Development of a Chitosan/Nickel Phthalocyanine Composite Based Conductometric Micro-sensor for Methanol Detection

Thin-film composite of chitosan/nickel phthalocyanine (NiPc) was electrochemically deposited on the fingers of interdigitated gold electrodes, applying chronoamperometric polymerization technique. The presence of crystallized NiPc in the chitosan was confirmed by EDX and FTIR analysis. Acetone, ethanol, and methanol gas-sensing properties of the films prepared at optimum conditions were studied at atmospheric temperature, through differential measurements at an optimized frequency of 10 kHz, using a lock-in amplifier. The conductometric sensor presents the highest sensitivity of 60.2 μS.cm⁻¹(v/v) for methanol and 700 ppm as the limit of detection. For validation, the methanol content of a commercial rubbing alcohol was determined.     

A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.     

Dynamic stability of a class of fractional-order nonlinear systems via fixed point theory

In this paper, the problem of the uniform stability for a class of fuzzy fractional-order genetic regulatory networks with random discrete delays, distributed delays, and parameter uncertainties is studied. Although there is a portion of literature on using fixed point theorems to study the stability of fractional neural networks, most of them required the fractional order to be in $\left(\frac{1}{2},1\right)$. However, the case of the fractional-order belonging to $(0,\frac{1}{2})$ has not been discussed. To solve it, this work proposes a novel idea of using fixed point theory to study the stability of fuzzy (0,1) order neural networks, the problem of the uniqueness of the solution of the considered genetic regulatory networks is resolved, and a novel sufficient condition to guarantee the uniform stability of above genetic regulatory networks is also derived. Eventually, an example is given to demonstrate that the obtained result is effective.     

The behavior of capillary suspensions at diverse length scales: From single capillary bridges to bulk

Liquid-liquid-solid systems are becoming increasingly common in everyday life with many possible applications. Here, we focus on a special case of such liquid-liquid-solid systems, namely, capillary suspensions. These capillary suspensions originate from particles that form a network based on capillary forces and are typically composed of solids in a bulk liquid with an added secondary liquid. The structure of particle networks based on capillary bridges possesses unique properties compared with networks formed via other attractive interactions where these differences are inherently related to the properties of the capillary bridges, such as bridge breaking and coalescence between adjacent bridges. Thus, to tailor the mechanical properties of capillary suspensions to specific requirements, it is important to understand the influences on different length scales ranging from the dynamics of the bridges with varying external stimuli to the often heterogeneous network structure.     

Stability analysis of fractional order fuzzy cellular neural networks with leakage delay and time varying delays

In this paper we investigated the stability of fractional order fuzzy cellular neural networks with leakage delay and time varying delays. Based on Lyapunov theory and applying bounded techniques of fractional calculation, sufficient criterion are established to guarantee the stability. Hybrid feedback control is applied to derive the proposed results. Finally, numerical examples with simulation results are given to illustrate the effectiveness of the proposed method.     

Multi-emissive 1D Cd(II) polymers with a biphenyl bridged bisazamacrocycle for ratiometric discrimination of nitroaromatics and selective visual detection of picric acid

Three one-dimensional ladder-like coordination polymers consisting of Cd₆ metalloring as the building unit, {[Cd₄LCl₄]·3H₂O}_n ( 1 ), {[Cd₃L(ClO₄)(H₂O)]ClO₄·3H₂O}_n ( 2 ), and {[Cd₆(L)₂(NO₃)₂(CH₃OH)(H₂O)](NO₃)₂·2CH₃OH·5H₂O}_n ( 3 ), were solvothermally constructed from a carboxylic functionalized bisazamacrocyclic ligand 4,4′-bis((4,7-bis(2-carboxyethyl)-1,4,7-triazacyclonon-1-yl)methyl)-1,1′-biphenyl (H₄L). These compounds dispersed in ethanol show the multiple emissions originating from the monomeric and intramolecularly overlapping biphenyl moieties which could be sensitively quenched by picric acid (PA) and 4-nitrophenol (4-NP) through the effective fluorescence resonance energy transfer process. The differential fluorescent responses of each compound on exposure to PA and 4-NP individually make the convenient ratiometric discrimination of two analytes based on the fluorescent intensity ratio (I₃₂₀/I₃₆₀) attainable, and 1 and 2 as ratiometric chemosensors for PA present a broad linear detection range from 4 to 300 μM with detection limits of 0.84 and 0.93 μM, respectively. Furthermore, the blue light emission of 1 under an ultraviolet lamp could be selectively quenched by PA even in the presence of all other interfering nitroaromatic pollutants, which empowers the fast visual detection of PA by naked eye.     

