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In-Situ Surface Modification of ITO Substrate via Bio-Inspired Mussel Chemistry for Organic Memory Devices. Biomimetics (Basel) 2022; 7:biomimetics7040237. [PMID: 36546937 PMCID: PMC9775351 DOI: 10.3390/biomimetics7040237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
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.
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Ma M, Cheng Y, Wei X, Chen Z, Zhou Y. Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN. BMC Med Inform Decis Mak 2021; 21:100. [PMID: 34330248 PMCID: PMC8323263 DOI: 10.1186/s12911-021-01438-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 02/15/2021] [Indexed: 11/10/2022] Open
Abstract
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.
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Affiliation(s)
- Mengnan Ma
- School of Biomedical Engineering, Sun Yat-sen University, No.132 Waihuan East Road, Guangzhou, 510006, China.,Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No.74 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yinlin Cheng
- School of Biomedical Engineering, Sun Yat-sen University, No.132 Waihuan East Road, Guangzhou, 510006, China.,Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No.74 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Xiaoyan Wei
- Minister of Science, Education and Data Management Department, Guangzhou Women and Children's Medical Center, National Children's Medical Center for South Central Region, Guangzhou Medical University, No.9 Jinsui Road, Guangzhou, 510623, China
| | - Ziyi Chen
- Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, No.58 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yi Zhou
- Department of Medical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, No.74 Zhongshan 2nd Road, Guangzhou, 510080, China. .,Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, No.74 Zhongshan 2nd Road, Guangzhou, 510080, China.
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Dashitsyrenova DD, Adonin SA, Gorokh ID, Kraevaya OA, Pavlova AV, Abramov PA, Frolova LA, Sokolov MN, Fedin VP, Troshin PA. Memory devices based on novel alkyl viologen halobismuthate(iii) complexes. Chem Commun (Camb) 2020; 56:9162-9165. [PMID: 32657295 DOI: 10.1039/d0cc03732j] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
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.
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Affiliation(s)
- Dolgor D Dashitsyrenova
- Institute for Problems of Chemical Physics RAS, Semenov Prospect 1, Chernogolovka, 142432, Russia
| | - Sergey A Adonin
- Nikolaev Institute of Inorganic Chemistry SB RAS, Lavrentieva St. 3, Novosibirsk, 630090, Russia. and South Ural State University, Lenina St. 76, 454080 Chelyabinsk, Russia
| | - Igor D Gorokh
- Nikolaev Institute of Inorganic Chemistry SB RAS, Lavrentieva St. 3, Novosibirsk, 630090, Russia. and Skolkovo Institute of Science and Technology, Nobel St. 3, Moscow, 143026, Russia
| | - Olga A Kraevaya
- Institute for Problems of Chemical Physics RAS, Semenov Prospect 1, Chernogolovka, 142432, Russia and Skolkovo Institute of Science and Technology, Nobel St. 3, Moscow, 143026, Russia
| | - Alina V Pavlova
- State Research Center Burnasyan Federal Medical Biophysical Center of Federal Medical and Biological Agency, Zhivopisnaya St. 46, Moscow, 123182, Russia
| | - Pavel A Abramov
- Nikolaev Institute of Inorganic Chemistry SB RAS, Lavrentieva St. 3, Novosibirsk, 630090, Russia.
| | - Lyubov A Frolova
- Institute for Problems of Chemical Physics RAS, Semenov Prospect 1, Chernogolovka, 142432, Russia and Skolkovo Institute of Science and Technology, Nobel St. 3, Moscow, 143026, Russia
| | - Maxim N Sokolov
- Nikolaev Institute of Inorganic Chemistry SB RAS, Lavrentieva St. 3, Novosibirsk, 630090, Russia.
| | - Vladimir P Fedin
- Nikolaev Institute of Inorganic Chemistry SB RAS, Lavrentieva St. 3, Novosibirsk, 630090, Russia.
| | - Pavel A Troshin
- Institute for Problems of Chemical Physics RAS, Semenov Prospect 1, Chernogolovka, 142432, Russia and Skolkovo Institute of Science and Technology, Nobel St. 3, Moscow, 143026, Russia
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Liu S, Chen X, Liu G. Conjugated polymers for information storage and neuromorphic computing. POLYM INT 2020. [DOI: 10.1002/pi.6017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Shuzhi Liu
- School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai China
| | - Xinhui Chen
- School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai China
| | - Gang Liu
- School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai China
- Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou China
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