Quantitative methods to detect phospholipids at the oil-water interface

A detection method for neuronal death indicates abnormalities in intracellular membranous components in neuronal cells that underwent delayed death

Acute neuronal degeneration is always preceded under the light and electron microscopes by a stage called microvacuolation, which is characterized by a finely vacuolar alteration in the cytoplasm of the neurons destined to death. In this study, we reported a method for detecting neuronal death using two membrane-bound dyes, rhodamine R6 and DiOC₆(3), which may be associated with the so-called microvacuolation. This new method produced a spatiotemporally similar staining pattern to Fluoro-Jade B in kainic acid-damaged brains in mice. Further experiments showed that increased staining of rhodamine R6 and DiOC₆(3) was observed only in degenerated neurons, but not in glia, erythrocytes, or meninges. Different from Fluoro-Jade-related dyes, rhodamine R6 and DiOC₆(3) staining is highly sensitive to solvent extraction and detergent exposure. Staining with Nile red for phospholipids and filipin III for non-esterified cholesterol supports that the increased staining of rhodamine R6 and DiOC₆(3) might be associated with increased levels of phospholipids and free cholesterol in the perinuclear cytoplasm of damaged neurons. In addition to kainic acid-injected neuronal death, rhodamine R6 and DiOC₆(3) were similarly useful for detecting neuronal death in ischemic models either in vivo or in vitro. As far as we know, the staining with rhodamine R6 or DiOC₆(3) is one of a few histochemical methods for detecting neuronal death whose target molecules have been well defined and therefore may be useful for explaining experimental results as well as exploring the mechanisms of neuronal death. (250 words).

Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm

Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to achieve. However, object detection algorithm in deep learning makes it feasible. This paper reports a new technique for evaluating Pickering emulsion properties through classification and quantification of the emulsion microstructure by object detection algorithm. The trained neural network models characterize the emulsion droplets by distinguishing between different individual emulsion droplets and morphological mechanisms from numerous microscopic images. The quantified results of the emulsion droplets presented in this study, provide details of statistical changes at different concentrations of the Pickering interface and storage temperatures enabling elucidation of the mechanisms involved. This methodology provides a new quantitative and statistical analysis of emulsion microstructure and properties.

Effect of Rhamnolipid Amidation on Biosurfactant Adsorption Loss and Oil-Washing Efficiency

Surfactant adsorption loss seriously hinders the economy of surfactant binary flooding technology for enhancing oil recovery, especially for biosurfactants with higher manufacturing costs. Here, biosurfactant rhamnolipid (RL) is chemically modified to develop a more efficient surfactant, rhamnolipid monoethanol amide (RL-MEA), which is characterized by decreased adsorption loss and increased oil-washing efficiency for enhanced oil recovery at a laboratory scale. Synthesis and characterization of the rhamnolipid monoethanol amide are carried out using high-performance liquid chromatography (HPLC), HPLC/MS, ¹H nuclear magnetic resonance (NMR), and Fourier transform infrared (FTIR) spectroscopy. The aggregation behavior is disclosed by surface tension, dynamic light scattering, and fluorescence spectra with pyrene as the probe. The applied performances of RL-MEA in the simulated enhanced oil recovery are researched, including the efficiency of oil washing, wettability to crude oil, and adsorption isotherms on silicates. Compared with the critical micelle concentration (CMC) of rhamnolipid of 14.23 × 10^-5 M in pure water and 9.02 × 10^-5 M in 0.2 M NaCl solution, the modified RL-MEA shows a significantly lower CMC of 7.15 × 10^-5 M in pure water and 5.34 × 10^-5 M in 0.2 M NaCl solution. More importantly, the modified RL-MEA reduces adsorption loss by 20% and enhanced oil-washing efficiency at higher temperatures and salt concentrations compared with the parent RLs. These findings would provide valuable information for developing efficient surfactant flooding agents for harsh reservoir geological conditions.