Optimal in situ electromechanical sensing of molecular species

TL;DR

This paper develops an analytical protocol for optimal molecular detection using graphene-based electromechanical sensors, accounting for environmental effects and sampling time to enhance sensing performance.

Contribution

It introduces a new analytical framework for optimizing electromechanical sensing protocols considering environmental perturbations and sampling effects.

Findings

Analytical expressions for current, susceptibility, and fluctuations under environmental influences.

Identification of the impact of thermally broadened transmission tails on signal-to-noise ratio.

A protocol for optimal sensing based on Fermi level modulation at fixed bias.

Abstract

We investigate protocols for optimal molecular detection with electromechanical nanoscale sensors in ambient conditions. Our models are representative of suspended graphene nanoribbons, which due to their piezoelectric and electronic properties, provide responsive and versatile sensors. In particular, we analytically account for the corrections in the electronic transmission function and signal-to-noise ratio originating in environmental perturbations, such as thermal fluctuations and solvation effects. We also investigate the role of the sampling time in the current statistics. As a result, we formulate a protocol for optimal sensing based on the modulation of the Fermi level at fixed bias, and provide approximate forms for the current, linear susceptibility, and current fluctuations. We show how the algebraic tails in the thermally broadened transmission function affect the behavior of the signal-to-noise ratio and optimal sensing. These results provide further insights into the operation of graphene deflectometers and other techniques for electromechanical sensing.