scholarly journals Electrowetting‐Mediated Transport to Produce Electrochemical Transistor Action in Nanopore Electrode Arrays

Small ◽  
2020 ◽  
Vol 16 (18) ◽  
pp. 1907249 ◽  
Author(s):  
Seung‐Ryong Kwon ◽  
Seol Baek ◽  
Kaiyu Fu ◽  
Paul W. Bohn
2021 ◽  
Vol 118 (39) ◽  
pp. e2022300118
Author(s):  
Yasutoshi Jimbo ◽  
Daisuke Sasaki ◽  
Takashi Ohya ◽  
Sunghoon Lee ◽  
Wonryung Lee ◽  
...  

Electrode arrays are widely used for multipoint recording of electrophysiological activities, and organic electronics have been utilized to achieve both high performance and biocompatibility. However, extracellular electrode arrays record the field potential instead of the membrane potential itself, resulting in the loss of information and signal amplitude. Although much effort has been dedicated to developing intracellular access methods, their three-dimensional structures and advanced protocols prohibited implementation with organic electronics. Here, we show an organic electrochemical transistor (OECT) matrix for the intracellular action potential recording. The driving voltage of sensor matrix simultaneously causes electroporation so that intracellular action potentials are recorded with simple equipment. The amplitude of the recorded peaks was larger than that of an extracellular field potential recording, and it was further enhanced by tuning the driving voltage and geometry of OECTs. The capability of miniaturization and multiplexed recording was demonstrated through a 4 × 4 action potential mapping using a matrix of 5- × 5-μm2 OECTs. Those features are realized using a mild fabrication process and a simple circuit without limiting the potential applications of functional organic electronics.


Nanoscale ◽  
2017 ◽  
Vol 9 (16) ◽  
pp. 5164-5171 ◽  
Author(s):  
Kaiyu Fu ◽  
Donghoon Han ◽  
Chaoxiong Ma ◽  
Paul W. Bohn

ACS Nano ◽  
2018 ◽  
Vol 12 (9) ◽  
pp. 9177-9185 ◽  
Author(s):  
Kaiyu Fu ◽  
Donghoon Han ◽  
Seung-Ryong Kwon ◽  
Paul W. Bohn

Small ◽  
2018 ◽  
Vol 14 (18) ◽  
pp. 1703248 ◽  
Author(s):  
Kaiyu Fu ◽  
Donghoon Han ◽  
Garrison M. Crouch ◽  
Seung-Ryong Kwon ◽  
Paul W. Bohn

2020 ◽  
Vol 12 (49) ◽  
pp. 55116-55124
Author(s):  
Seol Baek ◽  
Seung-Ryong Kwon ◽  
Kaiyu Fu ◽  
Paul W. Bohn

ACS Nano ◽  
2016 ◽  
Vol 10 (3) ◽  
pp. 3658-3664 ◽  
Author(s):  
Chaoxiong Ma ◽  
Wei Xu ◽  
William R. A. Wichert ◽  
Paul W. Bohn

The Analyst ◽  
2021 ◽  
Author(s):  
Hyein Do ◽  
Seung-Ryong Kwon ◽  
Seol Baek ◽  
Chinedu S. Madukoma ◽  
Marina K. Smiley ◽  
...  

Phenazine metabolites produced by Pseudomonas aeruginosa are selectively transported into nanopore electrode arrays for enhanced detection by redox cycling reactions at the dual electrodes while the larger bacteria are excluded.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.


2021 ◽  
pp. 2004033
Author(s):  
Estelle A. Cuttaz ◽  
Christopher A. R. Chapman ◽  
Omaer Syed ◽  
Josef A. Goding ◽  
Rylie A. Green

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