Green synthesis of nanomaterials finds the edge over chemical methods due to its environmental compatibility. Herein, we report green synthesis of silver nanoparticles (Ag NPs) mediated with dextran. Dextran was used as a stabilizer and capping agent to synthesize Ag NPs using silver nitrate (AgNO3) under diffused sunlight conditions.
The electrochemical behaviour of ferrocene (Fc) is investigated in six different deep eutectic solvents (DESs) formed by means of hydrogen bonding between selected ammonium and phosphonium salts with glycerol and ethylene glycol. Combinations of cyclic voltammetry and chronoamperometry are employed to characterise the DESs. The reductive and oxidative potential limits are reported versus the Fc/Fc(+) couple. The diffusion coefficient, D, of ferrocene in all studied DESs is found to lie between 8.49 × 10(-10) and 4.22 × 10(-8) cm(2) s(-1) (these do not change significantly with concentration). The standard rate constant for heterogeneous electron transfer across the electrode/DES interface is determined to be between 1.68 × 10(-4) and 5.44 × 10(-4) cm s(-1) using cyclic voltammetry. These results are of the same order of magnitude as those reported for other ionic liquids in the literature.
The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro-fuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time.