Ultra-Sensitivity Widefield, Confocal Surface Plasmon

Interferometry Using Sequential Coding

Suejit Pechprasarn

College of Biomedical Engineering, Rangsit University, Pathum Thani, Thailand

Keywords: Surface Plasmon Resonance, Confocal Microscope, Molecular Binding, Optical Instrumentation.

Abstract: Interferometry has been a standard technique for optical phase measurement. Most single-molecule sensitivity

measurements and imaging tools rely very much on the interferometric measurement of dual optical beams.

We have developed an embedded confocal interferometric microscope and demonstrated by theoretical

calculation that the system can achieve single molecule detection sensitivity. Of course, several challenges

need to be addressed to achieve such ultra-sensitivity. The confocal surface plasmon microscope is a beam

scanning system, and it also suffers from thermal variations due to long data acquisition time. Here we propose

a widefield quantitative confocal surface plasmon interferometric microscope configuration using orthogonal

coding. The proposed system sequentially illuminates a plasmonic sample with multiple focal points in the

sample plane based on an orthogonal code such as a Hadamard code. The images of the illumination sequences

are then captured and processed with the known Hadamard input sequence. Here we show that employing the

Hadamard time coding in the confocal surface plasmon interferometry enables us to (1) perform widefield

imaging, (2) higher signal-to-noise compared to the beam scanning system, (3) high sensitivity, (4) good

spatial resolution and (5) more stable compared to the confocal surface plasmon microscope.

1 INTRODUCTION

Doctors and biomedical scientists have been facing

more challenging diseases in these recent years.

Bacteria, germs, and viruses have become more

tolerant and resistant to chemicals and antibiotic

drugs (Boolchandani, D’Souza, & Dantas, 2019).

Fever and flu, such as COVID-19, have been severe

healthcare issues worldwide (Fricker Jr & Rigdon,

2018; Organization, 2020). Not only that, but there

are also symptoms and illnesses related to the aging

society; of course, one of the majority is

neurodegenerative diseases (Barnham, Masters, &

Bush, 2004), such as Alzheimer’s disease. The

number of patients diagnosed with the disease is

increasing yearly, and there is still no promising and

reliable early-stage diagnostic tool (Venazzi et al.,

2018). There is an increasing demand for highly

sensitive biosensors to tackle such situations and, of

course, for early-stage diagnosis of server illnesses.

From the literature review, the following are the

critical demands for next-generation diagnostic tools,

which can be summarized as:

1. In vitro imaging capability to understand the

physical interactions between cells, drugs, and

morphology of mutant bacteria or viruses (Xiao,

Parchur, Gilbertson, & Zhou, 2018).

2. Biosensing capability, besides imaging

capability ability to identify the presence of

genes or proteins, is also the key. This character

is where fluorescence labeling plays a crucial

role (Ounkomol, Seshamani, Maleckar,

Collman, & Johnson, 2018).

3. Highly sensitive, one of the hot topics in

biomedical sensors is single molecule detection

due to the high demand in the healthcare sector

(Zanchetta, Lanfranco, Giavazzi, Bellini, &

Buscaglia, 2017).

4. Quantitative imaging not only gives out imaging,

but it needs to be also able to perform a

quantitative measurement, such as showing

interaction strengths and binding kinetics

(Guerri et al., 2018).

5. High throughput screening different kinds of

binding arrays (Songa & Okonkwo, 2016).

6. Label-free, although fluorescence techniques

have their unique advantage, there are key issues

in those techniques, including photobleaching

and phototoxicity. They may also not represent

the natural behavior of the specimen due to the

Pechprasarn, S.

Ultra-Sensitivity Wideﬁeld, Confocal Surface Plasmon Interferometry Using Sequential Coding.

DOI: 10.5220/0011892300003408

In Proceedings of the 11th International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS 2023), pages 96-101

ISBN: 978-989-758-632-3; ISSN: 2184-4364

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

perturbation of the considerable molecular

weight of the fluoresce dyne (Zanchetta et al.,

2017).

7. Automatic feedback control system and self-

calibration (Hameed, Alrayk, & Obayya, 2016),

for example, automatic z-control for long-period

imaging, reference channel in the measurement

of binding kinetics.

Scientists and engineers are trying to provide

feasible solutions to medical needs. One of the

promising candidates that have been of interest to the

science community and healthcare sensor

manufacturers is surface plasmon resonance (SPR).

SPR is a confined electromagnetic wave

propagating along the surface of noble metals, such

as gold (Au), silver (Ag), and copper (Cu). The SPR

is sensitive to molecules and substances that bind to

the metal surface and appears as the change in wave

vector k

of the surface plasmons (SP) (Nguyen, Park,

Kang, & Kim, 2015). The SPR measurement is

carried out for biosensing applications using a

uniform layer of thin gold film 50nm thick coated on

a high refractive index prism or glass substrate. A p-

polarized light with sufficient light momentum

illuminates the gold sensor from the glass side, as

shown in Fig.1. The analyte and binding site are on

the other side of the gold, the so-called Kretschmann

configuration.

