Control of the ion current in a narrow nanotube

Developing the approaches to control transport in nanopores is an important task for natural systems, for example, as intercellular ion channels, and many industrial applications such as battery energy storage, DNA sequencing, desalination, gas and isotope separation, and molecular sensors [1]. However, understanding, predicting and controlling charge transport in nanopores still present formidable challenges to both nanotechnology and biophysics [2, 3].

Critical gaps recently identified [2, 3] in our understanding of ionic conduction in nano-channels include correlated transport and the conditions needed for the enhanced selectivity of particular ions. Modelling tools provide a pathway for a breakthrough in nanopore design, evaluation and optimisation for the wide range of 2D solid-state nanopores, including carbon nanotubes.

The project aims to develop a self-consistent computational tool for predicting the structure-functional nanopore relationship. The project's research objectives are:

Objective 1. To develop a computationally efficient and accurate method for analysing solute-solvent interactions based on the potential of mean force estimations. The method should address the issue of uncertainties in the force field descriptions of atomic interaction and, particularly, the influence of electronic polarisation. The method should incorporate molecular dynamics simulations with machine learning (AI)-based approaches for parametric uncertainties characterisation.

Objective 2. To apply the method for prediction of ionic selectivity and conductivity initially in carbon nanotube and then MoS2-based nanotube.

Objective 3. To identify the nanotube's parameters and essential functional modifications of nanotubes for selective conduction of mono- and divalent ions for battery applications.

The project will run in close collaboration with theoretical groups at Lancaster University and University College London.

The novelty of the proposed research lies in developing a methodology for drastically reducing the computational resources needed to assess uncertainties in the force field descriptions of atomic interaction and the influence of electronic polarisation. These factors, parametric uncertainties and polarisation, are significant factors slowing the impact of modelling prediction based on atomistic/molecular modelling of the nanotube structures.

The methodology is based on a combination of a coarse-grained description and machine learning/AI approaches for parametric uncertainty characterisation. The description is derived from relatively short, computationally inexpensive molecular dynamics and/or quantum mechanics simulations. The resulting coarse-grained model is then combined with a large family of approaches for uncertainty characterisation to predict the nanotube properties.

The latter are compared with available experimental outcomes, and the appropriate force-field description and the corresponding coarse-grained model are selected and used for further analysis and structure-function optimisation. References:

1. L. Wang, M. S. Boutilier, P. R. Kidambi, D. Jang, N. G. Hadjiconstantinou, and R. Karnik, "Fundamental transport mechanisms, fabrication and potential applications of nanoporous atomically thin membranes," Nat. Nanotechnol. 12, 509 (2017).

2. Faucher, et al, "Critical knowledge gaps in mass transport through single digit nanopores: A review and perspective," J. Phys. Chem. C 123, 21309-21326 (2019).

2. R. Aluru, et al, "Fluids and electrolytes under confinement in single-digit nanopores," Chem. Rev. 123, 2737-2831 (2023).