Amino Acid Sorting Based on Bifurcated Nanochannels with Tunable Surface Charge

Abstract

AbstractIn modern biology and biomedical engineering, the ability to accurately and efficiently separate and identify different biomolecules from complex biological samples is crucial for applications such as proteomics and cancer diagnostics. This study introduces a pioneering approach to amino acid sorting utilizing bifurcated nanofluidic channels constructed from stacked graphene membranes with tunable surface charges. Leveraging molecular dynamics simulations, periodic switching of an external electric field enables targeted sorting of amino acids with near 100% precision is demonstrated. Moreover, the velocity of sorting can also be controlled by varying surface charge, the strength of the electric field, and the temperature of the system. This approach significantly outperforms traditional methods by offering a rapid, scalable, and cost‐effective solution for analyzing complex biomolecular mixtures. The findings highlight the transformative impact of tunable surface charge in nanofluidics, not only providing breakthrough solutions to biomolecular sorting challenges, but also laying the groundwork for future applications in drug delivery and nanosurgery.