Master Thesis Proposals

Presented here are thesis proposals for students, supervised by staff at the Ultrasound, Microscopy and Optics group.  

The scope of these proposals can be adjusted and are equally suitable for students from the 5-year integrated masters (30 credits) and the 2-year master's program in Physics (60 credits) 

 

The Ultrasound, Microscopy and Optics group currently have the following project proposals:

Implementation and Use of an Oblique Linescanning Confocal System for Superresolution Fluorescence Microscopy

Supervisor:  Florian Ströhl

Introduction

In the realm of biological and medical research, superresolution fluorescence microscopy stands as a pivotal tool for observing molecular interactions at resolutions beyond the diffraction limit of light. The optics group at IFT is dedicated to advancing the capabilities of fluorescence microscopy through innovative technological developments. One such promising technique is our patented SOLIS method, essentially an oblique linescanning confocal system, which when combined with superresolution processing, can significantly enhance the performance of fluorescence images. This project aims to design, build, and utilize such an oblique linescanning confocal system to achieve superresolution imaging.

Research & Objectives

In this master project, the student will undertake the development and integration of an oblique linescanning confocal system combined with fluctuation microscopy techniques to achieve superresolution imaging. The project will be executed in several structured phases:

  1. System Assembly: Initially, the student will construct an oblique linescanning confocal microscope following a SOLIS design [1]. This will involve the integration of optical components, ensuring precise alignment, and calibration to meet the required specifications for high-resolution imaging.
  2. Integration with Fluctuation Microscopy: Subsequently, the student will implement control software necessary for capturing the required data.
  3. Experimental Validation and Optimization: Once the system is operational, the student will proceed with the experimental validation using selected biological samples. This phase focuses on optimizing imaging protocols and conditions to maximize the raw resolution and quality of the images obtained.
  4. Data Analysis and Evaluation: Following data acquisition, the student will create superresolved images using established methods [2] and analyse the collected data to assess the system’s performance. Key metrics such as resolution enhancement, image contrast, and quality will be evaluated and compared against standard confocal microscopy systems.
  5. Documentation and Scientific Communication: The final phase of the project involves documenting the methodologies, results, and conclusions. Potentially, the student will also have the opportunity to contribute to the drafting and submission of a scientific paper in the field of superresolution microscopy.

Training and Learning Outcomes

Throughout the project, the student will receive hands-on training in:

  • Building Electro-Optical systems, with a focus on modern microscopes
  • Advanced image processing and analysis
  • Scientific experimentation and data handling

The student will gain practical experience in engineering of optical systems and advanced image analysis. This project will also enhance the student's skills in scientific communication, both written and oral.

Project Requirements

The ideal candidate for this project should have a background in optics, physics, or a related field, with an interest in hands-on optical instrumentation development and image analysis. Prior experience in laboratory settings and familiarity with optical instrumentation will be advantageous but is not required. The project is research-intensive and requires a commitment to meticulous experimental procedures and data analysis.

References

[1] Florian Ströhl, Daniel Henry Hansen, Mireia Nager Grifo, and Åsa Birna Birgisdottir, "Multifocus microscopy with optical sectioning and high axial resolution," Optica (2022).

[2] K. Agarwal and R. Machan. "Multiple Signal Classification Algorithm for super-resolution fluorescence microscopy." Nature Communications (2016).

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Pushing the limits of on-chip spectroscopy

Supervisors:  Jana Jágerská and Roman Zakoldaev

Introduction

Photonic integrated circuits are a powerful analogy to electronic circuits that can perform information a myriad of operations including fast & power efficient data transmission, switching, gating, imaging, sensing, and many others. Of all possible applications, our team is focused on design, fabrication, and demonstration of extremely sensitive photonic waveguide sensors.

Out activity is driven by the desire to make diagnostic devices smarter and smaller by integrating functional components – a source, modulator, detector, and mainly sensing waveguides on a single chip [1]. Our group has recently demonstrated "an optimal" waveguide chip for detection of greenhouse gases and pollutants, showing detection limit of a 13C16O2 down to 30 ppb, which is about 3 orders of magnitude better detection limit that was reported on a chip by other groups, including IBM and MIT [2]. However, even better results could be achieved with optimized laser driving and detection strategy, and up-to-date spectroscopic data processing.

