Vertical CNT-Si Photodiode Array

Agências financiadoras: National Natural Science Foundation of China - 61204077; Shenzhen Science and Technology Innovation Commission - JCYJ20120614150521967

Square-wave voltametric method for determination of molinate concentration in a biological process using a hanging mercury drop electrode

A square-wave voltammetric (SWV) method using a hanging mercury drop electrode (HMDE) has been developed for determination of the herbicide molinate in a biodegradation process. The method is based on controlled adsorptive accumulation of molinate for 10 s at a potential of -0.8 V versus AgCl/Ag. An anodic peak, due to oxidation of the adsorbed pesticide, was observed in the cyclic voltammogram at ca. -0.320 V versus AgCl/Ag; a very small cathodic peak was also detected. The SWV calibration plot was established to be linear in the range 5.0x10-6 to 9.0x10-6 mol L-1; this corresponded to a detection limit of 3.5x10-8 mol L-1. This electroanalytical method was used to monitor the decrease of molinate concentration in river waters along a biodegradation process using a bacterial mixed culture. The results achieved with this voltammetric method were compared with those obtained by use of a chromatographic method (HPLC–UV) and no significant statistical differences were observed.

Deteção eletroquímica da norfloxacina e trimetoprim em amostras de urina utilizando polímero molecularmente impresso modificado e grafeno

A norfloxacina e o trimetoprim são dois antibióticos antibacterianos usados para o tratamento de infeções urinárias, intestinais e respiratórias. A maioria dos fármacos exige uma dosagem que garanta os níveis de segurança e eficácia de atuação. A necessidade de dosear os medicamentos e os seus metabolitos é assim um controlo imperioso e em muitos casos regular no tratamento de um paciente. Neste trabalho desenvolveram-se dois sensores eletroquímicos para a deteção da norfloxacina (NFX) e do trimetoprim (TMP), usando como superfície de suporte o carbono vítreo. A busca de novos materiais que conferiram maior seletividade e sensibilidade aos sistemas de deteção e por outro lado apresentem menores riscos para o paciente quando usados em dispositivos que permitam uma análise point-of-care, é especialmente importante e pode ser uma parte crucial do processo de decisão clínica. Assim, os polímeros molecularmente impresos enquadram-se nesse perfil e o seu uso tem vindo a ser cada vez mais avaliado. A impressão molecular é uma tecnologia capaz de produzir polímeros que incorporam as moléculas do analito e que após remoção por solventes específicos, permitem dotá-los de locais específicos de reconhecimento estereoquímico. A seleção do pirrol como polímero molecularmente impresso (MIP) permitiu construir com sucesso os sensores para doseamento dos antibióticos. A fim de aumentar a sensibilidade do método incorporou-se grafeno na superfície do elétrodo. Este material tem vindo a ser largamente utilizado devido às suas propriedades: estrutura molecular, condutividade elétrica e aumento da superfície são algumas das características que mais despertam o interesse para a sua aplicação neste projeto. Os sensores desenvolvidos foram incorporados em sistemas eletroquímicos. Os métodos voltamétricos aplicados foram a voltametria cíclica, a voltametria de onda quadrada e ainda a impedância. As condições de análise foram otimizadas no que respeita à polimerização do pirrol (concentração do polímero, número de ciclos de eletropolimerização e respetivos potenciais aplicados, tempo de incubação, solvente de remoção do analito), ao pH da solução do fármaco, à gama de concentrações dos antibióticos e aos parâmetros voltamétricos dos métodos de análise. Para cada um dos antibióticos um elétrodo não-impresso foi também preparado, usando o procedimento de polimerização mas sem a presença da molécula do analito, e foi usado como controlo. O sensor desenvolvido para o trimetoprim foi usado no doseamento do fármaco em amostras de urina. As amostras foram analisadas sem qualquer diluição, apenas foram centrifugadas para remoção de proteínas e algum interferente. Os sensores construídos apresentaram comportamento linear na gama de concentrações entre 102 e 107 mol/L. Os resultados mostram boa precisão (desvios padrão inferiores a 11%) e os limites de deteção foram de 8,317 e 1,307 mol/L para a norfloxacina e o trimetoprim, respetivamente. Para validação do método foram ainda efetuados ensaios de recuperação tendo obtido valores superiores a 94%.

