Maltol determination in food by microwave assisted extraction and electrochemical detection

An electrochemical method is proposed for the determination of maltol in food. Microwave-assisted extraction procedures were developed to assist sample pre-treating steps. Experiments carried out in cyclic voltammetry showed an irreversible and adsorption controlled reduction of maltol. A cathodic peak was observed at -1.0 V for a Hanging Mercury Drop Electrode versus an AgCl/Ag (in saturated KCl), and the peak potential was pH independent. Square wave voltammetric procedures were selected to plot calibration curves. These procedures were carried out with the optimum conditions: pH 6.5; frequency 50 Hz; deposition potential 0.6 V; and deposition time 10 s. A linear behaviour was observed within 5.0 × 10-8 and 3.5 × 10-7 M. The proposed method was applied to the analysis of cakes, and results were compared with those obtained by an independent method. The voltammetric procedure was proven suitable for the analysis of cakes and provided environmental and economical advantages, including reduced toxicity and volume of effluents and decreased consumption of reagents.

Luminol-doped nanostructured composite materials for chemiluminescent sensing of hydrogen peroxide

Silica based nanostructured composite materials doped with luminol and cobalt(II) ion were synthesized and characterized, resulting in a highly chemiluminescent material in the presence of hydrogen peroxide. A detection system with the CL light guided from the reaction tube to the photomultiplier tube using a one millimeter glass optical fiber was developed and assessed. A linear response was observed using a semi-logarithm calibration between 50–2000 µM hydrogen peroxide with 1 µM as the limit of detection.

Screening of grapes and wine for azoxystrobin, kresoxim-methyl and trifloxystrobin fungicides by HPLC with diode array detection

The Quinone outside Inhibitors (QoI) are one of the most important and recent fungicide groups used in viticulture and also allowed by Integrated Pest Management. Azoxystrobin, kresoxim-methyl and trifloxystrobin are the main active ingredients for treating downy and powdery mildews that can be present in grapes and wines. In this paper, a method is reported for the analysis of these three QoI-fungicides in grapes and wine. After liquid–liquid extraction and a clean-up on commercial silica cartridges, analysis was by isocratic HPLC with diode array detection (DAD) with a run time of 13 min. Confirmation was by solid-phase micro-extraction (SPME), followed by GC/MS determination. The main validation parameters for the three compounds in grapes and wine were a limit of detection up to 0.073mg kg-1, a precision not exceeding 10.0% and an average recovery of 93% ±38.

Multiplexed electrochemical immunosensor for detection of celiac disease serological markers

Celiac disease (CD) is a gluten-induced autoimmune enteropathy characterized by the presence of antibodies against gliadin (AGA) and anti-tissue transglutaminase (anti-tTG) antibodies. A disposable electrochemical dual immunosensor for the simultaneous detection of IgA and IgG type AGA and antitTG antibodies in real patient’s samples is presented. The proposed immunosensor is based on a dual screen-printed carbon electrode, with two working electrodes, nanostructured with a carbon–metal hybrid system that worked as the transducer surface. The immunosensing strategy consisted of the immobilization of gliadin and tTG (i.e. CD specific antigens) on the nanostructured electrode surface. The electrochemical detection of the human antibodies present in the assayed serum samples was carried out through the antigen–antibody interaction and recorded using alkaline phosphatase labelled anti-human antibodies and a mixture of 3-indoxyl phosphate with silver ions was used as the substrate. The analytical signal was based on the anodic redissolution of enzymatically generated silver by cyclic voltammetry. The results obtained were corroborated with commercial ELISA kits indicating that the developed sensor can be a good alternative to the traditional methods allowing a decentralization of the analyses towards a point-of-care strategy.

