Arginine-Functionalized Thin Film Composite Forward Osmosis Membrane Integrating Antifouling and Antibacterial Effects

Membrane fouling is an inevitable obstacle of polyamide composite forward osmosis (FO) membranes in oily wastewater treatment. In this study, zwitterionic arginine (Arg) is grafted onto nascent self-made FO polyamide poly(ether sulfone) (PA-PES) membrane, imparting superior hydrophilic, antifouling, and antibacterial properties to the membrane. Detailed characterizations revealed that the Arg-modified (Arg-PES) membrane presented obviously surface positively charged and unique morphology. Results showed that our strategy endowed the optimized membrane, the water flux increased by 113.2% compared to the pristine membrane, respectively, meanwhile keeping high NaCl rejection > 93.9% (with DI water as feed solution and 0.5 M NaCl as draw solution, FO mode). The dynamic fouling tests indicated that the Arg-PES membranes exhibited much improved antifouling performance towards oily wastewater treatment. The flux recovery ratios of the membrane were as high as 92.0% for cationic emulsified oil (cetyl pyridinium chloride, CPC), 87.0% for neutral emulsified oil (Tween-80), and 86.0% for anionic emulsified oil (sodium dodecyl sulfate, SDS) after washing, respectively. Meanwhile, the Arg-PES membranes assembled with guanidine cationic groups exhibited an enhanced antibacterial property against E. coli, which exhibited a high antibacterial efficiency of approximately 96%. Consequently, the newly arginine functionalized FO membrane possesses impressive antifouling performance, while simultaneously resisting bacterial invasion, thus rendering it an ideal alternative for oily wastewater treatment in the FO process.

Forecasting oil commodity spot price in a data-rich environment

Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.