Biosensor based on multi-walled carbon nanotubes paste electrode modified with laccase for pirimicarb pesticide quantification

This study focused on the development of a sensitive enzymatic biosensor for the determination of pirimicarb pesticide based on the immobilization of laccase on composite carbon paste electrodes. Multi- walled carbon nanotubes(MWCNTs)paste electrode modified by dispersion of laccase(3%,w/w) within the optimum composite matrix(60:40%,w/w,MWCNTs and paraffin binder)showed the best performance, with excellent electron transfer kinetic and catalytic effects related to the redox process of the substrate4- aminophenol. No metal or anti-interference membrane was added. Based on the inhibition of laccase activity, pirimicarb can be determined in the range 9.90 ×10- 7 to 1.15 ×10- 5 molL 1 using 4- aminophenol as substrate at the optimum pH of 5.0, with acceptable repeatability and reproducibility (relative standard deviations lower than 5%).The limit of detection obtained was 1.8 × 10-7 molL 1 (0.04 mgkg 1 on a fresh weight vegetable basis).The high activity and catalytic properties of the laccase- based biosensor are retained during ca. one month. The optimized electroanalytical protocol coupled to the QuEChERS methodology were applied to tomato and lettuce samples spiked at three levels; recoveries ranging from 91.0±0.1% to 101.0 ± 0.3% were attained. No significant effects in the pirimicarb electro- analysis were observed by the presence of pro-vitamin A, vitamins B1 and C,and glucose in the vegetable extracts. The proposed biosensor- based pesticide residue methodology fulfills all requisites to be used in implementation of food safety programs.

Laccase–Prussian blue film–graphene doped carbon paste modified electrode for carbamate pesticides quantification

A novel enzymatic biosensor for carbamate pesticides detection was developed through the direct immobilization of Trametes versicolor laccase on graphene doped carbon paste electrode functionalized with Prussianblue films (LACC/PB/GPE). Graphene was prepared by graphite sonication-assisted exfoliation and characterized by transmission electron microscopy and X-ray photoelectron spectro- scopy. The Prussian blue film electrodeposited onto graphene doped carbon paste electrode allowed considerable reduction of the charge transfer resistance and of the capacitance of the device.The combined effects of pH, enzyme concentration and incubation time on biosensor response were optimized using a 23 full-factorial statistical design and response surface methodology. Based on the inhibition of laccase activity and using 4-aminophenol as redox mediator at pH 5.0,LACC/PB/GPE exhibited suitable characteristics in terms of sensitivity, intra-and inter-day repeatability (1.8–3.8% RSD), reproducibility (4.1 and 6.3%RSD),selectivity(13.2% bias at the higher interference: substrate ratios tested),accuracy and stability(ca. twenty days)for quantification of five carbamates widely applied on tomato and potato crops.The attained detection limits ranged between 5.2×10−9 mol L−1(0.002 mg kg−1 w/w for ziram)and 1.0×10−7 mol L−1 (0.022 mg kg−1 w/w for carbofuran).Recovery values for the two tested spiking levels ranged from 90.2±0.1%(carbofuran)to 101.1±0.3% (ziram) for tomato and from 91.0±0.1%(formetanate)to 100.8±0.1%(ziram)for potato samples.The proposed methodology is appropriate to enable testing pesticide levels in food samples to fit with regulations and food inspections.

Electrochemical determination of the herbicide bentazone using a carbon nanotube b-Cyclodextrin modified electrode

An electrochemical sensor has been developed for the determination of the herbicide bentazone, based on a GC electrode modified by a combination of multiwalled carbon nanotubes (MWCNT) with b-cyclodextrin (b-CD) incorporated in a polyaniline film. The results indicate that the b-CD/MWCNT modified GC electrode exhibits efficient electrocatalytic oxidation of bentazone with high sensitivity and stability. A cyclic voltammetric method to determine bentazone in phosphate buffer solution at pH 6.0, was developed, without any previous extraction, clean-up, or derivatization steps, in the range of 10–80 mmolL 1, with a detection limit of 1.6 mmolL 1 in water. The results were compared with those obtained by an established HPLC technique. No statistically significant differences being found between both methods.

