首页|期刊导航|佛山科学技术学院学报(自然科学版)|基于Tb掺杂Y2O3催化发光的有机胺气体传感研究

基于Tb掺杂Y2O3催化发光的有机胺气体传感研究OA

The study of organic amine gas sensing based on cataluminescent characteristics of Tb-doped Y2O3

中文摘要英文摘要

胺类挥发性有机化合物是肉类中常见的有害物质,会对人体健康造成威胁.采用水热法制备一种掺杂Tb的Y2O3 纳米材料,并基于该材料的催化发光传感性质,建立一种有机胺气体的检测方法.结果显示,与无掺杂Y2O3 相比,Tb掺杂后的Y2O3 对胺类的响应显著增强.优化实验表明,Tb-Y2O3 对有机胺气体最佳传感参数为:工作温度200℃,检测波长555 nm,载气流速100 mL/min.在最优条件下,三甲胺的检出限(3σ)为 2.99 ppm,线性范围为 20~500 ppm,相关系数为 0.992,加标回收率为 97.05%~103.01%.将该传感器应用于肉类腐败过程中释放的有机胺检测,发现肉类在常温下存放 10 h内腐败不明显,而在 24 h后会进入快速腐败阶段.研究表明Tb掺杂的Y2O3 传感器在肉类新鲜度监测方面具有良好的应用潜力.

Amines,as volatile organic compounds(VOCs),are common harmful substances in meat and pose a threat to human health.In this study,a Tb-doped Y2O3 nanomaterial was synthesized,and a detection method for organic amine gases was developed based on the catalytic chemiluminescence sensing properties.Results showed that,compared to undoped Y2O3,the Tb-doped Y2O3 exhibited significantly enhanced response to amines.Optimization experiments indicated that the optimal sensing parameters were as follows:operating temperature of 200 ℃,detection wavelength of 555 nm,and carrier gas flow rate of 100 mL/min.Under these optimal conditions,the detection limit(3σ)for trimethylamine was 2.99 ppm,with the linear range was 20~500 ppm,the correlation coefficient of 0.992,and recovery rates ranging from 97.05%to 103.01%.The developed Tb-doped Y2O3 sensor was successfully applied to monitor organic amines released during meat spoilage.It was found that the meat did not show obvious spoilage within the first 10 hours at room temperature,but entered a rapid spoilage stage after 24 hours.This study demonstrates that the Tb-doped Y2O3 sensor holds great potential in monitoring meat freshness.

魏赞;李腾飞;魏峰;刘吉华;郭伟清

五邑大学轨道交通学院,广东 江门 529020||有研(广东)新材料技术研究院,广东 佛山 528000有研(广东)新材料技术研究院,广东 佛山 528000有研(广东)新材料技术研究院,广东 佛山 528000五邑大学轨道交通学院,广东 江门 529020有研(广东)新材料技术研究院,广东 佛山 528000

信息技术与安全科学

有机胺催化发光Tb掺杂Y2O3 纳米材料气体传感器

organic aminescataluminescencedopingY2O3 nanomaterialsgas sensors

《佛山科学技术学院学报(自然科学版)》 2026 (2)

71-78,8

广东省自然科学基金资助项目(2024A1515010130)

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