We report the encapsulation of low temperature carbon counter electrode based hole-conductor-free mesoscopic methylammonium lead iodide perovskite/TiO₂ heterojunction solar cells with polydimethylsiloxane. The solar cells demonstrate improved photovoltaic performance, where we obtain an optimal short-circuit photocurrent J_SC = 23.5 mA cm⁻², open-circuit photovoltage V_OC = 0.97 V, and fill factor FF = 0.474, corresponding to a light to electric power conversion efficiency of 10.8% under a standard AM 1.5 solar light of 100 mW cm⁻² intensity. The results exhibit a remarkable 54% enhancement over those without encapsulation. The cross-sectional SEM images indicate that the PDMS layer can condense the carbon electrode by filling the gaps in the mesoscopic carbon film during the solidification of PDMS, resulting in an improved MAPbI₃/carbon interface condition. The photoluminescence and electrical impedance spectroscopy measurements prove that the improved efficiency is ascribed to a more efficient charge transfer process and slower charge recombination between interfaces. In addition, the robust polydimethylsiloxane isolates water in air, avoiding the degradation of the CH₃NH₃PbI₃ perovskite and leading to an impressive stability during a testing period of 3000 h. Our work paves the way for realizing low cost, highly efficient and stable hybrid photovoltaic cells.

A reliable Cu–Cu bonding joint was achieved by using the highly sinterable Cu nanoparticle paste. Pure copper nanoparticles used in the preparation of nanoparticle paste were synthesized through simple routes, with an average size of 60.5 nm. Under an Ar-H2 gas mixture atmosphere, the Cu nanoparticle paste exhibited large areas of fusion after sintering at 300 °C and reached a low electrical resistivity of 11.2 μΩ cm. With the same temperature as sintering, a compact Cu–Cu bonding joint was achieved under the pressure of 1.08 MPa and the shear strength of the joint could achieve 31.88 MPa. The shear strength and the elemental composition of the bonded joint were almost unchanged after aging test, which proves that the Cu–Cu bonding with this process has excellent thermal stability.

In this work, we investigate the controlled fabrication of Sn-doped TiO₂ nanorods (Sn/TiO₂ NRs) for photoelectrochemical water splitting. Sn is incorporated into the rutile TiO₂ nanorods with Sn/Ti molar ratios ranging from 0% to 3% by a simple solvothermal synthesis method. The obtained Sn/TiO₂ NRs are single crystalline with a rutile structure. The concentration of Sn in the final nanorods can be well controlled by adjusting the molar ratio of the precursors. Photoelectrochemical experiments are conducted to explore the photocatalytic activity of Sn/TiO₂ NRs with different doping levels. Under the illumination of solar simulator with the light intensity of 100 mW/cm², our measurements reveal that the photocurrent increases with increasing doping level and reaches the maximum value of 1.01 mA/cm² at −0.4 V versus Ag/AgCl, which corresponds to up to about 50% enhancement compared with the pristine TiO₂ NRs. The Mott-Schottky plots indicate that incorporation of Sn into TiO₂ nanorod can significantly increase the charge carrier density, leading to enhanced conductivity of the nanorod. Furthermore, we demonstrate that Sn/TiO₂ NRs can be a promising candidate for photoanode in photoelectrochemical water splitting because of their excellent chemical stability.

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.