Overview: Surface-enhanced Raman scattering (SERS) affords a rapid, highly sensitive, and nondestructive approach for label-free and fingerprint diagnosis of a wide range of chemicals. This technique has been applied in explosives detection, pre-cancer diagnosis, food safety, and forensic analysis, where a small number of hazardous substances can seriously affect health of human beings. Thus, it is of great significance to prepare high-performance SERS sensors. In general, the signal intensity of SERS is determined by the following three factors: 1) The enhancement effect of surface nanostructure on local electric fields; 2) The number of molecules to be detected in hot spots; 3) Performance of the Raman spectrometer. Therefore, in order to achieve high-performance SERS detection of trace molecules, current research focuses on how to increase the density of hot spots and the number of analyte molecules in the detection area. An ultrafast laser has an ultra-short pulse width and ultra-high peak power, so it can interact with the majority of materials with high processing accuracy and excellent controllability. Meanwhile, it can rapidly construct a variety of large-area micro/nano-structures on material surfaces based on facile digital programming strategies. In addition, combined with multi-beam parallel fast scanning technology, low-cost and high-efficiency machining can be realized without a special requirement for the machining environment. Based on the above advantages, the ultrafast laser has become one of the important means for the fabrication of micro/nano-structures. This is important for the commercial preparation of high-performance SERS sensors. In this paper, we focus on two aspects to introduce the ultrafast laser preparation of high-performance SERS sensors, including how to increase the density of hot spots and the number of analyte molecules in the detection region. Ultrafast lasers can prepare micro/nano-structures with local field enhancement effects by both "bottom-up" and "top-down" processing strategies. The first is based on the "bottom-up" principle, where the reduction, deposition or polymerization of atoms, molecules or other nanoparticles is controlled by ultrafast lasers to achieve additive manufacturing of micro/nano-structures. The other is based on the "top-down" principle, where materials are removed by the ultrafast laser ablation to rapidly achieve hierarchical micro/nanostructures. These structures provide abundant active hot spots for SERS detection. In particular, the superhydrophobic surfaces prepared by the ultrafast laser are one of the most effective methods to achieve the enrichment of analyte molecules. Raman scattering can be excited more effectively by enriched molecules, which is conducive to obtaining higher detection limits and realizing ultra-trace detection. Finally, a prospect for the development of laser-prepared SERS substrates is provided.

冶金、 核工业、 污染检测和环境监测等领域对元素分析的需求是必不可少。 激光诱导击穿光谱技术作为一种新型的原子光谱分析技术, 具有实时快速、 对样品几乎无损、 可多元素同时分析等特点, 因此一直受到广泛的关注。 但其分析灵敏度较差的缺点一直限制着该技术的发展。 激光诱导荧光辅助激光诱导激光光谱技术能够通过激光共振激发提高分析灵敏度并高效检测样本元素种类, 通过光谱仪收集光谱信息并建立模型可对未知样本进行浓度预测。 但当基体原子与目标原子的特征谱线十分接近时, 基体谱线会受到影响, 此时一元定标准确度下降。 通过一元线性拟合和多元线性拟合两种方式对钢铁中的Ni和Cr元素分别建立线性模型。 首先, 选取样品光谱中的峰值谱线, 核实其是否为待测元素或基体元素所对应的特征谱线, 选定合适的特征谱线后, 将多个谱线的光谱强度以及对应该样品的待测元素浓度作多元线性拟合模型, 将各个谱线所对应的拟合系数由高到低进行排序, 并以多元线性拟合模型中各个特征谱线对应的光谱强度对浓度预测的贡献度为标准不断减少拟合维度, 使Ni和Cr拟合模型的决定系数分别由0.960 1提高至0.992 9和0.992 0提高至0.998 7, Ni和Cr元素含量的回归模型平均相对误差分别由38%降低至10%左右和55%降低至25%以内, Ni和Cr元素的线性回归模型的交叉验证均方根误差随着维度的增加分别由3.4%降低至2%左右和2.5%降低至1.5%左右。 选取多个谱线建立多元线性回归模型的方法较为有效的降低了激发干扰的影响, 以较小的工作量提高了对待测样品的待测元素浓度预测的准确度, 为推进激光诱导荧光辅助激光诱导激光光谱技术在元素分析的实际应用提出了一种可行的方案。