A density functional theory investigation on the formation mechanisms of DNA interstrand crosslinks induced by chloroethylnitrosoureas

2012 ◽  
Vol 113 (9) ◽  
pp. 1299-1306 ◽  
Author(s):  
Li-Jiao Zhao ◽  
Xin-Yan Ma ◽  
Ru-Gang Zhong
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Formation Mechanisms ◽  
Functional Theory ◽  
Interstrand Crosslinks ◽  
Dna Interstrand Crosslinks

scholarly journals Investigating 3,4-Bis(3-Nitrofurazan-4-Yl)furoxan Detonation with a Rapidly Tuned Density Functional Tight Binding Model

2021 ◽  
Author(s):  
Rebecca Lindsey ◽  
Sorin Bastea ◽  
Nir Goldman ◽  
Laurence E. Fried
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Tight Binding ◽  
Computational Cost ◽  
Formation Mechanisms ◽  
Extreme Conditions ◽  
Clear Understanding ◽  
Molecular Materials ◽  
Functional Theory ◽  
Complete Combustion

<p>Carbon rich materials lacking sufficient oxygen to undergo complete combustion have long been known to produce nanocarbon condensates of utility to industries spanning nanomedicine to quantum computing, when subject to strong shockwaves. However, the associated extreme conditions (e.g. 1000s of K and 10s of GPa) and rapid system evolution (e.g. 10s of ps) has precluded a clear understanding of early time phenomena giving way to carbon condensate formation. The semi-quantum density functional theory tight binding (DFTB) simulation method is ideal for studying chemistry on these timescales, offering much of the predictive power of density functional theory (DFT) at a fraction of the computational cost. However available parameterizations are not designed for application to organic molecular materials under extreme conditions.</p><p><i>Here, we describe a new machine learning (ML) approach for rapidly tuning DFTB models to simulate molecular materials under extreme conditions and demonstrate its application to modeling of 3,4-bis(3-nitrofurazan-4-yl)furoxan (i.e. DNTF), which has recently been shown to produce liquid carbon nanodroplets upon detonation that subsequently solidify into graphitic nano-onions</i>. We investigate early shockwave-driven decomposition chemistry to determine (1) major chemical kinetics steps, (2) the DNTF shock equation of state, and (3) implications of (1) and (2) for the DNTF nanocarbon formation mechanisms. We find evolution to be characterized by release of CO<sub>2</sub>, N<sub>2</sub>, and CO, as well as large C<i><sub>x</sub></i>N<i><sub>y</sub></i>O<i><sub>z</sub></i> species that are likely to be precursors to the experimentally observed carbon nano onions. Moreover, we find O purification (i.e. via CO<sub>2</sub> elimination) more rapid than that of N (i.e. via N<sub>2</sub> elimination), consistent with the experimentally observed N-containing species entrainment within the carbon condensates. Ultimately, we find the present developed ML-driven DFTB tuning approach well suited to the study of chemistry under extreme conditions, by providing a means of achieving long-timescale simulation with DFT-accuracy.</p><p> </p>

scholarly journals Density functional theory-based investigation of HCN and NH3 formation mechanisms during phenylalanine pyrolysis

RSC Advances ◽  
2020 ◽  
Vol 10 (47) ◽  
pp. 28431-28436
Author(s):  
Baizhong Sun ◽  
Chuanqun Liu ◽  
Deyong Che ◽  
Hongpeng Liu ◽  
Shuai Guo
Keyword(s):  
Amino Acids ◽  
Density Functional Theory ◽  
Density Functional ◽  
Nitrogen Transfer ◽  
Formation Mechanisms ◽  
Functional Theory ◽  
Nh3 Formation

As sludge pyrolysis produces large amounts of toxic NH3 and HCN, many works have studied nitrogen transfer during this process, commonly employing amino acids as models of sludge protein.

scholarly journals Investigating 3,4-Bis(3-Nitrofurazan-4-Yl)furoxan Detonation with a Rapidly Tuned Density Functional Tight Binding Model

2021 ◽  
Author(s):  
Rebecca Lindsey ◽  
Sorin Bastea ◽  
Nir Goldman ◽  
Laurence E. Fried
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Tight Binding ◽  
Computational Cost ◽  
Formation Mechanisms ◽  
Extreme Conditions ◽  
Clear Understanding ◽  
Molecular Materials ◽  
Functional Theory ◽  
Complete Combustion

<p>Carbon rich materials lacking sufficient oxygen to undergo complete combustion have long been known to produce nanocarbon condensates of utility to industries spanning nanomedicine to quantum computing, when subject to strong shockwaves. However, the associated extreme conditions (e.g. 1000s of K and 10s of GPa) and rapid system evolution (e.g. 10s of ps) has precluded a clear understanding of early time phenomena giving way to carbon condensate formation. The semi-quantum density functional theory tight binding (DFTB) simulation method is ideal for studying chemistry on these timescales, offering much of the predictive power of density functional theory (DFT) at a fraction of the computational cost. However available parameterizations are not designed for application to organic molecular materials under extreme conditions.</p><p><i>Here, we describe a new machine learning (ML) approach for rapidly tuning DFTB models to simulate molecular materials under extreme conditions and demonstrate its application to modeling of 3,4-bis(3-nitrofurazan-4-yl)furoxan (i.e. DNTF), which has recently been shown to produce liquid carbon nanodroplets upon detonation that subsequently solidify into graphitic nano-onions</i>. We investigate early shockwave-driven decomposition chemistry to determine (1) major chemical kinetics steps, (2) the DNTF shock equation of state, and (3) implications of (1) and (2) for the DNTF nanocarbon formation mechanisms. We find evolution to be characterized by release of CO<sub>2</sub>, N<sub>2</sub>, and CO, as well as large C<i><sub>x</sub></i>N<i><sub>y</sub></i>O<i><sub>z</sub></i> species that are likely to be precursors to the experimentally observed carbon nano onions. Moreover, we find O purification (i.e. via CO<sub>2</sub> elimination) more rapid than that of N (i.e. via N<sub>2</sub> elimination), consistent with the experimentally observed N-containing species entrainment within the carbon condensates. Ultimately, we find the present developed ML-driven DFTB tuning approach well suited to the study of chemistry under extreme conditions, by providing a means of achieving long-timescale simulation with DFT-accuracy.</p><p> </p>

Doping effects on the geometric and electronic structure of tin clusters

2019 ◽  
Vol 21 (44) ◽  
pp. 24478-24488 ◽  
Author(s):  
Martin Gleditzsch ◽  
Marc Jäger ◽  
Lukáš F. Pašteka ◽  
Armin Shayeghi ◽  
Rolf Schäfer
Keyword(s):  
Density Functional Theory ◽  
Electronic Structure ◽  
Density Functional ◽  
Beam Deflection ◽  
Functional Theory ◽  
Doping Effects ◽  
Depth Analysis ◽  
Trajectory Simulations

In depth analysis of doping effects on the geometric and electronic structure of tin clusters via electric beam deflection, numerical trajectory simulations and density functional theory.

What correlation effects are covered by density functional theory?

2000 ◽  
Vol 98 (20) ◽  
pp. 1639-1658 ◽  
Author(s):  
Yuan He, Jurgen Grafenstein, Elfi Kraka,
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Correlation Effects ◽  
Functional Theory
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