RESUMO
Two covalent organic framework (COF) films supported by a glass substrate were obtained by solvothermal reaction of an electron donor with electron acceptor 1,3,5-triformylbenzene (TF) or 2,4,6-triformylphloroglucinol (TFP), respectively. The TFP-BD film exhibits a nonlinear absorption coefficient of -3.01 × 105 cm GW-1. The TFP-BD film can aggregate electrons around the connected monomer through the D-A effect due to its highly polar and electronegative carbonyl oxygen atoms, thereby modulating the electronic structure of the COFs. This work provides a novel approach for the structural modulation of optical materials with strong nonlinearity.
RESUMO
Machine learning (ML) models have received increasing attention as a new approach for the virtual screening of organic materials. Although some ML models trained on large databases have achieved high prediction accuracy, the application of ML to certain types of organic materials is limited by the small amount of available data. On the other hand, metalloporphyrins and porphyrins (MpPs) have received increasing attention as potential photocatalysts, and recent studies have found that both HOMO/LUMO energy levels and energy gaps are important factors controlling the MpP photocatalysts. Since the training data of MpPs are insufficient and limited to porphyrin-based dyes, in this study, we proposed a deep transfer learning approach to rapidly predict the HOMO/LUMO energy levels and energy gaps of MpPs. To complement the open-source Porphyrin-based Dyes Database (PBDD), we curated a new database, the Metalloporphyrins and Porphyrins Database (MpPD), in which MpPs were specifically designed as potential photocatalysts and the HOMO/LUMO energies were calculated by advanced DFT functionals. We proposed PorphyBERT, a BERT-based regression model that was pre-trained with PBDD and fine-tuned with MpPD. The model performed satisfactorily in predicting HOMO and LUMO energies and energy gap with RMSEs of 0.0955, 0.0988, and 0.0787 eV and MAEs of 0.0774, 0.0824, and 0.0549 eV. Furthermore, due to its unique unsupervised pre-training phase, the model is not affected by the difference in computational functionals between pre-training and fine-tuning databases. Finally, we recommended 12 MpPs as potential photocatalysts for CO2 reduction with out-of-sample model predictions of energy gaps close to the values calculated by DFT.
RESUMO
The mechanism and origins of site-selectivity of Rh2(S-tfpttl)4-catalyzed C(sp3)-H bond aminations were studied using density functional theory (DFT) calculations. The synergistic combination of the dirhodium complex Rh2(S-tfpttl)4 with tert-butylphenol sulfamate TBPhsNH2 composes a pocket that can access both tertiary and benzylic C-H bonds. The nonactivated tertiary C-H bond was selectively aminated in the presence of an electronically activated benzylic C-H bond. Both singlet and triplet energy surfaces were investigated in this study. The computational results suggest that the triplet stepwise pathway is more favorable than the singlet concerted pathway. In the hydrogen atom abstraction by Rh-nitrene species, which is the rate- and site-selectivity-determining step, there is an attractive π-π stacking interaction between the phenyl group of the substrate and the phthalimido group of the ligand in the tertiary C-H activation transition structure. By contrast, such attractive interaction is absent in the benzylic C-H amination transition structure. Therefore, the DFT computational results clearly demonstrate how the synergistic combination of the dirhodium complex with sulfamate overrides the intrinsic preference for benzylic C-H amination to achieve the amination of the nonactivated tertiary C-H bond.
