In this paper, a novel data-driven single neuron predictive control strategy is proposed for non-Gaussian networked control systems with metrology delays in the information theory framework. Firstly, survival information potential (SIP), instead of minimum entropy, is used to formulate the performance index to characterize the randomness of the considered systems, which is calculated by oversampling method. Then the minimum values can be computed by optimizing the SIP-based performance index. Finally, the proposed strategy, minimum entropy method and mean square error (MSE) are applied to a networked motor control system, and results demonstrated the effectiveness of the proposed strategy.
Exfoliation of two-dimensional (2D) materials is an issue of concern among scientific researchers. This is because many solvents such as N, N-dimethylformamide and N-methyl-2pyrrolidone that are capable of better dispersion of 2D materials are relatively toxic and nonvolatile. This work focused on the reasonable design and mixture of two or three less toxic and volatile solvents based on Hansen solubility parameters theory to demonstrate the excellent exfoliation of 2D materials particularly reduced graphene oxide (rGO) and black phosphorus (BP). Polyvinylpyrrolidone (PVP) was introduced as a surfactant to functionalize rGO to help improve its dispersion. Results showed that PVP could effectively functionalize graphene. Few layers of rGO and BP were facilely achieved with 2-3 nm thickness from the use of the designed solvent mixtures, indicating the accomplishment of solvent mixtures in exfoliation/dispersion roles instead of the use of other toxic and nonvolatile solvents.
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