Antioxidant properties of hydroxycinnamic acids: a review of structure- activity relationships

Hydroxycinnamic acids (HCAs) are important phytochemicals possessing significant biological properties. Several investigators have studied in vitro antioxidant activity of HCAs in detail. In this review, we have gathered the studies focused on the structure-activity relationships (SARs) of these compounds that have used medicinal chemistry to generate more potent antioxidant molecules. Most of the reports indicated that the presence of an unsaturated bond on the side chain of HCAs is vital to their activity. The structural features that were reported to be of importance to the antioxidant activity were categorized as follows: modifications of the aromatic ring, which include alterations in the number and position of hydroxy groups and insertion of electron donating or withdrawing moieties as well as modifications of the carboxylic function that include esterification and amidation process. Furthermore, reports that have addressed the influence of physicochemical properties including redox potential, lipid solubility and dissociation constant on the antioxidant activity were also summarized. Finally, the pro-oxidant effect of HCAs in some test systems was addressed. Most of the investigations concluded that the presence of ortho-dihydroxy phenyl group (catechol moiety) is of significant importance to the antioxidant activity, while, the presence of three hydroxy groups does not necessarily improve the activity. Optimization of the structure of molecular leads is an important task of modern medicinal chemistry and its accomplishment relies on the careful assessment of SARs. SAR studies on HCAs can identify the most successful antioxidants that could be useful for management of oxidative stress-related diseases.

β-Lactams: chemical structure, mode of action and mechanisms of resistance

This synopsis summarizes the key chemical and bacteriological characteristics of β-lactams, penicillins, cephalosporins, carbanpenems, monobactams and others. Particular notice is given to first-generation to fifth-generation cephalosporins. This reviewalso summarizes the main resistancemechanism to antibiotics, focusing particular attention to those conferring resistance to broad-spectrum cephalosporins by means of production of emerging cephalosporinases (extended-spectrum β-lactamases and AmpC β-lactamases), target alteration (penicillin-binding proteins from methicillin-resistant Staphylococcus aureus) and membrane transporters that pump β-lactams out of the bacterial cell.

Mining protein structure data

The principal topic of this work is the application of data mining techniques, in particular of machine learning, to the discovery of knowledge in a protein database. In the first chapter a general background is presented. Namely, in section 1.1 we overview the methodology of a Data Mining project and its main algorithms. In section 1.2 an introduction to the proteins and its supporting file formats is outlined. This chapter is concluded with section 1.3 which defines that main problem we pretend to address with this work: determine if an amino acid is exposed or buried in a protein, in a discrete way (i.e.: not continuous), for five exposition levels: 2%, 10%, 20%, 25% and 30%. In the second chapter, following closely the CRISP-DM methodology, whole the process of construction the database that supported this work is presented. Namely, it is described the process of loading data from the Protein Data Bank, DSSP and SCOP. Then an initial data exploration is performed and a simple prediction model (baseline) of the relative solvent accessibility of an amino acid is introduced. It is also introduced the Data Mining Table Creator, a program developed to produce the data mining tables required for this problem. In the third chapter the results obtained are analyzed with statistical significance tests. Initially the several used classifiers (Neural Networks, C5.0, CART and Chaid) are compared and it is concluded that C5.0 is the most suitable for the problem at stake. It is also compared the influence of parameters like the amino acid information level, the amino acid window size and the SCOP class type in the accuracy of the predictive models. The fourth chapter starts with a brief revision of the literature about amino acid relative solvent accessibility. Then, we overview the main results achieved and finally discuss about possible future work. The fifth and last chapter consists of appendices. Appendix A has the schema of the database that supported this thesis. Appendix B has a set of tables with additional information. Appendix C describes the software provided in the DVD accompanying this thesis that allows the reconstruction of the present work.

The effect of fractional order in variable structure control

This paper studies fractional variable structure controllers. Two cases are considered namely, the sliding reference model and the control action, that are generalized from integer into fractional orders. The test bed consists in a mechanical manipulator and the effect of the fractional approach upon the system performance is evaluated. The results show that fractional dynamics, both in the switching surface and the control law are important design algorithms in variable structure controllers.

