Sensors International最新文献

Hybrid nanostructured materials currently offer a potential approach for a variety of applications due to improvements in their physio-chemical characteristics. Techniques for XRD, TEM-HRTEM, SAED, and UV-DRS were used to characterize the Cu₂ZnAl₂O₄ (CZA) material. Without any secondary phases and with an average crystallite size of 40 ​nm, X-ray diffraction pattern examination demonstrates the increased crystalline structure. A highly crystalline, polydisperse CZA nanostructure was visible using TEM-HRTEM and SAED. The CZA nanostructure's light-absorbing behavior is presented by UV-DRS analysis, which found that the predicted bandgap energy was 5.0 ​eV. In this article, we describe an easy chemical synthesis of a hybrid CZA nanostructure that works well as a catalyst to break down the acid red 88 (AR-88) dye under UV, sunlight, and low light conditions. Additionally, it was studied to determine how to modify the working electrode's surface to enable the detection of lead and tin metal ions. With 93.1% of degradation and comparison work on decolorizing AR-88 dye in the presence of both sunlight and darkness, CZA nanostructure was looked at as a potential catalyst for the decolorization of AR-88 dye. By using graphite electrode paste and cyclic voltammetry to analyze the synthesized sample in 1 ​N KCl, it was discovered that it had outstanding redox reaction and lead and tin detection capabilities.

An enhanced biosensor was developed for the determination of blood lactate in lacto-acidosis patients. The biosensor employed a nanohybrid composed of chitosan/iron oxide nanoparticles and carboxylated multiwalled carbon nanotubes (CHIT/Fe₃O₄NPs/c-MWCNTs), electrodeposited onto an Au electrode, followed by covalent immobilization of L-lactate oxidase (LOx) onto this nano-hybrid. The biosensor (LOx/CHIT/Fe₃O₄NPs/c-MWCNTs/AuE) exhibited notable improvements in its analytical characteristics such as a rapid response time (4s), a lower detection limit of 0.15 μM and a wider linear range of 1–3000 μM of L-lactic acid. Additionally, it displayed enhanced reproducibility and an extended shelf life of 100 days. The biosensor was employed to measure the concentration of L-lactate in the plasma of both apparently healthy individuals and lacto-acidosis patients. The results showed that the L-lactate concentrations ranged from 112 ± 1.24 to 183 ± 29.15 μmol/L in apparently healthy individuals, whereas it ranged from 2236 ± 33.29 to 4949 ± 72.39 μmol/L in lacto-acidosis patients, which is significantly higher than in apparently healthy individuals. Thus, the integration of the CHIT/Fe₃O₄NPs/c-MWCNTs hybrid film in the biosensor led to the enhanced analytical performance of the biosensor.

Spin exchange relaxation free (SERF) atomic magnetometer is one of the most sensitive magnetometers. High temperature and a non-magnetic environment are two key conditions to achieve ultra-high sensitivity. In this paper, a non-magnetic heating film with magnetic field self-suppression is optimized by the genetic algorithm method. Four structural parameters of the non-magnetic heating film including the wire spacing, wire width, wire thickness, and layer distance are optimized to minimize the magnetic field in the heated space. The simulation result based on the finite element analysis method shows that the magnetic field and the temperature field produced by a pair of heating films at the vapour cell are uniform. Finally, the magnetic field experiment proved that when the current is 10 ​mA, the magnetic field is 3.0520 ​nT. The temperature control experiment indicates that the temperature could be stabilized at 180 ​± ​0.2 ​°C. This study is significant for electric heating with a lower magnetic field and contributes to further improving the performance of atomic sensors.

Applying the renewable energy, such as the solar energy, would be a promising way to realize the self-powered and sustainable wireless sensing for temperature monitoring in food storage. This paper developed and proposed a solar energy harvesting and wireless charging based temperature monitoring system for food storage. The system includes the solar energy harvesting, wireless charging and wireless temperature sensing. The wireless charging performance between the wireless charging transmitter and wireless sensor node, the energy consumption performance of wireless sensor node, the wireless temperature monitoring performance and the deployment and improvement performance of the solar energy harvesting and wireless charging based temperature monitoring system were analyzed and evaluated. The proposed and developed system could effectively wirelessly monitor the temperature in real time by solar energy harvesting and wireless charging to ensure the food quality and safety in storage. The proposed system also has a potential application example for many kinds of food monitoring by renewable energy harvesting or wireless charging to improve the sustainability, quality and safety of food in storage or supply chain.

Phenolic compounds are well-known as antioxidants including many other important bioactive agents that have long been interested due to their benefits for human health, curing and preventing many diseases. Gallic acid (GA), is a naturally occurring most abundant phenolic acid compound found in groundnut, honey, mango, tea, wine, and various medicinal plants. Different techniques have been used for determination of GA. Electrochemical technique is of the best methods. A sensor with improved sensitivity for GA determination in real samples is developed. This work presents preparation of glycine modified carbon paste electrode (GMCPE) for square wave voltammetry determination of GA in groundnut and tea samples. Cyclic voltammetry and Electrochemical impedance spectroscopy results evidenced that modification of the surface of CPE by glycin improves the surface area and conductivity of the electrode. The appearance of an irreversible oxidative peak for GA with much reduced oxidative potential and about four folds current enhancement at GMCPE than the bare CPE showed the catalytic property of the modifier towards oxidation of GA. Under optimized conditions, SWV current response of GMCPE showed a linear dependence on the concentration of GA in the range 1.0 ​× ​10⁻⁶ – 2.0 ​× ​10⁻⁴ ​M, with LoD and LoQ 1.53 ​× ​10⁻⁸ ​M and 5.1 ​× ​10⁻⁸ ​M, respectively. Spike recovery results in groundnut and tea samples in the range 89.75–98.98% and 92.15–97.10%, respectively. Interference effect in the presence of selected potential interferents at their various levels with associated errors under 4.63% showed no interference on the current response of GA. The stability of the modifier with analysis time validated the applicability of the method for the determination of GA in real samples. Relative to previously reported works wider linear dynamic range, low LoD, high accuracy and selectivity make the present developed method an excellent candidate for determination of GA in real samples.

