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Lin Zhang (lizh0024@student.umu.se)

June 17, 2020



In demand for early and accurate diagnosis, plasmonic-based biosensors have emerged as an attractive solution that can achieve rapid, real-time, and label-free detection of various biomarkers. In this project, a portable system for fluorescence measurements based on plasmonic nanochip was demonstrated. I developed an image analysis program, which was used to perform image analysis to get the relationship between the fluorescence intensity of the image and the concentration of protein biomarkers. The project thus shows promising

results in building a portable detecting system for medical diagnostics, which is highly sensitive, multiple tests, easy to use.

Bachelor’s thesis in Physics at Umeå University Supervisors: Xueen Jia, Jonas Segervald




Abstract ... 1

Introduction ... 3


Theory ... 4

1.1 Localized Surface Plasmon Resonance (LSPR) ... 4

1.2 Metal Enhanced Fluorescence (MEF) ... 5


Experimental ... 6

2.1 Fabrication of Nanostructured Plasmonic Chip ... 6

2.1.1 Spin Coating ... 7

2.1.2 Nanoimprint Lithography (NIL) ... 8

2.1.3 Physical Vapor Deposition (PVD) ... 8

2.2 Fluorescence Measurements ... 9

2.3 Design of the Portable Detecting System ... 9

2.4 Image Analysis ... 10


Results & Discussion ... 12

3.1 Fluorescence Measurement ... 12

3.2 Image Analysis ... 13


Conclusion ... 17

References ... 18





Point-of-care testing (POCT), also known as near-patient testing, refers to any analytical test performed outside the laboratory and may be located either within a hospital as an adjunct to the main laboratory or for primary healthcare outside the hospital setting1. POCT is urgently needed in medical resource-limited regions, where they don’t have a laboratory or sensitive detecting device that can detect relatively low biomarker concentrations. To get a rapid and accurate diagnosis for the management of life-threatening infections, where immediate treatment is important, a portable and easy to use detect system that can do point-of-care testing is desired.

Noble metallic nanostructures have been demonstrated to be used in the application of biological sensing, imaging, and energy harvesting in recent years, mainly due to the advantage of their unique plasmonic property2,3,4. Au (Gold) is considered to be one of the primary building blocks for the next generation of sensing, imaging, and therapeutic devices5, besides the optical properties that Au has, its size and shape can be easily controlled. Its facile surface modification and biocompatibility make them a preferred material to be used in the ultrasensitive detection and imaging techniques needed for the treatment of fatal diseases, such as cancer6. Noble metals, like gold, show collective electromagnetic resonance due to their negative refractive index in the visible range, which leads to surface plasmon resonance (SPR) or localized surface plasmon resonance (LSPR). Both SPR and LSPR techniques are widely used for label-free biological sensing7. It is capable of multiplexed diagnostics, real-time analysis with high sensitivity, and has great potential for miniaturization —thus allowing for many integration possibilities into a point-of-care device8. Fluorescence is one of the predominant detection modalities due to the sensitivity and specificity that it enables9.

With the widespread use of smartphones, we can use smartphones as our platform for designing detection devices. Here, we demonstrated a portable nano-plasmonic, smartphone-based testing system for protein biomarkers, by using metal enhanced fluorescence (MEF) immunochemical assay. In this project, all the laboratory work like fabrication of the nanostructured plasmonic chip and data collecting were done by researchers in the Nano for Energy Group at Umeå University.



1. T


1.1 Localized Surface Plasmon Resonance (LSPR)

When the light wave enters the interface between the metal and the dielectric, the free electrons on the metal surface oscillate collectively, and the light waves and the free electrons on the metal surface couple to form a near-field electromagnetic wave propagating along the metal surface10. If the oscillation frequency of electrons matches the frequency of the incident light wave, resonance will occur. Under resonance, the energy of the electromagnetic field is effectively converted into the collective vibrational energy of free electrons on the metal surface. At this time, a special electromagnetic mode is formed. The electromagnetic field is confined to a very small range of the metal surface and is enhanced, which is called Surface Plasmon Resonance (SPR)11.

It mainly includes two modes, the Surface Plasmon Polaritons (SPP) propagating at the interface between the thin metal film and the medium, and the LSPR excited by the surface of the metal nanostructure. Fig.1 shows the mechanism of LSPR, because there are a large number of free electrons on the outside and inside of the metal particles, these electrons interact with each other to form an electron gas group. The compose of ions and free electrons gas is called plasma12. In the absence of light, these nanoparticles become electrically neutral. When being excited by light, due to the periodic change of the external electric field with time, the surface plasmon of the metal particles will change with the change of the electric field. When the frequency matches the natural frequency of the system, it will result in the so-called resonance, which is LSPR11. LSPR is the ability that all plasmonic nanomaterials have, also it’s highly controllable because they are sensitive to the feature that influences the surface electron density13.

