Microfluidic chips have shown their potential for applications in fields such as chemistry and biology, and 3D printing is increasingly utilized as the fabrication method for microfluidic chips. To address key issues such as the long printing time for conventional 3D printing of a single chip and the demand for rapid response in individualized microfluidic chip customization, we have optimized the use of DLP (digital light processing) technology, which offers faster printing speeds due to its surface exposure method. In this study, we specifically focused on developing a fast-manufacturing process for directly printing microfluidic chips, addressing the high cost of traditional microfabrication processes and the lengthy production times associated with other 3D printing methods for microfluidic chips. Based on the designed three-dimensional chip model, we utilized a DLP-based printer to directly print two-dimensional and three-dimensional microfluidic chips with photosensitive resin. To overcome the challenge of clogging in printing microchannels, we proposed a printing method that combined an open-channel design with transparent adhesive tape sealing. This method enables the rapid printing of microfluidic chips with complex and intricate microstructures. This research provides a crucial foundation for the development of microfluidic chips in biomedical research.

Micro–nano dual-scale embedded graphene/Ag architectures are designed for fabricating high-performance flexible transparent electrodes, thus realizing low-voltage-driven transparent electrothermal films.

Developing flexible sensors with high sensitivity, a wide sensing range, and good stability is a challenge.

The degree of lubricating oil contamination can directly reflect the wear state of mechanical equipment. This work proposes a microwave sensor based on microstrip line coupling split-ring resonator for detecting contaminants in lubricating oil. It consists of a printed circuit board and a microfluidic channel. The sensor can effectively distinguish the pollutant type by the resonance frequency’s offset direction, count the contaminant by the number of pulses, and estimate the contaminant size through the offset amplitude. The detection limits of the sensor are stainless steel particles with a diameter of 70 μm and air bubbles of 150 μm, with an accuracy of 94% and 93.3%, respectively. The sensor has an average sensitivity of 7.28 kHz/μm for detecting metal particles and 3.09 kHz/μm for detecting air bubbles. Furthermore, the sensor has a fast response speed and a high signal-to-noise ratio. This method provides a novel approach to detecting the health of mechanical equipment.