A robust real-time flow injection assay using Quartz Crystal Microbalance (QCM) biosensor has been developed for the detection of mesothelin, an antigen expressed on various malignant tumors including mesothelioma and ovarian cancers. A QCM sensor chip functionalized with self-assembled monolayer of cysteamine used to fabricate a sensitive immunosensor. Mesothelin specific antibody was immobilized on cysteamine modified gold surface of quartz crystal using N-ethyl-N’-(3-dimethylaminopropyl) carbodiimide and N-hydroxy succinimide coupling. Further, ethanolamine was used as a blocking reagent to prevent the non-specific adsorption on antibody functionalized gold surface. Various concentrations of mesothelin was tested and the resonant frequency variation of the crystal was observed by a quartz crystal microbalance until a stable response is obtained. A good correlation between the frequency changes and concentration of mesothelin tested was observed and the QCM biosensor detects mesothelin in the linear range of 100pg/mLto 50 ng/mL. Thus, QCM assay could be a promising technique for early diagnosis of mesothelioma, ovarian and pancreatic adenocarcinoma.

Graphene oxide based molecularly imprinted polymer was designed by incorporating vinyltrimethoxysilane into the layers of graphene oxide, which was copolymerized with functional monomers such as Itaconic acid (IA) and methyl methacrylate (MMA) was developed via bulk imprinting technique. The prepared polymer was studied for selective sensing the uric acid (UA) in blood serum. The electrode was constructed by modifying bare glassy carbon electrodes with the prepared molecularly imprinted polymer (MIP) via drop cast method. Electrochemical measurements were made by Cyclic voltammetric (CV) and Differential Pulse Voltammetric (DPV) response of the sensor. The physical and chemical properties of the resultant material will be characterized by FTIR spectroscopy, XRD and FESEM. The constructed sensor showed a regression coefficient (R²) of 0.9302 with limit of detection (LOD) of about 0.565 ​μM. The developed sensor is reusable without any compromise in its selectivity. All the results confirm that the constructed biosensor requires no pre-treatment of samples and is suitable for real sample analysis.

The influence of radiofrequency electromagnetic radiation on the wounding-induced electric potentials (EPs) in Aloe arborescens plants was investigated. Burn wounding-induced electrical potentials of mature A. arborescens plants were observed under the exposure of 2.45 ​GHz, 3.5 ​GHz and 5.5 ​GHz microwaves at incident power density 1.5 ​± ​0.2 ​W ​m⁻². Aloe leaves were subjected to flame wounding at the leaf tips and propagation EPs were recorded by inserting a glass Ag/AgCl microelectrode into the leaf pulp. The propagation of electrical potential and a standard deviation of the fluctuations in electrical potential (SDEF) were investigated. The flame wounding generated propagating characteristic electric potential, and the exposure of microwaves added extra characteristics to the signals by reversing the electrical potential temporally for a shorter duration. The characteristics appeared in the repolarization phase of the signal under 2.45 ​GHz and 3.5 ​GHz exposure; for the 5.5 ​GHz exposure, 3 out of 6 characteristics appeared during the depolarization phase. Averaged polarization rates of the characteristics were increased with the increased microwave frequency. Added characteristics to the electric potential may have resulted from a secondary signal triggered due to microwave exposure, which should be further studied. The repolarization and depolarization rates of the wound signals were not different between control and microwave exposures. SDEFs were also not affected by microwave exposure.

This paper presents the three-stage design of a grounded coplanar waveguide (GCPW) microstrip array antenna with via-holes beyond 100 ​GHz. The design was with a time-domain solver at the Computer Simulation Technology Microwave Studio (CST MWS) simulator, while the comparison was with the frequency-domain solver at the CST MWS simulator. As a result, a good agreement was achieved, which validated the proposed antenna. Furthermore, simulated results of the proposed antenna showed a bandwidth (BW), working frequencies, peak gain, and peak directivity of 24.22 ​GHz, 107.41–133.54 ​GHz, 12.1 ​dB, and 12.98 dBi, respectively. Thus, this GCPW microstrip array antenna can be suitable for beyond 100 ​GHz integrated vehicular autonomous and next-generation wireless communication networks at the sixth generation (6G) after a suitable experimental verification.

Malaria is a life-threatening parasitic disease spread by infected female Anopheles mosquitoes. After analyzing it, microscopists detect this disease from the sample of microscopic red blood cell images. A professional microscopist is required to conduct the detection process, such an analysis may be time-consuming and provide low-quality results for large-scale diagnoses. This paper develops an ensemble learning-based deep learning model to identify malaria parasites from red blood cell images. VGG16(Retrained), VGG19(Retrained), and DenseNet201(Retrained) are three models that are used in developing the adaptive weighted average ensemble models. To reduce the dispersion of predictions, a max voting ensemble technique is then applied in combination with adaptive weighted average ensemble models. A variety of image processing techniques are utilized including the data augmentation technique to increase the number of data and solve the overfitting problem of the model. Some other approaches of custom CNN, Transfer Learning, and CNN-Machine Learning (ML) classifier techniques are also implemented for comparing their performance with the ensemble learning model. The proposed ensemble learning model provides the best performance among all with an accuracy of 97.92% to classify parasitized and uninfected cells. Therefore, the deep learning model has the potential to diagnose malaria more accurately and automatically.