Figure 1. A representation of the localized surface plasmon resonance of a plasmonic



1.2 Metal Enhanced Fluorescence (MEF)

Before the experiment, it is important to know how metal can enhance fluorescence. If a fluorophore was excited, one of two processes will then occur, the fluorophore will release its sudden excess energy in the form of a photon (light) or as a phonon (vibration), i.e. through either a radiative or non-radiative transition14.

The rates of radiative and non-radiative are expressed in terms of 𝛤and 𝑘𝑛𝑟, respectively.

Eq.1 and Eq.2 indicate that the quantum yield 𝑄 and lifetime 𝜏 of a fluorophore are determined

by these two decay rates, 𝛤and 𝑘𝑛𝑟.

𝑄 = 𝛤

𝛤 + 𝑘𝑛𝑟 (1)

𝜏 = 1

𝛤 + 𝑘𝑛𝑟


Eq.3 and Eq.4 show that the quantum yield of a fluorophore 𝑄 denotes its emission efficiency.

The fluorescence lifetime 𝜏 is the length of time that the excited state a fluorophore exists before it returns to the ground state15.

When the same fluorophore is placed near the metallic surface or nanoparticles, the interaction between the two materials leads to a modified quantum yield 𝑄𝑚 and lifetime 𝜏𝑚. While 𝛤𝑚 is the modified radiative decay rate, 𝛤𝑚,𝑛𝑟 represents the additional non-radiative decay which is introduced by the metal nanoparticle14.

𝑄𝑚 = 𝛤0+ 𝛤𝑚 𝛤0+ 𝛤𝑚+ 𝛤𝑚,𝑛𝑟+ 𝑘𝑛𝑟 (3) 𝜏𝑚 = 1 𝛤0+ 𝛤𝑚+ 𝛤𝑚,𝑛𝑟+ 𝑘𝑛𝑟 (4) When a fluorophore is placed in proximity to a metal nanoparticle, the particle's effect on the fluorophore's emission intensity is 2-fold, as it modifies the rates of both the fluorophore's excitation and emission13. Metallic particles, like Au, has the spectral called LSPR15. Therefore, if we excite a particle at its LSPR wavelength, it will cause the absorption and scattering of the particle greatly amplified. The increased scattering will then generate an enhanced near-field intensity of the electric field surrounding the particle. If the fluorophore is placed within this near-field, it increases its rate of excitation and results in higher fluorescence13.

Second, as the particle and fluorophore are in such close vicinity to each other, the plasmon band of the particle and the dipole of the excited fluorophore couple together, generating new pathways through which energy can pass. This coupling enables the non-radiative transfer of excitation energy from the particle to the fluorophore and also the transmission of the fluorophore's energy as radiation to the far-field15.



2. E


2.1 Fabrication of Nanostructured Plasmonic Chip

Fig.2 shows the nanostructured plasmonic chip fabricated by researchers in the Nano for Energy

Group at Umeå University. I’ll give a brief introduction of production methods including spin coating, nanoimprinting, PVD, etc. For more in-depth experimental details, see Jonas’ Master’s


Figure 2.Illustration of the nano-plasmonic chip that fabricated by researchers in the Nano

for Energy Group at Umeå University.

We know that the substrate size is 76 𝑚𝑚 * 24𝑚𝑚 , the print area size is 75600 𝜇𝑚 ∗ 24000 𝜇𝑚. There is just one block on the substrate, the block size is 13000 𝜇𝑚 ∗ 13000 𝜇𝑚.

Fig.3 is the illustration of the nano-plasmonic chip after the surface treatment. There are

15 dots in the X-direction, 15 dots in the Y-direction. Therefore, there are 15 ∗ 15 testing dots. The feature diameter is 200 𝜇𝑚, the feature to feature distance is 650 𝜇𝑚. Rows 1-5 represent the Muc1-biotin, which is 50 𝜇𝑀 ; rows 5-10 represent the Fluorophore CF640R with a concentration of 3 𝜇𝑀. Rows 11-15 represent 12 𝜇𝑀 fluorophore CF640R. All of them are blocked with 25 𝑚𝑀 ethanolamine in 100 𝑚𝑀 sodium tetraborate buffer, incubated with Cy5-Streptavidin 1: 1000.



Figure 3. Illustration of the nano-plasmonic chip. 2.1.1 Spin Coating

During the spin coating process, a spin-coater was used to fabricate a thin film on a sample. (Fig.4). First, the substrate is locked to the platform using a vacuum pump, then the solution is dropped on using a pipette. Second, the solution was rotational spread out with the help of high speed. Last, baking at a proper temperature can remove the rest solvent.



