Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting
Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting
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DOI：
10.1038/s41467-019-13615-2
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发表时间：
2020-01-10
影响因子：
16.6
通讯作者：
Stevens, Molly M.
中科院分区：
文献类型：
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作者：
Kim, Nayoung;Thomas, Michael R.;Stevens, Molly M.
无标签表面增强拉曼光谱（SERS）可以通过直接识别其组分的独特物理化学性质来询问系统。然而，在复杂的生物系统中，这可能会产生高度重叠的光谱，阻碍样品鉴定。在这里，我们提出了一种人工鼻子启发的SERS指纹识别方法，其中光谱数据作为传感器表面化学功能的函数获得。在分子动力学模型的支持下，我们发现轻度选择性自组装单层膜可以影响分析物与等离子体表面相互作用的强度和结构，从而使SERS指纹图谱多样化。由于每个传感器产生一个调制的签名，增加数据集的维数的隐含价值是使用细胞溶解物为所有可能的组合多达9个指纹显示。通过每个额外的表面功能，平均判别精度可以可靠地提高到100%。这种阵列无标签平台说明了基于高维人工鼻子的传感系统的广泛潜力，可以更可靠地评估复杂的生物基质。
Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting their components' unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modeling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% are achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high-dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices.