An Open-Source Framework for Measurement and Analysis of Nanoscale Ionic Transport

TL;DR

This paper introduces an open-source, Python-based framework with GUIs for integrated measurement and analysis of nanoscale ionic transport, enhancing reproducibility and efficiency in nanofluidic research.

Contribution

It provides a unified, modular platform combining measurement and analysis tools for nanofluidic experiments, streamlining workflows and data processing.

Findings

Integrated measurement and analysis in one framework

Live data visualization during experiments

Improved reproducibility and workflow efficiency

Abstract

Nanofluidic systems exploit nanometre-scale confinement in channels and pores to regulate ionic transport, enabling functionalities such as osmotic energy harvesting and neuromorphic ionic memory. Studying such confined transport requires both precise electrical instrumentation and careful data analysis, yet, in practice, measurements are still often taken with vendor software, exported as files, and processed later in separate environments. In this work, we bring these steps together in a unified Python-based framework built around three interoperable graphical user interfaces (GUIs) for nanochannel, nanopore and memristor experiments. The framework is organised into two functional parts, measurement and analysis. On the measurement side, two GUIs drive Keithley Source Meters to run continuous voltage sweeps and user-defined memristive pulse sequences, while providing live plots, configuration management and controlled shutdown routines. On the analysis side, a dedicated nanochannel and nanopore GUI reads raw I-V datasets, applies unit-consistent processing, extracts conductance and ion mobility, evaluates selectivity and osmotic power, and is complemented by a web-based calculator that performs the same mobility analysis without a local Python installation. All three GUIs are implemented in Python/Tkinter with modular plotting and logging layers so that flexible control sequences and physics-based post-processing share a common data format, improving reproducibility, timing stability and day-to-day efficiency in nanofluidic and electronic device studies.