2022
DOI: 10.3390/s22082901
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Surface Plasmon Resonance (SPR) Spectroscopy and Photonic Integrated Circuit (PIC) Biosensors: A Comparative Review

Abstract: Label-free direct-optical biosensors such as surface-plasmon resonance (SPR) spectroscopy has become a gold standard in biochemical analytics in centralized laboratories. Biosensors based on photonic integrated circuits (PIC) are based on the same physical sensing mechanism: evanescent field sensing. PIC-based biosensors can play an important role in healthcare, especially for point-of-care diagnostics, if challenges for a transfer from research laboratory to industrial applications can be overcome. Research i… Show more

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Cited by 34 publications
(12 citation statements)
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“…PICs attract a lot of research interest and are shown to be promising candidates in a range of applications including sensing [40,41], especially in biomedical contexts [42,43] and photonic computing [44] but to date, research has been primarily driven the introduction and scaling of PICs for optical communications [45]. PICs offer strong advantages over electronic integrated circuits, namely higher available speed, large integration capacity, and compatibility with existing processing flows [46].PICs provide opportunities for miniaturisation of bulky optical systems, providing better access to bio-sensing spectroscopy applications to enable point-of-care diagnostics [47] and size and weight appropriate beam steering for light based sensing systems for utilisation in cars and drones [48].…”
Section: Photonic Integrated Circuits (Pics)mentioning
confidence: 99%
“…PICs attract a lot of research interest and are shown to be promising candidates in a range of applications including sensing [40,41], especially in biomedical contexts [42,43] and photonic computing [44] but to date, research has been primarily driven the introduction and scaling of PICs for optical communications [45]. PICs offer strong advantages over electronic integrated circuits, namely higher available speed, large integration capacity, and compatibility with existing processing flows [46].PICs provide opportunities for miniaturisation of bulky optical systems, providing better access to bio-sensing spectroscopy applications to enable point-of-care diagnostics [47] and size and weight appropriate beam steering for light based sensing systems for utilisation in cars and drones [48].…”
Section: Photonic Integrated Circuits (Pics)mentioning
confidence: 99%
“…[ 1–5 ] However, current detection methods, such as surface‐enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and photoacoustic (PA) imaging‐based detection, suffer from problems such as long signal collection time and require specialized equipment. [ 6–8 ] Hence, a self‐reporting platform that resolves the challenges faced by current detection techniques while enabling easy‐to‐use cancer diagnostics is a necessity.…”
Section: Introductionmentioning
confidence: 99%
“…[1][2][3][4][5] However, current detection methods, such as surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), and photoacoustic (PA) imaging-based detection, suffer from problems such as long signal collection time and require specialized equipment. [6][7][8] Hence, a self-reporting platform that resolves the challenges faced by current detection techniques while enabling easy-to-use cancer diagnostics is a necessity. Nanostructured carbon-based sensors fabricated from materials such as graphene, carbon nanotubes (CNT), and quantum dots (QD) can achieve targeted recognition and transduction properties by controlling their macro-and micro-structure using stimuli-responsive parameters.…”
Section: Introductionmentioning
confidence: 99%
“…The optical signal is produced by specific biological interactions with the analyte, as in other detection technologies, but with the advantage of not requiring a direct electrical connection. Different optical properties, such as absorption, fluorescence, internal reflection, and surface plasmon resonance, can be explored to monitor biorecognition in biosensors [ 9 , 10 , 11 , 12 ].…”
Section: Introductionmentioning
confidence: 99%