Intramolecular photochemical reactions of 4-(.omega.-alkenyloxy)-6-methyl-2-pyrones having an alkoxycarbonyl group at the olefinic carbon chain

1991 ◽  
Vol 56 (25) ◽  
pp. 7150-7154 ◽  
Author(s):  
Tetsuro Shimo ◽  
Jun Tajima ◽  
Takaaki Suishu ◽  
Kenichi Somekawa
1992 ◽  
Vol 57 (21) ◽  
pp. 5708-5712 ◽  
Author(s):  
Kenichi Somekawa ◽  
Hiroyuki Okuhira ◽  
Masayuki Sendayama ◽  
Takaaki Suishu ◽  
Tetsuro Shimo

ChemInform ◽  
2010 ◽  
Vol 24 (7) ◽  
pp. no-no
Author(s):  
K. SOMEKAWA ◽  
H. OKUHIRA ◽  
M. SENDAYAMA ◽  
T. SUISHU ◽  
T. SHIMO

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>


2018 ◽  
Author(s):  
Chandan Dey ◽  
Ronny Neumann

<p>A manganese substituted Anderson type polyoxometalate, [MnMo<sub>6</sub>O<sub>24</sub>]<sup>9-</sup>, tethered with an anthracene photosensitizer was prepared and used as catalyst for CO<sub>2</sub> reduction. The polyoxometalate-photosensitizer hybrid complex, obtained by covalent attachment of the sensitizer to only one face of the planar polyoxometalate, was characterized by NMR, IR and mass spectroscopy. Cyclic voltammetry measurements show a catalytic response for the reduction of carbon dioxide, thereby suggesting catalysis at the manganese site on the open face of the polyoxometalate. Controlled potentiometric electrolysis showed the reduction of CO<sub>2</sub> to CO with a TOF of ~15 sec<sup>-1</sup>. Further photochemical reactions showed that the polyoxometalate-anthracene hybrid complex was active for the reduction of CO<sub>2</sub> to yield formic acid and/or CO in varying amounts dependent on the reducing agent used. Control experiments showed that the attachment of the photosensitizer to [MnMo<sub>6</sub>O<sub>24</sub>]<sup>9-</sup> is necessary for photocatalysis.</p><div><br></div>


2018 ◽  
Author(s):  
Richard Kong ◽  
Mark Crimmin

<i>The formation of carbon chains by the coupling of COx (X = 1 or 2) units on transition metals is a fundamental step relevant to Fischer-Tropsch catalysis. Fischer-Tropsch catalysis produces energy dense liquid hydrocarbons from synthesis gas (CO and H2) and has been a mainstay of the energy economy since its discovery nearly a century ago. Despite detailed studies aimed at elucidating the steps of catalysis, experimental evidence for chain growth (Cn to Cn+1 ; n > 2) from the reaction of CO with metal complexes is unprecedented. In this paper, we show that carbon chains can be grown from sequential reactions of CO or CO2 with a transition metal carbonyl complex. By exploiting the cooperative effect of transition and main group metals, we document the first example of chain propagation from sequential coupling of CO units (C1 to C3 to C4), along with the first example of incorporation of CO2 into the growing carbon chain.</i><br>


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