Research & Objectives 

The purpose of the thesis is to build a small demonstrator based on free-standing thin-film waveguides developed by the group and optimize its performance towards sensitive and isotope-specific CO2 detection. First, you will become familiar with the design and fabrication of waveguides using photolithography on semiconductor wafers. You will familiarize yourself with the existing methodology of waveguide sensor characterization, learn how to operate a gas flow cell to introduce the desired amount of target molecules to the sensor, and how to determine the base line of the waveguide sensor and how to convert an output signal into spectroscopy data.

Your main tasks will include:

  • - Build a compact demonstrator combining a laser and a sensing chip
    • o Optimize and automate coupling
    • o Evaluate the spectral performance of the demonstrator system
    • o Quantify and mitigate back-reflection effects
  • - Optimize laser driving and data acquisition strategy
  • - Improve spectral data processing (LabView, Python)

You will gain broadly applicable theoretical and practical knowledge in design and operation of photonic integrated circuits and IR laser absorption spectroscopy, and hands-on experience related to construction and operation of optical setups, instrument control including laser driving, FPGA-assisted data acquisition, synchronization , spectral data processing. Based on the progress, you may be involved in field testing of the demonstrator for climate research (measurement of metabolic emission of bacteria from permafrost soils).

You will be immersed in the everyday work of an academic research team, and, in the meantime, developing hands on experience with photonic and electronic instrumentation desirable in R&D and industry. 1-2 publications related to the optimized gas detection and first field application are expected from this work.

Prerequisites:

Electromagnetism, measurement techniques, waves and optics, photonics (can be taken during the MSc project duration).

References

[1] Li, Ang, et al. "Advances in cost-effective integrated spectrometers." Light: Science & Applications  11.1 (2022): 174. https://doi.org/10.1038/s41377-022-00853-1

[2] Vlk et al. Light: Science & Applications (2021)10:26, https://doi.org/10.1038/s41377-021-00470-4

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On-chip methane sensing in functionalized porous waveguides

Supervisors:  Jana Jágerská and Sebastian Alberti

Introduction

Photonic integrated circuits are a powerful analogy to electronic circuits that can perform information a myriad of operations including fast & power efficient data transmission, switching, gating, imaging, sensing, and many others. Of all possible applications, our focus is on design, fabrication, and demonstration of extremely sensitive photonic sensors, that can be used in sensor networks, wearable electronics, lab-on-a-chip applications and point-of-care devices.  

Porous waveguides represent a latest trend in optochemical sensing: they can both confine light and analyte in the same region, thus providing strong interaction and high sensitivity. The MSc project will leverage this platform to detect methane, the principal component of natural gas and a potent climate forcer.

Research & Objectives 

Methane is a small, symmetrical molecule, and it is found in the atmosphere only at a small concentration of approx. 2 ppm (parts-per-million). To reliably detect such small concentrations on a chip, we propose a project that will investigate chemical pre-concentration inside the porous waveguide combined with optical detection. The tasks of the student will be as follows:

  • - Theoretically simulate the interaction of light and methane inside porous rib waveguides
  • - Perform methane detection on a chip prior to pre-concentration
  • - Functionalize porous waveguide with cryptophanes, and quantify signal enhancement due to pre-concentration. Both refractive index and spectroscopic detection will be explored.

The student will participate in a project on a leading research topic, and, in the meantime, develop hands-on experience with photonic simulation tools, optical setups, chemical functionalization, and IR spectroscopy. The broad experience and topic covered by the thesis provide a great basis for a career in academia, industrial R&D, or consulting jobs. The student is likely to author 1-2 publications related to the methane detection in porous waveguides.

Prerequisites:

Electromagnetism, measurement techniques, waves and optics, photonics (can be taken during the MSc project duration).

References

[1] Firehun Tsige Dullo, Susan Lindecrantz, Jana Jágerská, Jørn H. Hansen, Magnus Engqvist, Stian Andre Solbø, and Olav Gaute Hellesø, "Sensitive on-chip methane detection with a cryptophane-A cladded Mach-Zehnder interferometer," Opt . Express 23, 31564-31573 (2015)

[2] A. Datta, S. Alberti, M. Vlk, and J. Jágerská, "Spectroscopic Gas Detection Using Thin-film Mesoporous Waveguides," in 2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, OSA Technical Digest (Optica Publishing Group, 2021), paper ch_6_3.

