Taste Bud-Inspired Single-Drop Multitaste Sensing for Comprehensive Flavor Analysis with Deep Learning Algorithms.

The electronic tongue (E-tongue) system has emerged as a significant innovation, aiming to replicate the complexity of human taste perception. In spite of the advancements in E-tongue technologies, two primary challenges remain to be addressed. First, evaluating the actual taste is complex due to interactions between taste and substances, such as synergistic and suppressive effects. Second, ensuring reliable outcomes in dynamic conditions, particularly when faced with high deviation error data, presents a significant challenge. The present study introduces a bioinspired artificial E-tongue system that mimics the gustatory system by integrating multiple arrays of taste sensors to emulate taste buds in the human tongue and incorporating a customized deep-learning algorithm for taste interpretation. The developed E-tongue system is capable of detecting four distinct tastes in a single drop of dietary compounds, such as saltiness, sourness, astringency, and sweetness, demonstrating notable reversibility and selectivity. The taste profiles of six different wines are obtained by the E-tongue system and demonstrated similarities in taste trends between the E-tongue system and user reviews from online, although some disparities still exist. To mitigate these disparities, a prototype-based classifier with soft voting is devised and implemented for the artificial E-tongue system. The artificial E-tongue system achieved a high classification accuracy of ∼95% in distinguishing among six different wines and ∼90% accuracy even in an environment where more than 1/3 of the data contained errors. Moreover, by harnessing the capabilities of deep learning technology, a recommendation system was demonstrated to enhance the user experience.

High Performance Field-Effect Transistors Based on Partially Suspended 2D Materials via Block Copolymer Lithography.

Although various two-dimensional (2D) materials hold great promise in next generation electronic devices, there are many challenges to overcome to be used in practical applications. One of them is the substrate effect, which directly affects the device performance. The large interfacial area and interaction between 2D materials and substrate significantly deteriorate the device performance. Several top-down approaches have been suggested to solve the problem. Unfortunately, however, they have some drawbacks such as a complicated fabrication process, a high production cost, or a poor mechanical property. Here, we suggest the partially suspended 2D materials-based field-effect transistors (FETs) by introducing block copolymer (BCP) lithography to fabricate the substrate effect-free 2D electronic devices. A wide range of nanometer size holes (diameter = 31~43 nm) is successfully realized with a BCP self-assembly nanopatterning process. With this approach, the interaction mechanism between active 2D materials and substrate is elucidated by precisely measuring the device performance at varied feature size. Our strategy can be widely applied to fabricate 2D materials-based high performance electronic, optoelectronic, and energy devices using a versatile self-assembly nanopatterning process.