Sensing food quality by silicene nanosheets : a Density Functional Theory study

TL;DR

This study uses Density Functional Theory to model silicene nanosheets as sensors for food-emitted VOCs, showing fluorinated silicene's superior sensitivity and ability to distinguish multiple compounds, outperforming some existing materials.

Contribution

It demonstrates that fluorinated silicene nanosheets are highly effective and selective sensors for food VOCs, providing a theoretical basis for designing advanced nano-sensors.

Findings

Fluorinated silicene shows better sensitivity than unpassivated silicene.

Fluorinated silicene can distinguish four VOCs separately.

Passivated silicene outperforms r-GO in sensing food VOCs.

Abstract

Volatile organic compounds (VOCs) emitted by food products are considered markers for assessing quality of food. In this work, first-principles Density Functional Theory (DFT) and Non-equilibrium Green's function (NEGF) methods have been employed to model chemo-resistive gas sensor based on two-dimensional silicene based nanosheets that can sense the six different VOCs emitted by standard food products. Our calculations with unpassivated and flourine passivated silicene(F-silicene) sheets as sensor materials show that flourine passivated silicene has significantly better sensitivity towards all six VOC molecules (Acetone, Dimethylsulfide, Ethanol, Methanol, Methylacetate and Toluene). Moreover, flourinated silicene sensor is found to be capable of separately recognising four VOCs, a much better performance than r-GO used in a recent experiment. We analyse the microscopic picture influencing sensing capabilities of un-passivated and fluorinated silicene from the perspectives of adsorption energy, charge transfer and changes in the electronic structure. We find that better sensing ability of fluorinated silicene nanosheet can be correlated with the changes in the electronic structures near the Fermi level upon adsorption of different VOCs. The results imply that passivated silicene can work better as a sensor than r-GO in case of generic food VOCs. The results are important since modelling of various two-dimensional nano-sensors can be done in the similar way for detection of more complex VOCs emitted by specific food products.