Search Results (713)

Gel-type electrofluorochromic (EFC) devices, which reversibly modulate photoluminescence under electrical stimuli, have emerged as versatile platforms for advanced optoelectronic applications. By integrating redox-active luminophores with soft, ion-conductive gel matrices, these systems combine the structural robustness of solids with the ionic mobility of liquids, enabling a high-contrast, flexible, and multifunctional operation. This review provides a comprehensive overview of gel-based EFC technologies, outlining fundamental working principles, device architectures, and key performance metrics such as contrast ratio, switching time, and cycling stability. Gel matrices are categorized into ionogels, organogels, and hydrogels, and their physicochemical properties are discussed in relation to EFC device performance. Recent advances are highlighted across applications ranging from flexible displays and rewritable electronic paper to strain and biosensors, data encryption, smart windows, and hybrid energy-interactive systems. Finally, current challenges and emerging strategies are analyzed to guide the design of next-generation adaptive, intelligent, and energy-efficient optoelectronic platforms. Full article

Amperometric biosensors, due to their high sensitivity, fast response time, low cost, simple control, miniaturization capabilities, and other advantages, are receiving significant attention in the field of medical diagnostics, especially in monitoring blood glucose levels in diabetic patients. In this study, an amperometric glucose biosensor based on immobilized enzyme glucose oxidase (GOx) and bimetallic platinum cobalt (PtCo) nanoparticles was developed. The PtCo nanoparticles, deposited on a graphite rod electrode, exhibited peroxidase-like catalytic properties and were able to electrocatalyze the reduction of H₂O₂. After immobilization of the GOx, an amperometric signal generated by the biosensor was directly proportional to the glucose concentration in the range of 0.04–2.18 mM. The biosensor demonstrated a sensitivity of 19.38 μA mM⁻¹ cm⁻², with a detection limit of 0.021 mM and a quantification limit of 0.064 mM. In addition to this analytical performance, the biosensor exhibited excellent repeatability (relative standard deviation (RSD) was 4.90%); operational and storage stability, retaining 98.93% and 95.33% of its initial response after 26 cycles of glucose detection and over a 14-day period, respectively; and anti-interference ability against electroactive species, as well as exceptional selectivity for glucose and satisfactory reproducibility (RSD 8.90%). Additionally, the biosensor was able to detect glucose levels in blood serum with a high accuracy (RSD 5.89%), indicating potential suitability for glucose determination in real samples. Full article

In the present paper, the interaction between metal oxide nanoparticles and carbon materials was studied, and the results showed a synergetic effect, leading to an improvement in the properties of the obtained hybrid composites. The In₂O₃ NPs were prepared by the precipitation method and thermal treatment at 550 °C. The composites were obtained using an ex situ method, by mixing the In₂O₃ NPs with reduced oxide graphene (rGO) in a ratio of 10:1. The structural, morphological, and chemical composition studies of the In₂O₃ NPs and In₂O₃-rGO composites were investigates by FTIR and EDX spectroscopy, SEM microscopy, and XRD analysis. These techniques have highlighted the obtaining of In₂O₃ of high purity, and crystallinity, with the mean particle size in the range of 8–25 nm, but also, the dispersion of In₂O₃ NPs onto rGO sheets. We examined the influence of the In₂O₃ nanostructure morphology and In₂O₃-rGO composites on the electrochemical properties using cyclic voltammetry. The surface properties of the In₂O₃ and composite films were studied by contact angles, which indicate the maintenance of the hydrophilic nature. The obtained results establish the synergy between the main components to form In₂O₃-rGO, which can be used for the development of biosensors to enhance the device performance. Full article

This numerical study introduces a surface plasmon resonance (SPR)-based biosensor utilizing a kagome lattice-inspired hollow core photonic crystal fiber (PCF) for the highly sensitive detection of various blood biomarkers and analytical components. The sensor is designed to detect key blood biomarkers such as water, glucose, plasma, and hemoglobin (Hb), as well as analytical targets including krypton, sylgard, ethanol, polyacrylamide (PA), and bovine serum albumin (BSA), by monitoring shifts in the resonance wavelength (RW). A dual-polarization approach is employed by analyzing both transverse magnetic (TM) and transverse electric (TE) modes. The proposed sensor demonstrates exceptional performance, achieving maximum wavelength sensitivities (Sw) of 18,900 nm RIU⁻¹ for TM pol. and 16,800 nm RIU⁻¹ for TE pol. Corresponding peak amplitude sensitivities (S_A) of 71,224 RIU⁻¹ for TM pol. and 58,112 RIU⁻¹ for TE pol. were also observed. The peak sensor resolution (S_R) for both modes is on the order of 10⁻⁶ RIU, underscoring its high precision. Owing to its enhanced sensitivity, compact design, and robust dual-polarization capability, the proposed biosensor holds strong promise for point-of-care diagnostics and real-time blood component analysis. Full article

Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The system proposed was compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors were fabricated using silver nanoparticle-functionalized substrates to show high sensitivity and low-cost pollutant detection. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties compared with LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutants at different levels, and executing the models to test their accuracy in detecting pollutants and the latency of data processing. The TCN framework achieved a detection accuracy of 98.7%, which surpassed LSTM by 92.4%. In addition, TCN reduced latency in processing by 38% to enable fast data analysis and decision making. LoRaWAN allowed for perfect packet transmission of up to 15 km, while the loss rate stayed as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks. Full article

Organic photodetectors (OPDs) offer considerable promise for low-power, solution-processable biosensing and imaging applications; however, their performance remains limited by spectral mismatch and interfacial trap states. In this study, a highly sensitive polymer photodiode was developed via trace incorporation (0.8 wt%) of InP/ZnSe/ZnS quantum dots (QDs) into a PTB7-Th:PC₇₁BM bulk heterojunction (BHJ) matrix. This QD doping approach enhanced the external quantum efficiency (EQE) across the 540–660 nm range and suppressed the dark current density at −2 V by passivating interface trap states. Despite a slight decrease in optical absorption at the optimized composition, the internal quantum efficiency (IQE) increased significantly from ~80% to nearly 95% resulting in a net EQE improvement. This suggests that QD incorporation improved charge transport without compromising charge separation efficiency. As a result, the device achieved a specific detectivity (D*) of 1.8 × 10¹³ Jones, representing a 93% improvement over binary BHJs, along with an ultra-low dark current density of 7.76 × 10⁻¹⁰ A/cm². Excessive QD loading, however, led to optical losses and increased dark current, underscoring the need for precise compositional control. Furthermore, the enhanced detectivity led to a 4 dB improvement in the signal-to-noise ratio (SNR) of photoplethysmography (PPG) signals in the target wavelength range, enabling more reliable biophotonic sensing without increased power consumption. This work demonstrates that QD-based spectral and interfacial engineering offers an effective and scalable route for advancing the performance of OPDs, with broad applicability to low-power biosensors and high-resolution polymer–QD imaging systems. Full article

Next-generation pilot suits are evolving into intelligent, adaptive platforms that integrate advanced polymeric materials, smart textiles, and on-body artificial intelligence. High-performance polymers have advanced in mechanical strength, thermal regulation, and environmental resilience, with fabrication methods like electrospinning, weaving, and 3D/4D printing enabling structural versatility and sensor integration. In particular, functional nanomaterials and hierarchical nanostructures contribute critical properties such as conductivity, flexibility, and responsiveness, forming the foundation for miniaturized sensing and integrated electronics. The integration of flexible fiber-based electronics such as biosensors, strain sensors, and energy systems enables real-time monitoring of physiological and environmental conditions. Coupled with on-body AI for multimodal data processing, autonomous decision-making, and adaptive feedback, these systems enhance pilot safety while reducing cognitive load during flight. This review places a special focus on system-level integration, where polymers and nanomaterials serve as both structural and functional components in wearable technologies. By highlighting the role of nanostructured and functional materials within intelligent textiles, we underline a potential shift toward active human–machine interfaces in aerospace applications. Future trends and advancements in self-healing materials, neuromorphic computing, and dynamic textile systems will further elevate the capabilities of intelligent pilot suits. This review discusses interdisciplinary strategies for developing pilot wearables capable of responding to real-time physiological and operational needs. Full article

Due to the frequent occurrence and elevated concentrations of emerging contaminants (ECs) in water environments, as well as their high toxicity, these compounds have become a growing concern, threatening water safety, human health, and environmental health. Stricter regulations and routine monitoring are required to control EC pollution in water. Analytical chemistry-based techniques are the most widely used approach for quantifying ECs in environmental samples. However, high costs, complex sample preparation, time-consuming protocols, and labor-intensive processes limit their application for the routine and rapid detection of ECs. Biosensors are a promising biotechnological alternative that has received increased attention in recent years for the quantification of ECs. This review provides a comprehensive overview of the main types of biosensors used for monitoring ECs in aquatic environments, highlighting their underlying detection mechanisms and recent technological advancements. It also discusses key challenges associated with different biosensor platforms, such as stability, sensitivity, and development complexity. Potential future research directions to address these limitations and enhance the performance of biosensors include immobilization on hybrid nanomaterials, and the development of portable and multifunctional biosensors for on-site and real-time monitoring. By summarizing current progress and identifying future directions, this review will broaden the awareness and recognition of biosensors for monitoring ECs in water environments, contributing to water safety, sanitation, and sustainability. Full article

