Gold(III) complexes with imidazole,benzoxazole, purine and pyrimidine derivatives

Summary The synthesis and characterization of Au^III complexes with several heterocyclic ligands are reported. The compounds have general formula [AuX₃(L)], where L =N-methylimidazole (N-MeIz),N-ethylimidazole (N-EtIz),N-propylimidazole (N-PrIz), benzoxazole (BO), 2-methylbenzoxazole (2-MeBO), 2,5-dimethylbenzoxazole (2,5-diMeBO), 2-amino-pyrimidine (2-APm), 4(6) -hydroxy-pyrimidine [4(6)-hydrPm] or hypoxanthine (Hypox) and X = Cl or Br. Elemental analysis, conductivity measurements and spectral studies were used for the characterization of the complexes. A square-planar geometry withN-bonded heterocyclic ligands is suggested.     

Interaction of Pd(II) with Glutamic Acid

Pd(II) complexes with glutamic acid of the composition K[Pd(HGlu)Cl₂] (I) and [Pd(HGlu)₂] (II) were synthesized and studied by IR and electronic absorption spectroscopy methods. Pd²⁺–H₂O–Cl^– and Pd²⁺–H₂O–Cl^––H₂Glu systems were analyzed by pH-metric titration. The most essential Pd(II) complex forms were established by mathematical modeling and their formation constants were calculated. The electronic absorption spectra of complexes I and II were measured in aqueous and physiological solutions. Complex I was found to be biologically active and to exhibit antimetastatic properties.     

Rapid Removal of Organics and Oil Spills from Waters Using Silicone Rubber “Sponges”

We have developed a simple, robust, and efficient technology utilizing cheap and recoverable materials based on commercially available silicone elastomer networks for removing organic solvents and crude oil from waters. Hydrophobic and oleophilic properties of silicone elastomers endow poly(dimethyl siloxane) (PDMS) with the capacity to absorb a large variety of organics, including benzene (B), toluene (T), ethylbenzene (E), and xylene (X), commonly referred to as BTEX, and also crude oils, while at the same time enabling the organic “sponges” to float on waters, which facilitates straightforward handling. We developed a method for generating PDMS particles with variable sizes (ranging from hundreds nanometers to few millimeters) by drop-wise depositing siloxane/cross-linker mixtures into warm water, a process which leads to the cross-linking of the PDMS components. We have tested the capability of the PDMS particles to remove toluene and benzene from water. We also performed similar experiments by utilizing PDMS sheets. In both instances we observed a rapid sorption of the organic phase into PDMS; the amount of absorbed organic solvent depended on the concentration in water and the total mass (volume) of PDMS and did not depend on the geometry of the PDMS “sponge.” In addition, we have examined the uptake of toluene and benzene from toluene/benzene (T/B) mixtures dissolved in water. Our results indicate that the amount of benzene absorbed from the T/B mixtures into PDMS increases relative to the uptake from pure benzene/water solutions. This behavior is associated with toluene acting as a “surfactant” that effectively replaces the more unfavorable PDMS/B contacts with less costly T/B ones. Finally, a simple experiment demonstrates qualitatively that PDMS is also capable of removing crude oils from oil-contaminated waters.     

ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area.     

Possibility of preparation of Bi1-xLax)4Ti3O12 titanate powders in molten salts

The possibility of synthesizing (Bi_1-x La _x )₄Ti₃O₁₂ bismuth titanate powders in molten salts from low-melting Bi₂O₃ and LaCl₃ was explored. The developed methodical approaches can be used for preparing titanate powders of different dispersities, promising for producing state of the art electronic components.