Rapidly-customizable, scalable 3D-printed wireless optogenetic probes for versatile applications in neuroscience.

Optogenetics is an advanced neuroscience technique that enables the dissection of neural circuitry with high spatiotemporal precision. Recent advances in materials and microfabrication techniques have enabled minimally invasive and biocompatible optical neural probes, thereby facilitating in vivo optogenetic research. However, conventional fabrication techniques rely on cleanroom facilities, which are not easily accessible and are expensive to use, making the overall manufacturing process inconvenient and costly. Moreover, the inherent time-consuming nature of current fabrication procedures impede the rapid customization of neural probes in between in vivo studies. Here, we introduce a new technique stemming from 3D printing technology for the low-cost, mass production of rapidly customizable optogenetic neural probes. We detail the 3D printing production process, on-the-fly design versatility, and biocompatibility of 3D printed optogenetic probes as well as their functional capabilities for wireless in vivo optogenetics. Successful in vivo studies with 3D printed devices highlight the reliability of this easily accessible and flexible manufacturing approach that, with advances in printing technology, can foreshadow its widespread applications in low-cost bioelectronics in the future.

Rugged Island-Bridge Inorganic Electronics Mounted on Locally Strain-Isolated Substrates.

Various strain isolation strategies that combine rigid and stretchable regions for stretchable electronics were recently proposed, but the vulnerability of inorganic materials to mechanical stress has emerged as a major impediment to their performance. We report a strain-isolation system that combines heteropolymers with different elastic moduli (i.e., hybrid stretchable polymers) and utilize it to construct a rugged island-bridge inorganic electronics system. Two types of prepolymers were simultaneously cross-linked to form an interpenetrating polymer network at the rigid-stretchable interface, resulting in a hybrid stretchable polymer that exhibited efficient strain isolation and mechanical stability. The system, including stretchable micro-LEDs and microheaters, demonstrated consistent operation under external strain, suggesting that the rugged island-bridge inorganic electronics mounted on a locally strain-isolated substrate offer a promising solution for replacing conventional stretchable electronics, enabling devices with a variety of form factors.

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.

Instant, multiscale dry transfer printing by atomic diffusion control at heterogeneous interfaces.

Transfer printing is a technique that integrates heterogeneous materials by readily retrieving functional elements from a grown substrate and subsequently printing them onto a specific target site. These strategies are broadly exploited to construct heterogeneously integrated electronic devices. A typical wet transfer printing method exhibits limitations related to unwanted displacement and shape distortion of the device due to uncontrollable fluid movement and slow chemical diffusion. In this study, a dry transfer printing technique that allows reliable and instant release of devices by exploiting the thermal expansion mismatch between adjacent materials is demonstrated, and computational studies are conducted to investigate the fundamental mechanisms of the dry transfer printing process. Extensive exemplary demonstrations of multiscale, sequential wet-dry, circuit-level, and biological topography-based transfer printing demonstrate the potential of this technique for many other emerging applications in modern electronics that have not been achieved through conventional wet transfer printing over the past few decades.