Control of Enzymatic Degradation of Microbial Polyesters by Plasma Modification

The rate of enzymatic degradation of surface‐modified microbial polyesters, poly[(R)‐3‐hydroxybutyrate] and poly[(R)‐3‐hydroxybutyrate‐co‐3‐hydroxyvalerate], was studied. The plasma treatments were carried out in a CF₃H or O₂ environment. It was found that the CF₃H plasma‐treated polyesters exhibited significant retardation of enzymatic erosion because of the surface fluorocarbon groups induced by CF₃H plasma. These surface fluorocarbon groups act as retardants on enzymatic degradation due to increased hydrophobicity and of the inactivity of enzymes. However, the increased surface hydrophilicity of polyesters induced by O₂ plasma results in no significant acceleration of the enzymatic erosion, which may be due to the thin modified layer.

Weight loss profiles of P(3HB) film exposed to CF₃H plasma as a function of plasma exposure time.     

Convenient Synthesis of Phenyl Pyridyl-Ethers Utilizing Fluoride Ion

Aryl pyridyl ethers were prepared from phenols and active halopyridines under essentially neutral condition via fluoride ion assisted reaction.     

Hydrogen production and photocatalytic activity of g-C3N4/Co-MOF (ZIF-67) nanocomposite under visible light irradiation

Herein, cobalt (Co)-based metal–organic zeolitic imidazole frameworks (ZIF-67) coupled with g-C₃N₄ nanosheets synthesized via a simple microwave irradiation method. SEM, TEM and HR-TEM results showed that ZIF-67 were uniformly dispersed on g-C₃N₄ surfaces and had a rhombic dodecahedron shape. The photocatalytic properties of g-C₃N₄/ZIF-67 nanocomposite were evaluated by photocatalytic dye degradation of crystal violet (CV), 4-chlorophenol (4-CP) and photocatalytic hydrogen (H₂) production. In presence of visible light illumination, the photocatalytic dye results showed that 95% CV degradation and 53% 4-CP degradation within 80 min. The H₂ production of the g-C₃N₄/ZIF-67 composite was 2084 μmol g⁻¹, which is 3.84 folds greater than that of bare g-C₃N₄ (541 μmol g⁻¹).     

Linear Matrix Inequality Optimization Approach to Exponential Robust Filtering for Switched Hopfield Neural Networks

This paper is concerned with the delay-dependent exponential robust filtering problem for switched Hopfield neural networks with time-delay. A new delay-dependent switched exponential robust filter is proposed that results in an exponentially stable filtering error system with a guaranteed robust performance. The design of the switched exponential robust filter for these types of neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated using standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.     

Tuning of Supramolecular Architectures of l‐Valine‐Containing Dicyanoplatinum(II) 2,2′‐Bipyridine Complexes by Metal–Metal, π–π Stacking,and Hydrogen‐Bonding Interactions

A series of newly synthesized dicyanoplatinum(II) 2,2′‐bipyridine complexes exhibits self‐assembly properties in solution after the incorporation of the l ‐valine amino units appended with various hydrophobic motifs. These l ‐valine‐derived substituents were found to have critical control over the aggregation behaviors of the complexes in the solution state. On one hand, one of the complexes was found to exhibit interesting circularly polarized luminescence (CPL) signals at low temperature due to the formation of chiral spherical aggregates in the temperature‐dependent studies. On the other hand, systematic transformation from less uniform aggregates to well‐defined fibrous and rod‐like structures via Pt???Pt and π–π stacking interactions has also been observed in the mixed‐solvent studies. These changes were monitored by UV/Vis absorption, emission, circular dichroism (CD), and CPL spectroscopies, and morphologies were studied by electron microscopy.     

Switched exponential state estimation of neural networks based on passivity theory

In this paper, a new exponential state estimation method is proposed for switched Hopfield neural networks based on passivity theory. Through available output measurements, the main purpose is to estimate the neuron states such that the estimation error system is exponentially stable and passive from the control input to the output error. Based on augmented Lyapunov–Krasovskii functional, Jensen’s inequality, and linear matrix inequality (LMI), a new delay-dependent state estimator for switched Hopfield neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving delay-dependent LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.