Yao Kaibin, Xie Xin, Jiao Jiawei, Liu Anping, Huang Yingzhou
College of Physics, Chongqing University, Chongqing 400044, China; Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, Chongqing University, Chongqing 400044, China.
College of Physics, Chongqing University, Chongqing 400044, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Mar 5;308:123631. doi: 10.1016/j.saa.2023.123631. Epub 2023 Nov 8.
Limited by the narrow enhanced area of nanoscale on the metal surface, the sensitivity of surface-enhanced Raman spectroscopy (SERS) detection in solution is usually much lower than the detection in a solid substrate, which is dramatic in microfluidics for online detection. In this work, a cellulose microfilament embraced by Ag nanoparticles, called plasmonic cellulose microfilament, is located in a microchannel for SERS detection in microfluidics. Benefiting from the congestion caused by the plasmonic cellulose microfilament in a microchannel, the trace molecule in the solution is much easier to gather in Ag nanoparticles for Raman enhancement. To obtain high sensitivity, the structure of plasmonic cellulose microfilament is optimized. The SERS spectra collected in this novel microfluidics demonstrate that the plasmonic cellulose microfilament presents a high sensitivity at 10 M and good reproducibility in SERS detection. In addition, automatic identification of urea presence or absence was achieved based on deep learning (DL) here. The results show excellent diagnostic accuracy (99 %), which suggests that a fast, label-free urea screening tool can be developed. These results point out this SERS microfluidics with plasmonic cellulose microfilament has a great application prospective in online SERS detection with high sensitivity.
受限于金属表面纳米级增强区域的狭窄，溶液中表面增强拉曼光谱（SERS）检测的灵敏度通常远低于固体基质中的检测，这在用于在线检测的微流控技术中表现得很明显。在这项工作中，一种被银纳米颗粒包裹的纤维素微丝，称为等离子体纤维素微丝，位于微通道中用于微流控中的SERS检测。得益于等离子体纤维素微丝在微通道中造成的拥堵，溶液中的痕量分子更容易聚集在银纳米颗粒上以增强拉曼信号。为了获得高灵敏度，对等离子体纤维素微丝的结构进行了优化。在这种新型微流控技术中收集的SERS光谱表明，等离子体纤维素微丝在10⁻⁶ M时具有高灵敏度，并且在SERS检测中具有良好的重现性。此外，在此基于深度学习（DL）实现了尿素存在与否的自动识别。结果显示出优异的诊断准确率（99%），这表明可以开发一种快速、无标记的尿素筛查工具。这些结果表明，这种具有等离子体纤维素微丝的SERS微流控技术在高灵敏度在线SERS检测中具有巨大的应用前景。
