Separation of ionic liquids from dilute aqueous solutions using the method based on CO_2 hydrates

Ionic liquids （ILs） have been regarded as the potential novel solvents for improved analytical- and process-scale separation methods.The development of methods for the recovery of ILs from aqueous solutions to escape contamination and recycle samples will ultimately govern the viability of ILs in the future industrial applications. Therefore, in this paper a new method for separation of ILs from their dilute aqueous solutions and simultaneously purification of water was proposed on the basis of the CO2 hydrate formation. For illustration, the dilute aqueous solutions with concentrations of ILs ranging from 2× 10^-3 mol% to 2×10^-1 mol% were concentrated. The results show that the separation efficiency is very impressive and that the new method is applicable to aqueous solutions of both hydrophobic and hydrophilic ILs. Compared to the literature separation method based on the supercritical CO2, the new method is applicable to lower concentrations, and more importantly, its operation condition is mild.

MGCPN:An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast data.To address such a challenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM) for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement(IMERG) data,offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min.The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time.This issue is a common challenge in precipitation forecasting models.Furthermore,we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy.The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecasting in the TP region.The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success index,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33%,19%,and 8% compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet) models.