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As a type of clean energy resource, salinity gradient power between seawater and river water is important to satisfy the ever-growing energy demand on earth. In the recent years, the use of reverse electrodialysis in biomimetic nanofluidic systems has become a promising way for large-scale and high-efficiency harvesting of the salinity gradient power and surpasses the conventional polymeric ion-exchange membrane-based process. With regard to practical applications, significant efforts have been made towards the design and fabrication of high-performance and economically viable materials and devices. However, while extrapolating from single nanopores to multi-pore membrane materials, the commonly used linear amplification method causes severe deviation from the actual experimental value obtained on nanoporous membranes, particularly at a high pore density. An appropriate simulation method is therefore highly demanded and a great challenge. Herein, we present a general strategy for multi-pore nanofluidic systems by taking the influence of neighbouring nanopores into consideration. We have found that the fourth nearest-neighbor approximation is sufficiently precise for simulation in nanoporous systems. The simulation data are in good agreement with the experimental results. The simulation method provides insights for understanding the pore–pore interaction in porous nanofluidic systems and for the design of high-performance devices.

Salinity difference in ionic solutions is considered as a potential candidate for clean energy. Nowadays, nanofluidic reverse electrodialysis systems have received renewed attention for harnessing salinity gradient power. Towards practical applications, great efforts have been made in the fabrication of membrane-scale nanoporous materials. From a theoretical point of view, however, state-of-the-art simulation methods for multi-pore nanofluidic systems consume huge amounts of computational resources that frequently preclude simulation on lab-used computers. Here, we present a concise single-pore model to simulate the osmotic energy conversion in nanoporous materials. By regulating the geometric size of the solution reservoir, we show that the single-pore model is sufficiently accurate to simulate diffusive ion transport in multi-pore nanofluidic systems. More importantly, it largely reduces the computational scale by more than one order of magnitude. A benefit of this feature is that the model can incorporate more physical processes, such as the motion of fluid and heat conduction, which greatly expands the scope of the simulation method for understanding charge and mass transport behavior through nanoporous materials.

Natural Science Foundation of Fujian Province [2010J05034]; Fundamental Research Funds for the Central Universities [2012121034]; SRF for ROCS, SEM [44]; Program for New Century Excellent Talents in University of the Ministry of Education of China [NCET-09-0670]

Recent advances in materials science and nanotechnology have lead to considerable interest in constructing ion-channel-mimetic nanofluidic systems for energy conversion and storage. The conventional viewpoint suggests that to gain high electrical energy, the longitudinal dimension of the nanochannels (L) should be reduced so as to bring down the resistance for ion transport and provide high ionic flux. Here, counterintuitive channel-length dependence is described in nanofluidic osmotic power generation. For short nanochannels (with length L < 400 nm), the converted electric power persistently decreases with the decreasing channel length, showing an anomalous, non-Ohmic response. The combined thermodynamic analysis and numerical simulation prove that the excessively short channel length impairs the charge selectivity of the nanofluidic channels and induces strong ion concentration polarization. These two factors eventually undermine the osmotic power generation and its energy conversion efficiency. Therefore, the optimal channel length should be between 400 and 1000 nm in order to maximize the electric power, while balancing the efficiency. These findings reveal the importance of a long-overlooked element, the channel length, in nanofluidic energy conversion and provide guidance to the design of high-performance nanofluidic energy devices.