The Effect of Gluconate and EDTA on Thorium Solubility Under Simulated Cement Porewater Conditions

α-Hydroxy carboxylate ligands like gluconate or polyaminocarboxylate ligands such as ethylenediaminetetraacetate (EDTA) are frequently used in decontamination procedures at nuclear power plants. The presence of these organic substances among nuclear wastes could enhance the solubility of actinides by forming soluble complexes. Thermodynamic data on the stability of gluconate and EDTA with actinides are essential to predict their increase in mobility, especially in high pH systems characteristic of cement environments of a nuclear waste repository. In this work, the solubility of thorium oxyhydroxide in the presence of gluconate and EDTA has been studied. The results highlight the key role of these organics in increasing the solubility of thorium at pH_c = 12. The presence of calcium at concentrations below 10^?2 mol·dm^?3 (characteristic of cement porewaters corresponding to cement compositions at the second degradation stage) does not seem to affect significantly the thorium solubility under the studied conditions.     

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 log K_ow values.     

Experimental investigation on self-excited resonances of a cylindrical tube in cross-flow

This paper is devoted to the study of acoustic vibrations induced by a flow upon an air-filled cylindrical tube vertically placed in water. A water pump with adapted piping generates a turbulent flow horizontally canalized in a large laboratory tank (6 m × 4 m × 3 m). The tube is located across this flow and an accelerometer measures vibrations. The signal processing performed on the recorded signals brings out resonance modes of the tube excited by the flow. A theoretical study (tube in air) and complementary experiments (tube in air and in water) are conducted to identify these modes.     

Organic metabolites in exhaled human breath—A multivariate approach for identification of biomarkers in lung disorders

The gas chromatographic profiles of exhaled air from lung cancer patients have been investigated. The breath from healthy volunteers, smokers and non-smokers, and lung cancer patients without treatment and under radio and/or chemotherapy, was collected using Tedlar bags. Different profiles for healthy people and cancer patients could be recognized by multivariate analysis and significant diagnostic compounds could be established. Target compounds showed to be linear and branched hydrocarbons between C₁₄ and C₂₄. Solid phase microextraction (SPME) coupled to gas chromatography mass spectrometry GC–(TOF)-MS was used. The method showed good precision (RSD below 26%) and limit of detection ranged from 0.04 to 8.0 ppb. These findings show a high potential for establishment of laboratorial screening methods. Validation studies in a larger number of patients are being done.     

Neutron pick-up (n,2 n *) reaction

Direct²n^* contributions (²n^*-singlet n-n final state interaction) to the (n,2n) reaction cross sections are estimated.