Azulene, a bicyclic nonbenzenoid aromatic hydrocarbon, shows completely different physicochemical properties compared with its isomeric naphthalene. Herein, we made use of the diverse reactivity of each position on azulene to design a new synthetic strategy for azulene-based diimides bridged by phenyl or thieno[3,2-
]thiophenyl group, 2-(azulen-2'-yl)-5-(azulen-2''-yl)benzene-1,1':4,1''-tetracarboxylic diimides (
). The key step was double trifluoroacetylation at 1-position of two azulene moieties of the molecule followed by hydrolysis, anhydridization and imidization to obtain the target compounds. The single crystal structure analysis demonstrates that
has twisted molecular backbone. The adjacent two molecules form a dimer through the intermolecular π-π stacking (0.365 nm) between the five-membered ring and the seven-membered ring of two different azulene units. Strong π-π intermolecular interactions (0.355 nm) exist among the dimers to form a slipped one-dimensional (1D) packing motif in the crystal. For three compounds, the optoelectronic properties were investigated by UV-vis absorption spectra and cyclic voltammetry, and their energy levels of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and the energy gaps were calculated. The HOMO/LUMO energy levels of
are -5.56/-3.28 eV, -5.56/ -3.30 eV and -5.57/-3.42 eV, respectively. The end absorptions of
in thin films show obvious red-shift (13, 13 and 25 nm) relative to those in CHCl
solution, indicating strong intermolecular interactions in solid state. The charge carrier transport properties of three compounds were studied through organic field-effect transistors (OFETs). Bottom-gate and top-contact OFET devices of
were fabricated by spin-coated their respective solution on octadecyltrimethoxysilane (OTMS)-treated SiO
/Si substrates. Under nitrogen atmosphere, all of these three compounds displayed electron-dominated ambipolar organic semiconductor characteristics. The
atures above 60 ℃, while complex C3 bearing a 3,6-di-tert-butyl-substituted fluorenyl moiety showed lowest activities among the zirconocene series due to its overcrowded coordination sites. Compared with its zirconocene analogue, the hafnocene complex C4 activated with TIBA/TrB proved to be even more selective toward β-Me elimination, and meanwhile gave products with much lower molecular weights. At 100 ℃, the hafnocene system mainly oligomerized propylene to dimers and trimers. Studies on the dependence of the product molecular weight and the chain-release selectivity on monomer concentration suggested that both β-Me and β-H elimination involved in these systems mainly operate in a bimolecular pathway.
Article
Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane
Cai Chengzhi, Li Lifeng, Deng Xiaomei, Li Shuhua, Liang Hong, Qiao Zhiwei
Acta Chimica Sinica
2020, 78 (5):
427-436.
DOI: 10.6023/A20030065
Published: 16 April 2020
In this work, the separation performance of methane/ethane/propane (C1, C2 and C3) mixture in the 137953 hypothetical metal-organic frameworks (MOFs) is calculated by high throughput computational screening and multiple machine learning (ML) algorithms. First, to avoid the competitive adsorption of water vapor, 31399 hydrophobic MOFs (hMOFs) were screened out. Then, grand canonical Monte Carlo (GCMC) simulations were employed to calculate the adsorption behavior of a mixture with a mole ratio of C1:C2:C3=7:2:1 in these hMOFs, respectively. Second, the relationships among six MOF structures/energy descriptors (the largest cavity diameter (LCD), void fraction (f), volumetric surface area (VSA), Henry coefficient (K), heat of adsorption (Qst), density of MOF (ρ)) and three performance indicators of MOFs (selectivities (S), adsorption capacities (N) of C1, C2, C3 and their trade-offs (TSN)) were established. The LCDs were calculated by Zeo++software, and VSAs were calculated using RASPA software using He and N2 as probes, respectively, and Qst and K were calculated in an infinite dilution of each gas molecule in an infinite dilution state using NVT-MC method in RASPA software. Then, we found that there existed the "second peaks" of N and S in part of structure-property relationships, and all the optimal MOFs located in the range of second peaks, especially for the separation of C1 or C2. Third, the above-mentioned six MOF descriptors and three MOF performance indicators were trained, tested and predicted by four ML algorithms, including decision tree, random forest (RF), support vector machine and Back Propagation neural network. Although the predictive effect for the selectivity was very low, the introduction of TSN can significantly improve the accuracy of ML prediction, especially for RF algorithm (R=0.99). Therefore, the RF was used to quantitatively analyze the relative importance of each MOF descriptor, and found that three descriptors (K, LCD and ρ) possessed the highest importance for the separation of C1 and C2, and three other descriptors (K, Qst and ρ) for the separation of C3. Moreover, three simple and clear paths of optimal MOFs for C1, C2 and C3 adsorption were designed by the decision tree model with the descriptors. Based on those paths, there were 96%, 85%, 95% probability that we can search for high-performance MOFs, respectively. Finally, the best 18 MOFs were identified for different separation applications of C1, C2 and C3. This study reveals the second peaks and key MOF descriptors governing the adsorption of light alkane, develops quantitative structure-property relationships by ML, and identifies the best adsorbents from a large collection of MOFs for the separation of C1, C2 and C3 from natural gas.
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