Deep-Learning Technique To Convert a Crude Piezoresistive Carbon Nanotube-Ecoflex Composite Sheet into a Smart, Portable, Disposable, and Extremely Flexible Keypad

Abstract

An extremely simple bulk sheet made of a piezoresistive carbon nanotube (CNT)-Ecoflex composite can act as a smart keypad that is portable, disposable, and flexible enough to be carried crushed inside the pocket of a pair of trousers. Both a rigid-button-imbedded, rollable (or foldable) pad and a patterned flexible pad have been introduced for use as portable keyboards. Herein, we suggest a bare, bulk, macroscale piezoresistive sheet as a replacement for these complex devices that are achievable only through high-cost fabrication processes such as patterning-based coating, printing, deposition, and mounting. A deep-learning technique based on deep neural networks (DNN) enables this extremely simple bulk sheet to play the role of a smart keypad without the use of complicated fabrication processes. To develop this keypad, instantaneous electrical resistance change was recorded at several locations on the edge of the sheet along with the exact information on the touch position and pressure for a huge number of random touches. The recorded data were used for training a DNN model that could eventually act as a brain for a simple sheet-type keypad. This simple sheet-type keypad worked perfectly and outperformed all of the existing portable keypads in terms of functionality, flexibility, disposability, and cost.

Ecoflex polymer of different Shore hardnesses: Experimental investigations and constitutive modelling

“…Wearable and flexible stretch-sensors become emerging sectors for healthcare applications, cf. [9,10,11,12,13,14]. Furthermore, actuating materials in soft robotics and flexible rubber-like materials for energy harvesting from ambient motions such as human walking and ocean and tidal waves are also current areas of active research where appropriate soft and flexible materials have great demands [15,16].…”

“…Such composite sensors showed a low electrical percolation threshold of 0.3 wt%, with an elastic modulus as soft as the human skin in the forearm and palm dermis. More works in the area of stretch-based sensors using Ecoflex as a matrix material are due to Jiang et al [14], Kim et al [12], Lee et al [13]. Another important application of Ecoflex is related to tissue biomechanics.…”

An ultra-sensitive and wide measuring range pressure sensor with paper-based CNT film/interdigitated structure

“…In particular, flexible sensors have attracted significant attention due to their promising applications in electronic skin, wearable health monitoring devices, and human body posture detection systems [7,8]. On the basis of diverse sensing mechanisms, flexible sensors can be divided into piezoelectric [9,10], capacitive [11][12][13], triboelectric [14][15][16], and piezoresistive [17][18][19] sensors. In particular, piezoresistive sensors, which can transform a variety of pressure signals into a variety of resistance signals, show key advantages, such as high accuracy, simple signal collection and economical manufacturing.…”

Virtual microstructure design for steels using generative adversarial networks

“…Despite major issues with GAN, which are mode collapse, non-convergence, and training instability, 22 GAN has been one of the most interesting ideas in machine learning (ML) of the past 10 years. 13 Although conventional ML approaches based on supervised learning are well established in the materials research community, [23][24][25][26][27][28][29][30][31][32] GAN algorithms have just begun to be used for the materials research.…”

“…One neural network, called the generator, generates new data instances from randomly distributed sample data, and the other, the discriminator, judges them for authenticity; in other words, the discriminator determines whether each instance of data it examines belongs to the genuine training dataset or not. In contrast to the recent boom for deep learning-based artificial intelligence for use in both the chemistry and materials science research fields, [23][24][25][26][27][28][29][30][31][32] the unsupervised learning-based GAN is yet to be spotlighted in both the fields. Nevertheless, we have seen a certain degree of progress in the GAN utilization for the design of molecules, [34][35][36][37][38][39] and the drug discovery, [40][41][42] the inverse design of materials, 43 the design of crystal structure of inorganic materials.…”