Sensors International最新文献

Graphene and its derivatives have become essential materials in modern biomedical research due to their positive impact on various applications. Moreover, the integration of graphene-based materials with microfluidics technology has opened up new possibilities. The novelty of the current review is considering comprehensive analysis of the transformative impact of graphene and its derivatives in biomedical applications, particularly highlighting the integration with microfluidics technology. While many studies have focused on individual applications of graphene, this review uniquely present a holistic view of its potential across various biomedical fields, including drug delivery, gene delivery, tissue engineering, and photothermal treatment, detection, sensor with respect to conventional and microfluidics techniques. In this review, we analysed published research to unveil the increasing interest in graphene's potential applications in healthcare and medicine, as well as its prospects for further exploration. We explore the fundamental concepts of graphene, its properties, and its latest applications in medical implants and biological fields within the context of microfluidics and conventional prospects. The review also addresses the challenges and limitations of these materials and their promising future, recognizing that graphene research is still in its early stages compared to commercial 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%.

Air pollution is a significant problem in big cities due to the rapid increase of anthropogenic activities and severe traffic congestion. Therefore, real-time and micro tools for air monitoring are urgently necessary for fast and better policy decision-making. The current city air monitoring tool is typically static and serves a macro area. This study introduces technology development to integrate the air quality sensor with the satellite-based navigation receiver. This study used a carbon dioxide (CO₂) MH-Z19C sensor and real-time kinematic global navigation satellite system (RTK GNSS) U-Blox F9P with GNSS Trimble NetR9 receiver. The field air quality monitoring (CO₂ observed in ppm) and the movement velocity (vehicle speed observed in km/h) were recorded on two main roads of Jakarta by using a survey vehicle. The study compares the observation results of the non-integrated system (NIS) and integrated technology system (IS). The two systems generated the CSV database (CO₂ and vehicle speed); however, IS generated the automatic synchronized and error-free data output. The statistical regression analysis of CSV data (CO₂ and vehicle speed) between the NIS and IS reported significant results, which means both are reliable. Still, the NIS did not require manual synchronization, with some possibility of error. The R square values show a significant gap (speed 0.99 over CO₂ 0.144), indicating that IS needs further development as the CO₂ data varies due to technicality. The finding presents that integrating the CO₂ sensor and GNSS receiver generates a more effective time synchronization process and a reliable error removal technique in developing the CSV data. This finding is a significant reference in developing the integrated satellite-based receiver system with external environmental sensors.

‘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.

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.

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.

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 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.

An electrochemical biosensor for H₂O₂ detection was developed by using soybean peroxidase-copper phosphate mediated organic inorganic hybrid (OIH). The characterization of OIH was carried out by FESEM and FTIR. FESEM analysis showed a flower-like porous morphology of the OIH and FTIR confirmed the presence of soybean peroxidase and copper phosphate in the OIH. For sensor development, the paste of OIH was formulated in the presence of phosphate buffered saline (PBS) and was screen printed on indium tin oxide (ITO) coated glass substrate. Cyclic voltammetry analysis showed that the developed biosensor could detect H₂O₂ in the linear range of 20–100 ​μM with R² value of 0.963. The limit of detection (LOD) and sensitivity values calculated for H₂O₂ are 0.19 ​μM and 27.44 μA/(μM.cm2) respectively. Along with cyclic voltammetry experiments, electrochemical impedance spectroscopy (EIS) analysis was also carried out to study the sensing mechanism. The developed biosensor showed better selectivity towards H₂O₂ when tested against d-glucose, l-tyrosine, l-lysine, and l-ascorbic acid.

This work has been focused on developing a novel biosensor assisted by multivariate calibration methods to determine triglycerides (TGs, triacetin, tributyrin, tricaproin, tricaprylin, and tricaprin) in lyophilized serum samples. To achieve this goal, a bare glassy carbon electrode (GCE) was modified and used as the biosensing platform. To increase the sensitivity of the developed method, hydrodynamic methods were used to calibrate the biosensor response. To increase the selectivity of the developed biosensor, it was assisted by partial least squares-1 (PLS-1), radial basis function-PLS (RBF-PLS), and RBF-artificial neural network (RBF-ANN) for exploiting first-order advantage. After characterization of the modifications, the first-order advantage was exploited to increase the selectivity of the method by building a multivariate calibration set in a pre-analyzed lyophilized serum with different TGs concentrations, which were chosen according to the individual calibration curve. Calibration models were then built in the same pre-analyzed lyophilized serum and analyzed by PLS-1, RBF-PLS, and RBF-ANN. Therefore, their performances were examined to predict concentrations of a validation set. The results confirmed the successfulness of the calibration model developed by RBF-ANN. Finally, it was used to analyze two serum samples, and the results demonstrated that the method was successful because its results were compared with a reference method.