Search Results (265)

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

Prostate cancer diagnostics are rapidly advancing through innovations in nanotechnology, biosensing strategies, and molecular recognition. This review analyzes studies focusing on quantum dot (QD)-based biosensors for detecting prostate cancer biomarkers with high sensitivity and specificity. It covers diverse sensing platforms and signal transduction mechanisms, emphasizing the influence of the QD composition, surface functionalization, and bio interface engineering on analytical performance. Key metrics such as detection limits, dynamic range, and compatibility with biological samples, including serum, urine, and tissue, are critically assessed. Recent advances in green-synthesized QDs and smartphone-integrated diagnostic platforms are highlighted, including lateral flow assays, paper-based devices, and pH-responsive hydrogels for real-time, low-cost, and decentralized cancer screening. These innovations enable multiplexed biomarker detection and tumor microenvironment monitoring in point-of-care settings. This review concludes by addressing the current limitations, scalability challenges, and future research directions for translating QD-enabled biosensors into clinically viable diagnostic tools. Full article

Oblique angle deposition (OAD) holds significant potential for diverse applications, including energy harvesting devices, optoelectronic sensors, and electronic devices, owing to the creation of unique nanostructures. These nanostructures are characterized by their porosity and nanoscale columns, which can exist in numerous forms depending on deposition conditions. As a result, the engineering of nanostructures using OAD achieves the successful modulation of optical properties such as absorption, reflection, and transmission. This explains the current surge of attention toward photodetectors based on OAD technology. This review presents various photodetectors based on OAD technology and summarizes reported cases. It also explores current advancements, major applications, and future directions in photodetector development and nanostructure engineering. Ultimately, this review aims to provide a comprehensive overview of the research trends in photodetectors utilizing OAD technology and focus on their further development and application potential. Full article

Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors—including oscillatory, leaky integrate-and-fire (LIF), Hodgkin–Huxley (H-H), and stochastic dynamics—and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation. We review memristor-based neuron circuits regarding architectures, materials, and key performance metrics. At the system level, we summarize bio-inspired neuromorphic platforms integrating TSM neurons with visual, tactile, thermal, and olfactory sensors, achieving real-time edge computation with high accuracy and low power. Finally, we critically examine key challenges—such as stochastic switching origins, device variability, and endurance limits—and propose future directions toward reconfigurable, robust, and scalable memristive neuromorphic architectures. Full article

Silk fibroin (SF)-based intelligent wearable systems represent a frontier research direction in artificial intelligence and precision medicine. Their core efficacy stems from the inherent advantages of silk fibroin, including excellent mechanical properties, interfacial compatibility, and tunable structure. This article systematically reviews conductive modification strategies for silk fibroin and its research progress in the smart wearable field. It elaborates on the molecular structural basis of silk fibroin for use in smart wearable devices, critically analyzes five conductive functionalization strategies, compares the advantages, disadvantages, and applicable domains of different modification approaches, and summarizes research achievements in areas such as bioelectrical signal sensing, energy conversion and harvesting, and flexible energy storage. Concurrently, an assessment was conducted focusing on the priority performance characteristics of the materials across diverse application scenarios. Specific emphasis was placed on addressing the long-term functional performance (temporal efficacy) and degradation stability of silk fibroin-based conductive materials exhibiting high biocompatibility in implantable settings. Additionally, the compatibility issues arising between externally applied coatings and the native substrate matrix during conductive modification processes were critically examined. The article also identifies challenges that silk fibroin-based smart wearable devices currently face and suggests potential future development directions, providing theoretical guidance and a technical framework for the functional integration and performance optimization of silk fibroin-based smart wearable devices. Full article

ZnO/SiC nanocomposite materials possess significant potential for various technological fields due to their extraordinary optical, electrical, thermal, and mechanical properties. The synthesis methods, material properties, and diverse applications of ZnO/SiC composites have been systematically explored in this study. The potential application areas of this nanocomposite include their roles in photocatalysis, optoelectronic devices, gas sensors, and photovoltaic systems. The synergetic effects of ZnO and SiC are analyzed to highlight their advantages over their individual components. Future research directions must focus on the remaining challenges to optimize these nanoscale composite materials for industrial and emerging applications. Full article