2.1.2 Nanoimprint Lithography (NIL)

Here, nanoimprint lithography (NIL) was used. NIL is an approach to fabricate nanostructures, with the advantages of high throughput, high resolution, and low cost18. It uses a pre-prepared mold in combination with a mechanical press19, also heating is needed to make the material close to its glass transition temperature. Fig.5 shows the process of nanoimprint lithography.

The NIL process begins with creating predefined topological patterns on a mold. The imprint resist is spin-coated onto the prepared substrate. If appropriate force was used to press the mold against the resist-coated substrate, therefore, the template will be deformed. This action negatively imprints the topographical features onto the softer resists.

Figure 5. Illustration of the nanoimprint lithography process20.

2.1.3 Physical Vapor Deposition (PVD)

Physical vapor deposition (PVD) is a vaporization coating technique, involving the transfer of material on an atomic level under vacuum conditions21. The most common PVD processes are sputtering and evaporation. Here, the evaporation was used, Fig.6. (a) shows the flow diagram of the PVD process.

Fig.6. (b) shows the PVD setup used for evaporation. Evaporation is a common method

of thin-film deposition. The target material evaporates in vacuum. The vacuum makes the vapor particles travel directly to the substrate, where they condense back to the solid phase.



(a) (b)

Figure 6. a) Flow diagram of the PVD process; b) PVD setup used for


2.2 Fluorescence Measurements

The fluorescence measurements were done using LI-COR Odyssey SA. The image with varying fluorescence intensity of different pixels is created. These pixels intensity can then be analyzed to find the fluorescence intensity16.

2.3 Design of the Portable Detecting System

To design a portable detection system, simplicity and convenience are our first consideration. Therefore, I designed a peripheral detection system like this. It is just a “black box”, your phone can be placed on top of this “black box”. Fig.7 shows the illustration of the detecting device. The flat-surface LED (Light Emitting Diode) background can be mounted to the bottom of the device, which can be used to illuminate the sample area, providing certain wavelength light. So that the phone camera at the top of the device can get capture the uniform image, which reduces errors during image analysis.

The nano-plasmonic chip was fixed and supported by a piece of a glass slide, which is easy to remove/insert to the portable detecting platform. Opaque, weak reflective material was used to fix the gap between the black box and the sample holder. The smartphone and the top of the dark box fit as close as possible to reduce the gap. Therefore, we can create a “dark hood” to make sure the sample is only influenced by the certain wavelength LED, which can reduce the error.



Figure 7. Illustration of the detecting device.

An app can be used for capturing the image from a smartphone device. The application enabled the controllability of the values of camera settings such as ISO, exposure time, white balance, and gain factor, which helps to provide a consistent exposure condition in image analysis.

2.4 Image Analysis

The project aims to design a detecting system that can directly analyze the images captured by the smartphone camera to generate a protein biomarker analysis report. In this way, the user does not need a lot of medical knowledge, he can intuitively see the results he wants, which is convenient for the user to predict the condition of the patient, whether further treatment and diagnosis are needed.

Here we do the theoretical image analysis obtained from the laboratory, which is the first step of developing an app on the smartphone. In theory, the automatic analysis system is completely feasible. Fluorescence analysis was done by Matlab.

The nano-plasmonic chip was made for 15*15 features, while one protein corresponds to three dots. We need to get the fluorescence intensities of different features corresponding to the protein biomarkers’ concentration so that we can get the corresponding relationship between the fluorescence intensity and the concentration. Afterward, we can get the unknown concentration of the target protein by analyzing the fluorescence intensity of each feature, compare the standard value to determine whether the patient is sick.











Here, the results of the project were discussed. In Fig.9, we can see the schematic of the process of the whole project.

Figure 9. Schematic of the working flow of the project.

3.1 Fluorescence Measurement

Fig.10. shows the fluorescence test result, the ribbon-shaped fluorescent substance on the

figure is the glue that has not been completely cleaned up, which will affect the results and accuracy of subsequent image analysis.



3.2 Image Analysis

If we enlarge Fig.10, we can get 5 ∗ 3 features, shown in Fig.11. The coordinate marked in white next to the feature (A, B) represents the position of the feature, which represents the A row and B column. (2, 5), for example, this feature is in the second row and the fifth column in


Figure 11. Enlarged view of part of Fig.10.

The small fluorescent spot next to the feature is the ghost image caused by the shooting. We should ignore it when we analyze it, or it will affect the results of the data analysis.

We can get the intensity distribution of Fig.11, shown in Fig.12.



If we focus on one dot (1,5), for example then we get its intensity distribution.

Figure 13. a) Fluorescence test result of feature (1,5); b) Intensity distribution of (1,5).

We can delete some data by setting a threshold to eliminate the impact of background noise on image analysis so that we can focus on the fluorescence itself.

