The thermophysical properties, including composition, thermodynamic properties, transport coefficients and net emission coefficients, of thermal plasmas formed from pure iso-C4 perfluoronitrile C4F7N and C4F7N–CO2 mixtures are calculated for temperatures from 300 to 30 000 K and pressures from 0.1 to 20 atm. These gases have received much attention as alternatives to SF6 for use in circuit breakers, due to the low global warming potential and good dielectric properties of C4F7N. Since the parameters of the large molecules formed in the dissociation of C4F7N are unavailable, the partition function and enthalpy of formation were calculated using computational chemistry methods. From the equilibrium composition calculations, it was found that when C4F7N is mixed with CO2, CO2 can capture C atoms from C4F7N, producing CO, since the system consisting of small molecules such as CF4 and CO has lower energy at room temperature. This is in agreement with previous experimental results, which show that CO dominates the decomposition products of C4F7N–CO2 mixtures; it could limit the repeated breaking performance of C4F7N. From the point of view of chemical stability, the mixing ratio of CO2 should therefore be chosen carefully. Through comparison with common arc quenching gases (including SF6, CF3I and C5F10O), it is found that for the temperature range for which electrical conductivity remains low, pure C4F7N has similar ρCp (product of mass density and specific heat) properties to SF6, and higher radiative emission coefficient, properties that are correlated with good arc extinguishing capability. For C4F7N–CO2 mixtures, the electrical conductivity is very close to that of SF6 while the ρCp peak at 7000 K caused by decomposition of CO implies inferior interruption capability to that of SF6. The calculated properties will be useful in arc simulations.

BackgroundPolymerase chain reaction (PCR) is one of the most important developments in modern biotechnology. However, PCR is known to introduce biases, especially during multiplex reactions. Recent studies have implicated the DNA polymerase as the primary source of bias, particularly initiation of polymerization on the template strand. In our study, amplification from a synthetic library containing a 12 nucleotide random portion was used to provide an in-depth characterization of DNA polymerase priming bias. The synthetic library was amplified with three commercially available DNA polymerases using an anchored primer with a random 3’ hexamer end. After normalization, the next generation sequencing (NGS) results of the amplified libraries were directly compared to the unamplified synthetic library.ResultsHere, high throughput sequencing was used to systematically demonstrate and characterize DNA polymerase priming bias. We demonstrate that certain sequence motifs are preferred over others as primers where the six nucleotide sequences at the 3’ end of the primer, as well as the sequences four base pairs downstream of the priming site, may influence priming efficiencies. DNA polymerases in the same family from two different commercial vendors prefer similar motifs, while another commercially available enzyme from a different DNA polymerase family prefers different motifs. Furthermore, the preferred priming motifs are GC-rich. The DNA polymerase preference for certain sequence motifs was verified by amplification from single-primer templates. We incorporated the observed DNA polymerase preference into a primer-design program that guides the placement of the primer to an optimal location on the template.ConclusionsDNA polymerase priming bias was characterized using a synthetic library amplification system and NGS. The characterization of DNA polymerase priming bias was then utilized to guide the primer-design process and demonstrate varying amplification efficiencies among three commercially available DNA polymerases. The results suggest that the interaction of the DNA polymerase with the primer:template junction during the initiation of DNA polymerization is very important in terms of overall amplification bias and has broader implications for both the primer design process and multiplex PCR.

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Finding substitutes for sulfur hexafluoride (SF6), a gas with extremely high global warming potential, has been a persistent effort for years in the field of high voltage power equipment, which focuses on the evaluation of the electrical strength and boiling temperature for the practical purpose. Following up the previous proposed linear regression models, this work introduces machine learning algorithms including artificial neural network (ANN) and random forest (RF) as the potential approaches to predict the electrical strength and boiling temperature. Based on a series of descriptors derived from the molecular structure of 74 molecules, the performance of three different methods: multiple linear regression, artificial neural network and random forest are compared and assessed in terms of the sensitivity to the sample size, prediction accuracy and stability, and the interpretability of predictors. Considering the available data are limited, random forest shows superior performance with higher robustness and efficiency. The same approaches were applied to the boiling temperature and random forest produced better results as well. Besides, the variable importance ranked by RF improves understanding of the correlation between the molecular properties and electrical strength. It provides important insights to analyze the properties of the SF6 substitutes during the design and synthesis of the new eco-friendly gases in power equipment.

Air plasma has been widely applied in industrial manufacture. In this paper, both dry and humid air plasmas' thermodynamic and transport properties are calculated in temperature 300-100000 K and pressure 0.1-100 atm. To build a more precise model of real air plasma, over 70 species are considered for composition. Two different methods, the Gibbs free energy minimization method and the mass action law method, are used to determinate the composition of the air plasma in a different temperature range. For the transport coefficients, the simplified Chapman-Enskog method developed by Devoto has been applied using the most recent collision integrals. It is found that the presence of CO2 has almost no effect on the properties of air plasma. The influence of H2O can be ignored except in low pressure air plasma, in which the saturated vapor pressure is relatively high. The results will serve as credible inputs for computational simulation of air plasma.