Efficient Carbon-Based CsPbBr_3 Inorganic Perovskite Solar Cells by Using Cu-Phthalocyanine as Hole Transport Material

Metal halide perovskite solar cells(PSCs) have attracted extensive research interest for next-generation solution-processed photovoltaic devices because of their high solar-to-electric power conversion efficiency(PCE)and low fabrication cost. Although the world's best PSC successfully achieves a considerable PCE of over 20% within a very limited timeframe after intensive efforts, the stability, high cost, and up-scaling of PSCs still remain issues. Recently, inorganic perovskite material, CsPbBr_3, is emerging as a promising photo-sensitizer with excellent durability and thermal stability, but the efficiency is still embarrassing. In this work, we intend to address these issues by exploiting CsPbBr_3 as light absorber, accompanied by using Cu-phthalocyanine(CuPc) as hole transport material(HTM) and carbon as counter electrode. The optimal device acquires a decent PCE of 6.21%, over 60% higher than those of the HTM-free devices. The systematic characterization and analysis reveal a more effective charge transfer process and a suppressed charge recombination in PSCs after introducing CuPc as hole transfer layer. More importantly, our devices exhibit an outstanding durability and a promising thermal stability, making it rather meaningful in future fabrication and application of PSCs.

Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning

Tool failures in machining processes often cause severe damages of workpieces and lead to large quantities of loss,making tool condition monitoring an important,urgent issue.However,problems such as practicability still remain in actual machining.Here,a real-time tool condition monitoring method integrated in an in situ fiber optic temperature measuring apparatus is proposed.A thermal simulation is conducted to investigate how the fluctuating cutting heats affect the measuring temperatures,and an intermittent cutting experiment is carried out,verifying that the apparatus can capture the rapid but slight temperature undulations.Fourier transform is carried out.The spectrum features are then selected and input into the artificial neural network for classification,and a caution is given if the tool is worn.A learning rate adaption algorithm is introduced,greatly reducing the dependence on initial parameters,making training convenient and flexible.The accuracy stays 90%and higher in variable argument processes.Furthermore,an application program with a graphical user interface is constructed to present real-time results,confirming the practicality.