Early and accurate detection of plant diseases is critical for ensuring global food security and agricultural resilience. Ratoon stunting disease (RSD), caused by the bacterium Leifsonia xyli subsp. xyli (Lxx), is among the most economically significant diseases of sugarcane worldwide. Its cryptic nature—characterized by an absence of visible symptoms—renders timely diagnosis particularly difficult, contributing to substantial undetected yield losses across major sugar-producing regions. Here, we report the development of a potential-induced electrochemical (EC) nanobiosensor platform for the rapid, low-cost, and field-deployable detection of Lxx DNA directly from crude sugarcane sap. This method eliminates the need for conventional nucleic acid extraction and thermal cycling by integrating the following: (i) a boiling lysis-based DNA release from xylem sap; (ii) sequence-specific magnetic bead-based purification of Lxx DNA using immobilized capture probes; and (iii) label-free electrochemical detection using a potential-driven DNA adsorption sensing platform. The biosensor shows exceptional analytical performance, achieving a detection limit of 10 cells/µL with a broad dynamic range spanning from 10⁵ to 1 copy/µL (r = 0.99) and high reproducibility (SD < 5%, n = 3). Field validation using genetically diverse sugarcane cultivars from an inoculated trial demonstrated a strong correlation between biosensor signals and known disease resistance ratings. Quantitative results from the EC biosensor also showed a robust correlation with qPCR data (r = 0.84, n = 10, p < 0.001), confirming diagnostic accuracy. This first-in-class EC nanobiosensor for RSD represents a major technological advance over existing methods by offering a cost-effective, equipment-free, and scalable solution suitable for on-site deployment by non-specialist users. Beyond sugarcane, the modular nature of this detection platform opens up opportunities for multiplexed detection of plant pathogens, making it a transformative tool for early disease surveillance, precision agriculture, and biosecurity monitoring. This work lays the foundation for the development of a universal point-of-care platform for managing plant and crop diseases, supporting sustainable agriculture and global food resilience in the face of climate and pathogen threats. Full article

Under normal conditions, the novel zinc blende beryllium oxide (zb BeO) exhibits in a metastable crystalline phase, which is less stable than its wurtzite counterpart. Ultrathin zb BeO epifilms have recently gained significant interest to create a wide range of advanced high-resolution, high-frequency, flexible, transparent, nano-electronic and nanophotonic modules. BeO-based ultraviolet photodetectors and biosensors are playing important roles in providing safety and efficiency to nuclear reactors for their optimum operations. In thermal management, BeO epifilms have also been used for many high-tech devices including medical equipment. Phonon characteristics of zb BeO at ambient and high-pressure P ≠ 0 GPa are required in the development of electronics that demand enhanced heat dissipation for improving heat sink performance to lower the operating temperature. Here, we have reported methodical simulations to comprehend P-dependent structural, phonon and thermodynamical properties by using a realistic rigid-ion model (RIM). Unlike zb ZnO, the study of the Grüneisen parameter γ(T) and thermal expansion coefficient α(T) in zb BeO has revealed atypical behavior. Possible reasons for such peculiar trends are attributed to the combined effect of the short bond length and strong localization of electron charge close to the small core size Be atom in BeO. Results of RIM calculations are compared/contrasted against the limited experimental and first-principle data. Full article

The unique properties of graphene have allowed for the development of graphene-based field-effect transistors (GFETs) for applications in biosensors and chemical devices. However, the modeling and optimization of GFET performance exhibit great challenges. Herein, we propose a quantum transport simulation model for graphene-based field-effect transistors (GFETs) implemented in the open-source Octave programming language. The proposed simulation model (named SimQ) combines the Landauer–Büttiker formalism with self-consistent Schrödinger–Poisson solutions, enabling reliable simulations of transport phenomena. Our approach agrees well with established models, achieving Landauer–Büttiker transmission and tunneling transmission of 0.28 and 0.92, respectively, which are validated against experimental data. The model can predict key GFET characteristics, including carrier mobilities (500–4000 cm²/V·s), quantum capacitance effects, and high-frequency operation (80–100 GHz). SimQ offers detailed insights into charge distribution and wave function evolution, achieving an enhanced computational efficiency through optimized algorithms. Our work contributes to the modeling of graphene-based field-effect transistors, providing a flexible and accessible simulation platform for designing and optimizing GFETs with potential applications in the next generation of electronic devices. Full article