Study of optical and structural properties of Cu2ZnSnS4 thin films

Cu2ZnSnS4 is a promising semiconductor to be used as absorber in thin film solar cells. In this work, we investigated optical and structural properties of Cu2ZnSnS4 thin films grown by sulphurization of metallic precursors deposited on soda lime glass substrates. The crystalline phases were studied by X-ray diffraction measurements showing the presence of only the Cu2ZnSnS4 phase. The studied films were copper poor and zinc rich as shown by inductively coupled plasma mass spectroscopy. Scanning electron microscopy revealed a good crystallinity and compactness. An absorption coefficient varying between 3 and 4×104cm−1 was measured in the energy range between 1.75 and 3.5 eV. The band gap energy was estimated in 1.51 eV. Photoluminescence spectroscopy showed an asymmetric broad band emission. The dependence of this emission on the excitation power and temperature was investigated and compared to the predictions of the donor-acceptor-type transitions and radiative recombinations in the model of potential fluctuations. Experimental evidence was found to ascribe the observed emission to radiative transitions involving tail states created by potential fluctuations.

Detection of ZnS phases in CZTS thin-films by EXAFS

Copper zinc tin sulfide (CZTS) is a promising Earthabundant thin-film solar cell material; it has an appropriate band gap of ~1.45 eV and a high absorption coefficient. The most efficient CZTS cells tend to be slightly Zn-rich and Cu-poor. However, growing Zn-rich CZTS films can sometimes result in phase decomposition of CZTS into ZnS and Cu2SnS3, which is generally deleterious to solar cell performance. Cubic ZnS is difficult to detect by XRD, due to a similar diffraction pattern. We hypothesize that synchrotron-based extended X-ray absorption fine structure (EXAFS), which is sensitive to local chemical environment, may be able to determine the quantity of ZnS phase in CZTS films by detecting differences in the second-nearest neighbor shell of the Zn atoms. Films of varying stoichiometries, from Zn-rich to Cu-rich (Zn-poor) were examined using the EXAFS technique. Differences in the spectra as a function of Cu/Zn ratio are detected. Linear combination analysis suggests increasing ZnS signal as the CZTS films become more Zn-rich. We demonstrate that the sensitive technique of EXAFS could be used to quantify the amount of ZnS present and provide a guide to crystal growth of highly phase pure films.

Detection of quasi-periodic processes in complex systems: how do we quantitatively describe their properties?

It has been shown that in reality at least two general scenarios of data structuring are possible: (a) a self-similar (SS) scenario when the measured data form an SS structure and (b) a quasi-periodic (QP) scenario when the repeated (strongly correlated) data form random sequences that are almost periodic with respect to each other. In the second case it becomes possible to describe their behavior and express a part of their randomness quantitatively in terms of the deterministic amplitude–frequency response belonging to the generalized Prony spectrum. This possibility allows us to re-examine the conventional concept of measurements and opens a new way for the description of a wide set of different data. In particular, it concerns different complex systems when the ‘best-fit’ model pretending to be the description of the data measured is absent but the barest necessity of description of these data in terms of the reduced number of quantitative parameters exists. The possibilities of the proposed approach and detection algorithm of the QP processes were demonstrated on actual data: spectroscopic data recorded for pure water and acoustic data for a test hole. The suggested methodology allows revising the accepted classification of different incommensurable and self-affine spatial structures and finding accurate interpretation of the generalized Prony spectroscopy that includes the Fourier spectroscopy as a partial case.

Using mobile device detection approaches to augment the accuracy of web delivery content

Recent studies of mobile Web trends show a continuous explosion of mobile-friendly content. However, the increasing number and heterogeneity of mobile devices poses several challenges for Web programmers who want to automatically get the delivery context and adapt the content to mobile devices. In this process, the devices detection phase assumes an important role where an inaccurate detection could result in a poor mobile experience for the enduser. In this paper we compare the most promising approaches for mobile device detection. Based on this study, we present an architecture for a system to detect and deliver uniform m-Learning content to students in a Higher School. We focus mainly on the devices capabilities repository manageable and accessible through an API. We detail the structure of the capabilities XML Schema that formalizes the data within the devices capabilities XML repository and the REST Web Service API for selecting the correspondent devices capabilities data according to a specific request. Finally, we validate our approach by presenting the access and usage statistics of the mobile web interface of the proposed system such as hits and new visitors, mobile platforms, average time on site and rejection rate.

