## 1 Introduction  
Water flow at the nanoscale has gained increased research interest in nanofluidics, due to its key role in environmental, biological, and chemical processes that can lead the development of novel devices in the neighboring fields of physics, chemistry, and materials science [1]. As the classical [PERSON] theory of slip at the macroscale breaks down, predicting fluid behavior at the nanoscale and understanding the link between molecular structure and macroscopic behavior requires deep investigation over a number of parameters that can affect its flow and transport properties [2]. Only after having established deep understanding at these scales, we will be able to design devices with desired properties for practical applications [3]. The experimental investigation of devices at the nanoscale is uniquely important, as it would not only unravel phenomena taking place there, but also, would offer fabrication guidelines for commercial use. Nevertheless, the whole experimental processes are complex and challenging. Going down to less than 10 nm in pore sizes has only recently been accessible experimentally [4], and the best way to overcome these barriers emerges from numerical approaches [5]. Molecular dynamics (MD) simulations have given a boost in establishing water properties at the nanoscale through numerical calculations, bridging fluid mechanics, statistical mechanics, and condensed matter physics and setting the basis for the design and fabrication of nanofluidic devices [6]. Among these, novel desalination and water purification techniques have been proposed, covering membrane-based applications, reverse osmosis, Capacitive De-Ionization (CDI) and flow-electrode CDI [7; 8; 9]. Similar to the CDI philosophy, the recently proposed electric ion drift (EID) method does not use membranes or special electrodes [10; 11]. The EID method is of the same energy consumption as the capacitive deionization method. This method is approached by the Poisson-Nerst-Planck (PNP) theory, which can be less computationally demanding than MD but, on the other hand, it succeeds only at the bulk and cannot incorporate wall effects in regions near the channel walls. At the macroscale, the PNP scheme is a popular choice to explain the relation between the electrostatic problem and the mass transport [12,13]. System design, either in real applications or simulation models, has the main role in achieving an efficient desalination process. First, the choice of materials is primarily important. Towards minimization of energy consumption during the desalination process, novel materials have been employed, although sometimes they have been found to have only a slight impact on increasing energy efficiency [14]. On the other hand, carbon-based materials, such as graphene, are among the most popular choices and have been incorporated for the construction of permeable membranes due to their porous structure and wetting characteristics [15]. Recently, synthetic nano-conduits have been introduced, such as artificial water channels (AWCs), to develop highly selective membranes for water desalination [16]. In a neighboring research field with biological interest, bacterial membrane potential dynamics have been investigated for exploitation in microfluidic-based platforms [17]. Another significant parameter that should be borne in mind is the choice of the driving method that removes unwanted substances from the solution. The application of an external electric field, \(E\), parallel or perpendicular to the walls, is common choice. Care has to be taken as the presence of the electric field may alter the hydrogen bond network structure, relaxation times, and rotational dynamics of water [18]. Depending on \(E\) direction and strength, water molecules could be subject to phase change [19] but, in general, higher \(E\) value leads to higher ion rejection rate [20]. In salt solutions, the electric field along with ion concentration have also a considerable impact on the mutual diffusion coefficients [21, 22, 23].