journal article·2023-09

Enhancing fibre optic sensor signals via gold nanoparticle-decorated agarose hydrogels

Siti Rabizah Makhsin, Muhamad Hasbullah Zakaria, Muhammad Haziq Noor Akashah, Rozina Abdul Rani, Patricia J. Scully, Peter Gardner

Optical Materials· Volume 143
Publisher page

Abstract

The main objective of this work was to create a novel sensing material using different sizes of gold nanoparticles (AuNPs) embedded in agarose hydrogel (AG) encapsulated fibre optic sensors. AuNPs (4 nm–40 nm) were synthesized using the chemical reduction method with a reducing agent of sodium borohydride (1% w/v) and trisodium citrate (40 mM). The core of the fibre optic was coated either using a single-layer (4 nm AuNPs, 15 nm AuNPs and AG) or two-layer coating (Au-AG) with the first layer being AG then followed by AuNPs (4 nm or 15 nm). The signal resulting from the AuNPs-AG coated fibre optic probe was tested using spectroscopy with a scanning range from 200 to 1000 nm to establish which sample generated the highest sensing signal. The result demonstrated that the smaller size of AuNPs provides a higher signal compared to a larger size, while the two-layered coating of 4 nm Au-AG significantly boosts the signal of the fibre optic sensor up to 47%. The overall performance of the 4 nm Au-AG coated fibre optic probe was highly promising, demonstrating a refractive index (RI) sensitivity of 3669.8 ± 5.232 RIU−1, with an R2 value of 0.9802. This optimized configuration of coating materials presents the sensor platform as an excellent candidate for further exploration in designing specific sensing applications, such as gas sensors or biosensors. Its remarkable ability to enhance sensor sensitivity holds great promise for advancing sensing technologies.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Fiber Optic Sensors · Advanced Sensor and Energy Harvesting Materials · Biosensors and Analytical Detection

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

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