In recent years, researchers have used various methods to study flexible sensors based on carbon nanomaterials, among which impedance analysis has unique advantages in studying the sensor mechanism. In this paper, we investigated the impedance characteristics of a flexible zero-dimensional carbon nanocomposite strain sensor, establishing a simple electrical model by electrochemical impedance spectroscopy (EIS). Based on the model, the sensing mechanism of the flexible strain sensor was analyzed. The noise analysis theory of the flexible strain sensor was established, and it is proved that the noise of the sensor can be reduced significantly when an AC signal is applied, which will improve the accuracy of the sensor.
The measurement of cell viability is critical in the biomedical field. It is currently accomplished by staining cells with various stains and then manually or with instruments such as counters counting dead or live cells. However, the cell staining step is relatively time-consuming, and the stain is toxic. The internal structure of the cells is destroyed after staining, resulting in valuable cells that cannot be reused later. We proposed a label-free cell detection algorithm based on 2D bright-field images of T-cells and deep learning in this work. When used, this method eliminates the need for staining operations on cells, and cell viability is determined directly from the detection of bright-field cell images. The method based on YOLOX deep learning analysis has an excellent detection performance on bright-field images of T-cells, and the framework achieves the mAP (mean average precision) of more than 96.31% after cell detection. Experimental results show that combining 2D cell bright-field images with deep neural networks can yield a new label-free method for cell analysis.
Optical density is commonly used as a simple and rapid indirect measurement method to estimate biomass concentration in liquid cultures. However, the object of optical density detection is often algae cells, colonies, and other microorganisms, few studies adopt optical density to quantitatively measure the concentration of cancer cells. In this paper, different liquid media and cancer cells were used to implement a full-wavelength spectral analysis by microplate reader to find the corresponding robust wavelength. According to the experimental results, we suggest measuring cell concentration at nearinfrared wavelengths, such as 850 nm, to facilitate the subsequent unification of protocols applicable to different cell types and culture conditions. Meanwhile, a portable flow cell sensor based on optical density is demonstrated. The calibration curves under various experimental conditions have high regression coefficient R2 values, all greater than 0.99, which are expected to be used for online real-time measurement of cell concentration in biological reaction processes. This study provides the feasibility of using the optical density method as a quick and easy way for indirect quantitative measurement of cancer cell concentration.
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