Synthesis and Emission Behavior of 1,3-diarylisobenzofuran-5,6-dicarboximides and Their Transformation into Naphthalene-2,3:6,7-bis(dicarboximide)s
Haruki Shimosasa,
Ryuta Miyatake,
Naoki Kobayashi,
Mitsunori Oda
Issue:
Volume 4, Issue 2, April 2016
Pages:
16-23
Received:
10 March 2016
Accepted:
18 March 2016
Published:
6 April 2016
Abstract: Phosphine-assisted annulation of 2,5-diarylfuran-3,4-dicarbaldehydes with maleimides provided the title isobenzofurans in satisfactory yields. An effect of the substituents at the para position of the aryl groups in these isobenzofurans was demonstrated clearly by a red shift in their UV-vis absorption and emission spectra. They were transformed into the corresponding naphthalene-2,3:6,7-bis(dicarboximide)s by Diels-Alder reaction with another maleimide and subsequent dehydration with the aid of trifluoromethanesulfonic acid. Emission behavior of the title bis(dicarboximide)s is also described.
Abstract: Phosphine-assisted annulation of 2,5-diarylfuran-3,4-dicarbaldehydes with maleimides provided the title isobenzofurans in satisfactory yields. An effect of the substituents at the para position of the aryl groups in these isobenzofurans was demonstrated clearly by a red shift in their UV-vis absorption and emission spectra. They were transformed in...
Show More
Modelling of Normal Boiling Points of Hydroxyl Compounds by Radial Basis Networks
Issue:
Volume 4, Issue 2, April 2016
Pages:
24-29
Received:
3 May 2016
Published:
4 May 2016
Abstract: Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descriptors were selected to develop the RBN model. Simulated with the final optimum RBN model [8-35(64)-1], the root mean square errors (RMSE) for the training, the validation and the testing set were 5.55, 4.28, and 5.33, and the correlation coefficients R=0.994(training), 0.994(validation), 0.993(testing). The final RBN model was compared with the multiple linear regression approach and showed more satisfactory results.
Abstract: Radial basis networks (RBN) were applied to link molecular descriptor and boiling points of 168 hydroxyl compounds. The total database was randomly divided into a training set(134), a validation set(17) and a testing set(17). Each compound in the lowest energy conformation was numerically characterized with E-dragon software. Then 8 molecular descr...
Show More