Byeongho Lee

A carbon nanotube wall membrane for water treatment

Various forms of carbon nanotubes have been utilized in water treatment applications. The unique characteristics of carbon nanotubes, however, have not been fully exploited for such applications. Here we exploit the characteristics and corresponding attributes of carbon nanotubes to develop a millimetre-thick ultrafiltration membrane that can provide a water permeability that approaches 30,000 l m(-2) h(-1) bar(-1), compared with the best water permeability of 2,400 l m(-2) h(-1) bar(-1) reported for carbon nanotube membranes. The developed membrane consists only of vertically aligned carbon nanotube walls that provide 6-nm-wide inner pores and 7-nm-wide outer pores that form between the walls of the carbon nanotubes when the carbon nanotube forest is densified. The experimental results reveal that the permeance increases as the pore size decreases. The carbon nanotube walls of the membrane are observed to impede bacterial adhesion and resist biofilm formation.

Improvement of vertically aligned carbon nanotube membranes: desalination potential, flux enhancement and scale-up

AbstractCarbon nanotube (CNT) membranes are considered as next-generation membranes for desalination. Among the various types of CNT membranes, vertically aligned (VA) CNT membranes provide rapid water transport. However, when the water permeability of VA CNT membranes are compared with those of the commercial membrane, the VA CNT membranes only showed slightly higher water permeability due to their low pore densities. Additionally, the applicability of VA CNT membranes for desalination has been limited due to their larger pore sizes. Herein, we improved VA CNT membranes in terms of the desalination potential, flux enhancement, and scale-up. For the desalination potential, graphene oxide (GO) or polyamide (PA) were coated on a VA CNT membrane as a selective layer, which showed approximately 40–65% NaCl rejection, respectively. A pretreatment polyelectrolyte coating for a GO-coated VA CNT membrane increased the water permeability by approximately 50%. For the flux enhancement, the water permeability of a V...

Membrane of Functionalized Reduced Graphene Oxide Nanoplates with Angstrom-Level Channels

Membranes with atomic level pores or constrictions are valuable for separation and catalysis. We report a graphene-based membrane with an interlayer spacing of 3.7 angstrom (Å). When graphene oxide nanoplates are functionalized and then reduced, the laminated reduced graphene oxide (rGO) nanoplates or functionalized rGO membrane is little affected by an intercalated fluid, and the interlayer spacing of 3.7 Å increases only to 4.4 Å in wetted state, in contrast to the graphene oxide (GO) membrane whose interlayer spacing increases from 9 Å to 13 Å in wetted state. When applied to ion separation, this membrane reduced the permeation rate of small ions such as K+ and Na+ by three orders of magnitude compared to the GO membrane.

Cooperative localization considering estimated location uncertainty in distributed ad hoc networks

Localization is an essential service for numerous applications in wireless ad hoc networks. In particular, cooperative localization is a widely used technique for improving performance by utilizing information obtained from adjacent sensors. In general, distributed localization in ad hoc networks shows relatively low performance compared to centralized localization. This is partly due to the lack of information and partly because of error propagation. In this article, we propose a localization algorithm considering the location uncertainty of reference nodes. The proposed algorithm uses a dilution of precision, depending on the geometric deployment of reference nodes, as a representative value of uncertainty. The proposed algorithm estimates the position of a target node and re-estimates positions of reference nodes concurrently. Using the proposed algorithm, we can reduce the effect of accumulated error propagation and enhance the accuracy of estimated node positions. We verify the feasibility of the proposed algorithm and compare its performance with that of other localization schemes under several circumstances by performing simulations. The results show that the overall performance of the proposed algorithm outperformed that of other schemes.

Logarithmic-domain array interpolation for improved DOA estimation in automotive radars

In this paper, we propose a novel array interpolation method in logarithmic domain for enhanced direction of arrival (DOA) estimation. Array interpolation techniques are broadly utilized in the DOA estimation for achieving better angular resolution. Generally, to generate interpolated array elements from original array elements, the method of linear least squares (LLS) has been used. When we use the LLS method, the interpolated array elements are formulated by linear combinations of the original array elements. Therefore, amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In addition, through a transformation matrix from the LLS method, phases of the interpolated array elements are not precisely generated. For the DOA estimation, phase information of interpolated array elements is important. Therefore, we propose a novel array transformation matrix for generating accurate phases of the interpolated array elements to improve the DOA estimation performance. Verifying from simulation and measurement results, our method shows much better angular resolution and estimation accuracy.