A novel gas sensing principle based on quantum fluctuations

TL;DR

This paper introduces a new gas sensing method based on quantum fluctuations of electromagnetic fields, utilizing optically trapped nanoparticles and neural networks to detect very low gas concentrations with high accuracy.

Contribution

The work proposes a novel measurement scheme leveraging quantum fluctuations and neural networks for sensitive, non-invasive gas detection, differing from existing surface-based methods.

Findings

Detects CO2 concentrations down to 0.01%

Achieves 1 ppm accuracy in gas measurement

Enables fast, continuous monitoring without sample influence

Abstract

We present a model of a novel measurement scheme to detect small amounts of a gas species via the ground-state fluctuations of the electromagnetic field (dispersion forces) depending on the entire spectral properties of all objects. Here, we describe an experimental setup of optically trapped nanoparticles in a hollow-core fibre. We calculate the effects of the gases on the thermal motion of the nanoparticles and present a neural network-based method for reconstructing the gas concentrations. We present an example of one possible setup capable of detecting concentrations of CO2 down to 0.01 volume per cent with an accuracy of 1 ppm. Reliable detection of small concentrations of specific molecules in a gas is essential for numerous applications such as security and environmental monitoring, medical tests, and production processes. Unlike other measurement schemes, such as surface plasmons or functionalised surfaces, this allows for fast, continuous monitoring and using small sample quantities, without influencing the probe or the sensor system.