations of mass transfer, enzyme leakage, and cost inefficiency. Key innovations such as metal–organic frameworks (MOFs), magnetic nanoparticles, self-healing hydrogels, and 3D-printed scaffolds are highlighted for their ability to optimize enzyme orientation, stability, and catalytic efficiency under extreme conditions. Moreover, AI-driven predictive modeling and machine learning emerge as pivotal tools for rationalizing nanomaterial synthesis, multi-enzyme cascade design, and toxicity assessment, while microfluidic systems enable precise biocatalyst fabrication. This review also explores emerging carrier-free strategies, including cross-linked enzyme aggregates (CLEAs) and DNA-directed immobilization, which minimize diffusion barriers and enhance substrate affinity. Despite progress, challenges persist in regards to eco-friendly nanomaterial production, industrial scalability, and real-world application viability. Future directions emphasize sustainable hybrid material design, AI-aided lifecycle assessments, and interdisciplinary synergies between synthetic biology, nanotechnology, and data analytics. By connecting laboratory innovation with industrial needs, this work provides a forward-thinking framework to harness immobilized enzymes for achieving global sustainability goals, particularly in bioremediation, bioenergy, and precision medicine. Full article

Urushiol, the principal bioactive component of natural lacquer, has emerged as a promising candidate for developing eco-friendly antimicrobial coatings due to its unique catechol structure and long alkyl chains. This review systematically elucidates the molecular mechanisms underpinning urushiol’s broad-spectrum antimicrobial activity, including membrane disruption via hydrophobic interactions, oxidative stress induction through redox-active phenolic groups, and enzyme inhibition via hydrogen bonding. Recent advances in urushiol-based composite systems—such as metal coordination networks, organic–inorganic hybrids, and stimuli-responsive platforms—are critically analyzed, highlighting their enhanced antibacterial performance, environmental durability, and self-healing capabilities. Case studies demonstrate that urushiol derivatives achieve >99% inhibition against both Gram-positive and Gram-negative pathogens, outperforming conventional agents like silver ions and quaternary ammonium salts. Despite progress, challenges persist in balancing antimicrobial efficacy, mechanical stability, and biosafety for real-world applications. Future research directions emphasize precision molecular engineering, synergistic multi-target strategies, and lifecycle toxicity assessments to advance urushiol coatings in medical devices, marine antifouling, and antiviral surfaces. This work provides a comprehensive framework for harnessing natural phenolic compounds in next-generation sustainable antimicrobial materials. Full article

Nanofibers have emerged as transformative materials in the field of energy storage, offering unique physicochemical properties such as high surface area, porosity, and tunable morphology. Recent advancements have also introduced genetically modified fibers—engineered at the biological level to produce functionalized nanostructures with customizable properties. These bioengineered nanofibers add a sustainable and potentially self-healing component to energy storage materials. This paper reviews key applications of conventional and genetically modified nanofibers in lithium-ion and sodium-ion batteries, supercapacitors, hybrid systems, and flexible energy storage with a focus on how genetic and molecular engineering of fibrous materials enables new capabilities in ion transport, electrode architecture, and device longevity. Together, these advances contribute to the development of next-generation energy storage systems with enhanced performance, biocompatibility, and sustainability. This review therefore critically examines the current state, advantages, and limitations of both synthetic and biopolymer-based materials in energy storage applications. It discusses recent technological innovations, such as polymer–nanoparticle composites, functionalized polymer matrices, and next-generation polymer electrolytes. Future research should prioritize enhancing conductivity, improving scalability, and reducing environmental impact, ensuring that polymer-based materials contribute to the development of more efficient and sustainable energy storage technologies. Full article

