Fabricating BiOCl/BiVO4 nanosheets wrapped in a graphene oxide heterojunction composite for detection of an antihistamine in biological samples.

Antibiotics are essential medications for human and animal health, as they are used to battle urinary infections and bacterial diseases. Therefore, the rapid determination of antibiotic drugs in biological samples is necessary to address the current clinical challenge. Here, we developed a heterojunction ternary composite of BiOCl/BiVO4 nanosheets enriched with graphene oxide (BiOCl/BiVO4@GO) for accurate and minimal-level detection of an antihistamine (promethazine hydrochloride, PMZ) in urine samples. The BiOCl/BiVO4 nanosheets were prepared by a wet chemical approach using a deep eutectic green solvent. The spectroscopic and analytical methods verified the formation and interaction of the BiOCl/BiVO4@GO composite. Our results showed that the thoroughly exfoliated BiOCl/BiVO4@GO composite retained good electrical conductivity and fast charge transfer toward the electrode-electrolyte interface in neutral aqueous media. In addition, the experimental conditions were accurately optimized, and the BiOCl/BiVO4@GO composite showed excellent electrocatalytic activity toward the oxidation of PMZ. Indeed, the BiOCl/BiVO4@GO composite demonstrated a good linear response range (0.01-124.7 µM) and a detection level of 3.3 nM with a sensitivity of 1.586 µA µM-1 cm-2. In addition, the BiOCl/BiVO4@GO composite had excellent storage stability, good reproducibility, and reliable selectivity. Finally, the BiOCl/BiVO4@GO displayed a desirable recovery level of PMZ in urine samples for real-time monitoring.

Modeling & implementation of DRLA based partially shaded solar system integration with 3-ϕ conventional grid using constant current controller.

Renewable Energy Resources (RERs) are widely used on the concern of global environment protection. Solar energy systems play an important role in the generation of electrical energy, remarkably minimize the utilization of nonrenewable fuel sources. Solar energy can be extracted and transformed into electrical energy via solar photovoltaic process. Several traditional, soft computing, heuristic, and meta-heuristic maximum power point tracking (MPPT) techniques have been developed to extract Maximum Energy Point (MEP) from the solar photovoltaic modules under different atmospheric conditions. In this manuscript, the combination of reinforcement learning algorithm (RLA) and deep learning algorithm (DLA) called deep Reinforcement Learning Algorithm based MPPT (DRLAMPPT) is proposed under partial shading conditions (PSC) of the solar system. DRLAMPPT can deal with continuous state spaces, in contrast to RL it can be operated only with discrete action state spaces. In this proposed DRLAMPPT, deep deterministic policy gradient (DDPG) solves the problem of continuous state spaces are involved to reach the GMEP in photovoltaic systems especially under PSC. In DRLAMPPT, the representative's strategy is parameterized by an artificial neural network (ANN), which uses sensory information as input and directly sends out control signals. This work develops a 2 kW solar photovoltaic power plant comprises of a photovoltaic array, DC/DC step-up converter, 3-Φ Pulse Width Modulated Voltage Source Inverter (PWM-VSI) integrated with conventional power grid using Constant Current Controller (CCC Effectiveness of the proposed DRLAMPPT with CCC can be validated through an experimental setup and with MATLAB. Simulation and tested at different input conditions of solar irradiance. Experimental results prove that, in comparison to existing MPPTs, suggested DRLAMPPT not only attains the best efficiency and also adopts the change in environmental conditions of the photovoltaic system at a much faster rate and able to reach the GMEP within 0.8 s under PSC. Experimental and simulation results also prove that suggested CCC with LC filter makes the inverter output voltage and the grid voltage are in phase at the lower value of THD i.e. 1.1% and 0.98% respectively.

Efficient removal of anionic, cationic textile dyes and salt mixture using a novel CS/MIL-100 (Fe) based nanofiltration membrane.

The purification of hazardous textile dyeing wastewater has exhibited many challenges because it consists of a complex mixture, including dyestuff, additives, and salts. It is necessary to fabricate membranes with enhanced permeability, fouling resistance, stability, and superior dyes and salts removal from wastewater. Incorporating a highly water stable metal-organic framework (MOFs) into membranes would meet the requirements for the efficient purification of textile wastewater. In this study, nanofiltration (NF) membranes are fabricated by incorporating MIL-100 (Fe) into the chitosan (CS) through film casting technique. The effect of MIL-100 (Fe) loadings on chitosan characterized by FT-IR, XRD, contact angle measurement, FESEM-EDS, XPS, zeta potential, and surface roughness analysis. The membrane characterization confirmed the enhanced surface roughness, pore size, surface charge, and hydrophilicity. The CS/MIL-100 (Fe) membrane exhibited an improved pure water flux from 5 to 52 L/m2h as well as 99% rejection efficiency for cationic methylene blue (MB) and anionic methyl orange (MO). We obtained the rejection efficiency trend for the MB mixed salts in the order of MgSO4 (Mg2+ - 51.6%, SO42- - 52.5%) > Na2SO4 (Na+ - 26.3%, SO42- - 29.3%) > CaCl2 (Ca2+ - 21.4%, Cl- - 23.8%) > NaCl (Na+ - 16.8%, Cl- - 19.2%). In addition, the CS/MIL-100 (Fe) composite membrane showed excellent rejection efficiency and antifouling performances with high recycling stability. These stunning results evidenced that the CS/MIL-100 (Fe) nanofiltration membrane is a promising candidate for removing toxic pollutants in the textile dyeing wastewater.

A smart mobile pouch as a biomechanical energy harvester towards self-powered smart wireless power transfer applications.

A Smart Mobile Pouch Triboelectric Nanogenerator (SMP-TENG) is introduced as a promising eco-friendly approach for scavenging biomechanical energy for powering next generation intelligent devices and smart phones. This is a cost-effective and robust method for harvesting energy from human motion, by utilizing worn fabrics as a contact material. The SMP-TENG is capable of harvesting energy in two operational modes: lateral sliding and vertical contact and separation. Moreover, the SMP-TENG can also act as a self-powered emergency flashlight and self-powered pedometer during normal human motion. A wireless power transmission setup integrated with SMP-TENG is demonstrated. This upgrades the traditional energy harvesting device into a self-powered wireless power transfer SMP-TENG. The wirelessly transferred power can be used to charge a Li-ion battery and light LEDs. The SMP-TENG opens a wide range of opportunities in the field of self-powered devices and low maintenance energy harvesting systems for portable and wearable electronic gadgets.