scholarly journals Transition metal–assisted carbonization of small organic molecules toward functional carbon materials

2018 ◽  
Vol 4 (7) ◽  
pp. eaat0788 ◽  
Author(s):  
Zhen-Yu Wu ◽  
Shi-Long Xu ◽  
Qiang-Qiang Yan ◽  
Zhi-Qin Chen ◽  
Yan-Wei Ding ◽  
...  
2020 ◽  
Vol 16 ◽  
pp. 833-857 ◽  
Author(s):  
Maria A Theodoropoulou ◽  
Nikolaos F Nikitas ◽  
Christoforos G Kokotos

Photochemistry, the use of light to promote organic transformations, has been known for more than a century but only recently has revolutionized the way modern chemists are thinking. Except from transition metal-based complexes, small organic molecules have been introduced as catalysts or initiators. In this review, we summarize the potential that (aromatic or aliphatic) aldehydes have as photoinitiators. The photophysical properties and photoreactivity of benzaldehyde are initially provided, followed by applications of aldehydes as initiators for polymerization reactions. Finally, the applications to date regarding aldehydes as photoinitiators in organic synthesis are presented.


2014 ◽  
Vol 16 (20) ◽  
pp. 9327-9336 ◽  
Author(s):  
Yeping Xu ◽  
Tobias Watermann ◽  
Hans-Heinrich Limbach ◽  
Torsten Gutmann ◽  
Daniel Sebastiani ◽  
...  

Confinement effects on water, benzene and pyridine in mesoporous carbon materials were probed by 1H-MAS NMR and chemical shift calculations.


RSC Advances ◽  
2020 ◽  
Vol 10 (25) ◽  
pp. 14500-14509 ◽  
Author(s):  
Zhenghui Liu ◽  
Peng Wang ◽  
Yu Chen ◽  
Zhenzhong Yan ◽  
Suqing Chen ◽  
...  

A small organic molecule was tailored for the efficient synthesis of biphenyl and its derivatives from aryl iodides.


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>


ACS Omega ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 4995-5000 ◽  
Author(s):  
Jiaxiang Zhang ◽  
Junwen Yang ◽  
Ziyue Liu ◽  
Bin Zheng

Author(s):  
Mohamed R. Rizk ◽  
Muhammad G. Gamal ◽  
Amina Mazhar ◽  
Mohamed El-Deab ◽  
Bahgat El-Anadouli

In this work, we report a single-step preparation of porous Ni-based foams thin layer atop Cu substrate via a facile dynamic hydrogen bubble template technique (DHBT). The prepared porous Ni-based...


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