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Investigation of the gas sensing performance of FET devices at work: modeling the conduction mechanisms in 1D semiconducting metal oxide nanostructures by operando techniques

Grant number: 19/26333-2
Support Opportunities:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): October 01, 2021
Effective date (End): September 30, 2022
Field of knowledge:Engineering - Materials and Metallurgical Engineering - Nonmetallic Materials
Principal Investigator:Marcelo Ornaghi Orlandi
Grantee:Pedro Henrique Suman
Supervisor: Nicolae Barsan
Host Institution: Instituto de Química (IQ). Universidade Estadual Paulista (UNESP). Campus de Araraquara. Araraquara , SP, Brazil
Research place: Eberhard Karls Universität Tübingen, Germany  
Associated to the scholarship:16/20808-0 - Field-effect transistors (FET) based on 1D semiconducting nanostructures: impact of the electrical signal modulation on the gas sensor performance, BP.PD

Abstract

In this research project, a comprehensive phenomenological investigation of the conduction mechanisms associated with the gas sensing performance of field-effect transistors (FET) based on one-dimensional (1D) semiconducting metal oxide (SMOx) nanostructures is proposed. For this, FET devices with the channel/sensing element composed of single or multiple tin oxide (SnO2, SnO and Sn3O4) and copper oxide (CuO and Cu2O) nanostructures will be characterized by operando techniques. Special effort will be addressed to study a "3-in-1" device microfabricated using lithography and dual-beam microscopy (focused ion beam, FIB) with a p-n junction channel formed by crossing a single n-type and a p-type nanostructure. The gas sensor response of the transistors will be analyzed under identical experimental conditions towards several concentrations of oxidizing and reducing target gases at different temperatures and relative humidity levels. The head challenge of this work will be to conduct operando studies by diffuse reflectance infrared Fourier transformed spectroscopy (DRIFTS) and Kelvin Probe (KP) technique simultaneously with DC electrical measurements and also impedance spectroscopy (IS) in real operating conditions of the sensors. It is also intended to create a database for chemical sensors using machine learning algorithms for data processing and classification. The focus will be to propose models to describe the conduction mechanisms related to the surface chemistry and electronic properties of the sensing layers and their impacts on the obtained sensor response. From the results, we expect to understand how to optimize the sensing performance of FET devices and effectively contribute to their applications in strategic areas, including safety, environment and energy conservation. (AU)

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