Ms. Rima Mansour Profile

Rima Mansour

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Proceedings Article | 2 March 2022 Poster + Presentation + Paper

Colour and image processing for output extraction of a LSPR sensor

Rima Mansour, Vladan Stojkovic, Yuri Vygranenko, Paulo Lourenço, Rui Jusus, Alessandro Fantoni

Proceedings Volume 11950, 119500B (2022) https://doi.org/10.1117/12.2607463

KEYWORDS: Plasmonic sensors, Image segmentation, RGB color model, Sensors, Image processing, Refractive index, Color difference, Image processing algorithms and systems, Image sensors, Water

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Sensors based on the Local Surface plasmon Resonance (LSPR) are attractive due to their simple structure and good sensitivity, but the expensive optoelectronic part of the device is limiting the practical applications. There is a need for new strategies to bring the excellent detection properties of LSPR sensors to the playground of low-cost devices and materials. In this work, it is proposed a novel approach to the output extraction of from LSPR sensor whose sensing element is composed by metal nanoparticles (MNPs). Illuminated with an incident broad light source, the sensor produces a spectral transmission output where the MNPs act like a band-stop optical filter for a specific wavelength. An alteration of the refractive index in the surrounding medium corresponds directly to a shift of the filtering rejection band, which corresponds to a slight change in the colour of the light transmitted by the sensor elements. This colour change can be captured by a CMOS photo-camera, used as an image sensor. It is proposed in this paper an approach based on an automatized image processing algorithm for colour change detection, yielding to a system capable of detecting refractive index variations, avoiding the use of expensive spectrometers. The algorithm comprises three stages: (1) Region of interest detection: images are first cropped using the Otsu threshold binary image to remove the uninteresting areas in the image. (2) Image segmentation: using the watershed algorithm, the sensor elements (sample) area is detected automatically in the cropped image. The segmentation is done using the gradient image, where the watershed markers are the regions of low gradient and barriers are the areas of high values inside the image. (3) The resulted sample region is then processed to find its average or dominant LAB colour and then compare it to its corresponding sample image immersed in different mediums using the colour difference measurement CIEDE2000.

Proceedings Article | 5 March 2021 Presentation + Paper

Arrayed graphene enhanced surface plasmon resonance for sensing applications

Alessandro Fantoni, Yuri Vygranenko, Antonio Maçarico, Caterina Serafinelli, Miguel Fernandes, Rima Mansour, Rui Jesus, Manuela Vieira

Proceedings Volume 11682, 1168213 (2021) https://doi.org/10.1117/12.2583023

KEYWORDS: Graphene, Resonance enhancement, Sensors, Chemical elements, Surface plasmons, Sensing systems, Refractive index, Plasmonics, Oxides, Nanoparticles

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Combination of carbon-based nanomaterials (CNMs) with AuNPs has been demonstrated to enhance the LSPR response and facilitate the functionalization with specific and selective antibodies. Also, the introduction of CNMs in the plasmonic layer allows tuning of the LSPR central frequency. Joining the double dependence of the LSPR on the MNPs size and the presence of CNMs, it is possible to create a set of plasmonic layers whose LSPR wavelengths are distributed in a spectral range of few tenth of nanometers. This consideration paves the way to an LSPR sensor with an arrayed structure, where each element maximizes its specific LSPR at its own wavelength. Illumination with a broad light source produces a different response in each one of the elements. The working process underlying the sensing operation is that each element of the sensor array acts like a band-stop optical filter for a specific wavelength. The output can be extracted by the application of an image analysis approach to the spatially modulated light crossing the sensor area, based on a color recognition algorithm. A change in the refractive index over the sensor array will shift the rejection band of the sensing elements. An automatized method for color recognition can support the analysis of the refractive index variations yielding the final sensor output. A figure of merit, highlighting the LSPR central wavelength and spectral extension for different LSPR configurations, is also obtained for different sizes of the AuNPs and different flavors of CNMs.

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