Characteristics and gas sensor applications of ZnO-Perovskite heterostructure

In this study, zinc oxide (ZnO) nanofibers were prepared using the electrospinning method, and the effects of different spinning voltages and annealing temperatures on the fiber structure were tested. La_0.8Sr_0.2FeO₃ (LSFO) perovskite film was prepared by a sol-gel method. Then we dip LSFO on ZnO nanofiber and grow it on the interdigital gold electrode substrate for gas sensors. The results show that the ZnO/LSFO heterostructure gas sensor has a good sensing response to H₂S gas and exhibits good gas selectivity. The best gas response is 52.17% under 4 ppm H₂S and work temperature 200°C, which has good recovery and reproducibility.     

Improved optical and structural properties of cadmium sulfide nanostructures for optoelectronic applications

In this study, the effect of ZnO seed layer on the growth of uniform CdS nanostructures was investigated using chemical bath deposition technique. Besides, the influence of molar concentration of reagents on the surface morphology, structural and optoelectrical properties of the deposited CdS thin films were examined. The CdS nanostructures were grown on bare glass and ZnO/glass substrates with different reagent molar concentrations. The results indicated an improvement in the homogeneity and uniformity of the grown CdS nanostructures on ZnO seed layer which can be due to the low lattice mismatch between ZnO and CdS structures. The CdS/ZnO samples were optimized by changing the molar concentration of reagents. A three–dimensional intersecting vertical nanosheet morphology with hexagonal structure was obtained when modified chemical concentration of 0.5 M was applied. The XRD pattern of CdS nanosheets indicated the hexagonal phase of CdS which were strongly orientated along (002) plane. The elevated intensity of dominant peak related to this sample confirmed the improved crystal quality of this CdS nanostructure comparing to the other samples. The UV–Vis spectrum demonstrated a high absorption coefficient for CdS intersecting nanosheets which might be due to the high specific surface area and light trapping behavior of this sample. The photoluminescence study also showed an improvement in optical properties of optimized CdS nanostructures. In order to study the optoelectrical properties of CdS nanostructures, metal–semiconductor–metal photodetectors were fabricated with different CdS samples and their current–voltage characteristics were analyzed. The results indicated an enhancement in photosensitivity, responsivity, and speed of photodetectors based on optimized CdS nanostructures.     

Highly sensitive and wide-range flexible sensor based on hybrid BaWO4@CS nanocomposite

Flexible photoelectronic devices are in demand right now. In this work, a new family of biopolymer-based photodetectors is described. Chitosan (CS) was utilized as a safe, biodegradable host biopolymer, and nanostructured barium tungstate (BaWO₄) particles were used as the nanofiller of the biopolymer matrix to prepare flexible optical sensors. The co-precipitation process was used to produce the filler powders, which were then dried at room temperature without using any surfactants or hazardous solvents. The fabricated sensor showed high flexibility and sensitivity to UV/proton/alpha and laser irradiation. X-ray diffraction (XRD), XPS, FTIR, EDX-map analysis also confirmed the successful synthesis, related chemical binding, and elements in the nanocomposite structure. According to the TEM images, the average particle size of synthesized BaWO₄ NPs was obtained at about 110 nm. A considerable luminescence emission was observed in the constructed sensor's blue/green and ultraviolet spectral regions under various excitation sources. The developed sensor was nontoxic to the cells and provided soft, thin, antibacterial activity, flexible, and comfortable contact with skin, and promising ionizing ray detection applications in flexible optical sensors.     

Sensitivity of uncertainties in performance prediction of deteriorating concrete structures

Deterioration models for the condition and reliability prediction of civil infrastructure facilities involve numerous assumptions and simplifications. Furthermore, input parameters of these models are fraught with uncertainties. A Bayesian methodology has been developed by the authors, which uses information obtained through health monitoring to improve the quality of prediction. The sensitivity of prior and posterior predicted performance to different input parameters of the deterioration models, and the effect of instrument and measurement uncertainty, is investigated in this paper. The results quantify the influence of these uncertainties and highlight the efficacy of the updating methodology based on integrating monitoring data. It has been found that the probabilistic posterior performance predictions are significantly less sensitive to most of the input uncertainties. Furthermore, updating the performance distribution based on ‘event’ outcomes is likely to be more beneficial than monitoring and updating of the input parameters on an individual basis.     

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

The explosive growth in social networks that publish real-time content begs the question of whether their feeds can complement traditional sensors to achieve augmented sensing capabilities. One such capability is to explain anomalous sensor readings. In our previous conference paper, we built an automated anomaly clarification service, called ClariSense, with the ability to explain sensor anomalies using social network feeds (from Twitter). In this extended work, we present an enhanced anomaly explanation system that augments our base algorithm by considering both (i) the credibility of social feeds and (ii) the spatial locality of detected anomalies. The work is geared specifically for describing small-footprint anomalies, such as vehicular traffic accidents. The original system used information gain to select more informative microblog items to explain physical sensor anomalies. In this paper, we show that significant improvements are achieved in our ability to explain small-footprint anomalies by accounting for information credibility and further discriminating among high-information-gain items according to the size of their spatial footprint. Hence, items that lack sufficient corroboration and items whose spatial footprint in the blogosphere is not specific to the approximate location of the physical anomaly receive less consideration. We briefly demonstrate the workings of such a system by considering a variety of real-world anomalous events, and comparing their causes, as identified by ClariSense+, to ground truth for validation. A more systematic evaluation of this work is done using vehicular traffic anomalies. Specifically, we consider real-time traffic flow feeds shared by the California traffic system. When flow anomalies are detected, our system automatically diagnoses their root cause by correlating the anomaly with feeds on Twitter. For evaluation purposes, the identified cause is then retroactively compared to official traffic and incident reports that we take as ground truth. Results show a great correspondence between our automatically selected explanations and ground-truth data.     

