Understanding water permeation in graphene oxide membranes

Water transport through graphene-derived membranes has gained much interest recently due to its promising potential in filtration and separation applications. In this work, we explore water permeation in graphene oxide membranes using atomistic simulations, by considering flow through interlayer gallery, expanded pores such as wrinkles of interedge spaces, and pores within the sheet. We find that although flow enhancement can be established by nanoconfinement, fast water transport through pristine graphene channels is prohibited by a prominent side-pinning effect from capillaries formed between oxidized regions. We then discuss flow enhancement in situations according to several recent experiments. These understandings are finally integrated into a complete picture to understand water permeation through the layer-by-layer and porous microstructure and could guide rational design of functional membranes for energy and environmental applications.Comment: arXiv admin note: text overlap with arXiv:1308.536

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

Neural models have become ubiquitous in automatic speech recognition systems. While neural networks are typically used as acoustic models in more complex systems, recent studies have explored end-to-end speech recognition systems based on neural networks, which can be trained to directly predict text from input acoustic features. Although such systems are conceptually elegant and simpler than traditional systems, it is less obvious how to interpret the trained models. In this work, we analyze the speech representations learned by a deep end-to-end model that is based on convolutional and recurrent layers, and trained with a connectionist temporal classification (CTC) loss. We use a pre-trained model to generate frame-level features which are given to a classifier that is trained on frame classification into phones. We evaluate representations from different layers of the deep model and compare their quality for predicting phone labels. Our experiments shed light on important aspects of the end-to-end model such as layer depth, model complexity, and other design choices.Comment: NIPS 201