Fly Me to the Micron: Microtechnologies for Drosophila Research

Multicellular model organisms, such as Drosophila melanogaster (fruit fly), are frequently used in a myriad of biological research studies due to their biological significance and global standardization. However, traditional tools used in these studies generally require manual handling, subjective phenotyping, and bulk treatment of the organisms, resulting in laborious experimental protocols with limited accuracy. Advancements in microtechnology over the course of the last two decades have allowed researchers to develop automated, high-throughput, and multifunctional experimental tools that enable novel experimental paradigms that would not be possible otherwise. We discuss recent advances in microtechnological systems developed for small model organisms using D. melanogaster as an example. We critically analyze the state of the field by comparing the systems produced for different applications. Additionally, we suggest design guidelines, operational tips, and new research directions based on the technical and knowledge gaps in the literature. This review aims to foster interdisciplinary work by helping engineers to familiarize themselves with model organisms while presenting the most recent advances in microengineering strategies to biologists.

Sensing in Soft Robotics

Soft robotics is an exciting field of science and technology that enables robots to manipulate objects with human-like dexterity. Soft robots can handle delicate objects with care, access remote areas, and offer realistic feedback on their handling performance. However, increased dexterity and mechanical compliance of soft robots come with the need for accurate control of the position and shape of these robots. Therefore, soft robots must be equipped with sensors for better perception of their surroundings, location, force, temperature, shape, and other stimuli for effective usage. This review highlights recent progress in sensing feedback technologies for soft robotic applications. It begins with an introduction to actuation technologies and material selection in soft robotics, followed by an in-depth exploration of various types of sensors, their integration methods, and the benefits of multimodal sensing, signal processing, and control strategies. A short description of current market leaders in soft robotics is also included in the review to illustrate the growing demands of this technology. By examining the latest advancements in sensing feedback technologies for soft robots, this review aims to highlight the potential of soft robotics and inspire innovation in the field.

Propulsion Gait Analysis and Fluidic Trapping of Swinging Flexible Nanomotors

Micro- and nanomachines as feasible agents to exploit the microworld have attracted extensive research interest, particularly in the manipulation of soft nanorobots at small scales. Herein, we propose a model for regulating the motion of a swinging flexible nanomotor (SFN) driven by an oscillating magnetic field. Multisegments of an SFN are synthesized from nickel, gold, and porous silver. The coupling of magnetic actuation and the swinging pattern of SFNs are studied to reveal their mobility. Additionally, an optimal frequency occurs from the coupling of magnetic torque and structural deformation, rather than the simply considered step-out phenomenon. Meanwhile, a fluidic trapping region is formulated alongside the SFN. Such a trapping region is demonstrated by trapping a living neutrophil and accomplishing in vitro transportation using fluidic mediation. On-demand cargo delivery can be realized using a programmable magnetic field, and SFNs can be recycled with ease after manipulation owing to environmental concerns. In this study, we demonstrated the properties of SFNs that are useful bases for their customization and control. These flexible nanomotors may have the potential to promote drug delivery and biomedical operations in noncontact modes.

Soft Robotic Grippers

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

A Compliant End-Effector to Passively Limit the Force in Tele-Operated Tissue-Cutting

This paper presents a compliant end-effector that cuts soft tissues and senses the cutting forces. The end-effector is designed to have an upper threshold on cutting forces to facilitate safe handling of tissue during automated cutting. This is demonstrated with nonlinear finite element analysis and experimental results obtained by cutting inhomogeneous phantom tissue. The cutting forces are estimated using a vision-based technique that uses amplified elastic deformation of the compliant end-effector. We also demonstrate an immersive tele-operated tissue-cutting system together with a haptic device that gives real-time force feedback to the user.