Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning.
Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning.
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DOI：
10.1021/acsnano.7b00105
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发表时间：
2017-02-28
期刊：
影响因子：
17.1
通讯作者：
Ozcan A
中科院分区：
文献类型：
--
作者：
Ballard ZS;Shir D;Bhardwaj A;Bazargan S;Sathianathan S;Ozcan A
等离子体传感器已广泛用于生物和化学传感应用。新兴的纳米制造技术使这些传感器能够经济有效地在各种类型的基板上大规模制造。为了伴随这些进步，传感器读出设备也必须实现重大改进，以充分实现等离子体纳米传感器的广泛影响。在这里，我们提出了一种机器学习框架，可用于设计低成本和移动多光谱等离子体读取器，该读取器不使用传统上使用的笨重且昂贵的稳定光源或高分辨率光谱仪。通过在大量制造的等离子体纳米传感器上训练特征选择模型，我们选择创建最小误差折射率预测模型所需的最佳照明发光二极管组，该模型在统计上考虑了给定传感器设计的不同光谱响应和制造引起的可变性。这种计算传感方法使用模块化移动等离子体读取器进行了实验验证。我们测试了具有六边形和方形周期性纳米孔阵列的不同等离子体传感器，结果表明，最佳照明波段与根据传感器光谱特征（例如传输峰或谷）“直观”选择的照明波段不同。该框架为等离激元社区提供了设计低成本、移动多光谱读取器的通用工具，帮助将纳米传感技术转化为各种新兴应用，例如可穿戴传感、个性化医疗和护理点诊断。除了等离激元之外，基于光谱变化运行的其他类型的传感器也可以广泛受益于这种方法，包括例如支持适体的纳米颗粒检测和基于石墨烯的传感器等。
Plasmonic sensors have been used for a wide-range of biological and chemical sensing applications. Emerging nano-fabrication techniques have enabled these sensors to be cost-effectively mass-manufactured onto various types of substrates. To accompany these advances, major improvements in sensor read-out devices must also be achieved to fully realize the broad impact of plasmonic nano-sensors. Here, we propose a machine learning framework which can be used to design low-cost and mobile multi-spectral plasmonic readers that do not use traditionally employed bulky and expensive stabilized light-sources or high-resolution spectrometers. By training a feature selection model over a large set of fabricated plasmonic nano-sensors, we select the optimal set of illumination light-emitting-diodes needed to create a minimum-error refractive index prediction model, which statistically takes into account the varied spectral responses and fabrication-induced variability of a given sensor design. This computational sensing approach was experimentally validated using a modular mobile plasmonic reader. We tested different plasmonic sensors with hexagonal and square periodicity nano-hole arrays, and revealed that the optimal illumination bands differ from those that are ‘intuitively’ selected based on the spectral features of the sensor, e.g., transmission peaks or valleys. This framework provides a universal tool for the plasmonics community to design low-cost and mobile multi-spectral readers, helping the translation of nano-sensing technologies to various emerging applications such as wearable sensing, personalized medicine, and point-of-care diagnostics. Beyond plasmonics, other types of sensors that operate based on spectral changes can broadly benefit from this approach, including e.g., aptamer-enabled nanoparticle assays and graphene-based sensors, among others.