Assessing doping strategies for monolayer MoS2 towards non-enzymatic detection of cortisol: a first-principles study

Gabriele Boschetto, Aida Todri-Sanial (Corresponding author)

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2 Citaten (Scopus)

Samenvatting

In this work, we investigate by means of atomistic density functional theory simulations the interaction between cortisol (the target molecule) and monolayer MoS2 (the substrate). The aim is to assess viable strategies for the non-enzymatic chemical sensing of cortisol. Metal doping of the sensing material could offer a way to improve the device response upon analyte adsorption, and could also enable novel and alternative detection mechanisms. For such reasons, we explore metal doping of MoS2 with Ni, Pd, and Pt, as these are metal elements commonly used in experiments. Then, we study the material response from the structural, electronic, and charge-transfer points of view. Based on our results, we propose two possible sensing mechanisms and device architectures: (i) a field-effect transistor, and (ii) an electrochemical sensor. In the former, Ni-doped MoS2 would act as the FET channel, and the sensing mechanism involves the variation of the surface electrostatic charge upon the adsorption of cortisol. In the latter, MoS2 decorated with Pt nanoparticles could act as the working electrode, and the sensing mechanism would involve the reduction of cortisol. In addition, our findings may suggest the suitability of both doped and metal-doped MoS2 as sensing layers in an optical sensor.

Originele taal-2Engels
Pagina's (van-tot)1048-1058
Aantal pagina's11
TijdschriftPhysical Chemistry Chemical Physics
Volume24
Nummer van het tijdschrift2
DOI's
StatusGepubliceerd - 14 jan. 2022
Extern gepubliceerdJa

Bibliografische nota

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Financiering

The authors would like to thank Stefania Carapezzi for providing feedback and for many helpful discussions. The authors ackowledge funding from the European Union’s Horizon 2020 research and innovation programme, EU H2020 SmartVista project (www.smartvista.eu), grant agreement No. 825114. This work was granted access to the HPC/AI resources of CINES (Centre Informatique National de l’Enseignement Supérieur) under the allocation 2020-A0090811060 made by GENCI (Grand Équipement National de Calcul Intensif). The authors would like to thank Stefania Carapezzi for providing feedback and for many helpful discussions. The authors ackowledge funding from the European Union's Horizon 2020 research and innovation programme, EU H2020 SmartVista project (www.smartvista.eu), grant agreement No. 825114. This work was granted access to the HPC/AI resources of CINES (Centre Informatique National de l'Enseignement Sup?rieur) under the allocation 2020-A0090811060 made by GENCI (Grand ?quipement National de Calcul Intensif).

FinanciersFinanciernummer
Centre Informatique National de l'Enseignement Supérieur
European Union’s Horizon Europe research and innovation programme825114
Horizon 2020
Grand Équipement National De Calcul Intensif

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