Micromachines

The demand for increased accessibility and effectiveness of automated external defibrillators (AEDs) exists due to the life-threatening nature of sudden cardiac arrest (SCA). Timely access to AEDs can significantly increase the chances of survival in individuals experiencing SCA. Traditional AEDs are bulky and difficult to transport, primarily because of the battery size required to overcome transthoracic impedance (TTI). This study assessed the feasibility of microneedle (MN) electrodes to reduce the TTI and hence improve AED portability. An electrode prototype was developed, consisting of eight arrays of microneedles attached to a 3D printed flexible base. A circuit measuring interelectrode resistance was created to mimic an operational AED while also providing a quantitative measurement of the electrode–skin interface resistance. Testing was conducted on human chest skin. The MN electrode demonstrated a 24% reduction in human transthoracic skin resistance compared to conventional AED pads, suggesting the potential for improved AED effectiveness and portability. These findings indicate the potential for reduced device size, although further research is needed to validate the broader implications.

To address the issues of graphite flaking and interfacial microcrack propagation during conventional polishing of multiphase heterogeneous materials such as ductile cast iron, this study designed and validated a two-dimensional non-resonant flexible vibration-assisted polishing device (RVAPD) based on a series–parallel structure combined with a Scott Russell mechanism. The device decouples spatial motion from mechanical transmission through an irregular flexible hinge network. A theoretical model is established using the flexible matrix method and Lagrange equations, and static and dynamic simulations are conducted using ABAQUS finite element software. The simulation results show that the input and output stiffness values are 19,524 N/mm and 8197 N/mm, respectively. Experimental measurements revealed a natural frequency of 1120 Hz, a maximum output displacement of 12.58 μm, an output stiffness of 7200 N/mm, a static response delay of only 114 ms, and a positioning error below 2.76 μm, confirming the device’s high stiffness, wide bandwidth, and precise motion control capabilities. Polishing experiments indicate that 200 Hz provided the best surface-quality performance among the tested excitation frequencies, at which the surface roughness Sa decreased from 0.049 μm without vibration to 0.036 μm, while the maximum height Sz decreased from 1.927 μm to 0.983 μm. The polishing performance slightly deteriorated at 300 Hz, with Sa increasing to 0.043 μm. These results indicate that the tested excitation conditions can influence the surface-quality characteristics of the polished ductile cast iron. This research provides a basis for non-resonant vibration-assisted polishing of multiphase heterogeneous alloys.

Extracellular vesicles (EVs) carry key genetic biomarkers, such as somatic mutations, making them valuable targets for non-invasive cancer diagnostics. However, standard workflows that isolate EVs and detect low-abundance mutations typically require multi-step manual operations, leading to sample loss and potential cross-contamination. Here, we present an integrated microfluidic platform combined with a deep learning algorithm for the continuous processing and automated droplet analysis of EV-derived RNA mutations. The microfluidic chip integrates magnetic bead-based EV capture, online lysis, droplet generation, and arrayed RT-droplet digital PCR (RT-ddPCR) on a single device. To streamline signal readout, a deep learning framework based on Cellpose and K-means clustering was implemented to process individual fluorescence images, enabling droplet detection, background removal, gray-scale intensity extraction, and automatic positive/negative droplet classification. The system showed a linear response across a wide range of original-sample inputs (R² = 0.9950). The platform was evaluated using EV samples derived from three cancer cell lines (NCI-H1975, SK-LU-1, and HepG2) and the detection of EGFR L858R and KRAS mutations were achieved. By coupling continuous sample processing with automated image interpretation, this platform offers a practical reference for integrated EV nucleic acid analysis for liquid-biopsy applications.