Sulfination of Unactivated Allylic Alcohols via Sulfinate-Sulfone Rearrangement.

A dehydrative cross-coupling of unactivated allylic alcohols with sulfinic acids was achieved under catalyst-free conditions. This reaction proceeded via allyl sulfination and concomitant allyl sulfinate-sulfone rearrangement. Various allylic sulfones could be obtained in good to excellent yields with water as the only byproduct. This study expands the synthetic toolbox for constructing allylic sulfone molecules.

Directional, super-hydrophobic cellulose nanofiber/polyvinyl alcohol/montmorillonite aerogels as green absorbents for oil/water separation.

Nowadays, the problem of oil spill and organic solvent pollution has become more and more serious, and developing a green and efficient treatment method has become a research hotspot. Herein, the preparation of porous super-hydrophobic aerogel by directional freezing with cellulose nanofibre (CNF) as the base material, polyvinyl alcohol (PVA) as the cross-linking agent and montmorillonite (MMT) as the modifier and filler, followed by hydrophobic treatment with chemical vapour deposition is reported. The prepared composite aerogel presented three-dimensional inter-perforation network structure, low density (26.52 mg⋅cm-3 ), high porosity (96.1 %) and good hydrophobicity (water contact angle of 140°). Notably, the composite aerogel has a good adsorption effect on different oils and organic solutions, and its adsorption capacity can reach 40-68 times of its initial weight. After complete adsorption, the aerogel could be easily collected. More importantly, the composite aerogel had high strength, whose compressive stress at 70 % strain reached 0.15 MPa and could bear over 1290 times its weight without deformation after 2 weeks. A new, green, simple and efficient absorbent for the adsorption of oils and organic solvents is provided.

Intermittent Control for Quasisynchronization of Delayed Discrete-Time Neural Networks.

This article visits the intermittent quasisynchronization control of delayed discrete-time neural networks (DNNs). First, an event-dependent intermittent mechanism is originally designed, which is described by the Lyapunov function and three non-negative real regions. The distinctive feature is that the controller starts to work only when the trajectory of the Lyapunov function goes into the presupposed work region. The proposed method fundamentally changes the principle of the existing intermittent control schemes. Under the proposed framework of the intermittent mechanism, the work/rest time of the controller is aperiodic, unpredictable, and initial value dependent. Second, several succinct sufficient conditions in terms of linear matrix inequalities are developed to achieve the quasisynchronization of the considered DNNs. A simple optimization algorithm is established to compute the control gains and the Lyapunov matrices such that synchronization error is stabilized to the smallest convergence region. Finally, two simulation examples are provided to demonstrate the feasibility of the designed intermittent mechanism.

Finite-Time Decentralized Control of IT2 T-S Fuzzy Interconnected Systems With Discontinuous Interconnections.

This paper investigates the finite-time decentralized control problem for interconnected systems with discontinuous interconnections. By using the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model, a unified IT2 T-S fuzzy interconnected system is provided, in which the global system is described as a fuzzy blending of local subsystems under IF-THEN rules. In addition, based on the differential inclusion theory, the solutions of such discontinuous system are defined in the sense of Filippov. In order to stabilize the considered system in finite time, several decentralized discontinuous state feedback controllers are proposed. Furthermore, by the finite-time stabilization theory and generalized Lyapunov functional method, decentralized control is carried out and several sufficient criteria are derived to ensure the finite-time stabilization of the concerned system. Correspondingly, the settling times for stabilization are given. Finally, the proposed methodology is illustrated by an example.

Exponential Stabilization of Memristive Neural Networks via Saturating Sampled-Data Control.

This paper is concerned with the exponential stabilization of memristive neural networks (MNNs) by taking into account the sampled-data control and actuator saturation. On the one hand, the MNNs are converted into a tractable model by defining a class of logical switched functions. Based on this model, the connection weights of MNNs are dealt with by a robust analysis method. On the other hand, a saturating sampled-data controller containing an exponentially decaying term is designed. With the help of generalized sector condition and the Lyapunov stability theory, a novel sufficient condition ensuring the local exponential stability of the closed-loop systems is formulated in terms of linear matrix inequalities. In addition, three optimization problems are given to design the control gain with the aims of enlarging the sampling interval, expanding the estimation of the domain of attraction, and minimizing the size of actuators, while preserving the stability of the closed-loop systems. Two numerical examples are provided to illustrate the effectiveness of the obtained theoretical results.