Photocatalytic ozonation degradation of ciprofloxacin using ZnO nanoparticles immobilized on the surface of stones

Ciprofloxacin is used in the treatment of bacterial infections. Because ciprofloxacin is not effectively degraded by biological processes, advanced oxidation processes such as photocatalytic ozonation are applied to remove this antibiotic from wastewater. The aim of this study was to investigate photocatalytic ozonation for the removal of ciprofloxacin from aquatic environments and optimization of the effective parameters of the process. For this purpose, ZnO nanoparticles were synthesized using the thermal method and immobilized on the surface of stones. The structural properties of the nanoparticles were determined by XRD, TEM, Photoluminescence (PL) and SEM. Experiments were carried out in a Plexiglas reactor supported with the continuous injection of ozone. The effective parameters for removal efficiency were reaction time, initial concentration of ciprofloxacin, pH, photocatalyst concentration and reaction kinetics. The highest ciprofloxacin removal efficiency occurred at the following optimal conditions: pH of 7, reaction time of 30?min, photocatalyst concentration of 3?g/L and initial ciprofloxacin concentration of 10?mg/L. Removal efficiency of 96% was obtained under these conditions. Linear kinetic models showed that the process followed pseudo-first order and Langmuir-Hinshelwood kinetics. This process had a high removal efficiency and suitable for removal of ciprofloxacin from aquatic environments.GRAPHICAL ABSTRACT     

Energy dispersive X-ray fluorescence analysis of chloride in reforming catalysts

A relatively less expensive Phillips PV 9500/SY 585 automatic energy dispersive X-ray fluorescence (EDXFR) spectrometer with rhodium target, was modified using a commercial kitchen aluminium wrapping foil filter, for the determination of chloride in bifunctional reforming catalysts. Quantitation of chloride was carried out by the Claisse-Quintin method with a catalyst sample of known chloride content as an external standard. The results of EDXRF chloride analysis of different catalyst samples, containing varying amounts of percent carbon and chloride, compared well (p>0.75) with those obtained by silver chloride precipitation method. The small values of percent relative standard deviations at 0.4 to 4.5% showed a good reproducibility of the method. With the total analysis time of a few minutes combined with no sample preparation, the present method seems to be superior as compared to presently employed methods of chloride analysis in reforming catalysts.     

Radiochemical separation of99Mo from natural uranium irradiated with thermal neutrons

⁹⁹Mo was separated from uranium and insoluble fission product hydroxides. More than 98% of⁹⁹Mo radioactivity was extracted with bis (2-ethylhexyl)phosphoric acid. The organic phase was washed and⁹⁹Mo was back-extracted from the organic phase with NH₄OH solution. The percent recovery from the organic phase was 91% and the purity of⁹⁹Mo was more than 99%. Pure^99mTc was also extracted from the organic phase with a saline solution. Reversed-phase partition chromatography was used for the purification of⁹⁹Mo from¹³¹I and other fission products (10% HDEHP on kieselguhr bed).¹³¹I and other isotopes were quantitatively eluted with 0.1M H₂SO₄,⁹⁹Mo was eluted using a mixture of 0.5 M HCl and 30% H₂O₂.     

Thermoanalytical study of linkage isomerism in coordination compounds: Part I. Reinvestigation of thermodynamic and thermokinetic of solid state interconversion of nitrito (ONO) and nitro (NO2) isomers of pentaaminecobalt(III) chloride by means of DSC

Solid state thermal isomerization of [Co(NH₃)₅(ONO)]Cl₂ (nitrito isomer) to [Co(NH₃)₅(NO₂)]Cl₂ (nitro isomer) and reverse reaction were investigated by non-isothermal differential scanning calorimetry (DSC) and found to be essentially an equilibrium process. The interconversions are accelerated at above 65 °C and reach to equilibrium state at about 155 °C. After establishment of the equilibrium the relative amounts of two isomers at any temperature are governed by Gibbs free energy relationship. The experimental enthalpy changes of isomerization of pure nitrito and nitro solid samples to the equilibrium state are −4.67 (±0.19) and 0.99 (±0.05) kJ mol⁻¹, respectively. From these values, total enthalpy change was calculated as: ΔH°=−5.66(±0.20) kJ mol⁻¹. Using Gibbs free energy relationship, equilibrium constant, total free energy and entropy changes were estimated at 60 °C as: K=7.72(±0.8),  kJ mol⁻¹ and  J K⁻¹ mol⁻¹.An initial rate method has been developed to determine the kinetic parameters of these reactions from non-isothermal DSC data. Both nitro to nitrito and reverse reactions obey first order kinetic law in solid state. Estimated activation parameters of forward and reverse paths at 60 °C are , , and , respectively. The negative activation entropy of both directions support the intramolecular mechanism of isomerization, including formation of a seven coordinate transition state, which formerly suggested based on spectral and X-ray methods.     