The growing number of reinforced concrete (RC) structures nearing the end of their service life demands innovative strategies for preservation and retrofitting. Environmental degradation, aging infrastructure, and increased loading demands highlight the need for sustainable, durable, and cost-effective solutions. This paper reviews advancements in preserving and retrofitting RC and concrete infrastructure systems. Innovations include low-carbon binders, supplementary cementitious materials (SCMs), geopolymer concrete, and self-healing technologies to enhance durability and reduce environmental impact. Advanced retrofitting techniques, particularly fiber-reinforced polymer (FRP) systems, modularized steel reinforcement, and hybrid approaches, effectively improve resilience against environmental and operational stresses. Computational tools and machine learning offer promising pathways for optimizing mixture designs and enhancing sustainability. However, critical challenges remain, including scalability issues, performance variability, economic feasibility, and the lack of standardized guidelines. Addressing these challenges will require coordinated efforts across academia, industry, and regulatory bodies to establish performance-based guidelines, develop standardized testing protocols, and conduct comprehensive lifecycle assessments. The findings of this review contribute valuable insights for enhancing infrastructure resilience, reducing environmental impacts, and supporting global sustainability initiatives aimed at achieving net-zero emissions and climate resilience. Full article

The subway power supply system, as a critical component of urban rail transit infrastructure, plays a pivotal role in ensuring operational efficiency and safety. However, current systems remain heavily dependent on manual interventions for fault diagnosis and recovery, limiting their ability to meet the growing demand for automation and efficiency in modern urban environments. While the concept of “self-healing” has been successfully implemented in power grids and distribution networks, adapting these technologies to subway power systems presents distinct challenges. This review introduces an innovative approach by integrating multi-agent systems (MASs) with advanced artificial intelligence (AI) algorithms, focusing on their potential to create fully autonomous self-healing control architectures for subway power networks. The novel contribution of this review lies in its hybrid model, which combines MASs with the IEC 61850 communication standard to develop fault diagnosis, isolation, and recovery mechanisms specifically tailored for subway systems. Unlike traditional methods, which rely on centralized control, the proposed approach leverages distributed decision-making capabilities within MASs, enhancing fault detection accuracy, speed, and system resilience. Through a thorough review of the state of the art in self-healing technologies, this work demonstrates the unique benefits of applying MASs and AI to address the specific challenges of subway power systems, offering significant advancement over existing methodologies in the field. Full article

The extensive use of lithium-ion batteries and other energy storage systems (ESS) in recent years has resulted in a critical need for effective thermal management solutions that ensure safe and reliable operations. Carbon-based materials (C-bMs) are a promising candidate for addressing the thermal challenges in ESS due to their unique thermal, electrical, and structural properties. This article provides a concise overview of C-bM thermal management solutions for improved battery safety. The key thermal management requirements and failure modes associated with battery systems are highlighted, underscoring the importance of effective battery thermal management (BTM). Various forms of C-bMs, including graphite, graphene, carbon nanotubes, carbon foams, nanodiamonds, and graphdiyne, are examined for their potential applications in battery thermal management systems. The recent innovations and advancements in C-bM thermal management solutions, such as phase change composites, heat pipes, and thermal interface materials, are highlighted. Furthermore, the latest research trends focus mainly on the development of hybrid battery thermal management solutions, carbon-based aerogels, and complex C-bM structures with tailored thermal pathways for optimized thermal management. Most of the current innovations are still at the laboratory scale; hence, future research efforts will be focused on developing integrated multi-functional C-bMs, sustainable and scalable manufacturing techniques, self-healing C-bMs composites, intelligent C-bMs, and further explorations of uncommon C-bMs. These advancements are bound to enhance performance, sustainability, and application-specific adaptations for BTM. This article provides valuable insights for researchers, and stakeholders interested in leveraging C-bMs for BTM. Full article

