Sensors International最新文献

The current study demonstrates the manufacturing of highly sensitive aptasensr for the robust and effective detection of dengue virus antigen. The proposed electrochemical aptasensor employs both types of electrodes, namely commercialized screen-printed electrodes (C-SPEs) and self-fabricated screen-printed electrodes (SF-SPEs), were efficiently diagnose dengue virus antigen (DENV-Ag) and shows a lower limit of detection (LOD) i.e., 0.1 μg/ml. Both the electrode types were coated with chemically synthesized ZnO-Nanomaterial, which aids in electron transport, and to make it more selective highly specific DNA-aptamer was used against the DENV antigen. SEM and Uv–Vis spectra approaches were used to characterize the synthesized nanomaterial. To confirm the DENV-antigen detection results, electrochemical analysis was performed and the sensor cross-reactivity was also checked by a close member of the dengue virus i.e., chikungunya virus (CHIKV). The developed platform based on SF-SPEs & C-SPEs performed well in human serum. This investigation found that the SF-SPEs system had advanced sensitivity and responded very well to the C-SPEs. Consequently, the SF-SPEs system has emerged as a feasible choice for low-cost and highly sensitive DENV-detection and is also applicable for other analytes diagnostics.

Owing to their significant roles in multiple sectors, the demand for high-performance, rapid, user-friendly, and low-cost sensors is crucial for biosensing. This paper reports the performance of a commercial chip-based tunneling magnetoresistance (TMR) sensor for detecting green-synthesized magnetic nanoparticles (MNP) as potential magnetic labels. A Simple and low-cost design consisting of a TMR chip ALT-025 integrated with an Arduino microcontroller and a basic differential amplifier was developed to provide real-time and measurable digital readouts. Three kinds of ferrite MNPs (Fe₃O₄, CoFe₂O₄ and MnFe₂O₄) was synthesized by the coprecipitation method on the green synthesis approach utilizing Moringa Oleifera extracts. All sample have a face-centered cubic inverse spinel structure with average grain size of 10.3 nm, 9.2 nm and 6.1 nm for Fe₃O₄, CoFe₂O₄ and MnFe₂O₄, respectively. Furthermore, soft ferromagnetic behavior is identified for all sample with magnetization saturation of 55.3 emu/g, 37.6 emu/g, 19.3 emu/g for Fe₃O₄, CoFe₂O₄ and MnFe₂O₄, respectively. The sensor showed a promising performance in the detection of MNPs. For the three particles, the sensitivity exhibited a linear function of the MNPs concentration. The sensitivity is related not only to the particle size but also to the magnetization of the nanoparticles in the bias field. The change in the output voltage was proportional to the bias magnetization (M_Bias), indicating that particles with a higher bias magnetization can produce a stronger magnetic stray field on the TMR sensor surface. The sensor system successfully detected MNPs at different stray field intensities. Furthermore, a low limit of detection was achieved using these methods. Moreover, the remarkable stability and repeatability of the sensor is further validated by the steady signal acquired for 30s with an RSD of 0.5–28.5 %. Therefore, the integration of commercial chip-based TMR sensors and green-synthesized MNPs has great potential for advancing the detection of various biomolecules.

In this investigation, we employed a cost-efficient co-precipitation technique to synthesize nanostructures of Indium-doped ZnO, incorporating varying percentages of Indium (0.25 %, 0.5 %, 1 %, 2 %, and 4 %) into the ZnO lattice. These Indium atoms were introduced either by replacing oxygen (O₂) or occupying tetrahedral interstitial spaces within the structure. The resultant materials exhibited an average crystal size ranging from approximately 5 to 10 nm and displayed a highly crystalline nature. The UV–visible spectroscopy of these synthesized materials, revealing an excitation spectrum spanning 380 nm–395 nm. Photoluminescence measurements showed two distinct emission peaks at 390 nm and 471 nm, originates from the recombination of the free excitons through an exciton-exciton collision process and the presence of defects or impurities in the In–ZnO nanostructures. Defects in the crystal lattice, such as oxygen vacancies or interstitial defects, can create energy levels within the bandgap. Subsequently, we evaluated the suitability of these Indium-doped ZnO nanostructures for light sensor applications. Response and recovery times to infrared (IR), visible, and ultraviolet (UV) light was recorded. Remarkably, the nanostructures exhibited exceptional response and recovery times, in UV light compared to their performance with IR and visible light. This significant performance of synthesized materials in UV light shows the cost-effective co-precipitation method in fabricating Indium-doped ZnO nanostructures for UV light sensing applications.

