Viologen-based conjugated ionic polymer for nonvolatile rewritable memory device

In-Situ Surface Modification of ITO Substrate via Bio-Inspired Mussel Chemistry for Organic Memory Devices

The development of organic memory devices, regarding factors such as structure construction, principle exploration, and material design, has become a powerful supplement to traditional silicon-based information storage. The in-situ growth of materials on substrate surfaces can achieve closer bonding between materials and electrodes. Bio-inspired by mussel chemistry, polydopamine (PDA) was self-assembled on a flexible substrate as a connecting layer, and 2-bromoiso-butyryl bromide (BiBB) was utilized as an initiator for the polymerization of an iridium complex via surface-initiated atom-transfer radical polymerization (SI-ATRP). A device with the structure of Al/PDA-PPy₃Ir/ITO was constructed after the deposition of aluminum. The device exhibited a nonvolatile rewritable memory characteristic with a turn-on voltage of -1.0 V and an ON/OFF current ratio of 6.3 × 10³. In addition, the memory performance of the Al/PDA-PPy₃Ir/ITO device remained stable at bending states due to the intrinsic flexibility of the active layer, which can be expanded into the establishment of flexible memory devices. Spectroscopy and electrochemical characterization suggested that the resistive memory properties of the device stemmed from charge transfer between PDA and iridium polymer in the active layer (PDA-PPy₃Ir) under an applied voltage.

Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN

Background

Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed.

Methods

In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results.

Results

On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance.

Conclusion

The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG.

Memory devices based on novel alkyl viologen halobismuthate(iii) complexes

Four novel halobismuthate(iii) complexes with alkyl viologen cations: (R2Viol)2[Bi2X10] (R = n-butyl, n-pentyl, X = Cl, Br) have been synthesized. Both chloride complexes revealed photochromic behavior and were successfully utilized for the fabrication of OFET-based memory devices with high switching coefficients and good write-read-erase cycling stability.