Untethered capsules are capable of entering the gastrointestinal (GI) tract and collecting fluid samples containing microbial communities from specific locations, facilitating the study of chronic diseases. However, existing sampling capsules are designed for single-site sampling, making it challenging to gather samples from multiple targets. This paper reports a magnetic-driven capsule for multiple sampling within the GI tract and an on-demand magnetic-triggered fluid sampling strategy. The capsule consists of a body, a magnetic-triggered negative pressure unit, and a reservoir unit. Composed of an elastic membrane and Magnet I, the negative pressure unit controls pressure change inside the capsule cavity on demand to pump the sample by switching the magnetic field, while the embedded Magnet I also enables real-time magnetic localization for regional targeting and position tracking. The reservoir unit integrates three sampling papers for fluid absorption, two waterproof layers that maintain contamination levels below 25% to ensure reliable multi-site sampling, and a rotating arm embedded with Magnet II for posture adjustment of the sampling paper. The pumping and storage performance of the capsule was systematically evaluated and optimized. Meanwhile, the capsule, actuated by an external magnetic field, was evaluated for its active locomotion performance. Finally, the feasibility of using the capsule to perform active navigation and multi-target sampling in a porcine intestine was validated viaex vivoexperiments.

Biological machines that use biological cells and soft materials in combination to obtain a sense of the environment driven by bioenergy and generate driving force are called biohybrid actuators. With the development of tissue engineering and organoid technology, researchers have applied biohybrid actuators technology to the research of precision medicine and targeted drug delivery, but the research on feedback and evaluation of biohybrid actuation performance is limited to visual and simulation calculations. Therefore, we hope to develop an intelligent crawling skeleton for sensing function, which can be used to evaluate the actuation ability of muscle actuators, and eventually realize the high-precision control of biohybrid actuators. In this work, an intelligent crawling skeleton based on three-dimensional liquid metal is proposed to detect and feedback the crawling of C2C12 muscle actuators. Three-dimensional muscle tissue was composed of mixing hydrogels and cells, and the functionalization of muscle rings was promoted using static mechanical forces and external electric field stimulation. The composite crawling skeleton is fabricated by inverting mold and soft lithography technology. The skeleton can adapt to large deformations above 90 degrees and is more sensitive to deformations by adjusting materials with different elastic modulus. Inspired by the tendon-bone structure, the intelligent crawling skeleton can obtain the deformation degree of the biohybrid actuator in the crawling process according to the characteristics of the deformation from the muscle tissue, and put forward a good idea for the feedback and closed-loop control of the biohybrid actuators.

This study describes a microfluidics experiment with ready classroom applications, designed to enhance students' understanding of fluid dynamics, controlled mixing, and bubble formation. The materials employed are safe and readily accessible, such as vinegar and baking soda, combined with PDMS microfluidic chips and a high-resolution microscope, to provide real-time observation of gas-liquid interactions. A syringe pump delivers the reactants into a micro-channel through which the fluid flow behavior and bubble formation can be visualized and quantified.(/p)The focus of the experiment is on elucidating the effects of different soda concentrations on bubble generation in a controlled laminar flow. The results show a nonlinear trend between soda concentration and bubble features: lower concentrations produce fewer but larger bubbles, moderate concentrations produce small bubbles more frequently. At 0.2 M, the average bubble area was approximately 389 μm2, and at 0.4 M, there were smaller bubbles but more frequent occurrences. As concentrations increased above 0.6 M, bubbles became more uniform in size and more circular.Flow rates were varied from 3 to 15 μL/min to assess bubble behavior. Most bubbles functioned as wall bubbles in the micro-channel and were not perfectly spherical because of the influence of the local flow field and concentration gradients. The size distribution and circularity of the bubbles were measured using image analysis tools developed in Python.This affordable and visually appealing platform provides an alternative hands-on experience for students to learn the fundamental principles of microfluidics, thereby connecting classroom concepts with real-world observations. The lab activity promotes data analysis, hypothesis testing, and deepening understanding of concepts—skills essential for both academic and applied research.

Point cloud clustering is a promising method for 3D surface defect diagnosis in manufacturing but requires manual clustering parameter selection, reducing usability. This paper proposes an automatic point cloud clustering method to address this issue. It employs a strategy that progresses from coarse to fine. In the coarse searching stage, a K-Nearest Neighbor (KNN) graph analysis technique is developed to recognize potential defective regions in parallel. Moving on to the fine stage of extracting detailed defects, a modified DBSCAN algorithm is proposed, in which the clustering parameters are calculated automatically from the KNN graph analysis results. Experimental results showed that the proposed method achieved cloud clustering with automatically calculated clustering parameters for surface defect diagnosis. The proposed method outperformed the traditional region growing algorithm in accuracy (0.942 vs. 0.680) and processing speed (21500 points/sec vs. 8740 points/sec) without requiring manual intervention.Note to Practitioners—This paper presents a method for diagnosing defects on automobile and flat steel surfaces. Current 3D point cloud techniques for surface defect diagnosis require manual parameter adjustments, reducing usability. This paper proposes an automatic method without manual intervention. The proposed method uses a coarse-to-fine strategy. The 3D point cloud is divided into sub-blocks to locate potential defects, and a clustering algorithm then extracts detailed defects with automatically determined parameters. We mathematically characterize changes in point density caused by surface defects and show how these features can be used for clustering parameter calculation. Experimental results demonstrate the method’s efficiency on flat as well as some curved surfaces, but it has yet to be evaluated on complex structures. Future work will aim to broaden its application to include a more extensive variety of surfaces and integrate it with robotic vision systems.

Tactile information is the research hotspot of wearable flexible sensors due to its importance and complexity. With the innovation of wearable technology and robotics in healthcare, researchers are increasingly integrating wearable flexible sensors on the front end of robots to reproduce the hand tactile manipulation of human tissues. Therefore, it is hoped to develop a thin-film sensor that can be deployed in a small area to assist robots in surgery and data collection of human tissues. Here we use a one-step injection method to fabricate a novel composite sensor based on liquid metal. By laminating multiple PDMS microfluidic layers, the two parameters of pressure and deformation are measured simultaneously in a decoupled manner. The sensor is small and thin, making it easy to integrate into fingers/robot fingers for assistance. The finger/robot finger exerts pressure on the sensor and the sensor deforms with the material to identify the hardness of the material being touched. Separate performance tests of the two sensors show that the strain and pressure functions are decoupled from each other, and their ratios can identify and classify the hardness of different touched materials (glass, PDMS and silicone). This novel composite sensor we proposed can assist robots in manipulating human tissues during medical surgeries. At the same time, its function in tactile information feedback also has broad applications in medical treatment, rehabilitation and services.