Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: artificial neural network modeling and genetic algorithm optimization

Reusable nanosilver-coated magnetic particles for ultrasensitive SERS-based detection of malachite green in water samples

A novel nanosilver-deposited silica-coated Fe3O4 magnetic particle (Fe3O4@SiO2@Ag) with uniform size, good SERS activity and magnetic responsiveness was synthesized using amination polymer. The Fe3O4@SiO2@Ag magnetic particles have been successfully applied for ultrasensitive SERS detection of malachite green (MG) in water samples. The mechanism is that MG can be adsorbed on the silver surface of nanosilver-coated magnetic particles via one nitrogen atom, and the Raman signal intensity of MG is significantly enhanced by the nanosilver layer formed on the magnetic particles. The developed sensing system exhibited a sensitive response to MG in the range of 10 fM to 100 μM with a low limit of detection (LOD) 2 fM under optimal conditions. The LOD was several orders of magnitude lower than those of other methods. This SERS-based sensor showed good reproducibility and stability for MG detection. The silver-coated magnetic particles could easily be regenerated as SERS substrates only using low pH solution for multiple sensing events. The recovery of MG added to several water samples at different concentrations ranged from 90% to 110%. The proposed method facilitates the ultrasensitive analysis of dyes to satisfy the high demand for ensuring the safety of water sources.

Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network

Electronic nose technology is used in many areas, and frequently in the beverage industry for classification and quality-control purposes. In this study, four different aroma data (strawberry, lemon, cherry, and melon) were obtained using a MOSES II electronic nose for the purpose of fruit classification. To improve the performance of the classification, the training phase of the neural network with two hidden layers was optimized using artificial bee colony algorithm (ABC), which is known to be successful in exploration. Test data were given to two different neural networks, each of which were trained separately with backpropagation (BP) and ABC, and average test performances were measured as 60% for the artificial neural network trained with BP and 76.39% for the artificial neural network trained with ABC. Training and test phases were repeated 30 times to obtain these average performance measurements. This level of performance shows that the artificial neural network trained with ABC is successful in classifying aroma data.

Optimizing the biosorption of Bi3+ ions by Streptomyces rimosus using experimental design and applicability in kinetics and isotherm modeling

This investigation seeks to analyze bismuth biosorption onto Streptomyces rimosus in solutions following optimization using a Box–Behnken Design (BBD).

Simultaneous ultrasonic-assisted removal of malachite green and safranin O by copper nanowires loaded on activated carbon: central composite design optimization

The present study investigates the simultaneous ultrasound-assisted adsorption of malachite green (MG) and safranin O (SO) dyes from aqueous solutions by ultrasound-assisted adsorption onto copper nanowires loaded on activated carbon (Cu-NWs-AC).