Nanowire-Based Biosensors: From Growth to Applications

Abstract

Over the past decade, synthesized nanomaterials, such as carbon nanotube, nanoparticle, quantum dot, and nanowire, have already made breakthroughs in various fields, including biomedical sensors. Enormous surface area-to-volume ratio of the nanomaterials increases sensitivity dramatically compared with macro-sized material. Herein we present a comprehensive review about the working principle and fabrication process of nanowire sensor. Moreover, its applications for the detection of biomarker, virus, and DNA, as well as for drug discovery, are reviewed. Recent advances including self-powering, reusability, sensitivity in high ionic strength solvent, and long-term stability are surveyed and highlighted as well. Nanowire is expected to lead significant improvement of biomedical sensor in the near future.

Keywords: biological applications; medical applications; nanomaterial; nanowire; sensor.

Figure 1

Conceptual overview of field effect transistors (FET) (adopted with permission from [24]). (A) Schematics illustrating the differences between standard and nanowire field-effect transistors. (B) An overview of the functionalization process of the nanowire (based on 3-aminopropoyltriethoxysilane (APTES)). The APTES converts the silicon–oxygen bonds into a silane layer, after which, it becomes possible to bind this layer with several receptors (adopted with permission from [31]). (C) Illustrates the operating principles of nanowire sensors. The charged molecules captured by the receptors induce variations in the conductivity of nanowire (there is either an increase or a decrease in the current that passes through it). The nanowire sensors fabricated using (D) the bottom-up approach and (E) the top-down approach.

Figure 2

Top-down fabrication process and results using single-crystalline silicon (SCS) wafer. (A) (100)-silicon (adopted with permission from [51,52]). (B) (111)-silicon (adopted with permission from [53]).

Figure 3

Generalized strategy for ultrasensitive detection of protein and DNA using nanowires (adopted with permission from [62]). (A) Schematic representing the functionalization process to allow high sensitivity. (1) Coupling ethanolamine to the surface; (2) attaching PEG cross-linker; (3) binding of biomolecules by peptide-binding. (B) False-colored image of fluorophore antibodies bound to the ethanolamine and PEGylated surfaces. No statistical difference was found on the amount of fluorescence. (C) PEGylated surface has higher relative fluorescence. The schematic illustrated the spacing introduced by PEG significantly improved the binding efficiency of antibodies. (D,E) Detection of protein antigen (D) and ssDNA (E) using the developed devices. The LOD is 6 fM and 1 fM for protein and ssDNA, respectively.

Figure 4

Design nanowires for in vivo sensing. (A) Improving long-term stability by Al₂O₃ shell coating (adopted with permission from [89]). (B–D) Cytotoxic effects of SiC nanowires to cell behavior and differentiation (adopted with permission from [89]). (B) SEM pictures of SiC nanowires. (C) Quantification of adhesion and proliferation of hMSCs on nanowires and nanoparticles. (D) Quantification of differentiation potency of hMSCs on SiC nanowires. Alizarin Red S and Oil Red O stain was utilized to quantify the differentiation toward osteogenic and adipogenic lineage, respectively.

Figure 5

Integrate nanowires with disposable devices (adopted with permission from [97]). (A) Schematic and photograph of paper-based analytical devices (PADs) with ZnO nanowires. (B) Characterization of ZnO nanowires. From left to right: (1) SEM image of rough carbon surface before the growth of nanowire; (2) SEM image of nanostructured carbon surface with deposited ZnO nanowires; (3) TEM image for quantification of nanowires. (C) Calibration of sensor (output current vs. glucose concentration) in buffer. (D) Quantification of detected limits and linear range of the PADs in serum.

Figure 6

Signal processing strategy for multivariable nanowire biosensors. (A) Disease diagnostics based on machine learning: multivariable nanowire sensors were utilized to provide input for artificial neural networks (adopted with permission from [112]). (B) Big data strategy to correlate exhaled molecules detection by nanowire sensors with specific disease (adopted with permission from [113]).