Effects of team task structure on team climate for innovation and team outcomes

In this cross-sectional study we analyzed, whether team climate for innovation mediates the relationship between team task structure and innovative behavior, job satisfaction, affective organizational commitment, and work stress. 310 employees in 20 work teams of an automotive company participated in this study. 10 teams had been changed from a restrictive to a more self-regulating team model by providing task variety, autonomy, team-specific goals, and feedback in order to increase team effectiveness. Data support the supposed causal chain, although only with respect to team innovative behavior all required effects were statistically significant. Longitudinal designs and larger samples are needed to prove the assumed causal relationships, but results indicate that implementing self-regulating teams might be an effective strategy for improving innovative behavior and thus team and company effectiveness.

Management Structure and Work Team Processes; Responsibilities and Responsiveness

This paper explores the management structure of the team-based organization. First it provides a theoretical model of structures and processes of work teams. The structure determines the team’s responsibilities in terms of authority and expertise about specific regulation tasks. The responsiveness of teams to these responsibilities are the processes of teamwork, in terms of three dimensions, indicating to what extent teams indeed use the space provided to them. The research question that this paper addresses is to what extent the position of responsibilities in the team-based organization affect team responsiveness. This is done by two hypotheses. First, the effect of the so-called proximity of regulation tasks is tested. It is expected that the responsibility for tasks positioned higher in the organization (i.e. further from the team) generally has a negative effect on team responsiveness, whereas tasks positioned lower in the organization (i.e. closer to the team) will have a positive effect on the way in which teams respond. Second, the relationship between the number of tasks for which the team is responsible with team responsiveness is tested. Theory suggests that teams being responsible for a larger number of tasks perform better, i.e. show higher responsiveness. These hypotheses are tested by a study of 109 production teams in the automotive industry. The results show that, as the theory predicts, increasing numbers of responsibilities have positive effects on team responsiveness. However, the delegation of expertise to teams seems to be the most important predictor of responsiveness. Also, not all regulation tasks show to have effects on team responsiveness. Most tasks do not show to have any significant effect at all. A number of tasks affects team responsiveness positively, when their responsibility is positioned lower in the organization, but also a number of tasks affects team responsiveness positively when located higher in the organization, i.e. further from the teams in the production. The results indicate that more attention can be paid to the distribution of responsibilities, in particular expertise, to teams. Indeed delegating more expertise improve team responsiveness, however some tasks might be located better at higher organizational levels, indicating that there are limitations to what responsibilities teams can handle.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Bifacial dye-sensitized solar cells: a strategy to enhance overall efficiency based on transparent polyaniline electrode

Dye-sensitized solar cell (DSSC) is a promising solution to global energy and environmental problems because of its clean, low-cost, high efficiency, good durability, and easy fabrication. However, enhancing the efficiency of the DSSC still is an important issue. Here we devise a bifacial DSSC based on a transparent polyaniline (PANI) counter electrode (CE). Owing to the sunlight irradiation simultaneously from the front and the rear sides, more dye molecules are excited and more carriers are generated, which results in the enhancement of short-circuit current density and therefore overall conversion efficiency. The photoelectric properties of PANI can be improved by modifying with 4-aminothiophenol (4-ATP). The bifacial DSSC with 4-ATP/PANI CE achieves a light-to-electric energy conversion efficiency of 8.35%, which is increased by ,24.6% compared to the DSSC irradiated from the front only. This new concept along with promising results provides a new approach for enhancing the photovoltaic performances of solar cells.

Analysis of the nano-surface of a modified glassy carbon electrode by pseudo phase plane method

This paper presents the Pseudo phase plane (PPP) method for detecting the existence of a nanofilm on the nitroazobenzene-modified glassy carbon electrode (NAB-GC) system. This modified electrode systems and nitroazobenze-nanofilm were prepared by the electrochemical reduction of diazonium salt of NAB at the glassy carbon electrodes (GCE) in nonaqueous media. The IR spectra of the bare glassy carbon electrodes (GCE), the NAB-GC electrode system and the organic NAB film were recorded. The IR data of the bare GC, NAB-GC and NAB film were categorized into five series consisting of FILM1, GC-NAB1, GC1; FILM2, GC-NAB2, GC2; FILM3, GC-NAB3, GC3 and FILM4, GC-NAB4, GC4 respectively. The PPP approach was applied to each group of the data of unmodified and modified electrode systems with nanofilm. The results provided by PPP method show the existence of the NAB film on the modified GC electrode.