Electroanalytical Studies of Chromone Based Ionophores for the Selective Determination of Arsenite Ion

Two chemosensors 4H‐1‐benzopyran‐3‐carboxaldehyde, 4‐oxo‐, 3‐(2‐phenylhydrazone), [I₁] and 4H‐1‐benzopyran‐3‐carboxaldehyde, 4‐oxo‐, 3‐[2‐(2,4‐dinitrophenyl)hydrazone], [I₂] with hydrazone‐NH group as binding site have been shown excellent selectivity for arsenite ion. It is confirmed by the UV‐vis titration that I₂ is more selective than I₁. The performance of the coated graphite electrode (CGE) was found to be better than polymeric membrane electrode (PME) in terms of linear range of 4.89×10^?7–1.0×10^?1 mol L^?1, low detection limit of 8.31×10^?8 mol L^?1 and short response time. The proposed sensors were also used to determine the arsenite ion in different water samples.     

An Antibacterial Nonenzymatic Glucose Sensor Composed of Carbon Nanotubes Decorated with Silver Nanoparticles

A glucose sensor composed of silver nanoparticles decorated carbon nanotubes (Ag‐NPs/CNTs) prepared by ion implantation is described. Ag‐NPs with size of 2–4 nm are uniformly distributed in the CNTs after ion implantation. This process provides a strong combination between Ag‐NPs and CNTs and can effectively prevent the Ag‐NPs from aggregation. A linear range of 125 µM to 10 mM towards glucose determination was obtained. The Ag‐NPs/CNTs electrode shows minimal interferences from co‐existence species such as uric acid and ascorbic acid and an antibacterial rate of 94 % towards E. coli.     

Highly selective and sensitive paper-based colorimetric sensor using thiosulfate catalytic etching of silver nanoplates for trace determination of copper ions

A novel, highly selective and sensitive paper-based colorimetric sensor for trace determination of copper (Cu²⁺) ions was developed. The measurement is based on the catalytic etching of silver nanoplates (AgNPls) by thiosulfate (S₂O₃²⁻). Upon the addition of Cu²⁺ to the ammonium buffer at pH 11, the absorption peak intensity of AuNPls/S₂O₃²⁻ at 522 nm decreased and the pinkish violet AuNPls became clear in color as visible to the naked eye. This assay provides highly sensitive and selective detection of Cu²⁺ over other metal ions (K⁺, Cr³⁺, Cd²⁺, Zn²⁺, As³⁺, Mn²⁺, Co²⁺, Pb²⁺, Al³⁺, Ni²⁺, Fe³⁺, Mg²⁺, Hg²⁺ and Bi³⁺). A paper-based colorimetric sensor was then developed for the simple and rapid determination of Cu²⁺ using the catalytic etching of AgNPls. Under optimized conditions, the modified AgNPls coated at the test zone of the devices immediately changes in color in the presence of Cu²⁺. The limit of detection (LOD) was found to be 1.0 ng mL⁻¹ by visual detection. For semi-quantitative measurement with image processing, the method detected Cu²⁺ in the range of 0.5–200 ng mL⁻¹(R² = 0.9974) with an LOD of 0.3 ng mL⁻¹. The proposed method was successfully applied to detect Cu²⁺ in the wide range of real samples including water, food, and blood. The results were in good agreement according to a paired t-test with results from inductively coupled plasma-optical emission spectrometry (ICP-OES).     

Recent progress in applications of graphene oxide for gas sensing: A review

This paper is a review of the recent progress on gas sensors using graphene oxide (GO). GO is not a new material but its unique features have recently been of interest for gas sensing applications, and not just as an intermediate for reduced graphene oxide (RGO). Graphene and RGO have been well known gas-sensing materials, but GO is also an attractive sensing material that has been well studied these last few years. The functional groups on GO nanosheets play important roles in adsorbing gas molecules, and the electric or optical properties of GO materials change with exposure to certain gases. Addition of metal nanoparticles and metal oxide nanocomposites is an effective way to make GO materials selective and sensitive to analyte gases. In this paper, several applications of GO based sensors are summarized for detection of water vapor, NO₂, H₂, NH₃, H₂S, and organic vapors. Also binding energies of gas molecules onto graphene and the oxygenous functional groups are summarized, and problems and possible solutions are discussed for the GO-based gas sensors.     

