Organophosphorus (OP) compounds are one of the most hazardous chemicals used as insecticides/pesticide in agricultural practices. A large variety of OP compounds are hydrolyzed by organophosphorus hydrolases (OPH; EC 3.1.8.1). Therefore, OPHs are among the most suitable candidates which could be used in designing enzyme-based sensors for detecting OP compounds. In the present work, a novel nanobiosensor for the detection of paraoxon was designed and fabricated. More specifically, OPH was covalently embedded onto chitosan and the enzyme-chitosan bioconjugate was then immobilized on negatively charged gold nanoparticles (AuNPs) electrostatically. The enzyme was immobilized on AuNPs without chitosan as well to compare the two systems in terms of detection limit and enzyme stability under different pH and temperature conditions. Coumarin 1, a competitive inhibitor of the enzyme, was used as a fluorogenic probe. The emission of coumarin 1 was effectively quenched by the immobilized Au-NPs when bound to the developed nanobioconjugates. However, in the presence of paraoxon, coumarin 1 left the nanobioconjugate leading to enhanced fluorescence intensity. Moreover, compared to the immobilized enzyme without chitosan, the chitosan-immobilized enzyme was found to possess decreased Km value by over 50%, increased Vmax and Kcat values by around 15% and 74%, respectively. Higher stability within a wider range of pH (2-12) and temperature (25-90°C) was also achieved. The method worked in the 0 to 1050 nM concentration ranges, and had a detection limit as low as 5 × 10(-11) M.
Integrated environmental analysis using life cycle assessment for different fuel blends used in a single-cylinder diesel engine was performed to select the most eco-friendly fuel blend. More specifically, the inventory data in support of the integrated environmental analysis of water-emulsified 5% biodiesel/diesel blends (B5) containing different levels of carbon nanoparticles (i.e., 38, 75, and 150 µM) as a novel fuel nanoadditives at a fixed engine speed of 1000 rpm and four different engine loads (i.e., 25, 50, 75, and 100%) are presented. Neat diesel, B5, and B5 containing water (3 wt.%) were used as controls. Raw data related to the production and combustion of fuel blends were experimentally collected. Industrial (i.e., experiments at large scale) and laboratory (i.e., experiments at small scale) data were used for fuel blends production while experimental data obtained by engine tests were used for the combustion stage. Then raw data were processed with the IMPACT 2002+ methods by using the SimaPro software and EcoInvent database and were then converted into environmental impacts. Accordingly, six supplementary files including the inventory data on integrated environmental analysis of the different fuel blends are presented (Supplementary Files 1-6). The data could be applied for integrated environmental analysis in order to avoid subjective weighting of combustion parameters for selecting the most eco-friendly fuel blend for use in diesel engines. More specifically, by developing a single score indicator obtained through conducting integrated combustion analysis, comparison of various fuel blends is largely facilitated.
This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 μM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE