Abstract: Air quality and climate change are among the biggest societal challenges that we face today. An estimated 7 million people globally die prematurely each year due to air pollution. Atmospheric free radicals, particularly hydroxyl (•OH) and nitrate (•NO3), classified as atmospheric detergents, are the drivers of chemical processes. They determine the atmospheric composition and thus influence local and global air quality and climate. The ultimate aim of this project is to develop novel silicon junctionless nanowire transistor (Si JNT) sensors to detect hydroxyl and nitrate radicals. These JNT sensors are novel, low-cost, and easily accessible. JNTs will be functionalised with organic layers based on aromatics and alkanes with and without electron-withdrawing groups (e.g., perfluorinated alkanes, ketones) for selectively trapping and sensing radicals. Within this aim, to determine the suitability of Si JNT sensors in atmospheric sensing, initial tests were performed for different mixing ratios (ppb to ppm range) for the common atmospheric pollutant Nitrogen dioxide (NO2). The adsorption of NO2 molecules on Si-JNTs can lead to modulation of the concentration of holes and electrons in the conduction band of nanowires, resulting in a change in JNT parameters (e.g. on-current, mobility etc.), proving their sensitivity to gas environments. This presentation will provide an overview of the project on atmospheric radical sensing. Additionally, a summary of the latest results on the interaction of NO2 and Si JNT will be presented. Although challenging, JNT sensors not only have the potential to be rolled out on a global scale but can also be adapted to detect both atmospheric pollutants such as Nitrogen dioxide and atmospheric detergents such as •OH and •NO3. Additional Information: Vaishali Vardhan presented this talk for the HZDR Nanonet+ Workshop 2022. RADICAL represents a 'Fundamental Breakthrough in Detection of Atmospheric Free Radicals'. Find out more on the RADICAL project website: radical-air.eu The RADICAL project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 899282.