Doctoral School of Electrical Engineering 

BME-VIK, Department of Electronics Technology

Supervisor: Dr. BONYÁR Attila

Construction of a digital twin model of a localized surface plasmon resonance-based sensor

Introducing the research area

Affinity biosensors utilizing the phenomenon of surface plasmon resonance (SPR) can be considered the most sensitive optical principle-based devices among the label-free biosensor techniques of our time [1]. One of its major advantages is the possibility of a high degree of parallelization of measurements; on a typically gold thin-film chip surface; up to 100 measurement points can now be measured in parallel at the same time, allowing the creation of multi-biosensor constructs [2]. Surface plasmon resonance (SPR)-based biosensors found their applications in gas and water sensing [3,4], nucleic acid detection [5], and biosensing [6]. The aim of this research is to design a digital twin model of a sensor based on hexagonally arranged ellipsoidal gold nanoparticles.

Brief introduction of the research place

My research is carried out under the mentorship of Dr. Attila Bonyár at the Department of Electronic Technology, within the Nanotechnology and Sensors Research Group of BME, where they have been working on the research and development of electrochemical and optical biosensors for nearly 20 years, including working on the development of LSPR-based (localized surface plasmon resonance) biosensors for nearly 10 years.

History and context of the research

The phenomenon of localized surface plasmon resonance (LSPR) may open the possibility of implementing sensors using the transmission principle which is simpler than the reflection principle optics used by classical SPR devices, and thus integrate this sensitive technique into portable point-of-care (POC) devices while retaining all the advantages of classical SPRi technique. This requires the design of suitable nanostructures, taking into account all the important parameters of our device (sensitivity, detection threshold, reproducibility) and the cost of the design technology. One of the main advantages of LSPR-based sensors is their high sensitivity to changes in the refractive index of the surrounding medium. Localized surface plasmon polaritons are collective free electron oscillations (where the wavelength is longer than the characteristic size of the nanoparticle) excited by an external electric field on the surface of the gold nanoparticle. This effect can result in the enhancement of the scattering and absorption of incident electromagnetic waves and, due to its sensitivity to the refractive index of the surrounding medium, can be successfully exploited for sensing purposes. In my research, the development of a sensor element based on localized surface plasmon resonance was carried out using finite element simulations.

The research goals, open questions

 

The main objective of this research is the design of a localized surface plasmon resonance (LSPR) sensor based on hexagonally arranged ellipsoidal gold nanoparticles. Theoretical optimization of the geometric properties of the nanoparticle arrangements and their refractive index-sensitivity relationship is performed using finite element method (FEM) simulations. I investigate three different particle arrangements, with different particle distances between hexagonally arranged ellipsoidal nanoparticles under a periodic boundary condition, and also the effect of the substrate layer fitted in the model on the sensitivity. In addition, the effect of a non-periodic boundary condition on the different arrangements was investigated using the boundary element method (BEM).

Methods

I have investigated three different nanoparticle layouts, their experimental implementation has been detailed in earlier publications [9,10]. The SEM (scanning electron microscope) and STEM (scanning transmission electron microscope) images of the nanoparticle arrangements are shown in Figure 1.

1.      Figure The three different nanoparticle arrangements (types #1, #2 and #3). In the top SEM images, the nanoparticles were above the aluminum layer after synthesis. The lower STEM images were taken on the final sensing elements when the nanoparticles were already on top of the SiO2 nanoparticles/substrate. The image was reproduced from [10].

The finite element simulations were performed using Comsol Multiphysics 3.5 suit to investigate the sensitivity to different nanoparticle arrangements. The evaluation of the sensitivity of LSPR was based on the response of the nanoparticles to changes in the refractive index of the surrounding medium. The bulk refractive index sensitivity (RIS) was thus determined according to Equation 1, where Δλp is the shift of the peak of the extinction spectrum with the change of the refractive index of the dielectric medium. For this purpose, I modeled the environment surrounding the nanoparticles with a constant refractive index (e.g., air n1 = 1, water n2 = 1.33, so Δn=0.33)

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                                                                            (1)

 In addition, I used perfectly matched layers (PML) in the numerical simulations to absorb the incident wave and reflections. The finite element method allowed the implementation of a periodic boundary condition. For the BEM-based simulations, we used the MNPBEM Matlab Toolbox, which is specifically designed to study the plasmonic behavior of metallic nanomaterials [[11]]. The BEM solver was configured as discussed in the previous work of Dr. Attila Bonyár [12]. The different models are shown in Figure 2.

           

2.      Figure The different models: (a) non-periodic single particle inside a spherical PML, (b) non-periodic hexagonal particle arrangement, (c) periodic boundary condition with hexagonal unit cell, (d) periodic boundary condition with rectangular unit cell, (e) periodic model with added substrate, and (f) hexagonal BEM model.

Results

We compared the BEM and FEM approaches for the single-particle and hexagonal models and found that the LSPR peak positions and the resulting sensitivity values agree within 1 nm. In addition, a comparison of the FEM models of the hexagonal and rectangular unit cells is shown in Figure 3.

 

3.      Figure (a) comparison of FEM and BEM simulations, (b) comparison of hexagonal and rectangular unit cell results for nanoparticle #1 arrangement.

 

The results have been further detailed in a paper currently under submission [13]. The geometrical parameters, the interparticle spacing, the influence of the nanoparticle diameter and thickness on the position of the obtained LSPR peaks, and the sensitivity of the bulk refractive index have been studied in detail, considering the influence of the SiO2 substrate under the ellipsoidal particles (4. The figure also shows the comparison of simulation and experimental results (a) and the model with the substrate (c).

