The conventional mechanoluminescence (ML) mechanism of phosphors such as SrAl2O4:Eu and ZnS:Mn is known to utilize carrier trapping at shallow traps followed by stress (or strain)-induced detrapping, which leads to activator recombination in association with local piezoelectric fields. However, such a conventional ML mechanism was found to be invalid for the ZnS:Cu-embedded polydimethylsiloxane (PDMS) composite, due to the absence of luminescence with a rigid matrix and a negligibly small value of the piezoelectric coefficient (d33) of the composite. An alternative mechanism, namely, the triboelectricity-induced luminescence has been proposed for the mechanically driven luminescence of a ZnS:Cu-PDMS composite.
Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.
New theoretical solutions involving conventional crack propagation from static to dynamic fracture in terms of mechanoluminescence (ML) and the experimental techniques to trace the in situ crack and its instantaneous stress intensity factor (SIF) have been suggested in SrAl2O4:Eu2+, Dy3+ (SAO). The direct optical method to determine the moving crack tip on behalf of ML was verified by indirect crack mouth opening displacement (CMOD). The mode I SIF KIML calculated from the instantaneous cumulative ML fringes showed proper agreement with the SIFs KnormalInormalanormalML and KnormalInormalafalse(normalCMODfalse) acquired from conventional ASTM E‐399 measurements under quasidynamic condition. The magnitude and shape of the theoretically predicted crack tip stress field was in accordance with the experimental in situ ML evidence while determining the quasidynamic SIF from the cumulative ML intensity. Therefore, the use of ML technology could be one of the possible substitutive and substantial alternatives for structural health monitoring systems due to its simplicity but effectiveness in detecting arrested or propagating crack tips and in assessing the instantaneous structural integrity by means of the ML fracture parameters.
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.
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