Using device detection techniques in M-learning scenarios

Recent studies of mobile Web trends show the continued explosion of mobile-friend content. However, the wide number and heterogeneity of mobile devices poses several challenges for Web programmers, who want automatic delivery of context and adaptation of the content to mobile devices. Hence, the device detection phase assumes an important role in this process. In this chapter, the authors compare the most used approaches for mobile device detection. Based on this study, they present an architecture for detecting and delivering uniform m-Learning content to students in a Higher School. The authors focus mainly on the XML device capabilities repository and on the REST API Web Service for dealing with device data. In the former, the authors detail the respective capabilities schema and present a new caching approach. In the latter, they present an extension of the current API for dealing with it. Finally, the authors validate their approach by presenting the overall data and statistics collected through the Google Analytics service, in order to better understand the adherence to the mobile Web interface, its evolution over time, and the main weaknesses.

Adherend thickness effect on the tensile fracture toughness of a structural adhesive using an optical data acquisition method

Adhesive bonding is nowadays a serious candidate to replace methods such as fastening or riveting, because of attractive mechanical properties. As a result, adhesives are being increasingly used in industries such as the automotive, aerospace and construction. Thus, it is highly important to predict the strength of bonded joints to assess the feasibility of joining during the fabrication process of components (e.g. due to complex geometries) or for repairing purposes. This work studies the tensile behaviour of adhesive joints between aluminium adherends considering different values of adherend thickness (h) and the double-cantilever beam (DCB) test. The experimental work consists of the definition of the tensile fracture toughness (GIC) for the different joint configurations. A conventional fracture characterization method was used, together with a J-integral approach, that take into account the plasticity effects occurring in the adhesive layer. An optical measurement method is used for the evaluation of crack tip opening and adherends rotation at the crack tip during the test, supported by a Matlab® sub-routine for the automated extraction of these quantities. As output of this work, a comparative evaluation between bonded systems with different values of adherend thickness is carried out and complete fracture data is provided in tension for the subsequent strength prediction of joints with identical conditions.

Sensitive bi-enzymatic biosensor based on polyphenoloxidases–gold nanoparticles–chitosan hybrid film–graphene doped carbon paste electrode for carbamates detection

A bi-enzymatic biosensor (LACC–TYR–AuNPs–CS/GPE) for carbamates was prepared in a single step by electrodeposition of a hybrid film onto a graphene doped carbon paste electrode (GPE). Graphene and the gold nanoparticles (AuNPs) were morphologically characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, dynamic light scattering and laser Doppler velocimetry. The electrodeposited hybrid film was composed of laccase (LACC), tyrosinase (TYR) and AuNPs entrapped in a chitosan (CS) polymeric matrix. Experimental parameters, namely graphene redox state, AuNPs:CS ratio, enzymes concentration, pH and inhibition time were evaluated. LACC–TYR–AuNPs–CS/GPE exhibited an improved Michaelis–Menten kinetic constant (26.9 ± 0.5 M) when compared with LACC–AuNPs–CS/GPE (37.8 ± 0.2 M) and TYR–AuNPs–CS/GPE (52.3 ± 0.4 M). Using 4-aminophenol as substrate at pH 5.5, the device presented wide linear ranges, low detection limits (1.68×10− 9 ± 1.18×10− 10 – 2.15×10− 7 ± 3.41×10− 9 M), high accuracy, sensitivity (1.13×106 ± 8.11×104 – 2.19×108 ± 2.51×107 %inhibition M− 1), repeatability (1.2–5.8% RSD), reproducibility (3.2–6.5% RSD) and stability (ca. twenty days) to determine carbaryl, formetanate hydrochloride, propoxur and ziram in citrus fruits based on their inhibitory capacity on the polyphenoloxidases activity. Recoveries at two fortified levels ranged from 93.8 ± 0.3% (lemon) to 97.8 ± 0.3% (orange). Glucose, citric acid and ascorbic acid do not interfere significantly in the electroanalysis. The proposed electroanalytical procedure can be a promising tool for food safety control.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Electrospun Polyaniline-Reduced Graphene Oxide Composite Nanofibers Based High Sensitive Ammonia Gas Sensor

