The inhibition effect of some pyrimidine derivatives on austenitic stainless steel in acidic media

5-Benzoyl-4-(substitutedphenyl)-6-phenyl-3,4-dihydropyrimidine-2(1H)-(thio)ones (DHPMs) (I and II) were synthesized using the Biginelli three component cyclocondensation reaction of an appropriate β-diketone, arylaldehyde, and (thio) urea. The effect of these corrosion inhibitors on the corrosion of austenitic stainless steel in 0.5 M H₂SO₄ has been studied by electrochemical methods using Tafel plot, linear polarization and electrochemical impedance spectroscopy at 298 K. The inhibition efficiencies obtained from all the methods employed are in good agreement. The adsorption of the DHPMs onto the stainless steel surface was found to follow Langmuir and Dubinin–Radushkevich adsorption isotherm models. Negative values of ΔG_ads in the acidic media ensured the spontaneity of the adsorption process. Results show DHPM I to be the best inhibitor with a mean efficiency of 91% at 2 × 10⁻³ M additive concentration.     

Mycoflora and aflatoxin contamination in shelled pistachio nuts

A total of 143 pistachio nut samples collected during harvest, storage and processing were examined for mould growth and aflatoxin production. The mould count was in the range of 10³?10⁴ cfu g^?1 and 10⁵?10⁶ cfu g^?1 for the harvest and storage samples, respectively. The growth of Aspergillus flavus was 38-5-39-5% on the surface of the shells and 6–16% on the kernels without aflatoxin production. The contamination level of A flavus varied among samples collected from different regions. Peeling off the soft shell of pistachio nuts by hand reduced the contamination risk of A flavus to kernels. The predominant flora on stored pistachio nuts were Aspergillus, Penicillium, Cladosporium, Rhizopus, while the genera of Ulocladium, Trichothecium, Aureobasidium and Eurotium were less frequent. Thirty-five percent of the A flavus isolates produced aflatoxins on synthetic media.     

Effect of different drying methods on the essential oil yield,composition and antioxidant activity of <Emphasis Type="Italic">Origanum vulgare</Emphasis> L. and <Emphasis Type="Italic">Origanum onites</Emphasis> L.

Origanum onites and Origanum vulgare are important medicinal plants because of their flavoring, therapeutic, and preservative properties. Drying methods are important for obtaining high quality essential oil from spices. In this study, the effects of different drying processes on the essential oil yield, composition, and antiradical activity were evaluated. Plants were dried under sun light, in a ventilated shady place and in laboratory-type oven, and the oils were extracted with Clevenger type apparatus. Carvacrol and thymol were the major compounds in the essential oils of O. vulgare and O. onites, respectively. The highest oil yield and the highest antioxidant activity values were obtained from shade dried Origanum species followed by oven dried plants. The lowest essential oil yield and the lowest antioxidant activity for O. vulgare were found in fresh plants. The lowest essential oil yield, and the lowest antioxidant activity for O. onites belonged to sun dried plants. It could be concluded that essential oil yield, oil composition and antioxidant activity of both plants were greatly affected by the drying method.     

Double-Tailed Single-Head Amino Acid-Based Chiral Cationic Amphiphilic Molecules: Synthesis,Characterization, and Physicochemical Properties

Two double-tailed single-head amino acid-based chiral cationic amphiphilic molecules, l -aspartic acid hydrochloride dihexylester (L-ADHE, C₆–C₆) and l -aspartic acid hydrochloride dioctylester (L-ADOE, C₈–C₈), were synthesized. ¹H NMR, ¹³C NMR, and Fourier transform infrared (FT-IR) were used to characterize the structures of the esters. Micellization, surface, and structural properties of L-ADHE and L-ADOE in aqueous solution were studied by means of a number of techniques including conductivity, pH, surface tension, density, zeta potential, and dynamic light scattering (DLS). The critical micelle concentration (CMC) of L-ADHE and L-ADOE were found to be very low compared with those of corresponding single-tailed amino acid-based amphiphilic molecules. The pK_a values of L-ADHE and L-ADOE in aqueous solutions were determined via pH measurements, and it was observed that they had acidic properties giving low pK_a values. The excellent adsorption behaviors at the air/water interface of L-ADHE and L-ADOE in aqueous medium were obtained, i.e. they efficiently lowered the surface tension of water at CMC down approximately to 24.3 and 23.4 mN m⁻¹, respectively. From the density measurements, the volume change due to the micellization process for L-ADOE was found to be quite high compared to L-ADHE, i.e. about three times more. This result and the data obtained from the DLS revealed that the increase in aggregated structures of L-ADOE may be due to vesicle formation. The dye solubilization study was performed by UV–vis absorption spectroscopy for L-ADHE. Besides, the CMC value of L-ADHE was also successfully determined without using any dye through UV–vis spectroscopy.     

Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks

This study presents an artificial neural network (ANN) model to predict the torque and brake specific fuel consumption of a gasoline engine. An explicit ANN based formulation is developed to predict torque and brake specific fuel consumption of a gasoline engine in terms of spark advance, throttle position and engine speed. The proposed ANN model is based on experimental results. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. An ANN model based on a back-propagation learning algorithm for the engine was developed. The performance and an accuracy of the proposed ANN model are found satisfactory. This study demonstrates that ANN is very efficient for predicting the engine torque and brake specific fuel consumption. Moreover, the proposed ANN model is presented in explicit form as a mathematical function.     

Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine

This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this study is to develop an alternative robust formulations based on experimental data and to verify the use of GP for generating the formulations for gasoline engine torque and brake specific fuel consumption. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. Considerable good performance was achieved in predicting gasoline engine torque and brake specific fuel consumption by using GP. The performance of accuracies of proposed GP models are quite satisfactory (R² = 0.9878 for gasoline engine torque and R² = 0.9744 for gasoline engine brake specific fuel consumption). The prediction of proposed GP models were compared to those of the neural network modeling, and strictly good agreement was observed between the two predictions. The proposed GP formulation is quite accurate, fast and practical.     

Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method

Spark ignition (SI) engines have a nonlinear dynamic system with inherent uncertainties and unpredictable disturbances. The identification of a nonlinear system is vital in many fields of engineering. In this study, SI engine torque is identified from an input–output measurement. This study aims to propose a dynamic nonlinear model that uses an adaptive neuro‐fuzzy inference system and a nonlinear auto‐regressive with exogenous input structure to identify the dynamic nonlinear behavior of an SI engine. Considerable good performance is achieved using the adaptive neuro‐fuzzy inference system nonlinear auto‐regressive with exogenous input method. For model validation, the proposed method is compared with the more conventional identification approach called the Hammerstein method. The results show that the two methods are in excellent agreement. The Hammerstein model was chosen because its identification result of the SI system was studied previously by the author. Validation results prove that the ability of the proposed model can capture the highly nonlinear behavior of the SI system.