In this invention, the fluorinated graphene(FG) is obtained by the fluorination of electrochemically exfoliated graphene (ECG), which is found to be a one-step approach for the scalable preparation of FG. The precursor with fluorine atoms is mixed with the ECG followed by applying lower energy to produce the fluorinated graphene. This process demonstrates an eco-friend and safe as well as scalable features. We have demonstrated their multi-functional applications on surface coatings, including the outperformance anticorrosion passivation, hydrophobic surface with ultra-strong adhesion, and high safety energy storage device. This technology provided a new strategy for next-generation functional and atomic layered coatings.

A phosphate-sensing chip was constructed by a copper phosphate-deposited electrode. The modification of ionic liquidplasticizer can improve the selectivity of phosphate anionsreduce the interference of sulfate, nitratechloride anions. The sensing chip equipped with a portable electrochemical device can directly detect the phosphate concentration of solution extracted from soil samples in ten minutes by using amperometry. The sensing chip promises the feasibility of detecting the phosphate concentration without expensive instrumentlabor-intensively chemical colorimetry.

We propose a novel AI-based few-shot self-supervised learning method for automatic optical inspection image quality assessmentcomponent detection based on only few training images. Our method iteratively learns the feature representations of the components by using self-similarity of these components. With the large number of self-learned representations, the appearance variations of each component are then effectively learned in the AI model for component detectionmeasurement. The computation complexity of our method is significantly lower than that of deep learning methods.