Real-time measurement of oligomeric species in secondary organic aerosol with the aerosol time-of-flight mass spectrometer

Real-time detection of oligomers in secondary organic aerosols has been carried out with an aerosol time-of-flight mass spectrometer sampling particles generated in a smog chamber. The photooxidation products of 1,3,5-trimethylbenzene and NOx were studied over a range of initial 1,3,5-trimethylbenzene concentrations (137-1180 ppb), while keeping the 1,3,5-trimethylbenzene to NOx ratio nearly constant. The photooxidation products of a mixture of alpha-pinene (initial concentration 191 ppb), 1,3,5-trimethylbenzene (60 ppb), and NOx were also investigated. In both systems, ions were observed in the single-particle mass spectra up to 750 Da; the species observed differed in the two systems. These high-mass ions occur with characteristic spacing of 14 and 16 Da, indicative of oligomeric species. The results obtained agree well with off-line (matrix-assisted) laser desorption/ionization mass spectrometry results. The real-time capabilities of the aerosol time-of-flight mass spectrometer make it possible to investigate the temporal development of the oligomers with 5-min time resolution and also demonstrate that there are certain ions within the oligomer population that occur in nearly all of the particles and with relatively high signal intensity, suggesting that these ions have higher stability or that the species are formed preferentially.     

Energy and exergy analyses of an integrated SOFC and coal gasification system

This paper examines an integrated gasification and solid oxide fuel cell (SOFC) system with a gas turbine and steam cycle that uses heat recovery of the gas turbine exhaust. Energy and exergy analyses are performed with two different types of coal. For the two different cases, the energy efficiency of the overall system is 38.1% and 36.7%, while the exergy efficiency is 27% and 23.2%, respectively. The effects of changing the reference temperature on the exergy destruction and exergy efficiency of different components are also reported. A parametric study on the effects of changing the pressure ratio on the component performance is presented.     

Influence of fermentation temperature on volatile thiols concentrations in Sauvignon blanc wines

The effect of Saccharomyces cerevisiae strains on the amount of 4-mercapto-4-methylpentan-2-one, a major varietal aroma of Sauvignon blanc wines, was demonstrated by previous research work. However, the influence of different alcoholic fermentation parameters on the levels of volatile thiols (4-mercapto-4-methylpentan-2-one, 3-mercaptohexan-1-ol and 3-mercaptohexyl acetate) in wines has not yet been investigated. The impact of fermentation temperature on the final amount of volatiles thiols and on some other analytical parameters (ethanol, total acidity, residual sugars, volatile acidity) was determined in a model medium and in grape juice. Interaction between fermentation temperature and yeast strain was also tested. The fermentation temperature influenced the amount of volatile thiols irrespective of the yeast strain used. The final levels of 4MMP and 3MH in model medium and in wines were higher when the alcoholic fermentation is conducted at 20 degrees C than at 13 degrees C. The 3MHA, which was correlated with the amount of 3MH determined in wines, was also higher when the alcoholic fermentation was conducted at 20 degrees C. From a technological point of view, the choice of yeast strain and fermentation temperature has a decisive influence on the concentrations of the varietal aromas of Sauvignon blanc wines.     

Guest Editor's Introduction: Special Section on Power-Aware Parallel and Distributed Computing (PAPADS)

The ten papers in this special section focus on power-aware parallel and distributed computing (PAPADS).     

Durability of PEM fuel cell cathode in the presence of Fe and Al

The contamination effects of Fe³⁺ and Al³⁺ on the performance of polymer electrolyte membrane fuel cells were investigated by continuously injecting Fe³⁺ or Al³⁺ salt solution into the air stream of an operating fuel cell. Both metal ions individually caused significant cell performance degradation at a level of only 5 ppm mol in air. In addition, elevated temperature accelerated fuel cell performance degradation in the presence of Fe³⁺. Moreover, the presence of Fe³⁺ in an operating fuel cell resulted in the cell's sudden death, due to the formation of membrane pinholes that may have been promoted by the enhanced production of peroxy radicals catalyzed by Fe species. Half-cell tests in liquid electrolyte revealed that the presence of Al³⁺ in the electrolyte changed the kinetics and mechanisms of the oxygen reduction reaction by reducing the kinetic current densities and the electron transfer number.     

Degradation of Interfacial Chemistry of Epoxy/Silane/Aluminium Interfaces as a Result of Aqueous Attack

The degradation of a thin layer of adhesive on a grit-blasted aluminium substrate, as a result of aqueous attack, was investigated and compared with the behavior of the adhesive on a grit-blasted aluminium substrate treated with γ-glycidoxypropyl trimethoxy silane (GPS). The degradation study was achieved by examining aluminium coupons treated with adhesive that had been immersed in water at 25°C and an elevated temperature (50°C) for various treatment times ranging between 10 min and 1 day. All samples were characterized using X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). XPS and ToF-SIMS data indicated that the adhesive layer on both types of substrate was readily displaced by water. This is shown to be a two-stage process with bond rupture being identified by ToF-SIMS analysis and the displacement of the organic phase occurring at a later stage, as indicated by the XPS analysis, which showed a reduction in surface carbon concentration. When the substrates were directly in contact with water, a hydration process occurred and hydrated oxide species were formed on the surfaces. The results indicated that the hydration process was a postfailure event.     

A methodology for measuring interface fracture properties of composite materials

A methodology is presented for measuring interface fracture properties of composite materials. A bimaterial Brazilian disk specimen with a crack along the interface is employed. The specimen is analyzed by means of the finite element method and a conservative integral to determine stress intensity factors as a function of loading angle and crack length. A weight function is employed to determine the effect of residual curing stresses on the stress intensity factors. These are combined to determine the critical interface energy release rate _ic as a function of stress intensity phase angle for tests carried out on a glass/epoxy material pair.     

A 2 GHz Phase-Locked Loop Frequency Synthesizer with On-Chip VCO

This paper discusses the implementation of the building blocks for a 2 GHz phase-locked loop frequency synthesizer in a standard 0.5 m BiCMOS process. These blocks include a low-power optimized dual modulus prescaler which is able to operate with input frequencies up to 2.7 GHz, a phase detector with extremely constant gain throughout the input phase difference range, a chargepump with a rail-to-rail output, and an on-chip voltage-controlled oscillator.     

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Energy and security remain the main two challenges in Wireless Sensor Networks (WSNs). Therefore, protecting these WSN networks from Denial of Service (DoS) and Distributed DoS (DDoS) is one of the WSN networks security tasks. Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks. This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time. We examined the performance metrics of different machine learning classification categories such as K-Nearest Neighbour (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Gboost, Decision Tree (DT), Naïve Bayes, Long Short Term Memory (LSTM), and Multi-Layer Perceptron (MLP) on a WSN-dataset in different sizes. The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets, and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics. The performance metrics used in these validations were accuracy, F1-score, False Positive Ratio (FPR), False Negative Ratio (FNR), and the training execution time. Moreover, the test results showed the Gboost algorithm got 99.6%, 98.8%, 0.4% 0.13% in accuracy, F1-score, FPR, and FNR, respectively. At training execution time, it obtained 1.41 s for the average of all training time execution datasets. In addition, this paper demonstrated that for the numeric statistical data type, the best results are in the size of the dataset ranging from 3000 to 6000 records and the percentage between categories is not less than 50% for each category with the other categories. Furthermore, this paper investigated the effect of Gboost on the WSN lifetime, which resulted in a 32% reduction compared to other Gboost-free scenarios.