In this paper, we present a novel optical microfluidic cytometry scheme for label-free detection of cells that is based on the self-mixing interferometry (SMI) technique. This device enables simple, fast and accurate detection of the individual cell characteristics and efficient cell type classification. We also propose a novel parameter to classify the cell or particle size. Artificial polystyrene beads and human living cells were measured using this system, and the SMI signal properties were statistically evaluated. The capability of the proposed cytometer for cell type discrimination and size classification has been validated by the measurement results. Our study can provide a very simple technique for cell enumeration and classification without any extra devices such as high-speed camera, photomultiplier and spectrometer. Moreover, the fluorescence staining operation which is necessary in traditional flow cytometry methods is not required either in our system.

To construct a graphical neural network in vitro and explore the morphological effects of neural network structural changes on neurons, this study aimed to introduce a method for fabricating microfluidic array chips with different graphical structures based on 248-nm excimer laser one-step etching. Through the comparative analysis of the graphical neural network cultured on our microfluidic array chip with the one on the glass slide, the morphological effects of the neural network on the morphology of the neurons were studied. First, the design of the chip was completed according to the specific structure of the neurons and the simulation of the flow field. The chips were fabricated by excimer laser processing combined with the casting technology. Neurons were cultured on the chip, and a graphical neural network was formed. The growth status of the neural network was analyzed by microscopy and immunofluorescence technology, and compared with the random neural network cultured on glass slides. The results showed that the neurons on the array chips grew in microchannels, and neurites grew along the direction of the channel, interlacing to form a neural network. Furthermore, when the structure of the neural network was graphically changed, the internal neuron morphology changed: on the same culture days, the maximum length of the neurites of the graphical neural network was higher than the average length of the neurites of the random neural network. This research can provide the foundation for the exploration of the neural network mechanism of neurological diseases.

A novel carbon nanotubes/porous-network carbon micron tubes/silk fibroin (CPS) composite film neural electrode was presented and characterized. This electrode was fabricated through assembling dispersed carbon nanotubes (CNTs) by phytic acid onto the porous silk fibroin film. Afterward, the silk fibroin around the CNTs was carbonized by laser irradiation, producing porous-network carbon microtubes with good conductivity and biological compatibility. The structure of the CPS composite film was characterized by scanning electron microscopy and Raman spectroscopy. Cyclic voltammetry, electrochemical impedance spectroscopy, and safe charge injection limit (Q_inj) of the CPS electrode were also measured by the three-electrode system, and the results exhibited outstanding electrochemical properties with the achieved Q_inj of 5.7 mC/cm². This work provides a promising approach for the preparation of a long-term implanted neural electrode with high charge injection ability and good biocompatibility.