2results about How to "Good experimental repeatability" patented technology

This invention relates to the field of marine clean energy simulation technology, and discloses a rapid indoor simulation non-destructive evaluation method for hydrate-containing sediments, comprising the following steps: S1, preparing an indoor simulation system for simulation: the indoor simulation system includes a sample saturation component, a sample component, an acoustic testing component, a temperature control component, and a data acquisition component; S2, preparing and saturating the sample. This invention can rapidly generate hydrates and directly prepare saturated sediment samples under indoor conditions, significantly shortening the sample preparation cycle; it avoids hydrate decomposition or structural disturbance during saturation, improving sample structural stability and experimental repeatability; the sample remains within the ring cutter throughout the preparation and testing process, eliminating the need for demolding and significantly simplifying the experimental procedure; it achieves non-destructive evaluation of hydrate saturation through acoustic testing, and can combine temperature monitoring to reflect the internal phase transition process and latent heat effect of the sample in real time.

The invention discloses a 3D hepatotoxicity test monodisperse microgel preparation method, a 3D hepatotoxicity test monodisperse microgel preparation system and a medium, and belongs to the technical field of 3D cell culture and drug screening. The system comprises a preparation module, a statistical analysis module, a material processing module and a control system, the method comprises the following steps: collecting historical data to train an adaptive neural network, and establishing a mapping relation between control parameters and microgel quality indexes; a neural network is used as a prediction model, and optimal control input is solved through a model prediction controller; performing microgel preparation; acquiring images in real time to obtain and feed back actual particle sizes and variation coefficients; applying the new data to incremental updating of the neural network; and performing iteration to form a closed-loop optimization process. According to the method, multi-parameter self-adaptive optimization is realized through an artificial intelligence algorithm, the problem of manual exploration of parameters of different hydrogel materials is solved, the monodispersity and consistency of microgel are improved, and full-process automation is realized.