Multiscale Modeling of Phosphorene-Based Sensing Devices for Volatile Organic Compounds

Sensing of volatile organic compounds (VOCs) not only
in exhaled
air but also in environmental air is essential in rapid diagnostication
and in identifying potential hazards related to air quality, respectively.
In particular, respiratory diseases like influenza and tuberculosis
and other conditions like ketosis and diabetes can be investigated
based on the detection of specific biomarkers in the exhaled products.
At the same time, airborne pollutant contamination and air toxicity
must be strictly and efficiently monitored. A practical and accurate
VOC sensing device can be obtained using conductance changes induced
in properly functionalized phosphorene active layers, which ensure
specific binding sites for the targeted biomolecules. We develop here
a multiscale approach that embeds an atomistic description based on
density functional theory into a macroscopic transport model. In this
way, we account for the electronic structure modifications induced
in the active layer by the attached biomarkers with different configurations,
which are extracted from ab initio molecular dynamics simulations.
For the description of the device operation at the macroscopic level,
we introduce a statistical model, which takes into consideration the
proportion of the binding sites in the pristine active layer and the
fractions of attached molecules. Perturbing factors like carbon dioxide,
nitrogen, oxygen, and water molecules are investigated, and biomarker
detection limits are evaluated. To improve the specificity of the
biosensor, multiple sensing elements are employed, differently customized
by transition metal impurities, which enhance the biomarker recognition
capability. This allows the precise determination of the proportions
of the molecules adhered to the device with the accuracy strictly
dependent on the measurement resolution. Our results indicate that
acetone and cyclohexanone detection is possible down to a few tens
of ppm.