The structure–function relationship of CuO hierarchical morphologies in gas sensing has been revealed.

(Chao Jiang) associated with the oxygen vacancies explains the current-voltage behavior of these devices in this three layer structure. A schematic model is presented to illustrate the changes in oxygen vacancy concentration and switching processes, including the formation of new oxygen vacancies (triggered vacancies) to explain the large increase in current at the end of the setting process in these devices.

No abstract

No abstract

In this study, carbon fibers (Cf) coated with Ba0.6Sr0.4TiO3 (BST) (BST@Cf) were prepared by magnetron sputtering and subsequently heated in nitrogen to produce oxygen vacancies. BST@Cf and nitrogen-treated BST@Cf were cross-stacked on polyimide (PI) film to make a BST@Cf memristor. The electrical 2 properties of BST@Cf memristor were measured after being bent 3000 times. The device exhibits bipolar figure-of-eight (f8) hysteresis loop characteristics under applied voltage. The hysteresis loops narrow with increasing temperature of heat treatment in nitrogen, due to decrease in oxygen vacancy concentration. The hysteresis loops demonstrate the switching process of resistance between high resistance state (HRS) and low resistance state (LRS), with a maximum HRS/LRS ratio of 10 6 . The switching process can be divided into two parts, corresponding to Schottky Emission and Fowler-Nordheim (F-N) Tunneling. It is notable that no electroforming voltage is required to stimulate the memristor. The constructed memristor was cycled successfully 1000 times and retained the LRS 787 s during power cut off. In addition, the device exhibited synaptic behavior including learning and forgetting processes, in accordance with the paired-pulse facilitation (PPF) rule.The use of BST@Cf in the construction of the nonvolatile memristor imparts flexibility to the device allowing for the possibility of wearable flexible intelligent memristor based electronic devices in the future.

No abstract

Based on the hologram inpainting via a two-stage Generative Adversarial Network (GAN), we present a precise phase aberration compensation method in digital holographic microscopy (DHM). In the proposed methodology, the interference fringes of the sample area in the hologram are firstly removed by the background segmentation via edge detection and morphological image processing. The vacancy area is then inpainted with the fringes generated by a deep learning algorithm. The image inpainting finally results in a sample-free reference hologram containing the total aberration of the system. The phase aberrations could be deleted by subtracting the unwrapped phase of the sample-free hologram from our inpainting network results, in no need of any complex spectrum centering procedure, prior knowledge of the system, or manual intervention. With a full and proper training of the two-stage GAN, our approach can robustly realize a distinct phase mapping, which overcomes the drawbacks of multiple iterations, noise interference or limited field of view in the recent methods using self-extension, Zernike polynomials fitting (ZPF) or geometrical transformations. The validity of the proposed procedure is confirmed by measuring the surface of preprocessed silicon wafer with a Michelson interferometer digital holographic inspection platform. The results of our experiment indicate the viability and accuracy of the presented method. Additionally, this work can pave the way for the evaluation of new applications of GAN in DHM.

No abstract