Soil humic acid and arsenite binding by isothermal titration calorimetry and Dynamic Light Scattering: Thermodynamics and aggregation.

The arsenite-humic acid binding process was investigated using Isothermal Titration Calorimetry (ITC), Dynamic Light Scattering and Laser Doppler Electrophoresis techniques. The ITC data were successfully (R = 0.996-0.

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography.

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized.

Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons.

An interpretative strategy (factorial design experimentation+total resolution analysis+chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables.