Energy Gap between Photoluminescence and Electroluminescence as Recombination Indicator in Organic Small-Molecule Photodiodes

Charge transfer (CT) states at the donor–acceptor interfaces play an important role in organic optoelectronic devices, yielding photocarrier generation or recombination losses. In this study, we fabricate and characterize vacuum-deposited organic photodiodes (OPDs) composed of SubPc:C60 with various active-layer thicknesses and mixing ratios in terms of the charge separation efficiency (CSE) and charge collection efficiency (CCE). We demonstrate that the combined field-assisted quenching study using both photoluminescence (PL) and electroluminescence (EL) reveals detailed information on the device physics of bulk heterojunction photodiodes as related to the CT state. Our modified PL quenching efficiency approach allows us to reasonably evaluate the CSE. In addition, we find that the EL energy is closely related to the recombination loss factor, and the energy gap between PL and EL exhibits a strong linear relationship with the CCE. As a result, the energy gap is proved to be a meaningful indicator of the carrier transport properties, which allows prediction of the CCE in organic bulk heterojunctions

Silent Speech Recognition with Strain Sensors and Deep Learning Analysis of Directional Facial Muscle Movement

Silent communication based on biosignals from facial muscle requires accurate detection of its directional movement and thus optimally positioning minimum numbers of sensors for higher accuracy of speech recognition with a minimal person-to-person variation. So far, previous approaches based on electromyogram or pressure sensors are ineffective in detecting the directional movement of facial muscles. Therefore, in this study, high-performance strain sensors are used for separately detecting x- and y-axis strain. Directional strain distribution data of facial muscle is obtained by applying three-dimensional digital image correlation. Deep learning analysis is utilized for identifying optimal positions of directional strain sensors. The recognition system with four directional strain sensors conformably attached to the face shows silent vowel recognition with 85.24% accuracy and even 76.95% for completely nonobserved subjects. These results show that detection of the directional strain distribution at the optimal facial points will be the key enabling technology for highly accurate silent speech recognition