Science and technology of electrochemistry at nano-interfaces: concluding remarks

New horizons in nanoelectrochemistry: concluding remarks

The aim of this paper is to overview the meeting on New horizons in nanoelectrochemistry held at Nanjing University in China in October 2024 and to give some perspective to the work presented. This paper is based on my summary talk and breaks down the subjects in the following areas of nanoelectrochemistry presented at the meeting: nanowires, nanonets, and nanoarrays; nanopores; nanopipettes; spectroelectrochemistry, scanning ion-conductance microscopy and light-active processes at nanointerfaces; scanning electrochemical microscopy and scanning electrochemical cell microscopy; and nanosensors. I end with some discussion of online meetings and where the field might go including artificial intelligence and by asking AI to define the challenges and future of nanoelectrochemistry.

Spiers Memorial Lecture: New horizons in nanoelectrochemistry

This introductory lecture prefaces the 2024 New Horizons in Nanoelectrochemistry Faraday Discussion. A broad view of the previous Discussions related to nanoelectrochemistry is taken. Big ideas or concepts discussed at these previous meetings are identified, along with specific examples in each area. Closing comments aimed at a high level and related to where we are today and what is needed to continue to drive nanoelectrochemistry towards the horizon are considered.

Spiers Memorial Lecture. Next generation nanoelectrochemistry: the fundamental advances needed for applications

Nanoelectrochemistry, where electrochemical processes are controlled and investigated with nanoscale resolution, is gaining more and more attention because of the many potential applications in energy and sensing and the fact that there is much to learn about fundamental electrochemical processes when we explore them at the nanoscale. The development of instrumental methods that can explore the heterogeneity of electrochemistry occurring across an electrode surface, monitoring single molecules or many single nanoparticles on a surface simultaneously, have been pivotal in giving us new insights into nanoscale electrochemistry. Equally important has been the ability to synthesise or fabricate nanoscale entities with a high degree of control that allows us to develop nanoscale devices. Central to the latter has been the incredible advances in nanomaterial synthesis where electrode materials with atomic control over electrochemically active sites can be achieved. After introducing nanoelectrochemistry, this paper focuses on recent developments in two major application areas of nanoelectrochemistry; electrocatalysis and using single entities in sensing. Discussion of the developments in these two application fields highlights some of the advances in the fundamental understanding of nanoelectrochemical systems really driving these applications forward. Looking into our nanocrystal ball, this paper then highlights: the need to understand the impact of nanoconfinement on electrochemical processes, the need to measure many single entities, the need to develop more sophisticated ways of treating the potentially large data sets from measuring such many single entities, the need for more new methods for characterising nanoelectrochemical systems as they operate and the need for material synthesis to become more reproducible as well as possess more nanoscale control.

Machine Learning Techniques for Fluid Flows at the Nanoscale

Simulations of fluid flows at the nanoscale feature massive data production and machine learning (ML) techniques have been developed during recent years to leverage them, presenting unique results. This work facilitates ML tools to provide an insight on properties among molecular dynamics (MD) simulations, covering missing data points and predicting states not previously located by the simulation. Taking the fluid flow of a simple Lennard-Jones liquid in nanoscale slits as a basis, ML regression-based algorithms are exploited to provide an alternative for the calculation of transport properties of fluids, e.g., the diffusion coefficient, shear viscosity and thermal conductivity and the average velocity across the nanochannels. Through appropriate training and testing, ML-predicted values can be extracted for various input variables, such as the geometrical characteristics of the slits, the interaction parameters between particles and the flow driving force. The proposed technique could act in parallel to simulation as a means of enriching the database of material properties, assisting in coupling between scales, and accelerating data-based scientific computations.