Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

A competitive microcystin-LR immunosensor based on Au NPs@metal-organic framework(MIL-101)

An electrochemical immunosensor was developed for ultrasensitive detection of microcystin-LR in water. MIL-101, a porous metal-organic frameworks(MOFs) material based on trivalent chromium skeleton were synthesized by hydrothermal synthesis method, and loaded with Au nanoparticles(Au NPs) to prepare Au NPs@MIL-101 composite materials which were used as a marker to label anti microcystin-LR(Anti-MC-LR). The composite materials have strong catalytic properties to the oxidation of ascorbic acid. Anti-MC-LR was immobilized on glassy carbon electrode surface using electrodeposition graphene oxide(GO) as an immobilization matrix to construct a competitive microcystin-LR immunosensor. The electrochemical immunosensor display linear relationship in the range of 0.05 ng/mL-75 μg/mL with linear correlation coefficient of 0.9951 and detection limit of 0.02 ng/mL(S/N = 3). This sensor was used to detect microcystin-LR in the water sample. The recovery was 102.43%,which is satisfied. The good testing results indicate the sensor has a great prospect in practical application.