Effect of irradiation for sterilization on beverage reference materials for the analysis of food additives

Accreditation and Quality Assurance - Reference materials for proficiency testing (PT) were prepared for 6 years. The target analytes were food additives, sorbic acid, benzoic acid, and...     

Mechanical and electrical response variation of the polyurethane–tin oxide–carbon nanotube composite microfiber depending on the chemical solution

Toward the goal of smart sensor systems for wearable electronics, polymer microfiber‐based free‐standing sensors benefit from excellent flexibility, decent ductility, and easy wearability in comparison with thin‐film‐based sensing devices. Herein, we report a hydrophobic and conducting single‐strand microfiber‐based liquid‐phase chemical sensor consisting of polyurethane (PU), tin oxide (SnO₂), and carbon nanotube (CNT) composites with applying a (1H,1H,2H,2H‐heptadecafluorodec‐1‐yl) phosphonic acid (HDF‐PA)‐based self‐assembled monolayer. The free‐standing HDF‐PA‐treated PU–SnO₂–CNT composite microfiber showing selective filtering properties with the repellency of water and the penetration of an organic solvent is electrically and mechanically characterized. Finally, the single‐strand HDF‐PA‐treated PU–SnO₂–CNT composite microfiber‐based chemical sensor, which shows excellent mechanical properties and aqueous stability, is demonstrated to detect the presence of a chemical in pure water or counterfeit gasoline in pure gasoline by observing mechanical changes, especially variations in the length and diameter of the fiber, and monitoring the electrical resistance change. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2019 , 57, 495–502     

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.     

Pd nanoparticles immobilized on PNIPAM–halloysite: highly active and reusable catalyst for Suzuki–Miyaura coupling reactions in water

Poly(N‐isopropylacrylamide)–halloysite (PNIPAM‐HNT) nanocomposites exhibited inverse temperature solubility with a lower critical solution temperature (LCST) in water. Palladium (Pd) nanoparticles were anchored on PNIPAM‐HNT nanocomposites with various amounts of HNT from 5 to 30 wt%. These Pd catalysts exhibited excellent reactivities for Suzuki–Miyaura coupling reactions at 50–70 °C in water. In particular, Pd anchored PNIPAM/HNT (95:5 w/w ratio) nanocomposites showed excellent recyclability up to 10 times in 96% average yield by simple filtration. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Thermal stability of ester linkage in the presence of 1,2,3‐Triazole moiety generated by click reaction

The copper(I)‐catalyzed alkyne‐azide cycloaddition (CuAAC), so‐called “click” reaction, is one of most useful synthetic strategies to connect two polymer chains. 1,2,3‐Triazole ring (TA) produced by the click reaction has good thermal and chemical stability. However, we observed that block copolymers synthesized by the click reaction showed thermal degradation to give homopolymers when they are thermally annealed at high temperature, which is required for obtaining equilibrium microdomain structure. To investigate the origin of thermal instability of block copolymers, we synthesized model polystyrenes (PSs) using systematically designed bi‐functional atom transfer radical polymerization (ATRP) initiators containing TA. PS including both ester and TA groups showed thermal decomposition at relatively low temperature (e.g., 140 °C). MALDI‐TOF analysis clearly demonstrated that the cleavage site is the ester group adjacent to TA. We also found that the bromine group located at the polymer chain end plays an important role in pyrolysis of ester groups at low temperature. The pyrolysis occurs by syn‐elimination of the ester group. This result implies that the phase behavior of block copolymer synthesized by click reaction should be carefully investigated when high temperature thermal annealing is required. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017 , 55, 427–436     

Efficient and accurate procedure for selecting ground motions matching target response spectrum

Linear and nonlinear response history analyses have become popular in seismic design and seismic performance evaluation procedures. The accuracy of analysis results depends not only on the accurate analytic models for structures but also on the proper selection of input ground motions. The purpose of this study is to develop a computationally efficient and accurate procedure for selecting ground motions considering the target response spectrum mean and variance, and the correlations between response spectra of different periods. In this procedure, a number of response spectra are simulated equal to the number of ground motions to be selected, using a Monte Carlo simulation. Subsequently, ground motions are selected from a ground motion library to individually match the simulated response spectra, using the proposed selection procedure. This procedure is computationally efficient and accurate in selecting a ground motion that best matches a simulated response spectrum and in determining a scaling factor for the selected ground motion. In order to further improve the selection result, multiple sets of simulated response spectra are considered. The accuracy and efficiency of the proposed procedure are verified with numerical examples.