Recent advances in the use of organic-inorganic hybrid perovskites for optoelectronics have been rapid, with reported power conversion efficiencies of up to 22 per cent for perovskite solar cells. Improvements in stability have also enabled testing over a timescale of thousands of hours. However, large-scale deployment of such cells will also require the ability to produce large-area, uniformly high-quality perovskite films. A key challenge is to overcome the substantial reduction in power conversion efficiency when a small device is scaled up: a reduction from over 20 per cent to about 10 per cent is found when a common aperture area of about 0.1 square centimetres is increased to more than 25 square centimetres. Here we report a new deposition route for methyl ammonium lead halide perovskite films that does not rely on use of a common solvent or vacuum: rather, it relies on the rapid conversion of amine complex precursors to perovskite films, followed by a pressure application step. The deposited perovskite films were free of pin-holes and highly uniform. Importantly, the new deposition approach can be performed in air at low temperatures, facilitating fabrication of large-area perovskite devices. We reached a certified power conversion efficiency of 12.1 per cent with an aperture area of 36.1 square centimetres for a mesoporous TiO-based perovskite solar module architecture.

Dental caries and periodontal diseases have a close relationship with microbes such as Streptococcus mutans, Porphyromonas gingivalis and Fusobacterium nucleatum. Graphene oxide (GO), as the derivative of graphene, plays an important role in many areas including biology and medicine. In particular, it has been known as a promising antimicrobial nanomaterial. In this study, we focused on the antimicrobial property of GO against dental pathogens. With the utilization of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) reduced test, colony forming units (CFU) counting, growth curve observation, live/dead fluorescent staining, and confocal laser scanning microscopy (CLSM), we found GO nanosheets were highly effective in inhibiting the growth of dental pathogens. Transmission electron microscopy (TEM) images revealed that the cell wall and membrane of bacteria lost their integrity and the intracellular contents leaked out after they were treated by GO. Therefore, GO nanosheets would be an effective antibacterial material against dental pathogens and the potential applications in dental care and therapy are promising.

Operational stability is crucial for the success in large-scale application of metal halide perovskites devices. The diffusion of volatile iodide component of perovskites can induce irreversible device degradation. Here, low-dimensional diffusion barriers were introduced to increase the operational stability of highefficiency large-area PSC modules. A negligible decay was observed after 1,000 h under severe test condition for a 15% high-efficiency solar module.

Perovskite solar cells are a promising low-cost and highly efficient photovoltaic technology. However, there's still a big challenge in forming large area and uniform perovskite films with a high material utilization ratio. Here we provide a novel continuous processing method, soft-cover deposition, to control the formation of perovskite films in ambient air. High quality films were successfully deposited with less structural defects and high material utilization ratios. Excellent photovoltaic performance was also achieved in a 1 cm 2 unit solar cell, highly reproducible over a large area. The present deposition technology paves the way for future application of high cost-performance perovskite solar cells and the formation of solution processed thin-films.

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Inverted configuration perovskite solar cells containing a NiOx layer fabricated by using a low‐temperature process have yielded highly efficient, flexible devices. However, the instability of these devices has limited their commercial application. Here, we report high‐quality, ligand‐free NiOx nanocrystals, prepared directly through a facile organic solvent method, that form a stable ink when dispersed in an ethanol solvent. Rigid and flexible devices containing a NiOx layer (active area, 1.02 cm2) fabricated with this ink at low‐temperature achieved efficiencies of 18.49 and 15.89%, respectively. In addition, the devices retain 90% of their initial performance at 500 h in a damp‐heat test (85 °C and 85% relative humidity) due to there being less hydroxyl functional groups and H2O molecules on the NiOx surface, which degrade perovskite. Thus, we are able to successfully synthesize ligand‐free NiOx for the low‐temperature production of a high‐performance, stable perovskite solar cell.

Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings’ health condition. However, though these methods generally work well, they still have two potential drawbacks when facing massive fault data: (1) the feature extraction process needs prior domain knowledge, and therefore lacks a universal extraction method for various diagnosis issues, and (2) much training time is generally needed by the traditional intelligent diagnosis methods and by the newly presented deep learning methods. In this research, inspired by the feature extraction capability of auto-encoders and the high training speed of extreme learning machines (ELMs), an auto-encoder-ELM-based diagnosis method is proposed for diagnosing faults in bearings to overcome the aforementioned deficiencies. This paper performs a comparative analysis of the proposed method and some state-of-the-art methods, and the experimental results on the rolling element bearings data set show the effectiveness of the proposed method not only with adaptive mining of the discriminative fault characteristic but also at high diagnosis speed.

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert-Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.