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 (R2 = 0.996-0.936) interpreted by applying the MNIS model, enabling thermodynamic parameters to be determined. The MNIS model was adjusted to the arsenite-HA binding process assuming that hydrogen bonding is the dominant type of interaction in the system. Negative enthalpy change values indicated the arsenite-HAs binding as an exothermic process. Negative ΔG values (-(26.83-27.00) kJ mol-1) pointed out to spontaneous binding reaction, leading to the formation of the arsenite-HA complexes. The binding constant values ((7.57-5.02) 105 M-1) clearly demonstrate pronounced binding affinity. As ΔS values are obviously positive but close to zero, and ΔH>ΔS, the reaction can be considered enthalpy driven. Reaction heats and ΔH values (-(18.96-15.64) kJ mol-1) confirmed hydrogen bonds as the most ascendant interaction type in the arsenite-HA complex. Negative zeta potential values (-45 to -20 mV) had shown that arsenite-HA aggregates remained negatively charged in the whole molar charge ratio range. The HAs' aggregate size change is evident but not particularly pronounced (Zav = 50-180 nm). It can be speculated that aggregation during the titration process is not expressive due to repulsive forces between negatively charged arsenite-HA particles. Thermodynamic and reaction parameters clearly indicated that arsenite-HA complexes are formed at common soil pH values, confirming the possible influence of humic acids on increased As mobility and its reduced bioavailability.

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. The applied interpretative approach resulted in rather good agreement between the measured and the predicted retention times for PAHs in both one and two variable modeling. The ANN model, strongly affected by the number of input experiments, was shown to be less effective for one variable used, but quite successful when two input variables were used. All PAHs, including difficult to separate peak pairs (benzo(k)fluoranthene/benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene/dibenzo(a,h)anthracene), were separated in a standard (5% phenyl-95% dimethylpolysiloxane) capillary column at an optimum temperature ramp of 8.0 degrees C/min and final hold temperature in the range of 260-320 degrees C.

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. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK(a) and logK(ow) values.