Keywords

1 Introduction

Scientific measurements are indeed essential to periodically assess the contamination of water from the several supply sources. Pollutants from sewage entering the borehole, through corroded pipelines, bacterial growth on stagnated water, the residual chlorine levels in water are limited or feeble can be the source of contamination. The procedures for water quality monitoring are segregated into physical, chemical, bacteriological, and microscopic categories. It is possible to identify the traces of several microorganisms in water and harmful chemicals. The approaches of separation and detailing are mostly very tedious processes and time-taking. It is looking like it will be difficult to measure drinking water contamination for a pollutant on the fly [1]. The present context of work is mainly related to chemical analysis, which detects toxic chemicals and the detection of harmful organisms.

This paper presents an alternate approach to detecting pollutants in water using an optical sensor. The work shows design, modeling, and simulation of optical sensor using photonic crystal. A silicon photonic crystal-based micro square ring resonator structure is analyzed and designed using line and point defects in order to create a sensor that will be used for the detection of single waterborne bacterium like Shigella flexneri, Vibrio cholera, Salmonella enterica, E. coli, and others. The Lumerical FDTD simulation tool is used to design and model the proposed structure. The performance parameters sensitivity, quality factor, and fast response are used to evaluate the proposed structure.

2 Literature Survey

Miniaturization is the focus of current research. Using light technology, photonic crystal-based biosensors can now detect various bio-molecules, bacteria, viruses, and contaminants with high accuracy.

Many novel concepts and applications in one, two, and three dimensional, as well as structures such as dielectric, metallic, and acoustic, are being researched today. E. Yablonovitch explained the initial motivations for this study, which sprang from the need for a photonic band gap in quantum optics. Several practical and theoretical research were motivated by this necessity. This paper demonstrated how to dope a photonic semiconductor to create small electromagnetic cavities [2]. Granum and Lund presented about Bacillus cereus a form of bacteria and its water poisoning toxins. This is one of the early works of waterborne bacteria identification [3]. Tuminello et al. were the one to investigate the optical characteristics of Bacillus subtilis spores ranging in size from 0.2 to 2.5 num [4]. These research efforts led the foundation of using optical properties of bacteria for identification in water.

Sharan et al., worked on contaminants in drinking water using Ph.C. This identifies the impurities such as magnesium, calcium, iron, and cobalt [5]. Further research on salt in water resulted in further work, such as Lavanya et al., who proposed a concept of an optical sensor for detecting brininess in water [6]. Further, enhancement of the work using AdaBoost algorithm for analysis by S. G, Vivek et al. is interesting (S. G, Vivek et al. 2015). Praveen et al. created a Ph.C.-based nanoscaled sensor for detecting typhoid-causing chemicals in water. This work is a path breaking effort to help primary healthcare staffs in early detection of typhoid [7].

Sharma and Kalyani conducted early study on developing four-channel nano cavities linked Ph.C.-based biosensors for detection of bacteria in water. This is one of the unique efforts where multi-channel approach was experimented [8]. Roy and Sharan’s recent work on the creation of ultrahigh sensitivity biosensors to detect E. coli in water is in-depth research on waterborne E. coli detection. The research focuses on the use of SPR-based biosensors to detect E. coli [9].

Inspired by the advantage of using Ph.C. techniques for biosensing and optimization of outcome by machine learning, S. A. Nehal et al. published their work on AI for bacteria detection. This work uses the unique spectrum signature of the waterborne bacteria to identify water contamination [10].

A review of the literature was conducted, which included different studies and research contributions in the field of photonic crystal-based optical sensors. So, the main goal of this paper is to design and simulate a photonic crystal-based optical sensor capable of detecting waterborne bacterium as well as analyzing and comparing parameters such as sensitivity, quality factor, fast response, and so on.

