Unfolding of Poly-L-glutamic Acid by Microbubbling of Supercritical Carbon Dioxide
The conformational changes in α-helical poly-L-glutamic acid caused by microbubbling supercritical CO2 were investigated with circular dichroism spectra. After microbubbling using a micropore filter at 35 and 30 MPa for 30 min, α-helix content decreased to 37%, while without the filter it was 68%. The α-helix structure was significantly decomposed by a high density of CO2. No important changes were observed in heating, autoclaving, or pH-lowering.
pH-Defining Equilibrium between Water and Supercritical CO2. Influence on SFE of Organics and Metal Chelates
A direct measurement of the pH of water in contact with supercritical CO 2 was made by observing the spectra of a pH indicator with a UV-vis spectrophotometer. The pH was analyzed under pressures of 70-200 atm and temperatures of 25-70 °C. The measured pH varied from 2.80 to 2.95, and relative standard deviations of <1.5% were achieved. The effects of pH on the efficiency of supercritical fluid extraction of metals and ionizable organic species in water-containing systems are discussed.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.