In this study, 58 distinct TiO₂-coated glass samples were synthesized via Atmospheric Pressure Chemical Vapour Deposition (APCVD) under controlled synthesis conditions. The crystal properties, optical properties, surface properties and photogenerated charge carrier behaviour of all synthesized samples were characterized by X-ray diffraction (XRD), UV-visible spectroscopy, atomic force microscopy (AFM), and transient absorption spectroscopy (TAS), respectively. The photocatalytic activity of all coatings was systematically assessed against NO gas under near-ISO (22 197-1:2016) test conditions. The most active TiO₂ coating showed ∼22.3% and ∼6.6% photocatalytic NO and NO_x conversion efficiency, respectively, with this being ∼60 times higher than that of a commercial self-cleaning glass. In addition, we compared the accuracy of different machine learning strategies in predicting photocatalytic oxidation performance based on experimental data. The errors of the best strategy for predicting NO and NO_x removal efficiency on the entire data set were ±2.20% and ±0.92%, respectively. The optimal ML strategy revealed that the most influential factors affecting NO photocatalytic efficiency are the sample surface area and photogenerated charge carrier lifetime. We then successfully validated our ML predictions by synthesising a new, high-performance TiO₂-coated glass sample in accordance with our ML simulated data. This sample performed better than commercially available self-cleaning glass under a new metric, which comprehensively considered the visible light transmittance (VLT), NO degradation rate and NO₂ selectivity of the material. Not only did this research provide a panoramic view of the links between synthesis parameters, physical properties, and NO_x removal performance for TiO₂-coated glass, but also showed how ML strategies can guide the future design and production of more effective photocatalytic coatings.

中文翻译：

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实验和机器学习对用于光催化氮氧化物修复的增强型 TiO2 涂层玻璃的见解

在本研究中，通过大气压化学气相沉积(APCVD) 在受控合成条件下合成了58 种不同的TiO ₂涂层玻璃样品。所有合成样品的晶体性质、光学性质、表面性质和光生载流子行为分别通过X射线衍射（XRD）、紫外-可见光谱、原子力显微镜（AFM）和瞬态吸收光谱（TAS）进行表征。所有涂层的光催化活性均在接近 ISO (22 197-1:2016) 的测试条件下针对 NO 气体进行了系统评估。最活跃的TiO ₂涂层分别表现出约22.3%和约6.6%的光催化NO和NO _x转化效率，比商用自清洁玻璃高约60倍。此外，我们还根据实验数据比较了不同机器学习策略预测光催化氧化性能的准确性。在整个数据集上预测NO和NO _x去除效率的最佳策略的误差分别为±2.20%和±0.92%。最佳ML策略​​表明，影响NO光催化效率的最重要因素是样品表面积和光生载流子寿命。然后，我们根据 ML 模拟数据合成了一种新型高性能 TiO ₂涂层玻璃样品，成功验证了我们的 ML 预测。在综合考虑材料的可见光透过率（VLT）、NO降解率和NO ₂选择性的新指标下，该样品的性能优于市售自清洁玻璃。这项研究不仅全面介绍了TiO _{2镀膜玻璃的合成参数、物理性能和 NO x}去除性能之间的联系，而且还展示了机器学习策略如何指导未来更有效的光催化涂层的设计和生产。