 

4.      Figure (a) experimental and simulation results for nanoparticle arrangement #2, (b) circular polarization on the hexagonal unit cell model, (c) electric field map of the model with substrate.

It is shown that by optimizing the models, the refractive index sensitivities for the three different layouts are on average within 2.5 nm/RIU of the experimentally measured values. In addition, a detailed comparison of the simulation conditions and a detailed discussion of the effect of the geometric parameters on the obtained sensitivities can be particularly useful for the optimization of plasmonic sensor structures by numerical simulations. Using the realized nanocomposite plasmonic sensors' digital twin to optimize the fabrication parameters, the sensor performance can be improved in the future in different application areas.

 

Expected impact and further research

The aim of my research is to create a simulation model that will allow the design of an LSPR-based optical biosensor for the detection of arsenic, which could later form the basis for the development of a portable instrument for field testing. Due to the high prevalence of arsenic contamination worldwide, early detection and remediation have become an active area of research [7]. In humans, chronic exposure to arsenic can lead to fatal diseases such as gastric diseases and cancer. This is why there is an urgent need to develop an effective and reliable detection system. It is important to note that there is no purely electronic arsenic detector commercially available today, and testing is performed (state-of-the-art) using field test kits [8], which are complex and require trained personnel and are not very reliable.

Publications, references, links

List of corresponding own publications.

●       R. Kovács, A. Bonyár. Finite Element Investigation of the Plasmonic Properties of Hexagonally Arranged Gold Nanoparticles. In: 46th Spring Seminar on Electronics Technology ISSE 2023. (pp. 1-4)

 Under submission: Rebeka Kovács, Attila Bonyár. Towards digital twins of plasmonic sensors: constructing a complex numerical model of a plasmonic sensor based on hexagonally arranged gold nanoparticles. Nanomaterials. 2023.

Table of links.

BME ETT interactive online laboratory

List of references.

[1] H. Nguyen, J. Park, S. Kang, and M. Kim, ‘Surface Plasmon Resonance: A Versatile Technique for Biosensor Applications’, Sensors, vol. 15, no. 5, pp. 10481–10510, May 2015, doi: 10.3390/s150510481

[2] D. Wang et al., “Recent advances in surface plasmon resonance imaging sensors,” Sensors (Switzerland), vol. 19, no. 6. 2019. doi: 10.3390/s19061266

[3] Rodrigues MS, Borges J, Lopes C, Pereira RMS, Vasilevskiy MI, Vaz F. Gas Sensors Based on Localized Surface Plasmon Resonances: Synthesis of Oxide Films with Embedded Metal Nanoparticles, Theory and Simulation, and Sensitivity Enhancement Strategies. Applied Sciences. 2021;11(12):5388. doi:10.3390/app11125388

[4] Qiu G, Ng SP, Liang X, Ding N, Chen X, Wu CML. Label-Free LSPR Detection of Trace Lead(II) Ions in Drinking Water by Synthetic Poly(mPD- co -ASA) Nanoparticles on Gold Nanoislands. Anal Chem. 2017;89(3):1985-1993. doi:10.1021/acs.analchem.6b04536

[5] Thamm S, Csàki A, Fritzsche W. LSPR Detection of Nucleic Acids on Nanoparticle Monolayers. In: Zuccheri G, ed. DNA Nanotechnology. Vol 1811. Methods in Molecular Biology. Springer New York; 2018:163-171. doi:10.1007/978-1-4939-8582-1_11

[6] Austin Suthanthiraraj PP, Sen AK. Localized surface plasmon resonance (LSPR) biosensor based on thermally annealed silver nanostructures with on-chip blood-plasma separation for the detection of dengue non-structural protein NS1 antigen. Biosensors and Bioelectronics. 2019;132:38-46. doi:10.1016/j.bios.2019.02.036

[7] S. Thakkar, L. F. Dumée, M. Gupta, B. R. Singh, and W. Yang, ‘Nano-Enabled sensors for detection of arsenic in water’, Water Research, vol. 188, p. 116538, Jan. 2021, doi: 10.1016/j.watres.2020.116538.

[8] https://www.hach.com/p-arsenic-low-range-test-kit/2800000

[9] Lednický T, Bonyár A. Large Scale Fabrication of Ordered Gold Nanoparticle–Epoxy Surface Nanocomposites and Their Application as Label-Free Plasmonic DNA Biosensors. ACS Appl Mater Interfaces. 2020;12(4):4804-4814. doi:10.1021/acsami.9b20907

[10] S. Zangana, T. Lednicky A. Bonyar. “Three Generations of Surface Nanocomposites Based on Hexagonally Ordered Gold Nanoparticle Layers and Their Application for Surface-Enhanced Raman Spectroscopy” Chemosensors 2023. 11(4), 235

[11] Hohenester U, Trügler A. MNPBEM – A Matlab toolbox for the simulation of plasmonic nanoparticles. Computer Physics Communications. 2012;183(2):370-381. doi:10.1016/j.cpc.2011.09.009

[12] Bonyár A. Maximizing the Surface Sensitivity of LSPR Biosensors through Plasmon Coupling—Interparticle Gap Optimization for Dimers Using Computational Simulations. Biosensors. 2021;11(12):527. doi:10.3390/bios11120527

[13] Rebeka Kovács, Attila Bonyár. Towards digital twins of plasmonic sensors: constructing a complex numerical model of a plasmonic sensor based on hexagonally arranged gold
 nanoparticles. Nanomaterials. 2023.