Suppression of Charge Recombination by Vertical Arrangement of A Donor Moiety on Flat Planar Dyes for Efficient Dye-Sensitized Solar Cells.

In dye-sensitized solar cells (DSSCs), flat planar dyes (e. g., highly light-harvesting porphyrins and corroles) with multiple anchoring groups are known to adopt a horizontal orientation on TiO2 through the multiple binding to TiO2. Due to the strong electronic coupling between the dye and TiO2, fast charge recombination between the oxidized dye and an electron in TiO2 occurs, lowering the power conversion efficiency (η). To overcome this situation, an additional donor moiety can be placed on top of the planar dye on TiO2 to slow down the undesirable charge recombination. Here we report the synthesis and photovoltaic properties of a triarylamine (TAA)-tethered gold(III) corrole (TAA-AuCor). The DSSC with TAA-AuCor using iodine redox shuttle exhibited the highest η-value among corrole-based DSSCs, which is much higher than that with the reference AuCor. The transient absorption spectroscopies clearly demonstrated that fast electron transfer from the TAA moiety to the corrole radical cation in TAA-AuCor competes with the undesirable charge recombination to generate long-lived charge separated state TAA⋅+-Cor/TiO2⋅- efficiently. Consequently, the introduction of the TAA moiety enhanced the η-value remarkably, demonstrating the usefulness of our new concept to manipulate charge-separated states toward highly efficient DSSCs.

Exciton diffusion in amorphous organic semiconductors: Reducing simulation overheads with machine learning.

Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a significant computational overhead for studying exciton diffusion, especially in large and complex material datasets. While the idea of using machine learning to quickly predict these parameters has been explored previously, typical machine learning models require long training times, which ultimately contribute to simulation overheads. In this paper, we present a new machine learning architecture for building predictive models for intermolecular exciton coupling parameters. Our architecture is designed in such a way that the total training time is reduced compared to ordinary Gaussian process regression or kernel ridge regression models. Based on this architecture, we build a predictive model and use it to estimate the coupling parameters which enter into an exciton hopping simulation in amorphous pentacene. We show that this hopping simulation is able to achieve excellent predictions for exciton diffusion tensor elements and other properties as compared to a simulation using coupling parameters computed entirely from density functional theory. This result, along with the short training times afforded by our architecture, shows how machine learning can be used to reduce the high computational overheads associated with exciton and charge diffusion simulations in amorphous organic materials.

Mixed-Metal Cu-Zn Thiocyanate Coordination Polymers with Melting Behavior, Glass Transition, and Tunable Electronic Properties.

The solid-state mechanochemical reactions under ambient conditions of CuSCN and Zn(SCN)2 resulted in two novel materials: partially Zn-substituted α-CuSCN and a new phase CuxZny(SCN)x+2y. The reactions take place at the labile S-terminal, and both products show melting and glass transition behaviors. The optical band gap and solid-state ionization potential can be adjusted systematically by adjusting the Cu/Zn ratio. Density functional theory calculations also reveal that the Zn-substituted CuSCN structure features a complementary electronic structure of Cu 3d states at the valence band maximum and Zn 4s states at the conduction band minimum. This work shows a new route to develop semiconductors based on coordination polymers, which are becoming technologically relevant for electronic and optoelectronic applications.