journal article·2021-12Open access

Carbon-based SERS biosensor: from substrate design to sensing and bioapplication

Xiu Liang, Ning Li, Runhao Zhang, Penggang Yin, Chenmeng Zhang, Nan Yang, Kang Liang, Biao Kong

NPG Asia Materials· Volume 13· Issue 1

Abstract

Abstract The sensing of bioactive molecules based on photochemical techniques has become one of the fastest-growing scientific fields. Surface-enhanced Raman scattering (SERS) is a highly sensitive technique for the detection of low-concentration molecules, including DNA, microRNA, proteins, blood, and bacteria; single-cell detection and identification; bioimaging; and disease diagnosis, providing abundant structural information for biological analytes. One rapidly developing field of SERS biosensor design is the use of carbon-based nanomaterials as substrate materials, such as zero-dimensional carbon quantum dots, one-dimensional carbon nanotubes, two-dimensional graphene, and graphene oxide (GO) and three-dimensional spatial carbon nanomaterials or carbon-based core-shell nanostructures. In this review, we describe the recent developments in SERS biosensors, in particular carbon-based SERS, for the detection of bioactive molecules. We systematically survey recent developments in carbon nanomaterial-based SERS biosensors, focusing on fundamental principles for carbon-based materials for SERS biosensor design, fabrication, and operation, and provide insights into their rapidly growing future potential in the fields of biomedical and biological engineering, in situ analysis, quantitative analysis, and flexible photoelectric functional materials. As such, this review can play the role of a roadmap to guide researchers toward concepts that can be used in the design of next-generation SERS biosensors while also highlighting current advancements in this field.
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Research context

OpenAlex topics: Advanced biosensing and bioanalysis techniques · Gold and Silver Nanoparticles Synthesis and Applications · Advanced Nanomaterials in Catalysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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