To enhance the self-healing control capability of soft open points with energy storage (E-SOPs) and optimize the fault recovery performance in flexible interconnected distribution networks, this paper proposes a novel power supply restoration method based on E-SOP. The methodology begins with a comprehensive analysis of the E-SOP’s fundamental architecture and loss model. Subsequently, a dual-objective optimization function is formulated to maximize the sum of nodal active load restoration while minimizing network losses. The optimization problem is transformed into a second-order cone programming formulation under comprehensive operational constraints. To solve this complex optimization model, an innovative hybrid approach combining the Improved Whale Optimization Algorithm (IWOA) with second-order cone programming is developed. The proposed methodology is extensively validated using the IEEE 33-node test system. The experimental results demonstrate that this approach significantly enhances the power supply restoration capability of distribution networks while maintaining practical feasibility. Full article

The preservation of interfacial integrity in esthetic dental restorations remains a critical challenge, with hybrid layer degradation being a primary factor in restoration failure. This degradation is driven by a combination of host-derived enzymatic activity, including matrix metalloproteinases (MMPs), bacterial proteases, and hydrolytic breakdown of the polymerized adhesive due to moisture exposure. This review examines the multifactorial mechanisms underlying hybrid layer degradation and presents current advancements in restorative materials aimed at counteracting these effects. Principal strategies include collagen preservation through the inhibition of enzymatic activity, the integration of antimicrobial agents to limit biofilm formation, and the use of ester-free, hydrolysis-resistant polymeric systems. Recent research highlights acrylamide-based adhesives, which exhibit enhanced resistance to acidic and enzymatic environments, as well as dual functionality in collagen stabilization. Furthermore, innovations in bioactive resins and self-healing materials present promising future directions for developing adhesives that actively contribute to long-term restoration stability. These findings underscore the importance of continuous advancements in adhesive technology to enhance the durability and clinical performance of dental restorations. Full article

Hydrogels are known for their high water retention capacity and biocompatibility and have become essential materials in tissue engineering and drug delivery systems. This review explores recent advancements in hydrogel technology, focusing on innovative types such as self-healing, tough, smart, and hybrid hydrogels, each engineered to overcome the limitations of conventional hydrogels. Self-healing hydrogels can autonomously repair structural damage, making them well-suited for applications in dynamic biomedical environments. Tough hydrogels are designed with enhanced mechanical properties, enabling their use in load-bearing applications such as cartilage regeneration. Smart hydrogels respond to external stimuli, including changes in pH, temperature, and electromagnetic fields, making them ideal for controlled drug release tailored to specific medical needs. Hybrid hydrogels, made from both natural and synthetic polymers, combine bioactivity and mechanical resilience, which is particularly valuable in engineering complex tissues. Despite these innovations, challenges such as optimizing biocompatibility, adjusting degradation rates, and scaling up production remain. This review provides an in-depth analysis of these emerging hydrogel technologies, highlighting their transformative potential in both tissue engineering and drug delivery while outlining future directions for their development in biomedical applications. Full article

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network. Full article

Amino acid-derived self-assembled nanofibers comprising supramolecular chiral hydrogels with unique physiochemical characteristics are highly demanded biomaterials for various biological applications. However, their narrow functionality often limits practical use, necessitating the development of biomaterials with multiple features within a single system. Herein, chiral co-assembled hybrid hydrogel systems termed LPH-EGCG and DPH-EGCG were constructed by co-assembling L/DPFEG gelators with epigallocatechin gallate (EGCG) followed by cross-linking with polyvinyl alcohol (PVA) and hyaluronic acid (HA). The developed hybrid hydrogels exhibit superior mechanical strength, self-healing capabilities, and adhesive properties, owing to synergistic non-covalent interactions. Integrating hydrophilic polymers enhances the system’s capacity to demonstrate favorable swelling characteristics. Furthermore, the introduction of EGCG facilitated the hybrid gels to display notable antibacterial properties against both Gram-positive and Gram-negative bacterial strains, alongside showcasing strong antioxidant capabilities. In vitro investigation demonstrated enhanced cell adhesion and migration with the LPH-EGCG system in comparison to DPH-EGCG, thus emphasizing the promising prospects of these hybrid hydrogels in advanced tissue engineering applications. Full article