Some methods of training radial basis neural networks in solving the Navier‐Stokes equations

The purpose of this research is to analyze the application of neural networks and specific features of training radial basis functions for solving 2‐dimensional Navier‐Stokes equations. The authors developed an algorithm for solving hydrodynamic equations with representation of their solution by the method of weighted residuals upon the general neural network approximation throughout the entire computational domain. The article deals with testing of the developed algorithm through solving the 2‐dimensional Navier‐Stokes equations. Artificial neural networks are widely used for solving problems of mathematical physics; however, their use for modeling of hydrodynamic problems is very limited. At the same time, the problem of hydrodynamic modeling can be solved through neural network modeling, and our study demonstrates an example of its solution. The choice of neural networks based on radial basis functions is due to the ease of implementation and organization of the training process, the accuracy of the approximations, and smoothness of solutions. Radial basis neural networks in the solution of differential equations in partial derivatives allow obtaining a sufficiently accurate solution with a relatively small size of the neural network model. The authors propose to consider the neural network as an approximation of the unknown solution of the equation. The Gaussian distribution is used as the activation function.     

A stable pillared metal–organic framework constructed by H4TCPP ligand as luminescent sensor for selective detection of TNP and Fe3+ ions

A novel luminescent metal–organic framework ( Zn‐TCPP/BPY ) with pillared structure based on 2,3,5,6‐tetrakis(4‐carboxyphenyl)pyrazine (H₄TCPP) and 4,4′‐bipyridine (BPY) has been designed and synthesized through a solvothermal reaction. The [Zn₂(COO)₄] paddlewheel units are linked by TCPP^4? ligands to form two‐dimensional layers and further connected by BPY ligands as pillars to construct the twofold interpenetrating three‐dimensional framework. Interestingly, Zn‐TCPP/BPY possesses outstanding stability in organic solvents and water as well as maintains its structural rigidity in aqueous solutions of different pH values (3–12). After activation, Zn‐TCPP/BPY possesses permanent porosity with Brunauer–Emmett–Teller surface area of 630 m² g^–1. Remarkably, Zn‐TCPP/BPY displays excellent fluorescent property in virtue of the aggregation‐induced emission effect of the H₄TCPP ligand, which can be highly active and quenched by small amounts of 2,4,6‐trinitrophenol (TNP) and Fe³⁺ ions. Furthermore, the detection effect of Zn‐TCPP/BPY remains basically the same even after five cycles. The excellent stability, high sensitivity, and recyclability of Zn‐TCPP/BPY make it an outstanding chemical sensor for detecting TNP and Fe³⁺ ions.     

Tunable and highly sensitive temperature sensor based on graphene photonic crystal fiber

Optical fiber temperature sensors have been widely employed in enormous areas ranging from electric power industry, medical treatment, ocean dynamics to aerospace. Recently, graphene optical fiber temperature sensors attract tremendous attention for their merits of simple structure and direct power detecting ability. However, these sensors based on transfer techniques still have limitations in the relatively low sensitivity or distortion of the transmission characteristics, due to the unsuitable Fermi level of graphene and the destruction of fiber structure, respectively. Here, we propose a tunable and highly sensitive temperature sensor based on graphene photonic crystal fiber (Gr-PCF) with the non-destructive integration of graphene into the holes of PCF. This hybrid structure promises the intact fiber structure and transmission mode, which efficiently enhances the temperature detection ability of graphene. From our simulation, we find that the temperature sensitivity can be electrically tuned over four orders of magnitude and achieve up to ～ 3.34×10^-3 dB/(cm·℃) when the graphene Fermi level is ～ 35 meV higher than half the incident photon energy. Additionally, this sensitivity can be further improved by ～ 10 times through optimizing the PCF structure (such as the fiber hole diameter) to enhance the light-matter interaction. Our results provide a new way for the design of the highly sensitive temperature sensors and broaden applications in all-fiber optoelectronic devices.