New soluble MoS2 nanosheets covalently functionalized with poly(N-vinylcarbazole) (MoS2-PVK) were in situ synthesized for the first time. In contrast to MoS2 and MoS2 /PVK blends, both the solution of MoS2 -PVK in DMF and MoS2-PVK/poly(methyl methacrylate) (PMMA) film show superior nonlinear optical and optical limiting responses. The MoS2-PVK/PMMA film shows the largest nonlinear coefficients (βeff) of about 917 cm GW(-1) at λ=532 nm (cf. 100.69 cm GW(-1) for MoS2/PMMA and 125.12 cm GW(-1) for MoS2/PVK/PMMA) and about 461 cm GW(-1) at λ=1064 nm (cf. -48.92 cm GW(-1) for MoS2/PMMA and 147.56 cm GW(-1) for MoS2/PVK/PMMA). A larger optical limiting effect, with thresholds of about 0.3 GW cm(-2) at λ=532 nm and about 0.5 GW cm(-2) at λ=1064 nm, was also achieved from the MoS2-PVK/PMMA film. These values are among the highest reported for MoS2-based nonlinear optical materials. These results show that covalent functionalization of MoS2 with polymers is an effective way to improve nonlinear optical responses for efficient optical limiting devices.

Highly soluble polyacrylonitrile (PAN)-covalently grafted MoS2 nanosheets (MoS2–PAN) were synthesized in situ. This material shows superior optical limiting response in the visible and near-infrared ranges.

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A three-degrees-of-freedom model, including surge, sway and yaw motion, with differential thrusters is proposed to describe unmanned surface vehicle (USV) dynamics in this study. The experiment is carried out in the Qing Huai River and the data obtained from different zigzag trajectories are filtered by a Gaussian filtering method. A physics-informed neural network (PINN) is proposed to identify the dynamic models of the USV. PINNs combine the advantages of data-driven machine learning and physical models. They can also embed the speed and steering models into the loss function, which can significantly retain all types of information. Compared with traditional neural networks, the results show that the PINN has better generalization ability in predicting the surge and sway velocities and rotation speed with only limited training data.

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