Three-dimensional roselike α-Ni(OH)2 assembled from nanosheet building blocks for non-enzymatic glucose detection

Glucose detection plays very important roles in diagnostics and management of diabetes. The search for novel catalytic materials with appropriate architectures is the key step in the fabrication of highly sensitive glucose sensors. In this work, α-Ni(OH)₂ roselike structures (Ni(OH)₂-RS) assembled from nanosheet building blocks were successfully synthesized by a hydrothermal method through the hydrolysis of nickel chloride in the mixed solvents of water and ethanol with the assistance of polyethylene glycol (PEG). The structure and morphology of the roselike α-Ni(OH)₂ were characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), X-ray powder diffraction (XRD) and N₂ adsorption–desorption isotherm measurement. TEM and FE-SEM images showed that the synthesized Ni(OH)₂ was roselike and the size of the leaf-shaped nanosheet was about 5 nm in thickness, which leads to larger active surface areas and faster electron transfer for the detection of glucose. Compared with the bare GCE and bulk Ni(OH)₂/GCE, the Ni(OH)₂-RS/GCE had higher catalytic activity toward the oxidation of glucose. Under the optimal conditions, the Ni(OH)₂-RS/GCE offers a variety of merits, such as a wide linear response window for glucose concentrations ranging from 0.87 μM to 10.53 mM, short response time (3 s), a lower detection limit of 0.08 μM (S/N = 3), as well as long term stability and repeatability.     

The development of an artificial organic networks toolkit for LabVIEW

Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting‐edge algorithms are the time‐consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW? (AON‐TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy‐to‐use characteristics of the LabVIEW? software (e.g., graphical programming, transparent usage of other softwares and devices, built‐in programming event‐driven for user interfaces), to make it simple for the end‐user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so‐called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood–brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique. © 2015 Wiley Periodicals, Inc.     

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.     

Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks

Since wireless in terms of energy-restricted processes, dispersion radii, processing power limitations, buffers, bandwidth-limited connections, active network topologies, and network stream of traffic outlines, sensor networks provide difficult design issues. The number of hops and latency are decreased if there is a relay mote because it interacts directly with relay motes that are closer to the destination mote. The tremendous intensive research in the area of Wireless Sensor Networks (WSN) has gained a lot of significance among the technical community and research. The job of WSN is to sense the data using sensor motes, pass on the data to the destination detection mote which is associated with a processing center and can be used in multiple spans of Internet of Things (IoT) applications. Wireless sensor network has a set of sensor motes. By making use of sensor mote placement strategy all the sensor motes are spread in an area with each mote having its own exceptional location. Internet of things applications are delay sensitive those applications have a challenge of forming the complete path at a lower delay constraint. The proposal is to modify the game theory energy balancing algorithm by making use of relay motes so that overall network lifetime is increased. It has been proved that modified GTEB is better with respect to existing algorithms in terms of delay, figure of hops, energy depletion, figure of alive motes, figure of dead motes, lifespan ratio, routing overhead and throughput.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

Design of a coated thinly clad chalcogenide long-period fiber grating refractive index sensor based on dual-peak resonance near the phase matching turning point

A novel method for designing chalcogenide long-period fiber grating (LPFG) sensors based on the dual-peak resonance effect of the LPFG near the phase matching turning point (PMTP) is presented. Refractive index sensing in a high-refractive-index chalcogenide fiber is achieved with a coated thinly clad film. The dual-peak resonant characteristics near the PMTP and the refractive index sensing properties of the LPFG are analyzed first by the phase-matching condition of the LPFG. The effects of film parameters and cladding radius on the sensitivity of refractive index sensing are further discussed. The sensor is optimized by selecting the appropriate film parameters and cladding radius. Simulation results show that the ambient refractive index sensitivity of a dual-peak coated thinly clad chalcogenide LPFG at the PMTP can be 2400 nm/RIU, which is significantly higher than that of non-optimized gratings. It has great application potential in the field of chemical sensing and biosensors.