Nanoseed-assisted rapid formation of ultrathin ZnO nanorods for efficient room temperature NO2 detection

The influence of ZnO nanoseeds on the formation of ZnO nanorods from ε-Zn(OH)₂ in NaOH solution at 80 °C was investigated, using ZnO nanoparticles with a diameter of 4–10 nm as the seeds. The experimental results indicated that the presence of ZnO nanoseeds promoted the rapid heterogeneous formation of ultrathin ZnO nanorods. Compared with the ZnO submicron rods with a diameter of 0.5–1.0 µm, the ultrathin ZnO nanorods with a diameter of 10–15 nm were found to be more sensitive for detecting NO₂ at room temperature owing to their higher variation of channel conduction to the diameter.     

A critical analysis of the performance of plate- and point-electrodes for determination of electrical properties of the soil mass

In the recent past, researchers have started utilizing electrical properties of the soil mass for its characterization by employing either plate- or point electrodes. Though, plate-electrodes are easy to use for laboratory experiments, and quantification of geometrical characteristics of the electric field generated within them is easy to quantify, their application for in-situ experiments and for samples of cylindrical shape becomes difficult. On the other hand, usage of point-electrodes which are cylindrical in shape, are used for moisture content determination of the soil or migration of contaminants in it, for coarse-grained soils and fine-grained soils might yield erroneous results, due to the presence of inter particle voids and presence of cavities & anomalies, respectively. Furthermore, quantification of geometrical parameters of the electric field of point-electrodes is quite difficult which results erroneous measurements in determination of electrical properties of the material, in which they are installed. Hence, establishment of the uniqueness of electrical properties obtained from the plate- and point-electrodes, for identical samples becomes utmost important. With this in view, COMSOL Multiphysics® was employed to simulate the electrical response of various geomaterials in their uncontaminated and contaminated states and results were critically evaluated vis-à-vis those obtained from the impedance analysis (1 Hz to 40 MHz). Efforts have also been made to relate the geometrical dimensions of electrodes and electric field generated across the point electrodes, which would facilitate proper design and installation of sensors in a material to achieve the desired output. This study demonstrates the suitability and versatility of the point-electrodes for various (field and laboratory) applications where in moisture profiling and contaminant transport is to be established.     

Cloud enabled fractal based ECG compression in wireless body sensor networks

E-health applications deal with a huge amount of biological signals such as ECG generated by body sensor networks (BSN). Moreover, many healthcare organizations require access to these records. Therefore, cloud is widely used in healthcare systems to serve as a central service repository. To minimize the traffic going to and coming from cloud ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal based ECG lossy compression technique is proposed. It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios. The proposed technique is based on modifying the popular fractal model to be used in compression in conjunction with the iterated function system. The ECG signal is divided into equal blocks called range blocks. Subsequently, another down-sampled copy of the ECG signal is created which is called domain. For each range block the most similar block in the domain is found. As a result, fractal coefficients (i.e. parameters defining fractal compression model) are calculated and stored inside the compressed file for each ECG signal range block. In order to make our technique cloud friendly, the decompression operation is designed in such a way that allows the user to retrieve part of the file (i.e. ECG segment) without decompressing the whole file. Therefore, the clients do not need to download the full compressed file before they can view the result. The proposed algorithm has been implemented and compared with other existing lossy ECG compression techniques. It is found that the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.     

Active Sensing for Monitoring the Properties of Solid Rocket Motor Propellant Grains

Aging induced changes in the mechanical properties of solid propellant can lead to defects, such as cracks and grain‐liner separations, that limit the service lifetime of solid rocket motors. The use of embedded sensors is one approach that is being explored by various researchers to augment legacy inspection and prediction methods. We present herein an active sensing technique that is particularly suited for monitoring the properties of solid propellant, as it does not introduce electrical wires into the motor. Based on the use of magnetic induction for excitation and an optical fiber Bragg grating sensor to measure deformation, the method can be used to characterize the properties of a material with which it is in contact. In this paper, we first present proof‐of‐principal experiments demonstrating the utility of the method in characterizing the visco‐elastic properties of an adhesive, and in following changes in viscosity of an epoxy resin during cure. We next apply the method to solid propellant, and present data demonstrating that the method can be used to measure a deflection‐load curve of an aluminized HTPB propellant. In addition, we also show that the observed strain rate sensitivity matches that found in the literature and that the method had more than adequate resolution to observe the expected changes in material properties due to aging.     

Multifunctional Magnetic Optical Sensor Particles with Tunable Sizes for Monitoring Metabolic Parameters and as a Basis for Nanotherapeutics

Magnetic optical sensor particles with multifunctional cores and shells are synthesized via a facile nanoprecipitation method and the subsequent modification of the particle shell. The hydrophobic particle core includes optical oxygen indicators, a light harvesting system, photosensitizers, and magnetic nanoparticles. Further functionalities are introduced by modifying the shell with enzymes, antibodies, multiple layers of polyelectrolytes, stimuli‐responsive polymers, and luminescent indicator dyes. The hydrodynamic diameter is tunable by varying different precipitation parameters.