Search Results (2,832)

Printed biosensors have attracted increasing attention as platforms for rapid, low-cost, and portable diagnostics because they can be fabricated on flexible or rigid substrates using scalable printing techniques. Their performance is strongly influenced by both the printing process and the materials employed, since factors such as ink rheology, particle dispersion, interfacial behavior, and post-processing conditions directly affect device architecture, sensing performance, and manufacturing reliability. This review summarizes recent advances in printed biosensors from the combined perspectives of printing technologies and functional materials. Commonly employed printing techniques, including inkjet, screen, aerosol jet, and roll-to-roll gravure printing, are discussed with emphasis on their processing characteristics and material requirements. The review also examines key material platforms used in printed biosensors, including carbon-based nanomaterials, metal oxides, metal nanoparticles, conductive polymers, dielectric materials, and hybrid composites, highlighting their roles in electrical conductivity, catalytic activity, biomolecule immobilization, mechanical flexibility, and overall analytical performance. Finally, current challenges and emerging research directions are outlined with respect to ink stability, post-processing strategies, sensor reliability, manufacturability, and practical translation. Overall, this review emphasizes that the development of high-performance printed biosensors depends on the synergistic integration of rational material design with optimized printing strategies. Full article

Mine rescue operations are frequently conducted in hazardous underground environments characterized by damaged infrastructure, unstable communications, heat stress, and hypoxia risk, all of which threaten the safety of rescue personnel. To address these challenges, this study proposes a prototype-oriented mine-rescue monitoring framework that combines a Wi-Fi/optical-fiber communication architecture with flexible wearable sensing modules for physiological monitoring. The communication design employs Wi-Fi for local wireless data aggregation and optical fiber for reliable long-distance backhaul to the surface command side. For wearable monitoring, two flexible sensing modules were developed: a temperature sensor based on a polyaniline/graphene–polyvinyl butyral composite film and a PPG-oriented flexible optoelectronic module based on an ITO/Ag/ITO multilayer transparent electrode structure. Experimental results show that the temperature sensor exhibits a clear temperature-dependent resistance response within the tested range, while the optoelectronic module demonstrates low sheet resistance and acceptable electrical continuity under repeated bending. These results provide preliminary support for combining hybrid underground communication architecture with flexible wearable sensing components in mine-rescue scenarios. However, the present work remains at the stage of architecture design and component-level validation, and full end-to-end system verification under simulated or field rescue conditions will be the focus of future studies. Full article

Structural colors generated by interference, diffraction, or light scattering offer vivid visual effects without dyes or electronic components, making them promising for flexible optical sensing. This work reports a simple stretch–plasma–release (S-P-R) strategy to fabricate wrinkled photonic elastomers (WPEs). The flexible periodic structures exhibit mechanically responsive structural colors, as tensile strain alters the grating period, generating optical signals that can be visualized and quantified by spectroscopy. The wrinkle period is tunable in the range of 0.4–3.42 μm by adjusting plasma power, exposure time, pre-stretch ratio, and film thickness. A dumbbell-shaped substrate design reduces edge-induced stress concentration. It shows improved wrinkle uniformity, with the coefficient of variation reduced from 6.64% to 2.74%, and experimental colors agreeing well with modified Bragg condition predictions. The reflection peak shows a significant shift from 356 nm to 658 nm with varying viewing angles. Patterned plasma treatment enables the selective generation of wrinkled structures, producing bright color patterns. The structural color can be fully erased at a critical strain of 20% and recovered upon release, remaining stable over multiple loading–unloading cycles. With excellent mechanical compliance and optical tunability, these materials are well-suited for integration with hydrogel-based systems and show promise for wearable devices, security marking, and anti-counterfeiting applications. Full article

Undocumented orphan wells present challenges for subsurface characterization and environmental management due to their unknown locations and varied physical conditions. Magnetic surveys offer a promising pathway for identifying these wells by detecting the magnetic anomalies associated with steel casings. However, magnetometer data are typically high-volume, noisy, and complex, making them difficult to process efficiently with conventional methods. Existing processing methods require heavy preprocessing and achieve unsatisfactory recall scores. In this study, we propose a transformer-based deep learning framework designed to efficiently process hyper-resolute data without extensive downsampling. This is achieved through novel on-the-fly techniques as well as the use of sinusoidal positional encoders to allow the model relative positional awareness. Tests on purely synthetic data show that our model achieves F1-scores of over 90% for line spacings between successive flight paths up to 140 m, enabling surveys to take much sparser flight paths, resulting in more efficient coverage. When applied to real-life data, our model achieves a recall of 70%. This flexible and scalable framework enables the detection of orphan wells from drone data and can be readily adapted to other remote sensing applications. Full article

Flexible resistive sensors are promising for wearable and ergonomic applications because they can be easily fabricated on textiles or flexible substrates and enable real-time monitoring of human movement and posture, especially in health monitoring systems. This review presents an overview of recent developments in an interdisciplinary way and summarises advances in materials, fabrication methods, and ergonomic applications. A structured literature search was conducted across major databases, including only studies focused on resistive sensing. The selected works were analysed in terms of conductive materials, fabrication techniques (e.g., direct ink writing (DIW) and textile-based methods), and their integration into wearable systems. Flexible resistive sensors are widely used for monitoring joint motion, posture, and physiological signals in healthcare and industrial environments. However, several challenges remain, including limitations in sensitivity, signal stability, material durability, and the need for reliable calibration in real-world conditions. This review highlights current progress and existing limitations and outlines future research directions toward more robust and user-friendly wearable sensing solutions for ergonomic applications. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

