Transport of ionic solutions through graphene oxide (GO) membranes is a complicated issue because the complex and tortuous structure inside makes it very hard to clarify. Using molecular dynamics (MD) simulations, we investigated the mechanism of water transport and ion movement across multilayer GO. The significant flow rate is considerably influenced by the structural parameters of GO membranes. Because of the size effect on a shrunken real flow area, there is disagreement between the classical continuum model and nanoscaled flow. To eliminate the variance, we obtained modified geometrical parameters from density analysis and used them in the developed hydrodynamic model to give a precise depiction of water flow. Four kinds of solutions (i.e., NaCl, KCl, MgCl, and CaCl) and different configurational GO sheets were considered to clarify the influence on salt permeation. It is found that the abilities of permeation to ions are not totally up to the hydration radius. Even though the ionic hydration shell is greater than the opening space, the ions can also pass through the split because of the special double-deck hydration structure. In the structure of GO, a smaller layer separation with greater offsetting gaps could substantially enhance the membrane's ability to reject salt. This work establishes molecular insight into the effects of configurational structures and salt species on desalination performance, providing useful guidelines for the design of multilayer GO membranes.
Water
transport inside graphene-based materials has drawn much
attention nowadays because of its promising potential in ions filtration
applications. Using molecular dynamics (MD) simulations, we investigated
the mechanism of water transport inside the interlayer gallery between
graphene oxide layers. The model of slipped-Poiseuille flow was cited
to depict the characteristic transport of interlayer flow. This significant
flow rate was related to slip velocity of water, which is constrained
by hydrogen interactions between water molecules and hydroxyl groups.
We find that hydrogen-bond networks are correlated with both functionalization
and nanoconfinement. MD simulation results show that the slip velocity
is negatively correlated with oxide concentration while independent
of the array of hydroxyl groups, and the volumetric flux is linearly
dependent to the slip velocity. It reveals that graphene oxide layers
could get better water permeability after reduction.
Knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of HitRatio@1 on the dataset DBP15K.
We present a new type of in situ reduction-assembly approach to construct graphene hydrogel through simultaneous water evaporation and graphene oxide reduction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.