Electron transport layers (ETLs) in perovskite solar cells (PSCs) play a crucial role in determining their device performance, which form electron-selective contacts with perovskites, and boost the extraction rate of photoinduced electrons and tremendously affect the nucleation and subsequent growth of highly crystallized perovskite films. Herein, four different types of ETLs, i.e., compact titanium dioxide (c-TiO2), c-TiO2 and mesoporous titanium dioxide (m-TiO2) composite layers, fullerene C60, and c-TiO2 and C60 composite layers, are fabricated to compare their effects on the growth of perovskite absorbers and device photovoltaic performance. It is found that a single layer such as c-TiO2 and C60 shows a uniform, dense, and well-defined morphology with minimum roughness, and its film quality is much better than that of c-TiO2/m-TiO2 and c-TiO2/C60 composite layers. Meanwhile, a Cs+/Cl– codoped strategy is adopted and the optimized Cs0.1MA0.9PbI2.95Cl0.05 perovskites are synthesized and coated as light absorbers. Consequently, the c-TiO2-based PSCs show a relatively high efficiency of 20.15%; while the champion devices using the C60 ETL achieve a very high efficiency of 17.15%. To our knowledge, this is a rather high efficiency among pure C60-based PSCs with a regular n-i-p architecture. Moreover, the plastic photovoltaic device via a low-temperature C60 evaporation process is also successfully realized, and it provides a possibility for the application of flexible PSCs.

Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.

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In order to achieve replication of the ultra-thin metal X-ray focusing mirrors with high accuracy and efficiency, which is the key component of the EP satellite independently developed and manufactured in China, the simulation and experimental research on the demolding process of the X-ray focusing mirrors are carried out in this paper. The temperature and stress fields of the entire mandrel(aluminum) and the shell (nickel) during cooling process is simulated by finite element analysis, and the evolution of the interface stress during the demolding process is analyzed. When the temperature of the mandrel and shell decreases from 45 ℃ to 10℃, the equivalent stress at the interface between the mandrel and the mirror reaches 5.5MPa, which is larger than the adhesion strength between Au film and mandrel. Due to the difference in material thermal expansion coefficient between the mandrel and the mirror, it can be used to release the X-ray focusing mirror shell from the mandrel by cooling according to the experimental validation. Furthermore, the shell could separate from the mandrel by means of high precision demolding automatic device.After demolding, the angular resolution of the mirror is 25.1 "HPD (Half Power Diameter) by the X-ray testing, which meets the requirements of the project. The reliability and advancement of the technology are verified.