Simultaneous Increase of Solvent Flux and Rejection of Thin-Film Composite Membranes by Incorporation of Dopamine-Modified Mesoporous Silica.

Thin-film nanocomposite membranes have shown great promise in organic solvent nanofiltration. However, it is challenging to acquire high permeation flux without severe swelling, which might do harm to rejection and long-term stability. In this study, we introduced dopamine-modified mesoporous silica nanoparticles into the polyamide (PA) matrix via interfacial polymerization to fabricate a series of thin-film nanocomposite membranes. By using polyethyleneimine (PEI) as the aqueous monomer, the modified nanoparticles are designed to be cross-linked within the PA network, which allows the penetration of PEI into the mesopores, and therefore, the membranes show better resistance to solvent-induced swelling and pressure-induced densification. More importantly, the mesopores of nanoparticles provide additional fast channels for solvents, resulting in an unusual enhancement of solvent flux under reduced membrane swelling. Along with the permeation flux, the rejection performance of the nanocomposite membranes is simultaneously improved, thanks to the controlled swelling arising from the strong interfacial adhesion. Thin-film nanocomposite membranes with optimal filler concentration exhibit a high isopropanol permeance of 8.47 L m-2 h-1 bar-1 as well as a quite low-molecular-weight cutoff of 281 Da.

Examining Four Experimental Paradigms for EEG-Based Sleep Quality Evaluation with Domain Adaptation.

With the quick development of dry electrode electroencephalography (EEG) acquisition technology, EEG-based sleep quality evaluation attracts more attention for its objective and quantitative merits. However, there hasn't been a standard experimental paradigm. This situation hinders the development of sleep quality evaluation method and technique. In this paper, we experimentally examine the performance of four typical experimental paradigms for EEG-based sleep quality evaluation and develop a new EEG dataset recorded by dry-electrode headset. To eliminate individual variation caused by subjects, we evaluate the four experimental paradigms using domain adaptation (DA) methods. Experimental results demonstrate that a relaxing paradigm is more effective than other attention concentration paradigms and achieves the average accuracy of 76.01%. Domain Adversarial Neural Network outperforms other DA methods and obtains 18.69% improvement on accuracy compared with transfer component analysis.