The influence of pH and metallographic structure on the corrosion behavior of copper-drawn steel is studied with the simulated system. The effect of pH on the corrosion behavior of copper-drawn steel has been investigated using open-circuit potential, potentiodynamic polarization, galvanic current measurement, scanning electron microscopy and scanning vibrating electrode technique techniques. The steel is corroded as anode, while the corrosion of copper plate is protected as cathode. All the results revealed that pH and metallographic structure had a significant influence on the corrosion behavior of copper-drawn steel. With the decrease in pH value from 6 to 2.4, the corrosion rate of copper-drawn steel galvanic couple (Cu-Fe GC) obviously increased in the simulated solution of acidic red soil. The electric field formed by the Cu-Fe GC changes the direction of ion migration between the copper and drawn steel electrodes, which impacts the composition and microstructure of corrosion products formed on the electrode surface.

A facile strategy to boost anticorrosion potency of graphene oxide/silica hybrid sol-gel coating is developed through fully exploiting the capabilities of graphene oxide (GO). Together with a barrier to corrosives and crack inhibitor, GO was further explored herein as a regulator to regulate the gelation process and provide robust coating films with stratified microstructures and ultimately extended diffusion paths. The sol-gel coating with stratified microstructure achieved on AA5052 aluminum alloy surface afforded greatly enhanced corrosion protection capability as assessed by electrochemical measurements and immersion tests. The corrosion current density of the sample of a hybrid GO sol-gel film was about 30 times less than that of sample of pure sol-gel film sample. The regulation mechanism of GO during the film formation process and the anticorrosive protection properties of the film were discussed.

Background Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma are imbalanced. In this paper, we propose an effective solution to find the optimal imbalance machine learning model for predicting the classification of soft tissue sarcoma data. Methods In this paper, a large number of features are first obtained based on $$T_1$$ T 1 WI images using the radiomics methods.Then, we explore the methods of feature selection, sampling and classification, get 17 imbalance machine learning models based on the above features and performed extensive experiments to classify imbalanced soft tissue sarcoma data. Meanwhile, we used another dataset splitting method as well, which could improve the classification performance and verify the validity of the models. Results The experimental results show that the combination of extremely randomized trees (ERT) classification algorithm using SMOTETomek and the recursive feature elimination technique (RFE) performs best compared to other methods. The accuracy of RFE+STT+ERT is 81.57% , which is close to the accuracy of biopsy, and the accuracy is 95.69% when using another dataset splitting method. Conclusion Preoperative predicting pathological grade of soft tissue sarcoma in an accurate and noninvasive manner is essential. Our proposed machine learning method (RFE+STT+ERT) can make a positive contribution to solving the imbalanced data classification problem, which can favorably support the development of personalized treatment plans for soft tissue sarcoma patients.