3 Proposed Structure for Waterborne Bacteria Analysis

The Ph.C. structure used for sensor design is a square lattice structure composed of silicon (Si) rods placed in a silica slab (SiO2) (see Fig. 1). Inside the Ph.C. structure, an MSRR structure is created, forming a line and a point defect. Through numerical modeling, the optimal lattice constant (a) and silicon rods radius (r) are 540 nm and 100 nm, respectively, yielding a r/a ratio of value 0.185. The ring’s center is composed of 3 × 3 rods and arranged in a square pattern to improve responsiveness and sensitivity. The four Si rods are placed at the four corners of the ring to reduce losses such as scattering loss [11]. In this reported work, the proposed structure is placed in the water sample, based on the different bacteria contamination the effective refractive index of the water sample gets change. As a result, the light encounters change in refractive index surrounding the inner ring of the proposed structure, causing the resonance peak wavelength to vary.

Fig. 1
A square lattice structure with points and lines composed of silicon rods and silica slab depicts the sensor design for waterborne bacteria analysis.

Proposed sensor design

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The field pattern in a 2D Ph.C. proposed structure is as shown (see Fig. 2). In the TM mode, the field is perpendicular to the plane of periodicity for rods in air arrangement, i.e., parallel to the rods, while in the TE mode, the plane of periodicity runs around the holes.

Fig. 2
A graph depicts X on the X-axis and Y on the Y-axis values from minus 3 to 3. The color indicator is at the left of the graph.

Field distribution profile for proposed sensor using Lumerical FDTD

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As a result, for rod-type structures, TM mode is selected over TE mode polarizations. Furthermore, TM mode polarizations produce wider band gaps in rod type structures than TE mode polarizations. The TM mode provide large band gap which is because of the strong RI change between the rods and the analyte [12,13,14]. The bandgap structure is determined for the proposed design prior to the insertion of defect with the help of Opti-FDTD software which employs the plane wave expansion (PWE) (see Fig. 3) shows three PBG range of normalized frequency. They are from 0.49654 to 0.78543, 0.932657 to 1.01043, and 1.29936 to 1.30245 for TM mode, and the corresponding wavelength intervals are 1273.18 nm to 2013.93 nm, 989.677 nm to 1072.20, and 767.78 nm to 769.60 nm, respectively.

Fig. 3
A graph titled, P W E Band Solver. It depicts the wider band gaps in the T M mode of the proposed sensor by comparing frequency versus K vector index.

Band gap structure for TM mode

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The first wavelength interval is utilized for detecting purpose for the proposed structure which lies inside the third window of optical communication. As a result, the operating wavelength for input source is selected from 1500 to 1650 nm to enable efficient sensing. The perfectly matched layer (PML) is used as a boundary surface which reduces back reflections. The substrate material is SiO2 according to the study. The RI of material silicon rods in the suggested construction are surrounded by the RI of the analyte. The following Table 1 shows the proposed sensor design specification.

Table 1 Proposed sensor design parameter
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4 Simulation Results

The propagation of electromagnetic waves in conventional waveguides uses the total internal reflection concept. However, in photonic crystal-based structures, the waveguide is formed by utilizing line defect in the structure, and only a certain range of frequencies is permitted to propagate based on the photonic band gap generated by the periodical arrangements of two dielectric materials. The structure band gap is susceptible to modification by changing the lattice constant of the Ph.C. structure and the radius of the rods, allowing the structure to choose a certain frequency range to propagate. Because Ph.C.-based MSRRs have higher sensitivity than waveguide sensors, they are regarded as more promising structure for sensing applications. The Ansys FDTD tool is used to build and simulate the Ph.C.-based MSRR structure, and the shift in the peak wavelength caused by the various aquatic bacteria in the water sample is evaluated using the simulation results [15].

4.1 Results for Detection of Vibrio cholera (Refractive Index: 1.365)

The output transmission graph for pure water with refractive index 1.333 and Vibrio cholera with refractive index of 1.365 (see Fig. 4) and the obtained results.

Fig. 4
A graph of intensity versus wavelength depicts the output transmission for pure water and Vibrio cholera with refractive indexes of 1.333 and 1.365, respectively.

Transmission graph for pure water (RI:1.333) and water with Vibrio cholera bacteria (1.365)

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4.2 Results for Detection of E. coli (Refractive Index: 1.388)

The output transmission graph for pure water with refractive index 1.333 and E. coli with refractive index of 1.388 (see Fig. 5) and the obtained results.

