Internet of things based smart energy management in a vanadium redox flow battery storage integrated bio-solar microgrid.

In this paper, an optimized energy management scheme for Solar PV, Biogas, Vanadium Redox Flow Battery (VRFB) storage integrated grid-interactive hybrid microgrid system has been implemented using a low-cost Internet of Things (IoT) based smart communication platform. The energy monitoring and control architecture of the proposed system consists of four main parts; 1) Low-cost energy meter for real-time data acquisition for multiple renewable energy sources (Solar PV, Biomass), VRFB storage, grid and loads. 2) Monitoring, control & fault detection using Raspberry-Pi (Single Board Computer) platform and MODBUS over TCP/IP platform. 3) Cloud-based remote monitoring unit (RMU) using Message Queuing Telemetry Transport (MQTT) server and ThingSpeak Middleware. 4) Capacity measurement of biogas production along with automatic start/stop control of biogas engine-generator. 5) VRFB storage scheduling for peak demand shaving. A PSCAD simulation study has been done to realize the hybrid microgrid interconnection. The developed smart communication system performance is validated by a practical 10kWP solar PV, 15kVA biogas plant, 6 kWh VRFB storage integrated hybrid microgrid which satisfies peak demand management and ensures zero loss of power supply probability for dynamic load profile. Four real-life case studies have been done for the practical realization of the proposed energy management algorithm performance. Another significant contribution of this paper is the utilization of the solar PV power even during grid outage scenario at day time. It is made possible by intelligent interfacing of biogas power generator which acts as a reference AC bus for the grid-tied solar inverter and thus the available solar PV power can be used to serve the critical loads during grid outage condition. The proposed smart hybrid microgrid solution claims to be a generalized one, low cost compared to existing alternatives and applicable to satisfy scalable community energy security as well.

Performance Enhancement of Solar Cell by Incorporating Bilayer RGO-ITO Smart Conducting Antireflection Coating.

Multilayered graphene deposited on a flat resistive surface has twofold benefits. Less electronic scattering reduces the sheet resistance of the combined bilayer and high photon scattering through the unavoidable wrinkles on the chemically synthesized graphene layer leads to decreased effective reflection. In this paper, wet-chemically-synthesized reduced graphene oxide (RGO) has been employed on the top of the indium-doped tin-oxide (ITO) layer. The ITO layer of optimized thickness has been deposited as an alternative antireflection coating (ARC) on a p/n junction based crystalline silicon solar cell with standard textured surface. Variation in spectral response has been studied experimentally for different thickness and surface coverage of RGO on ITO. The combined effect of reduced sheet resistance due to high surface conductivity and increased photon injection efficiency due to scattering from the wrinkles of RGO results in significant improvement in the performance of the solar cell. By employing optimum thickness of RGO, percentage enhancements of about 18% and 10%, respectively, in efficiency and short-circuit current density have been achieved over the baseline cell structure. RGO also exhibits an additional benefit as a moisture repelling layer.

Outstanding Room-Temperature Hydrogen Gas Detection by Plasma-Assisted and Graphene-Functionalized Core-Shell Assembly of SnO2 Nanoburflower.

Here, we have reported the synthesis of three-dimensional, mesoporous, nano-SnO2 cores encapsulated in nonstoichiometric SnO2 shells grown by chemical as well as physical synthesis procedures such as plasma-enhanced chemical vapor deposition, followed by functionalization with reduced graphene oxide (rGO) on the surface. The main motif to fabricate such morphology, i.e., core-shell assembly of burflower-like SnO2 nanobid is to distinguish gases quantitatively at reduced operating temperatures. Electrochemical results reveal that rGO anchored on SnO2 surface offers excellent gas detection performances at room temperature. It exhibits outstanding H2 selectivity through a wide range, from ∼10 ppm to 1 vol %, with very little cross-sensitivity against other similar types of reducing gases. Good recovery as well as prompt responses also added flair in its quality due to the highly mesoporous architecture. Without using any expensive dopant/catalyst/filler or any special class of surfactants, these unique SnO2 mesoporous nanostructures have exhibited exceptional gas sensing performances at room temperature and are thus helpful to fabricate sensing devices in most cost-effective and eco-friendly manner.

Functionalized ZnO/ZnO2 n-N straddling heterostructure achieved by oxygen plasma bombardment for highly selective methane sensing.

Metal oxide semiconductors have been extensively used as reducing gas sensors with major limitations regarding selectivity and operating temperature which is relatively high for most of the cases making the device unusable in some critical situations. Higher operating temperature is also associated with the higher power consumption, which goes against the miniaturization of the device. In order to resolve these problems, here we introduced a ZnO/ZnO2 straddling 'n-N' isotype heterostructure as a highly selective and sensitive methane sensor at moderately low operating temperature. ZnO-Zn(OH)2 precursor films were treated in oxygen plasma in a pulsed DC magnetron sputtering system. Morphological analyses by field emission scanning electron microscopy showed flake like growth of the grains with high surface roughness, whereas X-ray diffraction (XRD) showed polycrystalline nature of the films. Polycrystalline ZnO2 peaks were observed in the XRD pattern in addition to the existing ZnO, which indicates modification of the precursor to oxygen rich heterostructure of ZnO/ZnO2. This was further supported by the shifting of the O1s peak in the X-ray photoelectron spectroscopic analysis. Plasma treated ZnO/ZnO2 heterostructured films were found to show high selectivity towards methane (with respect to H2S and CO) and sensitivity (∼96%) at a comparatively low operating temperature.

