SnO 2 has been well investigated in many successful state-of-the-art perovskite solar cells (PSCs) due to its favorable attributes such as high mobility, wide bandgap, and deep conduction band and valence band. Several independent studies show the performances of PSCs with SnO 2 are higher than that with TiO 2 , especially in device stability. In 2015, the first planar PSCs were reported with a power conversion efficiency over 17% using a low temperature sol-derived SnO 2 nanocrystal electron transport layer (ETL). Since then, many other groups have also reported high performance PSCs based on SnO 2 ETLs. SnO 2 planar PSCs show currently the highest performance in planar configuration devices (21.6%) and are close to the record holder of TiO 2 mesoporous PSCs, suggesting their high potential as ETLs in PSCs. The main concerns with the application of SnO 2 as ETL are that it suffers from degradation in high temperature processes and that its much lower conduction band compared to perovskite may result in a voltage loss of PSCs. Here, notable achievements to date are outlined, the unique attributes of SnO 2 as ETLs in PSCs are described, and the challenges facing the successful development of PSCs and approaches to the problems are discussed.

As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency. The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT. ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives. ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years. The risk of being attacked increases as a result. In order to protect these assets, intrusion detection systems (IDS) have drawn much attention. As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS. In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack. In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset. The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.

We introduce a facile route to synthesize SnO2 quantum dots colloidal solution at room temperature and superior homogeneous ETL is obtained by spin coating of the QDs colloidal solution with post-deposition annealing.

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