Fig. 5
A graph depicts the output transmission for pure water and water with E. coli bacteria with refractive indexes of 1.333 and 1.388, respectively.

Transmission graph for water (RI:1.333) and water with E. coli bacteria (1.388)

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4.3 Results for Detection of Shigella flexneri (Refractive Index: 1.388)

The output transmission graph for pure water with refractive index 1.333 and Shigella flexneri with refractive index of 1.422 (see Fig. 6) and the obtained results.

Fig. 6
A graph depicts the output transmission for pure water and water with Shigella flexneri with refractive indexes of 1.333 and 1.422, respectively.

Transmission graph for water (RI: 1.333) and water with Shigella flexneri (RI: 1.422)

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4.4 Calculation of Performance Parameter

Sensitivity Calculation

The sensitivity S is defined as the ratio of the change in the peak wavelength with the change in the refractive index of the water sample due to presence of waterborne bacterium present in the water given by Eq. 1 [16].

$$S = \frac{{\text{Change in Resonance Peak Wavelength}}}{{\text{Change in the RI of Water sample }}}$$
(1)

Sensor Response Characteristic

The sensor response characteristic was calculated using a numerical simulation approach based on FDTD. Figure depicts time monitors mounted at the input and output ports to calculate the time required by the proposed device to give output (see Fig. 7). The proposed single MSRR-based water analysis sensor seems to have a response time of around 80 fs from the characteristic graph (see Fig. 8).

Fig. 7
An image depicts the time monitors mounted at the input and output ports to calculate the sensor response characteristic using F D T D simulation.

Time monitor position on the proposed sensor to calculate response characteristic

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Fig. 8
A response time graph depicts the response characteristics of the input and output ports using the F D T D simulation.

Response characteristic of proposed sensor

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Quality Factor (Q-Factor)

Q-factor is calculated in terms of peak wavelength and 3 dB bandwidth of the spectral range as given in Eq. 2.

$${\text{Q-factor}} = \frac{{\text{Resonance Peak Wavelength}}}{{3\,{\text{dB Bandwidth of the spectral range}}}}$$
(2)

The optimized value of Q-factor is about 646.6 for the water sample at the resonance peak wavelength of 1525.98 nm.

Figure of Merit (FOM)

Figure of merit is defined as the ratio of the sensitivity of the sensor to the 3 dB bandwidth of the spectral range. FOM is calculation is done by given Eq. 3.

$${\text{FOM}} = \frac{S}{{{3}\,{\text{dB bandwidth of the Spectral range}}}}$$
(3)

The FOM for the proposed sensor is 392.61/RIU. Higher figure of merit of the sensor indicate a good label-free biosensor performance.

4.5 Result Observation

A photonic-based micro square ring resonator structure is designed for the detection of single waterborne bacterium present in water. Refractive index value, resonant wavelength shift, and sensitivity of normal water and water with various single waterborne are given in Table 2.

Table 2 Resonance peak wavelength shift and sensitivity for different waterborne bacterium
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The performance parameter of proposed sensor is compared with previous published research work, and it shows that the sensor is ultra-sensitive and compact in size. The following Table 3 gives the comparison between the proposed sensor and published research work.

Table 3 Comparison of performance parameter of proposed work with another published research work
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5 Conclusion

The detection of single cell waterborne bacterium is done with the help of designed Ph.C.-based MRR and simulated using Ansys Lumerical software. The proposed sensor is designed using square lattice structure and rods in air configuration and its operating wavelength range is from 1500 to 1650 nm. The proposed sensor is providing a quality factor of 646.6, sensitivity of 926.56 nm/RIU, 924.36 nm/RIU, and 920.44 nm/RIU for the waterborne bacterium Vibrio cholera, E. coli, and Shigella flexneri, respectively, and FOM of approximately 392.61/RIU. The research is mainly for the design of ultra-sensitive sensor for water analysis having high speed, compact, and low cost. So, the proposed sensor performance characteristic is compared with the published research work as shown in Table 3, and from the table we can conclude that the proposed sensor is ultra-sensitive for the detection of waterborne bacterium.