Figure 1: Kretschmann configuration.

No labeling agent is required in such

measurement, and the SPR measurement is a label-

free technique. A dark band dip appears on the

reflectance curve, as shown in Fig.1. This is evidence

of SPR coupling, as the presence of the dark band is

due to the SPR loss mechanisms in the SPR coupling

process (Pechprasarn, Chow, & Somekh, 2018).

When the analyte binds to the binding site, this dark

band dip moves to a higher wave vector position.

Since the SPR is an electromagnetic wave

propagation and, of course, phase detection is also

possible.

It has been very well established that phase

detection gives better sensitivity (Ho et al., 2006;

Huang, Ho, Kong, & Kabashin, 2012) due to the

sharper phase response of the phase curve compared

to the intensity measurement. However, it does

require an interferometric system.

The interferometric system usually requires well-

controlled experimental conditions, such as

temperature control and vibration isolation. This

paper will demonstrate how the proposed widefield,

confocal surface plasmon can overcome the

variations by employing time-coded illumination

using the Hadamard code. The Hadamard sequence is

an orthogonal sequence enabling a widefield,

confocal imaging capability.

2 MATERIALS AND METHODS

2.1 Optical Microscope Simulation

We address the issues of scanning confocal surface

plasmon interferometric system by employing a

rotating diffuser for incoherent illumination and a

digital micromirror device (DMD) as shown in Fig.2.

The DMD is for controlling the pattern of image plane

illumination using Hadamard sequence intensity

coding. The incoherent illumination ensures no

interference effect between any close point spread

functions.

Figure 2: Schematic diagram of SPR microscope with

digital micromirror device for image plane illumination

control.

An example of 64x64 Hadamard sequences is shown

in Fig.3. It is essential to point out that Hadamard

sequences are a series of orthogonal codes consisting

of -1 and 1. Each row of the Hadamard matrix can be

employed to code an individual pixel on the DMD

chip.

Ultra-Sensitivity Wideﬁeld, Confocal Surface Plasmon Interferometry Using Sequential Coding

Figure 3: A 64x64 Hadamard matrix.

One significant difference between Hadamard’s

code and the optical illumination is that the camera in

the microscope system can only detect light intensity.

Therefore, the Hadamard code in this research is

adjusted to 0 and 1, as shown in Fig.3a here, called 𝐻



Moreover, the conventional Hadamard with -1 and 1

coding, as shown in Fig.3b, is called 𝐻.

Figure 4: Shows (a) the conventional Hadamard 𝐻 with -1

and 1 coding for a 4 by 4 matrix and (b) the adjusted

Hadamard 𝐻 with 0 and 1 coding for a 4 by 4 matrix.

To demonstrate the feasibility of the proposed

method, let us assume that we would like to measure

confocal responses of 4 measurement positions in the

image plane 𝐼



,𝐼



,𝐼



and 𝐼



, respectively. These 4

points in the image plane are separated by arbitrary

distances and directions leading to different crosstalk.

Let 𝛼



is the crosstalk from point ‘b’ leaking into

point’ a’. Therefore, there are 12 crosstalk terms

𝛼



,𝛼



, 𝛼



,𝛼



,𝛼



,𝛼



, 𝛼



,𝛼



, 𝛼



,𝛼



,𝛼



and 𝛼



for the 4 points.

An essential property of the orthogonal matrix is that:

𝐻



𝐻𝑛𝐼 (1)

Where 𝐻



is the transpose of the Hadamard

matrix

𝐻 is the Hadamard matrix

𝑛 is the Hadamard matrix dimension

𝐼 is the identity matrix

These are applicable to reconstruct the reflectance for

each spatial position coded using the Hadamard

sequence as:

𝐻



𝐻•𝑅𝑛𝐼•𝑅 (2)

Where R is the reflectance corresponding to each

spatial position in the image plane.

Having mentioned that it is impossible to capture -1

code, this is replaced by 0 in this research. The

orthogonal code cannot correctly cancel out all the

crosstalk. Here we propose a set of the simultaneous

equation to reconstruct the correct intensity for each

spatial position in the image plane without crosstalk

from the other illuminating pixels. In the case of the

4x4 pixel, the 𝐻



•𝑅 can be expressed as:

(3)

(4)

The diagonal elements and the first column of the

matrix shown in Equation (4) allow us to determine

𝐼



,𝐼



,𝐼



and 𝐼



. The other elements also enable us to

determine all the crosstalk terms.

The reflectance point spread function of 1.49NA

with a linearly polarized laser beam at 633 nm was

simulated for a 50nm plasmonic gold sample coated

on a standard coverslip to demonstrate the proposed

method. The sample is then illuminated using the

PHOTOPTICS 2023 - 11th International Conference on Photonics, Optics and Laser Technology

Hadamard code in Fig.4b, in which each row in

Fig.4b represents a camera frame. Assuming that the

intensity of the reflectance point spread function is 1

for each position in the image plane. Fig.5 shows the

4 simulated camera frames corresponding to the

Hadamard sequences in Fig.4b. Fig.6a shows the 𝐻



•

𝑅 matrix and Fig.6b shows the 𝐻







𝐻



•𝑅



matrix

calculated using Equation (3) and Equation (4),

respectively. The reflectance of the 4 measurement

positions can then be determined from the diagonal

matrix and the first column of the 𝐻







𝐻



•𝑅



matrix and found to be all 1 as defined in this

example.

