Abstract: Spark source mass spectrometric analyses of doped carbon samples were performed at different pulse lengths, pulse frequencies and breakdown voltages. The pulse length and pulse frequencies had no influence on the analysis. The relation between breakdown voltage, spark gap and sample matrix was determined. With increasing breakdown voltage, the abundance of the multiply charged ions was found to decrease, whereas the abundance of the cluster ions increased. The breakdown voltage was also found to affect the absolute and relative intensities of different elements in different manners. Interpretation of these results led to some new considerations on the phenomena in a spark discharge.

Abstract: Bilayers of carbon, consisting of a layer of natural carbon and a second layer highly enriched in 13C, were investigated with the LAMMA 500-® instrument. When such a bilayer is perforated by the laser beam, comparable amounts of 12C and 13C are evaporated and partly ionized so that geometry effects and recombination reactions during laser-induced ionization can straightforwardly be studied.

Abstract: The effects on river water quality of sewer overflows are not well known. Since the duration of the overflow is in the order of magnitude of minutes to hours, continuous measurements of water quality are needed and traditional grab sampling is unable to quantify the pollution loads. The objective of this paper was to demonstrate the applicability of high frequency measurements for assessing the impacts of waste water treatment plants on the water quality of the receiving surface water. In our in situ water quality monitoring setup, two types of multiparameter sensors mounted on a floating fixed platform were used to determine the dynamics of dissolved oxygen, specific conductivity, ammonium-N, nitrate-N and dissolved organic carbon downstream of a waste water treatment plant (WWTP), in combination with data on rainfall, river discharge and WWTP overflow discharge. The monitoring data for water quantity and water quality were used to estimate the pollution load from waste water overflow events and to assess the impact of waste water overflows on the river water quality. The effect of sewer overflow on a small river in terms of N load was shown to be significant. The WWTP overflow events accounted for about 1/3 of the river discharge. The NH4-N loads during overflow events contributed 29% and 21% to the August 2010 and June 2011 load, respectively, in only 8% and 3% of the monthly time span. The results indicate that continuous monitoring is needed to accurately represent the effects of sewer overflows in river systems.

Abstract: A chemical kinetics model was developed to characterise the gas‐phase dynamics of H₂production in nanosecond‐pulsed CH₄plasmas. Pulsed behaviour was observed in the calculated electric field, electron temperature and species densities at all pressures. The model agrees reasonably with experimental results, showing CH₄conversion at 30% and C₂H₂and H₂as major products. The underlying mechanisms in CH₄dissociation and H₂formation were analysed, highlighting the large contribution of vibrationally excited CH₄and H₂to coupling energy from the plasma into gas‐phase heating, and revealing that H₂synthesis is not affected by applied pressure, with selectivity remaining unchanged at ~42% in the 1–5 bar range.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image