RESUMEN
The catalysis reaction mechanism at nano/atomic scale attracted intense attention in the past decades. However, most in situ characterization technologies can only reflect the average information of catalysts, which leads to the inability to characterize the dynamic changes of single nanostructures or active sites under operando conditions, and many micro-nanoscale reaction mechanisms are still unknown. The combination of in situ transmission electron microscopy (TEM) holder system with MEMS chips provides a solution for it, where the design and fabrication of MEMS chips are the key factors. Here, with the aid of finite element simulation, an ultra-stable heating chip was developed, which has an ultra-low thermal drift during temperature heating. Under ambient conditions within TEM, atomic resolution imaging was achieved during the heating process or at high temperature up to 1300 °C. Combined with the developed polymer membrane seal technique and nanofluidic control system, it can realize an adjustable pressure from 0.1 bar to 4 bar gas environment around the sample. By using the developed ultra-low drift gas reaction cells, the nanoparticle's structure evolution at atomic scale was identified during reaction.
RESUMEN
It is crucial to monitor the status of aquaculture objects in recirculating aquaculture systems (RASs). Due to their high density and a high degree of intensification, aquaculture objects in such systems need to be monitored for a long time period to prevent losses caused by various factors. Object detection algorithms are gradually being used in the aquaculture industry, but it is difficult to achieve good results for scenes with high density and complex environments. This paper proposes a monitoring method for Larimichthys crocea in a RAS, which includes the detection and tracking of abnormal behavior. The improved YOLOX-S is used to detect Larimichthys crocea with abnormal behavior in real time. Aiming to solve the problems of stacking, deformation, occlusion, and too-small objects in a fishpond, the object detection algorithm used is improved by modifying the CSP module, adding coordinate attention, and modifying the part of the structure of the neck. After improvement, the AP50 reaches 98.4% and AP50:95 is also 16.2% higher than the original algorithm. In terms of tracking, due to the similarity in the fish's appearance, Bytetrack is used to track the detected objects, avoiding the ID switching caused by re-identification using appearance features. In the actual RAS environment, both MOTA and IDF1 can reach more than 95% under the premise of fully meeting real-time tracking, and the ID of the tracked Larimichthys crocea with abnormal behavior can be maintained stably. Our work can identify and track the abnormal behavior of fish efficiently, and this will provide data support for subsequent automatic treatment, thus avoiding loss expansion and improving the production efficiency of RASs.
