Preparation and Characterization of Gemini Surfactant Intermedium

This paper studies the preparation process and characterization of gemeni-diol, an intermedium compound for synthesizing anionic gemini surfactant. Firstly, as a material to synthesize anionic gemini surfactant, high purity ethylene glycol diglycidyl ether (EGDGE) is obtained by distill epoxy resin thinner at a reduced pressure. Based on gas chromatogram, 94.51 percent of liquid at cut points of 116-119℃/5mmHg is EGDGE. Then the effects of catalyst and reaction time on the reaction of nonylphenol and EGDGE are investigated. The results show the optimized conditions to synthesize gemini-diol are as following: using 0.25%KOH and 0.25% phosphorus triphenyl as catalyst to keep the reaction of nonylphenol and EGDGE at 110℃ for 3-5h. The yield of gemini-diol is 88.2% under these conditions

The Effect of Betaine Surfactant on Carbonate Reservoir Wettability in Self-Diverting Acidizing Stimulation

Contact angle alterations of carbonate cores after immersing in spent acid with oleyl amido propyl betaine surfactant were measured to clarify the effect of viscoelastic surfactant on the wettability of carbonate reservoir during self-diverting acidizing. The results showed that spent acid solutions with hydrochloric acid and betaine surfactant induced core wettability to water-wetting for initially oil-wet rocks, and oil-wetting for initially water-wet rocks. Longer immersion time and higher concentration of surfactant enhanced the effects. The adverse wettability reversal for water-wet reservoir was eliminated by mutual solvent or brine postflush. Chemical mechanisms of the wettability alteration were interpreted

Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading

Whole slide images (WSI) provide valuable phenotypic information for histological assessment and malignancy grading of tumors. The WSI-based grading promises to provide rapid diagnostic support and facilitate digital health. Currently, the most commonly used WSIs are derived from formalin-fixed paraffin-embedded (FFPE) and Frozen section. The majority of automatic tumor grading models are developed based on FFPE sections, which could be affected by the artifacts introduced from tissue processing. The frozen section exists problems such as low quality that might influence training within single modality as well. To overcome these problems in the single modal training and achieve better multi-modal and discriminative representation disentanglement in brain tumor, we propose a mutual contrastive low-rank learning (MCL) scheme to integrate FFPE and frozen sections for glioma grading. We first design a mutual learning scheme to jointly optimize the model training based on FFPE and frozen sections. In this proposed scheme, we design a normalized modality contrastive loss (NMC-loss), which could promote to disentangle multi-modality complementary representation of FFPE and frozen sections from the same patient. To reduce intra-class variance, and increase inter-class margin at intra- and inter-patient levels, we conduct a low-rank (LR) loss. Our experiments show that the proposed scheme achieves better performance than the model trained based on each single modality or mixed modalities without reducing the efficiency of inference, and even improves the feature extraction in classical attention-based multiple instances learning methods (MIL). The combination of NMC-loss and low-rank loss outperforms other typical contrastive loss functions. The source code is in https://github.com/uceclz0/MCL_glioma_grading.</p

Preparation of Nano-Porous Carbon-Silica Composites and Its Adsorption Capacity to Volatile Organic Compounds

Carbon-silica composites with nanoporous structures were synthesized for the adsorption of volatile organic compounds (VOCs), taking tetraethyl orthosilicate (TEOS) as the silicon source and activated carbon powder as the carbon source. The preparation conditions were as follows: the pH of the reaction system was 5.5, the hydrophobic modification time was 50 h, and the dosage of activated carbon was 2 wt%. Infrared spectrum analysis showed that the activated carbon was dispersed in the pores of aerogel to form the carbon-silica composites material. The static adsorption experiments, dynamic adsorption-desorption experiments, and regeneration experiments show that the prepared carbon-silica composites have microporous and mesoporous structures, the adsorption capacity for n-hexane is better than that of conventional hydrophobic silica gel, and the desorption performance is better than that of activated carbon. It still has a high retention rate of adsorption capacity after multiple adsorption-desorption cycles. The prepared carbon-silica composites material has good industrial application prospects in oil vapor recovery, providing a new alternative for solving organic waste gas pollution