Application of Machine Learning for Calibrating Gas Sensors for Methane Emissions Monitoring.

Methane leaks are a significant component of greenhouse gas emissions and a global problem for the oil and gas industry. Emissions occur from a wide variety of sites with no discernable patterns, requiring methodologies to frequently monitor these releases throughout the entire production chain. To cost-effectively monitor widely dispersed well pads, we developed a methane point instrument to be deployed at facilities and connected to a cloud-based interpretation platform that provides real-time continuous monitoring in all weather conditions. The methane sensor is calibrated with machine learning methods of Gaussian process regression and the results are compared with artificial neural networks. A machine learning approach incorporates environmental effects into the sensor response and achieves the accuracies required for methane emissions monitoring with a small number of parameters. The sensors achieve an accuracy of 1 part per million methane (ppm) and can detect leaks at rates of less than 0.6 kg/h.

Terahertz spectroscopy studies on epitaxial vanadium dioxide thin films across the metal-insulator transition.

We present results on terahertz (THz) spectroscopy on epitaxial vanadium dioxide (VO(2)) films grown on sapphire across the metal-insulator transition. X-ray diffraction indicates the VO(2) film is highly oriented with the crystallographic relationship: (002)(film)//(0006)(sub) and [010](film)//[2 ̅1 ̅10](sub). THz studies measuring the change in transmission as a function of temperature demonstrate an 85% reduction in transmission as the thin film completes its phase transition to the conducting phase, which is much greater than the previous observation on polycrystalline films. This indicates the crucial role of microstructure and phase homogeneity in influencing THz properties.