In this paper, we present and investigate a novel approach for self-referenced sensing using a multilayer structure in Kretschmann configuration. The obtained results show that the structure can support two modes, plasmon-induced transparency and waveguide mode. The sensing performance of the structure was evaluated by calculating the sensor Sensitivity, Quality Factor, and Figure of Merit. Moreover, to quantify the capability of our approach for self-referencing sensing we calculated the self-referencing figure of merit. We demonstrate that the PIT mode-based approach has the best simulation results in terms of Figure of Merit of 5950/RIU, Quality Factor of 292.5/RIU, and Self-Referencing Figure of Merit of 5.7. The designed biosensors can be used for accurate and reliable sensing applications.

The effectiveness and dependability of network communication within the Internet of Things (IoT) depends on the energy-harvesting capabilities of IoT sensors. It is imperative to efficiently handle energy resources to fulfill computational requirements, ensuring optimal performance and continuous operation of IoT sensors across various applications. This investigation examines the challenges associated with energy harvesting in commonly used IoT sensors and their corresponding communication technologies. This encompasses wireless communication, cyber–physical systems (CPS), machine-to-gateway communication (M2G), wireless power transmission (WPT), and IoT infrastructure and protocols such as IPv6, 6LoWPAN, MQTT, CoAP. Furthermore, the study explores routing algorithms within the IoT network context, recognizing their crucial role in addressing challenges related to sensor battery lifespan and energy conservation. Challenges in energy resource management, which include considerations of sensor types, spatial relationships, and connection stability, are also discussed. The study investigates the energy consumption of different types of connections in an IoT network during data transfer, considering factors such as jitter, packet loss, overhead, congestion, distance between nodes, network protocol (MQTT), and data size (32MB). Two scenarios are explored: one where the minimum frequency band and data rate are fixed, revealing that Sigfox consumes more energy than others, while Bluetooth v5.0 is more energy-efficient; and another where the maximum frequency band and data size are fixed, showing that 5G consumes more energy, whereas NB-IoT is more energy-efficient. Finally, the research investigates the energy consumption increments for various network connections (2G, 3G, 4G, 5G, Bluetooth V5.0, Sigfox, WiMAX, LoRaWAN, Zigbee, and NB-IoT) as the frequency band and network data rate increase from minimum to maximum values, revealing increments within the range of 7% to 71%.

‘Give a man a biosensor, and you enable him to unlock a world of cost-effective solutions for research, diagnosis, and personalized healthcare’.

Biosensors have emerged as a game-changer in the realms of research sciences and healthcare, offering exceptional value for money. The integration of biosensors into these fields holds immense promise, empowering researchers and medical practitioners to unlock intricate mysteries in food and water safety, human biology, and health assessment. These state-of-the-art technologies are a breath of fresh air, revolutionizing disease detection and tracking to unprecedented levels, elevating the ability to monitor the body's response. They have become the linchpin of numerous cost-effective, highly efficient, and streamlined medical devices prevalent in modern healthcare. By harnessing the sensitivity and specificity of biosensors, healthcare professionals can hit the nail on the head, identifying even the subtlest biomarkers and indicators of various ailments, and enabling timely intervention and treatment. The superior quality of these biosensors ensures unrivaled diagnostic accuracy, leading to more reliable and effective healthcare outcomes. In a nutshell, biosensors have raised the bar, making research, public safety, and tailored healthcare options a walk in the park, ultimately enhancing overall health and well-being.

Biosensors offer immense potential in medical diagnostics due to their user-friendly nature, scalability, and efficient manufacturing. With intelligent wearable features, they facilitate seamless health monitoring for the elderly, bridging the gap between self-care and healthcare providers. This exchange of medical information reduces interference and hospital visits, opening avenues in wellness, fitness, and athletics for consumers and commercial entities.

This paper explores the advancements in Biosensors technology and their promising benefits in medicine, focusing on cardiovascular diseases and using informative diagrams. It examines fourteen key applications of Biosensors in the medical field, highlighting the integration of biomedical devices, apps, firmware, and advanced algorithms. These developments pave the way for innovative medical therapies, real-time evidence-based insights, customized solutions, and informed guidance, shaping a bright future for healthcare.

