SnO₂ Nanostructure Applied as Gas Sensors with Reinforcement Learning for the Categorization of Greenhouse Gases

The application of conventional tin dioxide (SnO₂) gas sensors is inadequate because of their high operating temperature, poor selectivity, and low gas response. Nanomaterials enhance gas-sensing properties through surface effects, nanoscale dimensions, and quantum-size effects. The development of SnO₂ nanomaterial-based sensors through nanotechnology substantially improves gas sensing performance. The increasing concentration of greenhouse gases significantly contributes to climate change and global warming. Conventional gas sensing strategies face challenges in terms of selectivity, sensitivity, and adaptability under unpredictable environmental situations. To address these concerns, this article proposes a hybrid approach that combines SnO₂ nanostructure-based semiconductor gas sensors with a reinforcement learning (RL) model to achieve accurate detection and greenhouse gas categorization. SnO₂ nanostructure provides efficient electron transport, larger surface area, and enhanced adsorption properties, while the RL model enables adaptive and intelligent gas categorization. Experimental results show that SnO₂ nanostructure combined with the RL algorithm achieves faster response time, improves gas-sensing performance, and provides more reliable discrimination for detecting greenhouse gas.