Over the past two decades, titanium dioxide nanotubes (TiO₂ NTs) have gained considerable attention as multifunctional materials in sensing technologies. Their large surface area, adjustable morphology, chemical stability, and photoactivity have positioned them as promising candidates for diverse sensor applications. This review presents a broad overview of the development of TiO₂ NTs in sensing technologies for medical diagnostics over the last two decades. It further explores strategies for enhancing their sensing capabilities through structural modifications and hybridization with nanomaterials. Despite notable advancements, challenges such as device scalability, long-term operational stability, and fabrication reproducibility remain. This review outlines the evolution of TiO₂ NT-based sensors for medical diagnostics, highlighting both foundational progress and emerging trends, while providing insights into future directions for their practical implementation across scientific and industrial domains. Full article

The rapid expansion of the Internet of Things (IoT) ecosystem has transformed industries but also exposed significant cybersecurity vulnerabilities. Traditional centralized methods for securing IoT networks struggle to balance privacy preservation with real-time threat detection. This study presents a Federated Learning-Driven Cybersecurity Framework designed for IoT environments, enabling decentralized data processing through local model training on edge devices to ensure data privacy. Secure aggregation using homomorphic encryption supports collaborative learning without exposing sensitive information. The framework employs GRU-based recurrent neural networks (RNNs) for anomaly detection, optimized for resource-constrained IoT networks. Experimental results demonstrate over 98% accuracy in detecting threats such as distributed denial-of-service (DDoS) attacks, with a 20% reduction in energy consumption and a 30% reduction in communication overhead, showcasing the framework’s efficiency over traditional centralized approaches. This work addresses critical gaps in IoT cybersecurity by integrating federated learning with advanced threat detection techniques. It offers a scalable, privacy-preserving solution for diverse IoT applications, with future directions including blockchain integration for model aggregation traceability and quantum-resistant cryptography to enhance security. Full article

A brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices, enabling users to control them through thought. It records brain signals and classifies them into specific commands for external devices. Among various classifiers used in BCI, those directly classifying covariance matrices using Riemannian geometry find broad applications not only in BCI, but also in diverse fields such as computer vision, natural language processing, domain adaption, and remote sensing. However, the existing Riemannian-based methods exhibit limitations, including time-intensive computations, susceptibility to disturbances, and convergence challenges in scenarios involving high-dimensional matrices. In this paper, we tackle these issues by introducing the Bures–Wasserstein (BW) distance for covariance matrices analysis and demonstrating its advantages in BCI applications. Both theoretical and computational aspects of BW distance are investigated, along with algorithms for Fréchet Mean (or barycenter) estimation using BW distance. Extensive simulations are conducted to evaluate the effectiveness, efficiency, and robustness of the BW distance and barycenter. Additionally, by integrating BW barycenter into the Minimum Distance to Riemannian Mean classifier, we showcase its superior classification performance through evaluations on five real datasets. Full article

Two-dimensional (2D) materials have revolutionized the field of optoelectronics by offering exceptional properties such as atomically thin structures, high carrier mobility, tunable bandgaps, and strong light–matter interactions. These attributes make them ideal candidates for next-generation photodetectors operating across a broad spectral range—from ultraviolet to mid-infrared. This review comprehensively examines the recent progress in 2D material-based photodetectors, highlighting key material classes including graphene, transition metal dichalcogenides (TMDCs), black phosphorus (BP), MXenes, chalcogenides, and carbides. We explore their photodetection mechanisms—such as photovoltaic, photoconductive, photothermoelectric, bolometric, and plasmon-enhanced effects—and discuss their impact on critical performance metrics like responsivity, detectivity, and response time. Emphasis is placed on material integration strategies, heterostructure engineering, and plasmonic enhancements that have enabled improved sensitivity and spectral tunability. The review also addresses the remaining challenges related to environmental stability, scalability, and device architecture. Finally, we outline future directions for the development of high-performance, broadband, and flexible 2D photodetectors for diverse applications in sensing, imaging, and communication technologies. Full article

