A green approach for the synthesis of gold nanotriangles using aqueous leaf extract of Callistemon viminalis

In this present work, we developed a green method for the synthesis of stable Au NPs that does not require any of the conventional stabilizing ligands. This synthesis can be performed in minutes rather than hours, under very mild conditions using aqueous leaf extract of Callistemon viminalis. Gold nanotriangles so prepared were well characterized by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), quasi elastic light scattering (QELS), UV-visible spectroscopy techniques etc. Powder X-ray diffraction plot revealed the presence of only gold in zero oxidation state.     

Surfactant‐Assisted Dispersive Liquid–Liquid Microextraction for Sensitive Spectrophotometric Determination of Iron in Food and Water Samples and Comparison with Atomic Absorption Spectrometry

A technique of selective and sensitive surfactant‐assisted dispersive liquid–liquid microextraction combined with spectrophotometry was developed for determination of iron in water and food samples. This method involves the formation of a red‐colored iron‐thiocyanate complex in the presence of cetyltrimethylammonium bromide (CTAB) as cationic surfactant. The use of CTAB assisted in color formation and effective extraction of the complex into the organic solvent through micelle formation prior to spectrophotometric and flame atomic absorption spectrometry measurement. Optimum absorbance and extraction of the iron complex was obtained with concentrations of ammonium thiocyanate, N‐phenylbenzimidoyl thiourea, CTAB and sodium chloride of 0.30 M, 3.0 × 10^?3 M, 0.40 × 10^?3 M and 1.0 %, respectively. The calibration curve was linear over a range of 20–350 ng mL^?1 iron with correlation of estimation (R²) of 0.997. The proposed method was successfully applied to the determination of iron in food (cereal, fruit and vegetable) and water samples.     

Multi-Objective Optimization During Magnetic Field Assisted ECDM of Alumina-Zirconium Dioxide Ceramic Composite using Battle Royale Optimization

Alumina-Zirconium dioxide (Al₂O₃-ZrO₂) Ceramic Composite Material (CCM) is specifically known for its enhanced mechanical and corrosion resistance properties and is widely used as raw material for mechanical parts like, pump components, die inserts, bearings, etc. As a result, industrialists are searching for an efficient method for machining this Al₂O₃-ZrO₂ material. In this regard, a hybrid unconventional machining process called Electrochemical Discharge Machining (ECDM) is adapted to analyze the machinability of Al₂O₃-ZrO₂ CCM. Besides, to ensure the efficiency of the ECDM process, a magnetic field is also given to the tool electrode during this study to improve the Material Removal Rate (MRR) of the ECDM machine. The experiment is designed using Response Surface Methodology (RSM) by changing the magnitudes of input controls, namely Electrolytic Concentration (EC), Inter-electrode Gap (IEG) and Applied Voltage (AV). Moreover, a novel hybrid machine learning optimization strategy called Deep Belief Network based Battle Royal Optimization (DBN-BRO) algorithm is developed to predict and optimize the ECDM process. Finally, the optimum results are perceived from 55 V AV, 22.727% EC and 40.909 mm IEG input levels. The proposed method shows less than 0.6207 Root Mean Square Error (RMSE) and tools nearly 80 iterations for optimizing the results.