[Accurate calculation of spectral line profiles by considering influence of varying pressure and temperature in a gas].

Accurate calculation of spectral line profiles of a gas is very important for gas sensing. As we know, a variation in pressure (temperature) of a gas will result in the corresponding variation in temperature (pressure) of the gas. In the present paper we calculated spectral line profiles of a gas by considering the changes in both temperature and pressure. The authors found that in our case the Lorentzian profile has broader applicable ranges of pressure and temperature, and the Gaussian profile is only applicable in some extreme conditions. Furthermore, the authors found that the influence of variations in pressure and temperature has to be considered in calculating the peak values of the spectral line profiles such as Gaussian, Lorentzian, and Voigt; otherwise the resultant relative errors of the calculated peak values can exceed 0.1. The similar observations were also found for other gases such as CH4, CO2, CO, and NO, although the parameters such as wavelength, coefficient of pressure-broadening, relative molecular mass, and temperature were different.

Transition State Theory-Inspired Neural Network for Estimating the Viscosity of Deep Eutectic Solvents.

The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R 2 of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.