Project description

Chemical imaging is a powerful tool for studying complex systems such as soils. Soils release N₂O, which contributes to global warming. Chemical microenvironments within the soil, notable O₂, NH₄⁺, and H⁺ levels affect the amount of N₂O emissions. However, our understanding of how these factors interact is still lacking. It is crucial to have a tool that can map these parameters in 2D with high spatial and temporal resolutions to develop strategies for reducing N₂O emissions. To achieve this, I have proposed a project that integrates optical sensors (optodes), hyperspectral imaging, and machine learning (chemometrics). This project will focus on developing a methodology for the simultaneous and direct imaging of O₂, NH₄⁺, and H⁺ levels.

The goal of the project is to provide farmers and policymakers with the tools to optimize fertilizer use, reduce costs, and ultimately support Europe's mission for healthier soils and lower greenhouse gas emissions.

Short Bio

I am a postdoctoral researcher at Aarhus University, specializing in ion-selective optodes and digital color analysis. I received my Ph.D. in physical chemistry in 2021 from St. Petersburg State University in Russia. My interests also include computer simulations of sensor responses, the behavior of liquid/liquid interfaces, multivariate optical measurements, and visualizing ionic gradients. With extensive experience in theoretical and experimental analysis, my goal is to enhance the analytical capabilities of optode-based systems for environmental and biomedical applications.