Polymer–graphene composites have emerged as an advantageous class of functional materials that combine the exceptional electrical, mechanical, and surface properties of graphene with the ability to be processed, modified, and made more flexible through polymers. Polymer–graphene composites have recently seen rapid growth in environmental applications, including water treatment, pollutant degradation, sensing, and energy–environment interfaces. This review critically examines recent advancements in polymer–graphene composites for catalysis (including photocatalysis, electrocatalysis, hydrogenation, and energy conversion) and environmental applications (such as water treatment, dye degradation, heavy-metal removal, and oil–water separation). There is considerable discussion about structure–property–performance relationships, catalytic and adsorption mechanisms, and the role of polymer matrices. Current challenges, scalability issues, and future research directions for sustainable, industrially viable polymer–graphene systems are highlighted. Full article

Inclusive education (IE) aims to promote meaningful participation and a sense of belonging for all learners. However, limited research has examined how students with learning difficulties (LDs) experience inclusion in everyday school life. This study explored how primary school students with mild LDs perceive their participation, relationships with teachers and peers, and the role of inclusive classes (ICs) within mainstream Greek primary education. A qualitative design was adopted, and data were collected through semi-structured interviews with ten Grade 6 students receiving support through ICs. Transcripts were analyzed using thematic analysis. Findings indicated that participation was associated with perceived competence in academic tasks, with language-based activities frequently described as cognitively demanding and stressful. Belonging was predominantly felt through peer acceptance and supportive teacher practices rather than solely through classroom placement. The ICs were perceived as providing individualized support and emotional safety, although some ambivalence regarding withdrawal from the mainstream classroom was reported. Students stressed the need for flexible assessment and clearer instructional guidance to enhance fairness and participation. Overall, the findings show that inclusion is experienced as a dynamic interaction between academic accessibility, interpersonal relationships, and supportive learning environments. They also underline the importance of incorporating student voice into inclusive practice. Full article

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article

High-sensitivity rapid detection of ammonia (NH₃) in environmental monitoring, industrial safety, early diagnosis, and other fields is of great significance. Covalent organic frameworks (COFs) have shown great potential in the field of gas sensing due to their designable porous structure and active sites. However, the traditional solvothermal synthesis method of COFs has problems such as cumbersome steps, high energy consumption and serious environmental pollution. Therefore, it is of great significance to invent a new method for COF synthesis that is green and efficient and makes it easy to conduct flexible ammonia gas sensing. This study first reported a solvent-free synthesis of imine connection 1,3,5-Triformylbenzene (TFB) and p-Phenylenediamine (PDA)—a new strategy for COF. This method innovatively employs zinc trifluoromethyl sulfonate (Zn(OTf)₂) as a bifunctional catalyst. This catalyst not only efficiently catalyzes para-phenylenediamine, but its zinc ions also play a unique structural guiding role, guiding the reactants to be arranged in a directional manner, thereby constructing a highly ordered porous crystal structure. A series of characterizations confirmed that the obtained TFB-PDA-COF had good crystallinity and a high proportion of imine bonds (C=N). The powder material was coated onto a flexible polyimide (PI) substrate, successfully constructing a resistive ammonia gas sensor that operates at room temperature. The test results show that this sensor has a high response value, rapid response/recovery capability, and good selectivity for ammonia gas. More importantly, based on a flexible PI substrate, the device can maintain stable sensing performance even under repeated bending conditions, demonstrating its great potential in practical flexible electronic applications. This work not only provides a brand-new “zinc ion-guided” paradigm for the green and controllable synthesis of COF but also lays a material foundation for their application in the next-generation flexible sensing field. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

With advancements in Earth observation capabilities, the demand for large-scale mapping using remote sensing images has increased significantly. However, selecting an optimal image set for the area of interest (AOI) from a large collection of remote sensing images remains challenging. On the one hand, it is crucial to select images with minimal redundancy and low cloud cover to enhance production efficiency and the effective coverage of mapping products. On the other hand, adjacent selected images should transition naturally so that the resulting mapping products appear visually cohesive. Unfortunately, most existing remote sensing image selection algorithms focus only on the former, with little attention to visual consistency. Meanwhile, images from the same swath inherently offer advantages in both redundancy reduction and visual consistency. However, a larger coverage area also carries the potential for greater variation in cloud cover, and cloud distribution within a swath can be highly complex. Managing the relationships among swaths, images, and cloud cover is also challenging. To address these issues, this paper proposes a novel image selection model, SwathSel. Candidate images are grouped through a composite grouping strategy based on swaths, cloud cover, and topological connectivity, thereby expanding the fundamental unit for image selection from individual scenes to connected image subsets. A dynamic adjustment mechanism is introduced to enhance grouping flexibility. Additionally, local and global swath consistency constraints are designed to strengthen visual consistency among images, and a subset evaluation module is used to comprehensively assess swath consistency, coverage, cloud cover, and metadata information. Through a greedy strategy combined with a rapid refinement technique, the final selected image set is obtained. Experiments were conducted on four datasets, and four quantitative metrics were designed to evaluate the visual consistency of the results. Compared with baseline models, SwathSel achieves lower redundancy and cloud cover while delivering superior visual consistency. Full article