Abstraction and addition kinetics of C2H radicals with CH4, C2H6, C3H8, C2H4, and C3H6: CVT/SCT/ISPE and hybrid meta-DFT methods

2015 ◽  
Vol 17 (5) ◽  
pp. 3142-3156 ◽  
Author(s):  
Manas Ranjan Dash ◽  
B. Rajakumar
Keyword(s):  
Density Functional ◽  
Transition State Theory ◽  
State Theory ◽  
Rate Coefficients ◽  
Dft Methods ◽  
Functional Theory ◽  
Variational Transition State Theory ◽  
Wide Range ◽  
P Rate ◽  
Kinetics Of

Rate coefficients for the reactions of C2H radicals with methane (k1), ethane (k2), propane (k3), ethylene (k4), and propylene (k5) were computed using canonical variational transition state theory (CVT) coupled with hybrid-meta density functional theory (DFT) over a wide range of temperatures from 150 to 5000 K.

scholarly journals Estudo cinético da abstração do hidrogênio do acetato de metila

2020 ◽  
Author(s):  
Leandro da Silva Pereira ◽  
Leonardo Baptista
Keyword(s):  
Transition State Theory ◽  
Methyl Esters ◽  
Hydrogen Abstraction ◽  
State Theory ◽  
Basis Set ◽  
Rate Coefficients ◽  
Dft Methods ◽  
Carbon Chains ◽  
Kinetics Of ◽  
Good Agreement

Biodiesel is a fuel formed by methyl esters with large carbon chains. The investigation of the hydrogen abstraction reactions of small methyl esters can be helpful to the improvement and development of kinetics models of biodiesel combustion. For this reason, the present study aims to study the thermochemistry and kinetics of hydrogen abstraction of methyl ethanoate using DFT methods and transition state theory. The abstraction reactions by O2, O, HO2, and H were studied with the B3LYP-D3 and M06-2X functionals with cc-pVDZ, ccpVTZ, aug-cc-pVDZ, and aug-cc-pVTZ basis set. At 298 K, the rate coefficients evaluated are in good agreement with the literature’s coefficients and the faster reaction occurs in the presence of O atoms. The hydrogen abstraction by O2 molecule it is not important at 298 K, but should be included in the present study since it should be important at higher temperatures.

scholarly journals Kinetics of the Reactions of Ozone with Halogen Atoms in the Stratosphere

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1053
Author(s):  
S. Vijayakumar ◽  
Duminda S. Ranasinghe ◽  
David M. Wilmouth
Keyword(s):  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Transition State Theory ◽  
State Theory ◽  
Rate Coefficients ◽  
Variational Transition State Theory ◽  
Halogen Atoms ◽  
Reaction Cycles ◽  
Energy Surfaces ◽  
Kinetics Of

It is well established that reaction cycles involving inorganic halogens contribute to the depletion of ozone in the atmosphere. Here, the kinetics of O3 with halogen atoms (Cl, Br, and I) were investigated between 180 and 400 K, expanding the temperature range relative to prior studies. Canonical variational transition state theory including small curvature tunneling correction (CVT/SCT) were considered, following the construction of the potential energy surfaces. MRCI + Q/aug-ano-pVTZ//MP2/aug-cc-pV(T + d)Z and MRCI + Q/aug-ano-RCC-VTZP//MP2/aug-cc-pV(T + d)Z levels of theory were used to calculate the kinetic parameters. Calculated rate coefficients were used to fit the Arrhenius equations, which are obtained to be k1 = (3.48 ± 0.4) × 10−11 exp[(−301 ± 64)/T] cm3 molecule−1 s−1, k2 = (3.54 ± 0.2) × 10−11 exp[(−990 ± 35)/T] cm3 molecule−1 s−1 and k3 = (1.47 ± 0.1) × 10−11 exp[(−720 ± 42)/T] cm3 molecule−1 s−1 for the reactions of O3 with Cl, Br, and I atoms, respectively. The obtained rate coefficients for the reactions of O3 with halogen atoms using CVT/SCT are compared to the latest recommended rate coefficients by the NASA/JPL and IUPAC evaluations. The reactivity trends and pathways of these reactions are discussed.

scholarly journals A machine learning platform for the discovery of materials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carl E. Belle ◽  
Vural Aksakalli ◽  
Salvy P. Russo
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Gw Approximation ◽  
Dft Methods ◽  
Functional Theory ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

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