Learning from the locomotion of natural organisms is one of the most effective strategies for designing microrobots. However, the development of bioinspired microrobots is still challenging because of technical bottlenecks such as design and seamless integration of high-performance actuation mechanism and high-density energy source for untethered locomotion. Directly harnessing the activation energy and intelligence of living tissues in synthetic micromachines provides an alternative route to developing biohybrid microrobots. Here, we propose an approach to engineering the genetic and nervous systems of a nematode worm, Caenorhabditis elegans, and creating an untethered, highly controllable living soft microrobot (called “RoboWorm”). A living worm is engineered through optogenetic and biochemical methods to shut down the signal transmissions between its neuronal and muscular systems while its muscle cells still remain optically excitable. Through dynamic modeling and experimental verification of the worm crawling, we found that the phase difference between the worm body curvature and the muscular activation pattern generates the thrust force for crawling locomotion. By reproducing the phase difference via optogenetic excitation of the worm body muscles, we emulated the major worm crawling behaviors in a controllable manner. Furthermore, with real-time visual feedback of the worm crawling, we realized closed-loop regulation of the movement direction and destination of single worms. This technology may facilitate scientific studies on the biophysics and neural basis of crawling locomotion of C. elegans and other nematode species.

Many applications of plasmonic nanoparticles require precise control of their optical properties that are governed by nanoparticle dimensions, shape, morphology and composition. Finding reaction conditions for the synthesis of nanoparticles with targeted characteristics is a time-consuming and resource-intensive trial-and-error process, however closed-loop nanoparticle synthesis enables the accelerated exploration of large chemical spaces without human intervention. Here, we introduce the Autonomous Fluidic Identification and Optimization Nanochemistry (AFION) self-driving lab that integrates a microfluidic reactor, in-flow spectroscopic nanoparticle characterization, and machine learning for the exploration and optimization of the multidimensional chemical space for the photochemical synthesis of plasmonic nanoparticles. By targeting spectroscopic nanoparticle properties, the AFION lab identifies reaction conditions for the synthesis of different types of nanoparticles with designated shapes, morphologies, and compositions. Data analysis provides insight into the role of reaction conditions for the synthesis of the targeted nanoparticle type. This work shows that the AFION lab is an effective exploration platform for on-demand synthesis of plasmonic nanoparticles.

An integrated microfluidic flow-focusing platform for on-chip fabrication and filtration of cell-laden microgels.