High-performance nonfused ring electron acceptor with a steric hindrance induced planar molecular backbone

Three nonfused ring electron acceptors (NFREAs) TTC6,TT-C8T and TT-TC8 were purposefully designed and synthesized.The molecular geometry can be adjusted by the steric hindrance of lateral substituents.According to the DFT calculations,from TTC6 to TT-C8T and TT-TC8,planarity of the molecular backbone is gradually improved,accompanying with the enhancing of intramolecular charge transfer effect.As for TT-TC8,the two phenyl substituents are almost perpendicular to the molecular backbone,which endues the acceptor with good solubility and suppresses it to form over-aggregation.Multidirectional regular molecular orientation and closer molecular stacking are formed in TT-TC8 film.As a result,TT-TC8 based devices afford the highest PCE of 13.13%,which is much higher than that of TTC6 (4.41%) and TT-C8T (10.42%) and among the highest PCE values based on NFREAs.     

Phase Transitions in Ferroelectric KNO3/Al2O3 Nanocomposites

Russian Physics Journal - The temperature dependences of the linear dielectric permittivity ε′ and the third harmonic coefficient γ3ω for nanocomposites obtained by embedding a...     

Simulation of Dampers Made of Viscoelastic Materials

Technical Physics - Taking damping into account in the problems of structural dynamics is an important and nontrivial problem. Its complexity is primarily determined by the required specification...     

Behavior of Andreev states for topological phase transition

Theoretical and Mathematical Physics - We consider three one-dimensional superconducting structures: 1) the one with $$p$$ -wave superconductivity; 2) the main experimental model of a nanowire with...     

Small-Angle Neutron Scattering at the Pulsed Reactor IBR-2: Current Status and Prospects

Crystallography Reports - Neutron diffraction studies on the small-angle neutron spectrometer YuMO (Joint Institute for Nuclear Research, Dubna), based on the IBR-2 pulsed reactor, have been...     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

Equation for Condensed Matter Phase Transitions

Russian Physics Journal - A unified equation for the pressure drop in the apparatus with the stationary and fluidized granular layers is derived. The resulting recurrent equation is used to...     

The Interference of Magnetoelastic Waves in an Yttrium Iron Garnet Film

Physics of the Solid State - The interference of excited and received by current antennae magnetoelastic waves counterpropagating in an yttrium iron garnet film is experimentally studied. It is...