Modeling of the aniline with nitrobenzene reaction was carried out by PM6 method with tetramethylammonium hydroxide. Calculated data prove that the stage of transfer of hydride ion from the p-[sigma]-complex to the acceptor, i.e. nitrobenzene or 4- nitrodiphenylamine determines the rate of aniline with nitrobenzene condensation. Herein, intermolecular transfer mechanism that has lower activation energy is the most likely one if compared with intramolecular mechanism. It is shown that tetramethylammonium cation can form ionic and ion-dipole complexes with the components of the reaction mixture and its field influences the distribution of electron density in the reactants and their reactivity
Modeling of the condensation reaction to diphenylamine was carried out by PM7 method with acid catalysts: tetrafluoroborate, oxytrifluoroborate and anilinium oxytetratrifluoroborate. The calculated data prove that the formation of a few protonated forms of aniline is possible during the reaction of aniline with acids. Only positively charged p- and o-[sigma]-complexes are capable of further interaction with aniline. The stage of intramolecular proton transfer from the primary to the secondary amino groups of intermediates of aniline reaction with proton aniline ?-complex determines the condensation rate of aniline to diphenylamine with acid catalysts. A catalyst anion can form ionic and ion-dipole complexes with reaction mixture components and can influence the distribution of electron density in reactants and their reactivity with its field
Rational development of oil and gas reservoirs is possible only with efficient monitoring by various well logging techniques. This paper presents algorithms for processing data acquired by spectral noise logging (SNL) in memory mode. The SNL technology is designed to identify flowing reservoir intervals, cross-flows behind casing and tubing and casing leaks by spectral analysis of recorded noise signals. While moving through a reservoir, fluids and gases create turbulence and rock vibrations that in turn generate noise. This acoustic noise is recorded with a noise logging memory tool consisting of a high-sensitivity piezoelectric hydrophone sensor and an amplifier and data collection module. The tool records acoustic signals in the frequency range of 15 Hz to 60 kHz. The existing SNL technology excludes intense broadband noise created by the movement of the tool in the well. Useful information is extracted from background noise using a technique based on wavelet thresholding. Spectral noise density in the depth-frequency plane undergoes a wavelet transform. At each measurement depth, several tens of noise signals are recorded to determine mean wavelet coefficients and their typical variance. Then, they are analysed to remove statistically insignificant details from the signal spectrum and to suppress noise components that are present throughout large depth intervals. The processing of data acquired in tens of wells from various fields has show that the noise features identified by wavelet filtering correlate with open-hole data and are confirmed by conventional well logging techniques.
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