Tuning phosphorene and MoS2 2D materials for detecting volatile organic compounds associated with respiratory diseases

Abstract

Efficient identification of volatile organic compounds (VOCs) is essential for the rapid diagnostication of respiratory diseases. By detecting specific biomarkers associated with different pathologies one may distinguish between tuberculosis, nosocomial pneumonia, Aspergillus fumigatus, influenza and SARS-CoV-2 virus infections. Phosphorene and MoS₂ are potential candidates from the class of 2D graphene-like materials, which can be used as active layers for sensing elements. However, as the target molecules poorly adhere to the pristine layers, binding centers are created by introducing substitutional impurities. The adsorbed VOCs induce modifications in the electrical properties of the customized active layers. For each biomarker and a sequence of substitutional impurities, a pattern of conductivities is obtained, which enables the detection of an unknown test specimen. Exploring multiple biosensor configurations we find an optimal design yielding a considerable selectivity for the five biomarker compounds.

Fig. 1. Biosensor design workflow and the working principle for biomarker detection. Binding centers are created in the active layers (BP and MoS₂) using substitutional impurities. The selected biomarkers attached to the active layers induce modifications in the electronic properties, observed in the electrical conductivities. Depending on their response with regards to a specific biomarker, the biosensor can embed one or up to ten functionalized monolayers. The conductivity patterns are used to identify an unknown specimen.

Fig. 2. Device structures comprising the active layers (BP and MoS₂) with substitutions and attached biomarker, on top of insulating hBN. The selected biomarkers associated to the respiratory diseases are depicted below.

Fig. 3. (a and b) Bonding lengths corresponding to susbtitutional atoms and biomolecules, for BP@hBN and MoS₂@hBN devices, respectively. (c and d) The binding energies of the molecules which adhere to the active layer, indicated for the same structures. The system index counts for each of the ten substitutions, four molecule configurations relative to the substrate and each of the five biomarkers, resulting in a total of 200 structures per device. The colors mark the three groups of substitutions: group-IV (C, Si, Ge), group-VI (S, Se) and TMs (Mn, Fe, Co, Ni, Cu).

Fig. 4. (a and b) Density of states (DOS) in logarithmic scale and (c and d) scaled conductivities, G/G₀ for the systems indicated in Fig. 3. The same color codes apply. The conductances are normalized to the highest value, G₀, obtained from the data sets of the two devices.

Fig. 5. Identifying biomarkers based on R² coefficient of determination: the test sample is compared with the reference and the predicted biomarker is determined based on the largest R². The reference data sets, corresponding to the five selected biomarkers, are depicted by colored symbols, the test cases are represented by solid lines.

Fig. 6. Typical sensor performance with respect to identifying one of the five possible biomarkers, for the two devices, (a) BP@hBN and (b) MoS₂@hBN.

Fig. 7. Distribution of charge density differences as the molecules are attached to active layers. For the two devices, BP@hBN (a–e) and MoS₂@hBN (f–j), we represented δρ(r⃑) for silicon substitutions, for each five molecules. Larger amount of charge transfer can be correlated with the performance chart shown in Fig. 6. Charge accumulations and charge depletion are represented by violet and blue colors, respectively.

Fig. 8. Charge transfer between the molecule and devices, BP@hBN (a) and MoS₂@hBN (b), reflecting the particularities of the substitution–molecule pairs. The red dots indicate the devices with Si substitutions. The systems are indexed as in Fig. 3.