Figure 5: Simulated reflectance from a 50 nm thick gold

sensor using (a) the 1

frame of the adjusted Hadamard

code in Fig.4b, (b) the 2

frame of the adjusted Hadamard

code in Fig.4b, (c) the 3

frame

of the adjusted

Hadamard code in Fig.4b and (d) the 4

frame of the

adjusted Hadamard code in Fig.4b.

Figure 6: Shows (a) 𝐻



•𝑅 and (b) 𝐻







𝐻



•𝑅



calculated

using Equation (3) and Equation

(4).

2.2 Optical Simulation Parameters

The system in Fig. 2 consists of a linearly polarized

laser at 633 nm wavelength (HeNe). The objective

lens is an oil immersion objective lens 1.49NA with

x100 magnification. The plasmonic sensor is a

uniform sensor made of 50nm gold coated on a

standard coverslip with 0.17mm thickness. The

refractive index of the coverslip is n

glass

of 1.52, and

the refractive index of the immersion oil noil of 1.52.

The complex refractive index of gold at 633 nm

wavelength is n

gold

of 0.1834+3.4332i (Johnson &

Christy, 1972). The gold sensor is coated with 10 nm

thick Bovine Serum Albumin (BSA) protein with a

refractive index of 1.4 (Chow, Pechprasarn, Meng, &

Somekh, 2016). A water environment with a

refractive index of 1.33 backs the sensor.

3 RESULTS

This section provides simulation results to

demonstrate how the widefield SPR phase

measurement can be achieved. The following is a list

of optical and physical parameters computed in the

simulation. These parameters can be realized under

standard optical experimental conditions. The system

configuration is the same as shown in Fig.2.

The back focal response (BFP) of the microscope

objective, as shown in Fig.7, is calculated by Fresnel

equations and a transfer matrix approach computed in

Matlab. Fig.7a shows the intensity response of the

BFP, and Fig.7b shows the phase response of the

corresponding BFP.

Figure 7: (a) Intensity response of the BFP and (b) phase

response of the simulated BFP.

The microscope point spread function for different z

defocus ranging from -6 microns to 2 microns, as

shown in Fig. 8 , was then computed by multiplying

defocus phase function exp(i2kzcosθ) to the BFP

response, where k is the wave vector given by

2πn

/λ. θ is the incident angle, and z is the axial

defocus distance of the sample stage.

Ultra-Sensitivity Wideﬁeld, Confocal Surface Plasmon Interferometry Using Sequential Coding

Figure 8: V(z) responses for (a) z of 6 microns (b) z

of 3 microns (c) z of 0 microns and (d) z of 2 microns.

The reflectance of 4 measurement positions separated

by 100 nm was then computed by convolution

calculation, as shown in Fig.9. The adjusted

Hadamard coding shown in Fig.4b was then applied

to the 4 measurement positions. Note that the 4

measurement positions were purely for illustration

here; the proposed method can be appliable with

much larger pixels.

Figure 9: (a) Reflectance at z of 6 microns, (b) reflectance

at z of 3 microns, (c) reflectance at z of 0 microns, and (d)

reflectance at z of 2 microns.

Figure 10: V(z) confocal responses of the 4 measurement

positions. Note that the 4 V(z) curves are the same.

Equation (4) was then applied to separate the

crosstalk effect. The V(z) confocal responses (Zhang,

Pechprasarn, & Somekh, 2012; Zhang, Pechprasarn,

Zhang, & Somekh, 2012) corresponding to the 4

measurement positions are shown in Fig.10. The V(z)

curves of the 4 measurement positions are

successfully reconstructed. They all have the same

amplitude, shape, and ripple period. Note that the

results in Fig.8 and Fig.9 were displayed in intensity,

whereas the V(z) curves in Fig.10 were plotted in the

square root of intensity.

-6 -5 -4 -3 -2 -1 0 1 2

z defocus, m

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

PHOTOPTICS 2023 - 11th International Conference on Photonics, Optics and Laser Technology

100

4 CONCLUSION

Here, we have proposed and analyzed an optical

widefield, confocal surface plasmon configuration

based on time-coded illumination. It is more robust

and has a better signal-to-noise ratio than a

conventional scanning confocal surface plasmon

microscope allowing multiple confocal scanning

point spread functions to scan over the sample

simultaneously. The crosstalk between the

overlapping point spread functions can be suppressed

and reconstructed using the property of orthogonal

coding in the image plane of the microscope objective.

The proposed method can be integrated with standard

microscope systems to provide widefield, confocal

imaging.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the Research

Institute of Rangsit University for supporting the

research; and the College of Biomedical Engineering,

Rangsit University, for the computing power and

research laboratory used in this work.

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