Ji Sik Kim

Mechanoluminescence of SrAl 2 O 4 :Eu 2+ , Dy 3+ under cyclic loading

The mechanoluminescence (ML) of SrAl2O4:Eu(+), Dy(3+) (SAO) has been of particular interest based on the possibility that these materials could be used as nondestructive, reproducible stress (or load) sensors. However, there has been no in-depth study of ML under a cyclic load. It was found that a cyclic load generated harmonics in the ML response. The second harmonic term exhibiting a doubled frequency was significant, but the others could be ignored. In addition, hysteresis behavior was observed in the ML and was examined by comparison with the hysteresis that is typical in piezoelectricity.

A Mechanoluminescent ZnS:Cu/Rhodamine/SiO2/PDMS and Piezoresistive CNT/PDMS Hybrid Sensor: Red-Light Emission and a Standardized Strain Quantification

We developed a hybrid strain sensor by combining mechanoluminescent ZnS:Cu/rhodamine/SiO2/PDMS composites and piezoresistive CNT/PDMS for qualitative and quantitative analysis of onsite strain. The former guarantees a qualitative onsite measure of strain with red-light emission via mechanoluminescence (ML) and the latter takes part in accurate quantification of strain through the change in electrical resistance. The PDMS matrix enabled a strain sensing in a wider range of strain, spanning up to several hundred percent in comparison to the conventional rigid matrix composites and ceramic-based ML sensors. Red-light emission would be much more effective for the visualization of strain (or stress) when ML is used as a warning sign in actual applications such as social infrastructure safety diagnosis, emergency guide lighting, and more importantly, in biomedical applications such as in the diagnosis of motility and peristalsis disorders in the gastrointestinal tract. Despite the realization of an efficient red-light-emitting ML in a ZnS:Cu/rhodamine/SiO2/PDMS composite, the quantification and standardization of strain throughout the ML has been far from complete. In this regard, the piezoresistive CNT/PDMS compensated for this demerit of mechanoluminescent ZnS:Cu/rhodamine/SiO2/PDMS composites.

Mechanically driven luminescence in a ZnS:Cu-PDMS composite

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.

An extremely simple macroscale electronic skin realized by deep machine learning

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.

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

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.