Ammonia is an important gas in many power plants and industrial processes so its detection is of extreme importance in environmental monitoring and process control due to its high toxicity. Ammonia’s threshold limit is 25 ppm and the exposure time limit is 8 h, however exposure to 35 ppm is only secure for 10 min. In this work a brief introduction to ammonia aspects are presented, like its physical and chemical properties, the dangers in its manipulation, its ways of production and its sources. The application areas in which ammonia gas detection is important and needed are also referred: environmental gas analysis (e.g. intense farming), automotive-, chemical- and medical industries. In order to monitor ammonia gas in these different areas there are some requirements that must be attended. These requirements determine the choice of sensor and, therefore, several types of sensors with different characteristics were developed, like metal oxides, surface acoustic wave-, catalytic-, and optical sensors, indirect gas analyzers, and conducting polymers. All the sensors types are described, but more attention will be given to polyaniline (PANI), particularly to its characteristics, syntheses, chemical doping processes, deposition methods, transduction modes, and its adhesion to inorganic materials. Besides this, short descriptions of PANI nanostructures, the use of electrospinning in the formation of nanofibers/microfibers, and graphene and its characteristics are included. The created sensor is an instrument that tries to achieve a goal of the medical community in the control of the breath’s ammonia levels being an easy and non-invasive method for diagnostic of kidney malfunction and/or gastric ulcers. For that the device should be capable to detect different levels of ammonia gas concentrations. So, in the present work an ammonia gas sensor was developed using a conductive polymer composite which was immobilized on a carbon transducer surface. The experiments were targeted to ammonia measurements at ppb level. Ammonia gas measurements were carried out in the concentration range from 1 ppb to 500 ppb. A commercial substrate was used; screen-printed carbon electrodes. After adequate surface pre-treatment of the substrate, its electrodes were covered by a nanofibrous polymeric composite. The conducting polyaniline doped with sulfuric acid (H2SO4) was blended with reduced graphene oxide (RGO) obtained by wet chemical synthesis. This composite formed the basis for the formation of nanofibers by electrospinning. Nanofibers will increase the sensitivity of the sensing material. The electrospun PANI-RGO fibers were placed on the substrate and then dried at ambient temperature. Amperometric measurements were performed at different ammonia gas concentrations (1 to 500 ppb). The I-V characteristics were registered and some interfering gases were studied (NO2, ethanol, and acetone). The gas samples were prepared in a custom setup and were diluted with dry nitrogen gas. Electrospun nanofibers of PANI-RGO composite demonstrated an enhancement in NH3 gas detection when comparing with only electrospun PANI nanofibers. Was visible higher range of resistance at concentrations from 1 to 500 ppb. It was also observed that the sensor had stable, reproducible and recoverable properties. Moreover, it had better response and recovery times. The new sensing material of the developed sensor demonstrated to be a good candidate for ammonia gas determination.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.

A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks

IEEE 802.11 is one of the most well-established and widely used standard for wireless LAN. Its Medium Access control (MAC) layer assumes that the devices adhere to the standard’s rules and timers to assure fair access and sharing of the medium. However, wireless cards driver flexibility and configurability make it possible for selfish misbehaving nodes to take advantages over the other well-behaving nodes. The existence of selfish nodes degrades the QoS for the other devices in the network and may increase their energy consumption. In this paper we propose a green solution for selfish misbehavior detection in IEEE 802.11-based wireless networks. The proposed scheme works in two phases: Global phase which detects whether the network contains selfish nodes or not, and Local phase which identifies which node or nodes within the network are selfish. Usually, the network must be frequently examined for selfish nodes during its operation since any node may act selfishly. Our solution is green in the sense that it saves the network resources as it avoids wasting the nodes energy by examining all the individual nodes of being selfish when it is not necessary. The proposed detection algorithm is evaluated using extensive OPNET simulations. The results show that the Global network metric clearly indicates the existence of a selfish node while the Local nodes metric successfully identified the selfish node(s). We also provide mathematical analysis for the selfish misbehaving and derived formulas for the successful channel access probability.