Musho, Terence D. | Search NASA

DOE OSTI2021

Direct Measurement of the Selective Microwave-Induced Heating of Agglomerates of Dipolar Molecules: The Origin of and Parameters Controlling a Microwave Specific Superheating Effect

Agglomerates of polar molecules in nonpolar solvents are selectively heated by microwave radiation. The magnitude of the selective heating was directly measured by using the temperature dependence of the intensities of the Stokes and anti-Stokes bands in the Raman spectra of p-nitroanisole (pNA) and mesitylene. Under dynamic heating conditions, a large apparent temperature difference (ΔT) of over 100 °C was observed between the polar pNA solute and the nonpolar mesitylene solvent. This represents the first direct measurement of the selective microwave heating process. The magnitude of the selective microwave heating was affected by the properties of the agglomerated pNA. As the concentration of the pNA increases, the magnitude of the selective heating of the pNA was observed to decrease. This is explained by the tendency of the pNA dipoles to orient in an antiparallel fashion in the aggregates as measured by the Kirkwood g value, which decreased with increasing concentration. This effect reduces the net dipole moment of the agglomerates, which decreases the microwave absorption. After the radiation was terminated, the effective temperature of the dipolar molecules returned slowly to that of the medium. The slow heat transfer was modeled successfully by treating the solutions as a biphasic solvent/solute system. Based on modeling and the fact that the agglomerate can be heated above the boiling temperature of the solvent, an insulating layer of solvent vapor is suggested to form around the heated agglomerate, slowing convective heat transfer out of the agglomerate. Furthermore, this is an effect unique to microwave heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A machine learning approach for increased throughput of density functional theory substitutional alloy studies

In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network (NN) approach to predict the initial position of minority and majority ions prior to DFT relaxation. The second advancement is to allow the NN to predict the total energy for every possibility minority ion position and select the most stable configuration in the absence of relaxing each trial minority configuration. A bismuth oxide materials system, (Bi x La y Yb z ) 2 MoO 6 , was used as the model system to demonstrate the developed methods and quantify the resulting computational speedup. Compared to a brute force method that requires the calculation of every permutation of minority configuration and subsequent DFT relaxation, a 1.3× speedup was realized if the NN predicted the initial configuration of ions prior to relaxation. Implementation of the second advancement allowed the NN to predict the total energy for all possible trial configurations and downselect the most stable configurations prior to relaxation, resulting in a speedup of approximately 37×. Validation was done by comparing position and energy between the NN and DFT predictions. A maximum position vector mean squared error (MSE) of 1.6 × 10 -2 and a maximum energy MSE of 2.3 × 10 -7 was predicted for the worst case configuration. This method demonstrates a significant computational speedup, which has the potential for even greater computational savings for larger compositional design spaces.

36 MATERIALS SCIENCE↗