A review on amperometric-type immunosensors based on screen-printed electrodes.

In this brief review, we summarize the recent research activities involved in the development of amperometric-type immunosensors based on screen-printed electrodes (SPEs). We focus on the underlying principle involved in these types of sensors, their fabrication and electrode surface modification. We also discuss the various factors involved in the designing of such immunosensors and how they affect their performances. Finally we provide an insight into the drawbacks associated with these SPEs.

Palladium-silver-activated ZnO surface: highly selective methane sensor at reasonably low operating temperature.

Metal oxide semiconductors (MOS) are well known as reducing gas sensors. However, their selectivity and operating temperature have major limitations. Most of them show cross sensitivity and the operating temperatures are also relatively higher than the value reported here. To resolve these problems, here, we report the use of palladium-silver (70-30%) activated ZnO thin films as a highly selective methane sensor at low operating temperature (∼100 °C). Porous ZnO thin films were deposited on fluorine-doped tin oxide (FTO)-coated glass substrates by galvanic technique. X-ray diffraction showed polycrystalline nature of the films, whereas the morphological analyses (field emission scanning electron microscopy) showed flake like growth of the grains mainly on xy plane with high surface roughness (107 nm). Pd-Ag (70-30%) alloy was deposited on such ZnO films by e-beam evaporation technique with three different patterns, namely, random dots, ultrathin (∼1 nm) layer and thin (∼5 nm) layer as the activation layer. ZnO films with Pd-Ag dotted pattern were found show high selectivity towards methane (with respect to H2S and CO) and sensitivity (∼80%) at a comparatively low operating temperature of about 100°C. This type of sensor was found to have higher methane selectivity in comparison to other commercially available reducing gas sensor.

Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system.

The paper attempts to improve the accuracy of a fuzzy expert decision making system by tuning the parameters of type-2 sigmoid membership functions of fuzzy input variables and hence determining the most appropriate type-1 membership function. The current work mathematically models the variability of human decision making process using type-2 fuzzy sets. Moreover, an index of accuracy of a fuzzy expert system has been proposed and determined analytically. It has also been ascertained that there exists only one rule in the rule base whose associated mapping for the ith linguistic variable maps to the same value as the maximum value of the membership function for the ith linguistic variable. The improvement in decision making accuracy was successfully verified in a medical diagnostic decision making system for renal diagnostic applications. Based on the accuracy estimations applied over a set of pathophysiological parameters, viz. body mass index, glucose, urea, creatinine, systolic and diastolic blood pressure, appropriate type-1 fuzzy sets of these parameters have been determined assuming normal distribution of type-1 membership function values in type-2 fuzzy sets. The type-1 fuzzy sets so determined have been used to develop an FPGA based smart processor. Using the processor, renal diagnosis of patients has been performed with an accuracy of 98.75%.

ASIC design of a digital fuzzy system on chip for medical diagnostic applications.

The paper presents the ASIC design of a digital fuzzy logic circuit for medical diagnostic applications. The system on chip under consideration uses fuzzifier, memory and defuzzifier for fuzzifying the patient data, storing the membership function values and defuzzifying the membership function values to get the output decision. The proposed circuit uses triangular trapezoidal membership functions for fuzzification patients' data. For minimizing the transistor count, the proposed circuit uses 3T XOR gates and 8T adders for its design. The entire work has been carried out using TSMC 0.35 µm CMOS process. Post layout TSPICE simulation of the whole circuit indicates a delay of 31.27 ns and the average power dissipation of the system on chip is 123.49 mW which indicates a less delay and less power dissipation than the comparable embedded systems reported earlier.

Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient.

The paper describes the design and training of a fuzzy neural network used for early diagnosis of a patient through an FPGA based implementation of a smart instrument. The system employs a fuzzy interface cascaded with a feed-forward neural network. In order to obtain an optimum decision regarding the future pathophysiological state of a patient, the optimal weights of the synapses between the neurons have been determined by using inverse delayed function model of neurons. The neurons that are considered in the proposed network are devoid of self connections instead of commonly used self connected neurons. The current work also find out the optimal number of neurons in the hidden layer for accurate diagnosis as against the available number of CLB in the FPGA. The system has been trained and tested with renal data of patients taken at 10 days interval of time. Applying the methodology, the chance of attainment of critical renal condition of a patient has been predicted with an accuracy of 95.2%, 30 days ahead of actually attaining the critical condition. The system has also been tested for pathophysiological state prediction of patients at multiple time steps ahead and the prediction at the next instant of time stands out to be the most accurate.