L-tryptophan (L-Trp) is a vital amino acid that sways neuronal function, immunity, and gut homeostasis, and its accurate detection in food samples is crucial. The aim of this study is to integrate zinc cobaltite (ZCO) nanoparticles and 3D porous reduced graphene oxide (rGO) on a screen-printed carbon electrode (SPCE) surface for building a novel electrochemical sensor to sensitively detect L-Trp in food products. A low-temperature aqueous solution method was employed in ZCO nanoflower synthesis and a hydrothermal approach was utilized to prepare 3D rGO. SEM, elemental mapping, XRD, Raman spectroscopy, XPS, and EIS characterizations were performed on the prepared nanocomposite, ZCO/3DrGO. Electrochemical experiments conducted with the cyclic and differential pulse voltammetric techniques were used for effectively assessing the catalytic power of ZCO/3DrGO/SPCE. With a low detection limit of 3 nM, high sensitivity of 19.53 μAμM⁻¹cm⁻², and a broad linear range of 0.08–5.93; 5.93–87.18 μM, the sensor demonstrated promising electrocatalytic activity towards L-Trp. Further, the reliability of the sensor was proved by analyzing its stability, repeatability, reproducibility, and selectivity towards L-Trp. The successful detection of L-Trp in dairy products (yogurt, milk, and cottage cheese) using the proposed sensor evinced its practical feasibility with high recovery of 98.16%–101.16% and low RSD of 2.8%.

This research highlights the significant role of green synthesis in the production of copper oxide (CuO) nanoparticles by using natural extracts as reducing agents. These nanoparticles have shown promising potential in two key applications: photocatalytic degradation of industrial dye effluents and electrochemical sensing of ciprofloxacin. The study found that Arundinaria gigantea leaf extract is an effective reducing agent for synthesizing well-defined crystalline structure CuO nanoparticles, with an average size of 36 nm. The CuO nanoparticles have demonstrated high efficiency in photocatalytic applications, effectively degrading AR88 dye under UV irradiation, making them a viable solution for eco-friendly water purification. Additionally, when incorporated into an electrochemical sensor, these CuO nanoparticles have improved sensitivity and selectivity in detecting ciprofloxacin in aqueous solutions with high accuracy and precision. This study emphasizes the versatility and effectiveness of green-synthesized CuO nanoparticles for various practical uses.

Silicon photonics is a rapidly developing field that offers cost-effective biosensors with improved sensitivity and the potential for interconnecting with other electronic devices for instant disease diagnosis. The Mach Zehnder interferometer architecture (MZI) is a key technology for biosensors, as it detects changes in refractive index (RI) caused by the presence of biomolecules. In this study, a silicon-polymer double-slot waveguide-based MZI was designed, with a small mode area and a large evanescent field to enhance light-analyte interaction. The waveguide was optimized by converting a normal slot waveguide into a double-slot waveguide with varying slot widths. In transmission spectrum, the wavelength shift was measured for both normal and disease samples. Additionally, the loss at a specific wavelength was analyzed to understand the impact of the biomolecule on the sensor performance. The results show that this sensor has a high sensitivity of 2.39 X10^5 nm/RIU, making it a promising candidate for biosensing applications.

Worldwide, there has been an increasing prevalence of kidney disorders for several years. Kidney disorders are characterized by abnormal kidney biomarkers like uric acid, urea, cystatin C, creatinine, kidney injury molecule-1, C-related protein, etc., in the human body. A person suffering from kidney disorders is prone to several other serious health consequences, such as cardiac diseases and renal failure, which can lead to death. However, early diagnosis of kidney disorders requires effective disease management to prevent disease progression. Existing diagnostic techniques used for monitoring kidney biomarker concentration include chromatographic assays, spectroscopic assays, immunoassays, magnetic resonance imaging (MRI), computed tomography (CT), etc. They also necessitate equipped laboratory infrastructure, specific instruments, highly trained personnel working on these instruments, and monitoring kidney patients. Hence, these are expensive and time-consuming. Since the past few decades, a number of biosensors, like electrochemical, optical, immunosensors, potentiometric, colorimetric, etc., have been used to overcome the drawbacks of conventional and modern techniques. These biosensing systems have many benefits, such as being cost-effective, quick, simple, highly sensitive, specific, requiring a minimum sample amount, reliable, and easy to miniaturize. This review article discusses the uses of effectual biosensors for kidney biomarker detection with their potential advantages and disadvantages. Future research needs to be implicated in developing highly advanced biosensors that must be sensitive, economical, and simple so that they can be used for on-site early detection of kidney biomarkers to assess kidney function.