For feature (1, 5), the sum pixel intensity of the fluorescence is 1.7 × 107, the average intensity is 160.1. Then repeated operations were done to get the sum/average intensity of each feature, summarized in tables. Table.1 is the sum intensity of each feature, while Table.2 summarizes the average intensity of each feature.

Table 1. The sum intensity of each dot.

Row / Column 4 5 6 7 8

1 1.9 × 107 1.7 × 107 2.1 × 107 2.0 × 107 2.1 × 107 2 1.9 × 107 1.6 × 107 1.7 × 107 1.7 × 107 1.6 × 107 3 1.1 × 107 1.5 × 107 2.0 × 107 1.8 × 107 8.1 × 107



Table 2. The average intensity of each dot.

Row / Column 4 5 6 7 8

1 169.1 160.1 172.8 168.5 174.2

2 170.1 153.2 162.6 159.3 158.4

3 170.6 149.0 167.5 179.3 193.4

Fig.14 shows the intensity distribution of the 15 features shown in Fig.11. It’s clear that feature

(3, 4) is less than others, feature (3,8) shows its abnormal performance. It might be caused by the glue or incomplete cleaning of the substrate.

Figure 14. The intensity distribution of 15 features separately.

We know that Row 1-3 represents the Muc1-biotin, which is 50 𝜇𝑀 . Then we ignored unexpected data, (3, 4) and (3, 8), the average fluorescence intensity can be calculated. The fluorescence intensity of the image and the concentration of the target protein biomarker can

be linked to obtain the relationship between them.

In the future, we can prepare samples with different concentrations, get the fluorescence test results, and the intensity of the target feature though the image analysis. Then the concentration of the target protein can be obtained. Therefore, we could link each average intensity to the concentration we studied. By doing this repeatedly, we can get a graph of intensity vs. concentration, a more accurate relationship can be obtained, we can use it to directly calculate the concentration through the intensity.



The mean intensity of 13 features we got is 164.9, then we can get the relation between the intensity of the target feature and the concentration of Muc1-biotin, which is

𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 Muc1 − biotin = 0.3032 ∗ 𝐼𝑚𝑎𝑔𝑒 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 (5) However, the relation obtained through this analysis is inaccurate, we still need to do more tests to verify this linear relation, we cannot get the right relation just from this one concentration tests. Theoretically, the relationship between other proteins’ concentration and image fluorescence intensity can be obtained through the above data analysis, but because of the lack of data sources, no further analysis can be performed. But once we get the relation between the fluorescence intensity and concentration, we can use the relation to get the concentration rapidly. This can then be used for rapid diagnosis, but we need to improve accuracy so it can be used for medical purposes. Also, the nano-plasmonic chip has 225 features, while one protein corresponds to three dots, multiple tests can be done.



4. C


In summary, a portable detecting system based on a nano-plasmonic chip is designed, which theoretically can do fluorescence measurements and data analysis. Matlab was used to get the intensity distribution of different features. A relationship between the image intensity and target protein concentration was found. Because of lacking data sources, further analysis can’t be done. Through the project, I have a full grasp of how to make the nanostructured chip, understand the concept of plasmonic effect, nanotechnology, etc., thanks for Xueen and Jonas’ help.

Because of the coronavirus, I was unable to enter the laboratory to perfect this design, but many other functions can be added later, such as color recognition, automatic camera recognition on the mobile phone to generate reports, and we can make it more compact and easier to carry. We can also use 3D printing technology to complete the construction of the model, and truly manufacture a portable detecting device.

Gold is used in this project, other noble metals also have the enhancement effect, like silver and alloys. later, we can use them to test their ability. Also, we can try to optimize the nanochip, so we can get a better result and a better resolution. A portable detecting device will be popularized in areas with poor medical conditions and will contribute to curbing the deterioration of the disease and curbing the spread of infectious diseases.





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clc; clear; close all;

% Open a picture selection interface, and return the name and address of the picture you need

[filename, pathname] = uigetfile({'*.jpg'; '*.bmp';

'*.gif'; '*.png'},'Select Image');

% there is no image if filename == 0 return; end origin_img = imread([pathname,filename]); figure(1); imshow(origin_img);

% After drawing, change the mouse into a cross to select the area of interest

h = imrect;

position = getPosition(h);

% Drag the mouse to get the area of interest, pos has four values, the pixel coordinates of the upper left corner of the area of interest and the length and width of the area

roi = imcrop(origin_img,position); figure(2); imshow(roi); imwrite(roi,'roi.jpg'); A=imread('roi.jpg'); a=double(A(:,:,1)); mesh(a);

resultA=A(find(A>100)) %set a threshold

mean(resultA(:)) sum(resultA(:))





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