Electrochemically-gated Graphene Broadband Microwave Waveguides for Ultrasensitive Biosensing

TL;DR

This paper introduces a graphene-based microwave biosensor that uses electrochemical gating and microfluidics to detect DNA sequences with single-base resolution at extremely low concentrations, achieving high accuracy with machine learning.

Contribution

The novel sensor combines microwave waveguides, electrochemical gating, and microfluidics for highly sensitive DNA detection, surpassing existing field-effect transistor and microwave sensor capabilities.

Findings

Achieves >97% classification accuracy for DNA sequences.

Detects DNA at concentrations as low as 1 aM.

Outperforms state-of-the-art biosensors in sensitivity.

Abstract

Identification of non-amplified DNA sequences and single-base mutations is essential for molecular biology and genetic diagnostics. This paper reports a novel sensor consisting of electrochemically-gated graphene coplanar waveguides coupled with a microfluidic channel. Upon exposure to analytes, propagation of electromagnetic waves in the waveguides is modified as a result of interactions with the fringing field and modulation of graphene dynamic conductivity resulting from electrostatic gating. Probe DNA sequences are immobilised on the graphene surface, and the sensor is exposed to DNA sequences which either perfectly match the probe, contain a singlebase mismatch or are unrelated. By monitoring the scattering parameters at frequencies between 50 MHz and 50 GHz, unambiguous and reproducible discrimination of the different strands is achieved at concentrations as low as 1 attomole per litre (1 aM). By controlling and synchronising frequency sweeps, electrochemical gating, and liquid flow in the microfluidic channel, the sensor generates multidimensional datasets. Advanced data analysis techniques are utilised to take full advantage of the richness of the dataset. A classification accuracy > 97% between all three sequences is achieved using different Machine Learning models, even in the presence of simulated noise and low signal-to-noise ratios. The sensor exceeds state-of-the-art sensitivity of field-effect transistors and microwave sensors for the identification of single-base mismatches.