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In this work the magnesium based Janus micromotors were prepared by an asymmetric coating of Co-Au bilayer on the surface of Mg microparticles. The micromotor could efficiently self-propel in phosphate buffer saline (PBS) with a highest speed of 221 μm·s−1 without any extra additives through the macrogalvanic corrosion and pitting corrosion mechanism. The influence of pH value of PBS buffer on the motion of micromotor was also investigated. Moreover, we demonstrated that the motion of micromotor could be controlled by an external magnetic field rapidly and accurately, indicating the potential application in biomedicine.

Ammonia nitrogen is one of the most hazardous water pollution parameters. It is crucial to monitor the concentration of ammonia nitrogen to minimize ammonia nitrogen pollution in river water. This study aims to develop a reliable model to accurately predict ammonia nitrogen concentration. Langat River was selected as the study area. Two soft computing techniques namely Backpropagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed for the model development. Different model architectures were developed and evaluated. ANFIS model VI appears as an effective tool to serve the main objective where it has a considerably high coefficient of determination, low mean absolute and root mean squared errors, and small average percentage error. The model has an average percentage error of 23%, indicating it is able to provide an estimation accuracy of at least 77%.