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Registration Number

0224U001065, 0122U001611 , R & D reports

Title

Multifunctional nanocomposites based on silicon and carbon for intelligent environmental and radiation monitoring systems

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Head

Monastyrskyi Liubomyr S., Доктор фізико-математичних наук

Registration Date

16-01-2024

Organization

Ivan Franko National University of Lviv

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A B S T R A C T Report on the scientific research work: 103 p., 51 figures, 2 tables, 1 supplement, 123 sources. GAS ADSORPTION SENSORS, GRAPHENE, CARBON NANOTUBES, POROUS SILICON, RADIATION SENSORS, PHOTODETECTORS. Research object: the sensory properties of nanocomposites based on silicon and carbon nanostructures for intelligent sensor systems of radiation and environmental monitoring. The purpose of the work is the development of new and improvement of known technologies of obtaining sensor elements based on silicon and carbon nanocomposites with improved functional properties for the needs of modern sensor electronics and their approbation in intelligent sensor systems for radiation, environmental and biomedical monitoring. Research methods: mathematical and computer modeling, scanning electron microscopy, determination of sensory properties, measurement of electrical and photoelectric characteristics, impedance spectroscopy. Obtained results. The conditions for the formation of nanocomposites based on porous silicon, carbon nanotubes, and reduced graphene oxide with optimal structural characteristics for sensor applications have been established using computer modeling and experimental studies. The main regularities of electronic processes in sensor nanomaterials based on silicon and carbon under the influence of the adsorption of molecules of gases and organic compound vapors, photons of visible light, and ionizing radiation have been established. The obtained nanocomposites were used as sensitive elements of gas sensors and detectors of electromagnetic and ionizing radiation. A method of gas identification with a single-element sensor based on a graphene field-effect transistor using machine learning technology is proposed.

Product Description

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Boyko Yaroslav Vasylovych
Olenych Igor B.
Sinkevych Oleh O.
Sokolovskii Bohdan Stepanovych

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2024-01-16