Installation of carbon chain onto 2-cyclohexene-1,4-diol monoacetate

Alkylation of cyclohexenyl monoacetate 1 with R₂Cu(CN)(MgCl)₂ or RMgBr/CuCN (cat.) in Et₂O produced trans 1,2-isomers 4, while arylation and alkenylation of 1 was accomplished with lithium borates 5 and a nickel cat. to afford trans 1,4-isomers 3 selectively. Furthermore, several transformations of the products were carried out to demonstrate synthetic advantages of the present reactions.     

Ion pairing, H-bonding, and π–π interactions in copper(II) complex-organo-networks derived from a proton-transfer compound of the 1,10-phenanthroline-2,9-dicarboxylic acid

The one-pot reaction between the novel proton transfer compound (pydaH₂)²⁺(phendc)²⁻, LH₂, and Cu(II) afforded the compounds (pydaH)₂[Cu(phendc)₂]·10H₂O, 1, and (pydaH)₂[Cu(phendc)(phendcH)]₂·5H₂O, 2, where pyda=2,6-diaminopyridine, and phendcH₂=1,10-phenanthroline-2,9-dicarboxylic acid. The single crystal X-ray diffraction analysis of 1 and 2 revealed that these are two novel self-assembled 3D Cu(II) complex-organo-networks, in which (pydaH)⁺ ions and [Cu(phendc)₂]²⁻ or complex units are held together by ion pairing, H-bonding, and π–π interactions. Magnetic measurements over the temperature range 1.8–310 K revealed no significant magnetic coupling between Cu(II) centers in 1 or 2.     

Ketoconazol‐Triiodide Ion Pair Complex as a Suitable Carrier in an Iodide Selective Membrane Electrode

A novel membrane sensor for selective monitoring of iodide, consisting of a triiodide‐ketoconazole ion pair complex dispersed in a PVC matrix, plasticized with a mixture of 2‐nitrophenyl octyl ether and dioctylphtalate with unique selectivity toward iodide ions, is described. The influence of membrane composition, pH of test solution and foreign ions on the electrode performance were investigated. The optimized membrane demonstrates a near‐Nernstian response for iodide ions over a wide linear range from 1.0 × 10^?2 to 1.0 × 10^?5 M, at 25 ± 1 °C. The electrode could be used over a wide pH range 3–10 and has the advantages of high selectivity, fast response time and good lifetime (over 4 months). It was successfully used as indicator electrode in potentiometric titrations and direct potentiometric assay of iodide ions.     

Nanostructured pure gamma-Fe2O3 via forced precipitation in an organic solvent

Pure maghemite, gamma-Fe(2)O(3), was prepared as ultra fine particles in the nanometer-sized range via the forced precipitation method in an organic solvent. The precipitation of iron(III) ions, from iron(III) chloride in 2-propanol led selectively to highly dispersed particles of ferrihydrite, which upon treatment with temperatures higher than 200 degrees C under dynamic vacuum resulted in high-surface-area particles of gamma-Fe(2)O(3). Precipitation in water also led to ferrihydrite, but the final product, after heating at 300 degrees C, contained a mixture of gamma-Fe(2)O(3) and alpha-Fe(2)O(3) (hematite). The precipitation from iron(III) nitrate in water resulted in goethite which was converted to hematite upon heating. On the other hand, the final product in 2-propanol was a mixture of maghemite and hematite. The products were characterized by FTIR, TGA, XRD, and gas sorption analysis. Nitrogen gas adsorption studies for the pure gamma-Fe(2)O(3) samples revealed mesoporous particles with high surface areas in the range of 70-120 m(2) g(-1) after heat treatment at 300 degrees C. The gamma-Fe(2)O(3) particles retained their gamma-phase as well as their mesoporous structure at relatively high temperatures, as high as 400 degrees C.     

Corrected Debye-Hückel analysis of surface complexation; III. Spherical particle charging including ion condensation

Statistical mechanics has been used to derive a model for the charging of a spherical particle in a salt solution to complement our experimental studies and gain a deeper understanding of the processes involved in surface complexation. Our chosen model goes beyond the equilibrium constants and the Gouy-Chapmann theory currently used in surface complexation models. The proton adsorption is taken to occur at a harmonic potential well on the surface characterized by a frequency v and a well depth u(0). Outside the particle surface there is a capacitor layer of width w(c) which is impenetrable to the salt ions. The diffuse screening of the charged particle is described by a corrected Debye-Hückel analysis accounting for ion size in the ion-ion interactions. To account also for nonlinear electrostatic response a layer of condensed counterions has been introduced. The criterion for the onset of ion condensation is that the electrostatic field exceeds a linear response criterion. Ion size effects are accounted for in terms of hole-corrected electrostatic energies and excluded volume. The model has been applied to titrated surface charge data on goethite (alpha-FeOOH) at various background concentrations and good agreement between the experimental data and the model was obtained. Both the size of the screening ions and the central particle size were shown to be of importance for the surface charge.     

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.