A dual stimuli-responsive smart soft carrier using multi-material 4D printing.

This paper proposes a 4D printed smart soft carrier with a hemispherical hollow and openable lid. The soft carrier is composed of a lid with a slot (with a shape of 4 legs), a border, and a hemisphere. The soft carrier is fabricated by 4D printing using smart hydrogels. Specifically, the lid, border, and hemisphere are fabricated using a thermo-responsive poly(N-isopropylacrylamide) (PNIPAM) hydrogel, a non-responsive polyethylene glycol (PEG) hydrogel with superparamagnetic iron oxide nanoparticles (SPIONs), and a PEG hydrogel, respectively. Since the SPIONs are included in the border, the slot in the center of the lid is opened and closed according to the temperature change caused by near-infrared (NIR) irradiation, and the proposed soft carrier is magnetically driven by an external magnetic field. The hemisphere enables the storage and transport of cargo. The proposed soft carrier can control the opening and closing of the slot and movement to a desired position in water. Several cargo delivery experiments were conducted using various shapes and numbers of cargo. In addition, the proposed soft carrier can successfully handle small living marine organisms. This soft carrier can be manufactured by 4D printing and operated by dual stimuli (NIR and magnetic field) and can safely deliver various types of cargo and delicate organisms without leakage or damage. The flexibility of 4D printing enables the size of the soft carrier to be tailored to the specific physical attributes of various objects, making it an adaptable and versatile delivery approach.

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.