The integration of Internet of Things (IoT) devices and Artificial Intelligence (AI) has opened new frontiers in mental health, particularly in stress detection and management. This review explores the current literature, examining how IoT-enabled wearables, sensors, and mobile applications, combined with AI algorithms, are utilized to monitor physiological and behavioral indicators of stress. Advancements in real-time stress detection, personalized interventions, and predictive modeling are highlighted, alongside a critical evaluation of existing technologies. While significant progress has been made in the field, several limitations persist, including challenges with the accuracy of stress detection, the scalability of solutions, and the generalizability of AI models across diverse populations. Key challenges are further analyzed, such as ensuring data privacy and security, achieving seamless technological integration, and advancing model personalization to account for individual variability in stress responses. Addressing these challenges is essential to developing robust, ethical, and user-centric solutions that can transform stress management in mental healthcare. This review concludes with recommendations for future research directions aimed at overcoming current barriers and enhancing the effectiveness of IoT- and AI-driven approaches to stress management. Full article

While atmospheric pressure plasma sources are emerging as potentially innovative instruments in medicine, some aspects of the interaction between plasma and biological substrates remain unclear. The high diversity in both sources and applications in the literature, and the lack of a systematic testing protocol, has resulted in a wide variety of devices that cannot be efficiently compared with one another. In this work, an integrated benchmark involving physical, chemical, and biological diagnostics is proposed. The setup is designed to be stable and fixed, while remaining adaptable to different sources. Three different sources, for a total of five configurations, are compared, demonstrating the possibility of obtaining multimodal data. Comparing the biological effects in terms of E. coli abatement between direct and indirect treatments allowed for the exclusion of short-timescale species and phenomena to have a key role in the abatement. The chemical characterisation describes the equilibrium of reactive oxygen and nitrogen species in treated samples, whose presence in the water has been found to be coherent with the plasma operating gas and the nitrogen vibrational temperatures. Nitrate, nitrite and peroxide are excluded from having an autonomous role in the inactivation biochemistry, suggesting the presence of a synergistic effect. Full article

Machine learning has brought about a revolutionary transformation in healthcare. It has traditionally been employed to create predictive models through training on locally available data. However, privacy concerns can sometimes impede the collection and integration of data from diverse sources. Conversely, a lack of sufficient data may hinder the construction of accurate models, thereby limiting the ability to produce meaningful outcomes. Especially in the field of healthcare, collecting datasets centrally is challenging due to privacy concerns. Indeed, federated learning (FL) emerges as a sophisticated distributed machine learning approach that comes to the rescue in such scenarios. It allows multiple devices hosted at different institutions, like hospitals, to collaboratively train a global model without sharing raw data. In addition, each device retains its data securely on locally, addressing the challenges of time-consuming annotation and privacy concerns. In this paper, we conducted a comprehensive literature review aimed at identifying the most advanced federated learning applications in cancer research and clinical oncology analysis. Our main goal was to present a comprehensive overview of the development of federated learning in the field of oncology. Additionally, we discuss the challenges and future research directions. Full article

The aim of the study was to determine the prevalence rates of respiratory pathogens using syndromic tests and also to show which respiratory viruses were detected in suspected cases, especially during and after the pandemic period. A total of 1984 different respiratory tract samples from various departments were included and studied with the QIAstat-Dx device in 2021–2023. The samples were studied with the QIAstat-Dx1 Respiratory SARS-CoV-2 Panel. The kit used was a fully automated, multiplex syndromic test that detected SARS-CoV-2 and 21 other respiratory tract pathogens. As a result of the study, the prevalence of Rhinovirus/Enterovirus (RV/EV) (18.59%), RV/EV-SARS-CoV-2 (42.74%), SARS-CoV-2 (5.04%), and Influenza A Virus (IAV) (5.59%) agents was found to be higher than other agents during the period investigated. Among the 1984 patients examined, 959 (48.33%) had a single viral agent, 156 (7.86%) had double coinfection, 11 (0.55%) had triple coinfection and 1 patient had quadruple coinfection. Nearly half of the patients had a straightforward infection, which helps clinicians in directing specific treatment methods. The study results demonstrate that during the pandemic period, the detection of respiratory pathogens such as SARS-CoV-2 and RV/EV was not only critical for accurate diagnosis but also served as an important indicator of the broader epidemiological trends in respiratory infections. The seasonal distribution showed that while RV/EV was frequently present, its coinfection with SARS-CoV-2 was notably observed only in the first trimester. In light of our findings showing high rates of SARS-CoV-2 and RV/EV detection, along with diverse patterns of coinfection in clinical samples, such comprehensive testing not only assists in rapid diagnosis but also informs public health strategies by reflecting the evolving landscape of respiratory infections in the pandemic and post-pandemic era. Full article

Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency. Full article