NITROCAT- A Kinetic Modeling Approach for the Optimization of Photocatalytic Nitrate Reduction to Nitrogen]

• Applicant: Dr. Girlie Eunice Lopez
• Subject Area: Solid State and Surface Chemistry, Material Synthesis ; Physical Chemistry of Solids and Surfaces, Material Characterisation
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 573686000

Nitrate (NO₃⁻) is one of the major surface and groundwater pollutants being regulated and monitored globally due to its serious health risks. In Europe, 14.1% of groundwater across EU Member States exceeded the NO₃⁻ concentration limits set for drinking water (50 mg/L) between 2016 and 2019. In response to this widespread issue, the European Commission has intensified efforts to ensure compliance with the Nitrates Directive, which is critical for achieving the European Green Deal. Photocatalysis has emerged as a promising approach to environmental remediation, utilizing light as a sustainable energy source to reduce NO₃⁻ to harmless nitrogen gas (N₂). Despite extensive research on titanium dioxide (TiO₂) and modified TiO₂ systems for photocatalytic NO₃⁻ reduction report 100% selectivity, the overall NO₃⁻ conversion rates are typically low (8% to 50%). Moreover, variations in reaction conditions, such as pH, initial NO₃⁻ concentration, and light intensity, lead to inconsistent product selectivity and conversion, and their influence on photocatalytic NO₃⁻ reduction is still poorly understood. Clearly, NO₃⁻ reduction is a complex reaction in which product distribution or selectivity varies significantly depending on these interdependent parameters. In most cases, high selectivity and conversion cannot be reliably achieved in isolated experiments without a deeper understanding of the underlying mechanisms and operational conditions. The NITROCAT project addresses these limitations by introducing, a combined approach that integrates complex kinetic modeling and model-based design of experiments (MBDoE). Kinetic modeling enables the extraction of catalyst-specific descriptors such as rate constants, quantum yields, and recombination rates which provides a deeper understanding of how reaction conditions and changes in the catalyst can influence photocatalytic performance, ultimately guiding the rational design of more selective and efficient NO₃⁻ reduction systems. MBDoE complements this by identifying the optimal experimental conditions to maximize the information obtained from each experiment. Together, these two powerful tools offer a systematic and efficient approach for understanding the role of reaction parameters in the photocatalytic NO3- reduction process and optimizing the reaction selectivity. The anticipated impact of NITROCAT is extensive. This project will not only advance the fundamental knowledge in NO₃⁻ remediation but also establish clear guidelines for optimizing reaction conditions (and the use of MBDoE to do so), which are essential for sustainable photocatalytic NO₃⁻ reduction. By tackling the pressing issue of NO₃⁻ pollution, this project also supports global efforts toward environmental sustainability, aligning with the United Nations Sustainable Development Goal 6 (Clean Water and Sanitation) and the European Green Deal.

• DFG Programme: WBP Position