DFT B3LYP/6-311+G* calculation study of a new nonclassical mechanism of electrophilic functionalization of aromatic C–H bond via aryl cation formation

1. Download : Download high-res image (84KB)
2. Download : Download full-size image

     

Intermolecular interactions between natural humic substances and tricyclic antidepressants

1. Download : Download high-res image (128KB)
2. Download : Download full-size image

     

Trimetallic NiCoM catalysts (M = Mn,Fe, Cu) for methane conversion into synthesis gas

1. Download high-res image (195KB)
2. Download full-size image

     

Hydrothermal synthesis,thermal decomposition and optical properties of Fe2F5(H2O)(Htaz)(taz)(Hdma)

Crystal structure of Fe₂F₅(H₂O)(Htaz)(taz)(Hdma) which crystallizes in the triclinic system space group $\text{P}\overline{1}$ with unit cell parameters a = 8.8392(5) Å, b = 9.1948(5) Å, c = 9.5877(5) Å, α = 82.070(3)°, β = 63.699(3)°, γ = 89.202(3)°, Z = 2, and V = 690.91(7) ų, was synthesized under hydrothermal conditions at 393 K for 72 h, by a mixture of FeF₂/FeF₃, 1,2,4-triazole molecule (Htaz), and hydrofluoric acid solution (HF 4%) in dimethylformamide solvent (DMF). The main feature of this material is the coexistence of two oxidation states for iron atoms (Fe²⁺, Fe³⁺) in the unit cell, which associate by opposite fluorine corners of FeF₅N and FeF₂N₄ octahedra, and/or triazole molecule which originates the 2D produces material. The structure determination, performed from single crystal X-ray diffraction data, lead to the R₁/_WR₂ reliability factors 0.031/0.087. Thermal stability studies (TG/DTG/DTA) show that the decomposition provides in the temperature range 473–773 K and no mass loss was detected before 473 K. Mass spectrometry (MS) has been used. The optical absorption of the solid was measured at the corresponding λ_max using UV–vis diffuse-reflectance spectrum.     

Order-Stability in Complex Biological,Social, and AI-Systems from Quantum Information Theory

This paper is our attempt, on the basis of physical theory, to bring more clarification on the question “What is life?” formulated in the well-known book of Schrödinger in 1944. According to Schrödinger, the main distinguishing feature of a biosystem’s functioning is the ability to preserve its order structure or, in mathematical terms, to prevent increasing of entropy. However, Schrödinger’s analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schrödinger also appealed to the ambiguous notion of negative entropy. We apply quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of classical entropy. We consider a complex biosystem S composed of many subsystems, say proteins, cells, or neural networks in the brain, that is, S=(Si). We study the following problem: whether the compound system S can maintain “global order” in the situation of an increase of local disorder and if S can preserve the low entropy while other Si increase their entropies (may be essentially). We show that the entropy of a system as a whole can be constant, while the entropies of its parts rising. For classical systems, this is impossible, because the entropy of S cannot be less than the entropy of its subsystem Si. And if a subsystems’s entropy increases, then a system’s entropy should also increase, by at least the same amount. However, within the quantum information theory, the answer is positive. The significant role is played by the entanglement of a subsystems’ states. In the absence of entanglement, the increasing of local disorder implies an increasing disorder in the compound system S (as in the classical regime). In this note, we proceed within a quantum-like approach to mathematical modeling of information processing by biosystems—respecting the quantum laws need not be based on genuine quantum physical processes in biosystems. Recently, such modeling found numerous applications in molecular biology, genetics, evolution theory, cognition, psychology and decision making. The quantum-like model of order stability can be applied not only in biology, but also in social science and artificial intelligence.