Noncontact Photodiode-Based Sensing Architecture for Alpha Particle Detection in Radon Monitoring IoT Systems

This paper presents a novel non-contact sensor architecture for indoor radon gas detection based on a silicon photodiode sensor optimized for alpha particle detection. Unlike traditional detection systems that require bulky, contact-based enclosures, the proposed design utilizes a light-shielded, air-permeable black box environment to isolate and amplify ionization events resulting from radon decay. The sensor module includes a multi-stage analog core that detects pulses proportional to the number of alpha particles using a microcontroller. Data processing and real-time wireless transmission are performed here as an IoT node. Calibration and validation are performed against the commercial Radon Eye RD200 detector using radon-emitting rock in a controlled environment. The system demonstrates a detection accuracy of over 90% with a high correlation to commercial standards while maintaining low power consumption. The proposed architecture is suitable for continuous indoor monitoring in residential and occupational environments and offers a promising direction for compact, low-cost, and reliable radon detection solutions. The innovation of this work lies in the combination of non-contact photodiode-based alpha detection and low-power IoT integration, which provides a compact, scalable, and cost-effective solution for real-time radon monitoring in residential, occupational, and remote environments.