基于纳米金类过氧化物酶活性构建RGB可视化检测体系的研究

Study on the construction of RGB visual detection system based on the peroxidase-like activity of gold nanoparticles

中文关键词:  纳米金  过氧化物酶  RGB比色法  可视化检测

英文关键词:gold nanoparticles  peroxidase  RGB colorimetry  visual detection

中文摘要:

      目的 利用金纳米粒子(gold nanoparticles, AuNPs)的类过氧化物酶催化活性, 结合智能手机RGB识别进行可视化信号采集, 构建颜色-RGB数值-模拟曲线响应模型。方法 采用柠檬酸钠还原法合成粒径均一的AuNPs分散液。利用一级动力学模型对AuNPs催化活性进行测定并探究不同反应条件对催化活性的影响。借助智能手机软件Color Meter获得反应后图像的RGB参数信息, 选择最优定量公式拟合实验数据。结果 AuNPs表观催化活性为3.5×10?3 s?1·mL?1。反应条件在40~50℃、pH 3.5~4.0、醋酸钠缓冲液浓度大于1.0 mmol/L、反应时间大于15 min时AuNPs催化活性最高, AuNPs的量与反应后溶液显色程度在一定范围内呈现良好的线性关系。结论 AuNPs可通过控制反应参数呈现不同的类过氧化物酶催化活性, 利用智能手机RGB识别技术可快速、直观地展现不同量AuNPs催化显色反应的结果, 完成定量分析。该方法具有良好的准确度和可靠性, 可作为一种可视化检测手段, 为利用AuNPs构建的生物传感检测体系提供参考。

英文摘要:

      Objective To construct a color-RGB numeric-simulation curve response model by utilizing the peroxidase-like activity of gold nanoparticles (AuNPs) combined with the RGB recognition function of smart phone for visual signal acquisition. Methods AuNPs disperse solution with uniform particle size were synthesized by sodium citrate reduction method. The first order kinetic model was used to determine the catalytic activity of AuNPs and explore the effects of different reaction conditions on the catalytic activity. Color Meter, a smart phone software, was used to obtain the RGB parameter information of the image after reaction, and the optimal quantitative formula was selected to fit the experimental data. Results The apparent catalytic activity of AuNPs was 3.5×10?3 s?1·mL?1. The highest catalytic activity of AuNPs was observed at 40?50℃, pH 3.5?4.0, sodium acetate buffer concentration more than 1.0 mmol/L, and reaction time more than 15 min. The AuNPs content had a good linear relationship with the color change degree of solution after reaction. Conclusion AuNPs can present different peroxidase-like catalytic activities by controlling the reaction parameters, the results of different amounts of AuNPs catalyzed color reaction can be quickly and intuitively displayed by using the RGB recognition technology of smart phone, and the quantitative analysis is completed. This method has good accuracy and reliability, and can be used as a visual detection method to provide a reference for the biosensing detection system constructed by AuNPs.