Biomarkers play a pivotal role in disease diagnosis, therapeutic efficacy evaluation, prognostic assessment, and drug screening. However, the trace concentrations of these markers in complex physiological environments pose significant challenges to efficient detection. It is necessary to avoid interference from non-specific signals, which may lead to misjudgment of other substances as biomarkers and affect the accuracy of detection results. With the rapid advancements in electrochemical technologies and artificial intelligence (AI) algorithms, intelligent electrochemical biosensors have emerged as a promising approach for biomedical detection, offering speed, specificity, high sensitivity, and accuracy. This review focuses on elaborating the latest applications of AI-empowered electrochemical biosensors in the biomedical field, including disease diagnosis, treatment monitoring, drug development, and wearable devices. AI algorithms can further improve the accuracy, sensitivity, and repeatability of electrochemical sensors through the screening and performance prediction of sensor materials, as well as the feature extraction and noise reduction suppression of sensing signals. Even in complex physiological microenvironments, they can effectively address common issues such as electrode fouling, poor signal-to-noise ratio, chemical interference, and matrix effects. This work may provide novel insights for the development of next-generation intelligent biosensors for precision medicine. 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

The extensive diversity of volatile organic compounds, along with their minor structural variations, presents significant challenges in the development of chemosensory-based biosensors. Previously, we generated sensor cells expressing insect odorant receptors (ORs) in Sf21 cells, demonstrating their potential as cell-based odorant sensor elements. However, it remains unclear whether the selectivity of cells expressing ORs in vitro for diverse compounds aligns with the receptor’s in vivo performance, aside from the response to target compounds. To address this, we assessed the ligand responses of sensor cells expressing ORs from Drosophila melanogaster using a high-throughput calcium imaging system. Our results demonstrate that in vitro receptor responses exhibit ligand selectivity comparable to in vivo conditions across different chemical categories. Broadly tuned OR-expressing sensor cells (Or13a, Or47a, and Or98a) displayed differential affinities, whereas the narrowly tuned Or56a-expressing sensor cells selectively responded to geosmin. Moreover, cell responses varied with subtle differences in chemical structure, including carbon chain length and functional group positioning. These findings provide valuable insights into insect OR–ligand interactions in vitro, demonstrating that receptor selectivity in sensor cells closely mirrors in vivo conditions. In addition to this consistency, our results highlight the subtle ligand differentiation capabilities of sensor cells enabling fluorescence-based visualization of receptor–ligand interactions. Full article

Nanomaterials’ special features enable their extensive application in chemical and biochemical nanosensors for food assays; food packaging; environmental, medicinal, and pharmaceutical applications; and photoelectronics. The analytical strategies based on novel nanomaterials have proved their pivotal role and increasing interest in the assay of key food components. The choice of transducer is pivotal for promoting the performance of electrochemical sensors. Electrochemical nano-transducers provide a large active surface area, enabling improved sensitivity, specificity, fast assay, precision, accuracy, and reproducibility, over the analytical range of interest, when compared to traditional sensors. Synthetic routes encompass physical techniques in general based on top–down approaches, chemical methods mainly relying on bottom–up approaches, or green technologies. Hybrid techniques such as electrochemical pathways or photochemical reduction are also applied. Electrochemical nanocomposite sensors relying on conducting polymers are amenable to performance improvement, achieved by integrating redox mediators, conductive hydrogels, and molecular imprinting polymers. Carbon-based or metal-based nanoparticles are used in combination with ionic liquids, enhancing conductivity and electron transfer. The composites may be prepared using a plethora of combinations of carbon-based, metal-based, or organic-based nanomaterials, promoting a high electrocatalytic response, and can accommodate biorecognition elements for increased specificity. Nanomaterials can function as pivotal components in electrochemical (bio)sensors applied to food assays, aiming at the analysis of bioactives, nutrients, food additives, and contaminants. Given the broad range of transducer types, detection modes, and targeted analytes, it is important to discuss the analytical performance and applicability of such nanosensors. Full article