Influence of junction resistance on spatiotemporal dynamics and reservoir computing performance arising from an SWNT/POM 3D network formed via a scaffold template technique.

For scientists in numerous fields, creating a physical device that can function like the human brain is an aspiration. It is believed that we may achieve brain-like spatiotemporal information processing by fabricating an in materio reservoir computing (RC) device because of a complex random network topology with nonlinear dynamics. One of the significant drawbacks of a two-dimensional physical reservoir system is the difficulty in controlling the network density. This work reports the use of a 3D porous template as a scaffold to fabricate a three-dimensional network of a single-walled carbon nanotube polyoxometalate nanocomposite. Although the three-dimensional system exhibits better nonlinear dynamics and spatiotemporal dynamics, and higher harmonics generation than a two-dimensional system, the results suggest a correlation between a higher number of resistive junctions and reservoir performance. We show that by increasing the spatial dimension of the device, the memory capacity improves, while the scale-free network exponent (γ) remains nearly unchanged. The three-dimensional device also displays improved performance in the well-known RC benchmark task of waveform generation. This study demonstrates the impact of an additional spatial dimension, network distribution and network density on in materio RC device performance and tries to shed some light on the reason behind such behavior.

Development of haptic based piezoresistive artificial fingertip: Toward efficient tactile sensing systems for humanoids.

Haptic sensors are essential devices that facilitate human-like sensing systems such as implantable medical devices and humanoid robots. The availability of conducting thin films with haptic properties could lead to the development of tactile sensing systems that stretch reversibly, sense pressure (not just touch), and integrate with collapsible. In this study, a nanocomposite based hemispherical artificial fingertip fabricated to enhance the tactile sensing systems of humanoid robots. To validate the hypothesis, proposed method was used in the robot-like finger system to classify the ripe and unripe tomato by recording the metabolic growth of the tomato as a function of resistivity change during a controlled indention force. Prior to fabrication, a finite element modeling (FEM) was investigated for tomato to obtain the stress distribution and failure point of tomato by applying different external loads. Then, the extracted computational analysis information was utilized to design and fabricate nanocomposite based artificial fingertip to examine the maturity analysis of tomato. The obtained results demonstrate that the fabricated conformable and scalable artificial fingertip shows different electrical property for ripe and unripe tomato. The artificial fingertip is compatible with the development of brain-like systems for artificial skin by obtaining periodic response during an applied load.