Molecularly imprinted electrochemical sensor for ochratoxin A detection in food samples

A novel electrochemical sensor for ochratoxin A (OTA) detection was fabricated through the modification of a glassy carbon electrode (GCE) with multiwalled carbon nanotubes (MWCNTs) and a molecularly imprinted polymer (MIP). The MWCNTs dramatically promoted the sensitivity of the developed sensor, while polypyrrole (PPy) imprinted with OTA served as the selective recognition element. The imprinted PPy film was prepared by electropolymerization of pyrrole in the presence of OTA as a template molecule via cyclic voltammetry (CV). The electrochemical oxidation of OTA at the developed sensor was investigated by CV and differential pulse voltammetry (DPV). The developed MIP/MWCNT/GCE sensor showed a linear relationship, when using DPV, between peak current intensity and OTA concentration in the range between 0.050 and 1.0 μM, with limits of detection (LOD) and quantification of 0.0041 μM (1.7 μg/L) and 0.014 μM (5.7 μg/L) respectively. With the developed sensor precise results were obtained; relative standard deviations of 4.2% and 7.5% in the evaluation of the repeatability and reproducibility, respectively. The MIP/MWCNT/GCE sensor is simple to fabricate and easy to use and was successfully applied to the determination of OTA in spiked beer and wine samples, with recoveries between 84 and 104%, without the need of a sample pre-treatment step.

“Green Electrodes” Modified with Au Nanoparticles Synthesized in Glycerol, as Electrochemical Nitrite Sensor

A new environmentally friendly Au nanoparticles (Au NPs) synthesis in glycerol by using ultraviolet irradiation and without extra-added stabilizers is described. The synthesis proposed in this work may impact on the non-polluting production of noble nanoparticles with simple chemicals normally found in standard laboratories. These Au NPs were used to modify a carbon paste electrode (CPE) without having to separate them from the reaction medium. This green electrode was used as an electrochemical sensor for the nitrite detection in water. At the optimum conditions the green sensor presented a linear response in the 2.0×10−7–1.5×10−5 M concentration range, a good detection sensitivity (0.268 A L mol−1), and a low detection limit of 2.0×10−7 M of nitrite. The proposed modified green CPE was used to determine nitrite in tap water samples.

MnFe2O4@CNT-N as novel electrochemical nanosensor for determination of caffeine, acetaminophen and ascorbic acid

For the first time, a glassy carbon electrode (GCE) modified with novel N-doped carbon nanotubes (CNT-N) functionalized with MnFe2O4 nanoparticles (MnFe2O4@CNT-N) has been prepared and applied for the electrochemical determination of caffeine (CF), acetaminophen (AC) and ascorbic acid (AA). The electrochemical behaviour of CF, AC and AA on the bare GCE, CNT-N/GCE and MnFe2O4@CNT-N/GCE were carefully investigated using cyclic voltammetry (CV) and square-wave voltammetry (SWV). Compared to bare GCE and CNT-N modified electrode, the MnFe2O4@CNT-N modified electrode can remarkably improve the electrocatalytic activity towards the oxidation of CF, AC and AA with an increase in the anodic peak currents of 52%, 50% and 55%, respectively. Also, the SWV anodic peaks of these molecules could be distinguished from each other at the MnFe2O4@CNT-N modified electrode with enhanced oxidation currents. The linear response ranges for the square wave voltammetric determination of CF, AC and AA were 1.0 × 10−6 to 1.1 × 10−3 mol dm−3, 1.0 × 10−6 to 1.0 × 10−3 mol dm−3 and 2.0 × 10−6 to 1.0 × 10−4 mol dm−3 with detection limit (S/N = 3) of 0.83 × 10−6, 0.83 × 10−6 and 1.8 × 10−6 mol dm−3, respectively. The sensitivity values at the MnFe2O4@CNT-N/GCE for the individual determination of AC, AA and CF and in the presence of the other molecules showed that the quantification of AA and CF show no interferences from the other molecules; however, AA and CF interfered in the determination of AC, with the latter molecule showing the strongest interference. Nevertheless, the obtained results show that MnFe2O4@CNT-N composite material acted as an efficient electrochemical sensor towards the selected biomolecules.

Multi-agent simulation of competitive electricity markets: Autonomous systems cooperation for European market modeling

The electricity market restructuring, and its worldwide evolution into regional and even continental scales, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in a rising complexity in power systems operation. Several power system simulators have been developed in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex and constantly changing environment. The main contribution of this paper is given by the integration of several electricity market and power system models, respecting to the reality of different countries. This integration is done through the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The continuous development of Multi-Agent System for Competitive Electricity Markets platform provides the means for the exemplification of the usefulness of this ontology. A case study using the proposed multi-agent platform is presented, considering a scenario based on real data that simulates the European Electricity Market environment, and comparing its performance using different market mechanisms. The main goal is to demonstrate the advantages that the integration of various market models and simulation platforms have for the study of the electricity markets’ evolution.

Electrochemical sensor for simultaneous determination of herbicide MCPA and its metabolite 4-chloro-2-methylphenol. Application to photodegradation environmental monitoring

The development and application of a polyaniline/carbon nanotube (CNT) cyclodextrin matrix (PANI-β-CD/MWCNT)-based electrochemical sensor for the quantitative determination of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) and its main transformation product 4-chloro-2-methylphenol in natural waters are described. A simple cyclic voltammetry-based electrochemical methodology, in phosphate buffer solution at pH 6.0, was used to develop a method to determine both MCPA and 4-chloro-2-methylphenol, without any previous extraction or derivatization steps. A linear concentration range (10 to 50 μmol L−1) and detection limits of 1.1 and 1.9 μmol L−1, respectively, were achieved using optimized cyclic voltammetric parameters. The proposed method was successfully applied to the determination of MCPA and 4-chloro-2-methylphenol in natural water samples with satisfactory recoveries (94 to 107 %) and in good agreement with the results obtained by an established high-performance liquid chromatography technique, no significant differences being found between the methods. Interferences from ionic species and other herbicides used for broad-leaf weed control were shown to be small. The newly developed methodology was also successfully applied to MCPA photodegradation environmental studies.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Electrochemical characterization of Dps, a DNA-protecting protein

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Modeling assembly program with constraints. A contribution to WCET problem

Dissertação para obtenção do Grau de Mestre em Lógica Computacional

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Spatio-Temporal data modeling in response to deforestation monitoring (a case study of small region in Riau Province, Indonesia)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Hydrologic modeling and uncertainty analysis of an ungauged watershed using mapwindow-swat

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

Stochastic modeling of the thermal and catalytic degradation of polyethylene using simultaneous DSC/TG analysis

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

New spectroscopic and electrochemical insights on a class I superoxide reductase: evidence for an intramolecular electron-transfer pathway

Biochem. J. (2011) 438,485–494 doi:10.1042/BJ20110836