Abstract
Nanoparticles can be described as a particulate material whose dimensional range lies in nanoscale (i.e., 1–100 nm). Nanoparticles of various shapes such as spheres, rods, wires, planes, stars, cages, multipods can be tailored using different nanochemical procedures. Efforts are being applied in fashioning different hybridized shapes like core–shell nanostructures, spheres dispersed in nanosheets to exploit the physical and chemical properties of the different chemical entities used as well as their varying surface chemistry with change in geometry of the particulate. The unique advantageous properties exhibited by the nanoparticles unlike their bulk counter parts include high surface-to-volume ratio, higher surface energy, and unique mechanical, thermal, electrical, magnetic, and optical behaviors. Nanomaterials are considered to have a commendable potential in the field of biosensors for its capability of efficient signal capture of the biological recognition event (transduction) at nanoscale level. Efficient transduction at such a low length scale is the defining parameter of an efficient and robust biomedical sensor. Translation of the interaction of the biological analytes with that of the bioinspired receptors (nanomaterial) into molecular level range is the key of high sensitivity of any biosensor, and this property can be further exploited for medical diagnosis of cancer at very initial stages and many other physiological ailments. This chapter will be mainly dealing with nanoparticles-based diagnostics and correspondingly their sensitivity validated by their physical and chemical characterization. It will also include a section with a discussion on related recent advancements and future prospects which are to be explored on the field of nanoparticles-based diagnostics.
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Manoharan, K., Saha, A., Bhattacharya, S. (2018). Nanoparticles-Based Diagnostics. In: Bhattacharya, S., Agarwal, A., Chanda, N., Pandey, A., Sen, A. (eds) Environmental, Chemical and Medical Sensors. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-10-7751-7_11
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