玉米秸秆基柔性表面增强拉曼基底制备与应用

Preparation and Application of Flexible Surface-enhanced Raman Substrates Based on Corn Straw

由于表面增强拉曼光谱检测技术具有特异性强、灵敏度高等优点,而被广泛应用在痕量化合物的检测中。用于检测的SERS基底成为近年来的研究热点,然而研究者大多关注对检测限的降低,而对基底的成本考虑较少。利用廉价的玉米秸秆原料制备了玉米秸秆基醋酸纤维素膜,之后耦合金纳米颗粒,制备了测试性能稳定且检测限较低的玉米秸秆基柔性表面增强拉曼基底。针对水体中福美双农药的测试显示,过滤富集测试较滴加测试能达到更低的检测限,其检测限可达10^(-7)g/mL,并在10^(-7)~10^(-6)g/mL质量浓度范围具有良好的线性回归关系。对基底的适用性研究可知其对水中的多种农药和染料具有良好的检测效果。

Due to the advantages of strong specificity and high sensitivity,surface-enhanced Raman spectroscopy(SERS)has been widely used in the detection of trace compounds.SERS substrates used for detection had become a research highlight in recent years.However,researchers mostly focused on the reduction of limits of detection,and seldom considered the cost.Cellulose acetate membrane was prepared by using cheap corn straw.Then,coupled with gold nanoparticles,an SERS substrate with stable test performance and low limits of detection was prepared.The characterization of the SERS substrates showed that the gold nanoparticles formed regular multilayer structure on the surface of cellulose acetate,which was conducive to the formation of“hot spots”and increased the signal.Abundant pore structure and hydrophilicity of the SERS substrates made the enrichment test feasible.It also had a certain thermal stability,to ensure that a short time of laser irradiation would not burn the substrate.The SERS test results showed that the enrichment test had a lower limit of detection than the drop test.It can reach 10^(-7) g/mL,and had a good linear relationship in the concentration range of 10^(-7)~10^(-6) g/mL.Applicability analysis showed that it had good detection effect on several kinds of water pollutants,and provided a technological process for preparing surface-enhanced Raman substrates from corn straw.Due to the cheap raw material and simple preparation process,the preparation cost of SERS substrates could be effectively reduced.Thus,it laid the application foundation for high value utilization of straw and rapid and high sensitivity detection of water pollutants.