 
 
 

Computational super-resolution imaging of nanoparticles using label-free microscopy with polarization separated imaging

Supervisors: Krishna Agarwal and Zicheng Liu

Introduction

Label-free microscopy is soon catching up as a preferred form of imaging living systems in a non-toxic manner. Super-resolution in label-free far-field microscopy is an open challenge, if elastic scattering has to be exploited for imaging. In such situations, a full 3D electromagnetic scattering model, including multiple scattering, has to be considered. In addition, it is preferred to measure quantities directly proportional to electric far field and across the complete aperture, ie the 4πsolid angle. If these characteristics are satisfied, it has been shown that super-resolution can be achieved [1,2]. Achieving measurements with these two characteristics is close to impossible in practical microscopes in the visible spectrum. Then, it is important to understand the limitations on super-resolution in the case of limited aperture and absence of electric field measurements. It is also required to design super-resolution algorithms optimized for these limitations.

Research & Objectives

The student will simulate a simplified version of computational microscope capable of simulating the electric field and intensity on the camera for nanoparticles under different orientations and polarizations of incident light. Then, the student will implement an existing algorithm [1] for performing computational imaging of the nanoparticles and study the effect of limited aperture and camera pixilation on resolution, assuming that electric field measurements are hypothetically possible. The student will develop an intensity only super-resolution approach for computational imaging based directly on [1,2] and study the artefacts arising from the unavailability of electric field measurements. Finally, depending upon the progress, observations, and available time, the student may explore the design of new computational imaging algorithm for super-resolved imaging of nanoparticles. The student will learn about computational modeling, applied electromagnetism, optics, and super-resolution imaging. The student will therefore gain experience of developing theoretical concepts rooted in physics and mathematics to apply in technology development. The student will receive a direct exposure of academic research, in the meantime developing orientation for computation and programming intensive tasks desirable in industry.

Prerequisites:

The student is required to have studied electromagnetism, linear algebra, and preferably computational modeling and calculus.

References

[1] Chen, Xudong, and Yu Zhong. "MUSIC electromagnetic imaging with enhanced resolution for small inclusions." Inverse Problems 25.1 (2008): 015008.

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Nanoscopy-associated image and data processing tools on JavaPython platform

Supervisors: Krishna Agarwal and Sebastian Acuna.

Introduction

Fluorescence microscopy has enabled countless breakthroughs in medical and biological research. It combines the basic idea of ​​'seeing is believing' with molecular or biochemical specificity using specially-targeted fluorescent labelling. The optical nanoscopy group at UiT has expertise in and access to a variety of advanced microscopy techniques, including several that we have developed ourselves. Multiple Signal Classification Algorithm (MUSICAL) is one such nanoscopy technique; it uses an algorithm to super-resolve the distribution of fluorophores by utilizing fluctuations in fluorescence intensity[1]. It can support 100-50 nm resolution with short acquisition time (seconds), which makes it a valuable tool for imaging biological samples. In this project, the student will create nanoscopy data processing tools for MUSICAL in order to allow the user to explore various options and perform advanced image processing tasks on MUSICAL's raw microscopy and processed nanoscopy images.

Research & Objectives

The student will develop user friendly toolkit on java (FIJI) or python platforms to allow powerful investigation of MUSICAL, such as including tools for

1. Threshold suggestion and automatic thresholding

2. Options for indicator functions

3. Contrast enhancements

4. Video nanoscopy and temporal multi-scale MUSICAL analysis

5. Denoising as pre- or post-processing

6. Artefact suppression and drift correction using deep learning

7.Raw data and MUSICAL image analytics and statistics generation

8.Foreground segmentation/background suppression, etc.

The project stands at a junction of advanced software programming, new algorithm development, physics-based deep learning, statistics, user-interface design, etc.

It is preferred that the student has taken a course in microscopy or nanoscopy (or is taking it this semester). There is a publication component and potential to participate in business plan development of MUSICAL.

References

[J1] K. Agarwal and R. Machan. "Multiple Signal Classification Algorithm for super-resolution fluorescence microscopy." Nature Communications(2016).

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On-chip super-resolution microscopy of pathology and tissue sections

Supervisors: Balpreet Singh Ahluwalia, Vishesh Dubey, Luis Enrique Villegas Hernandez

Introduction

In several diseases, change in the tissue morphology is considered as the marker of disease. Until now histology is the most preferred method to visualize these changes in the morphology. Unfortunately, the present technique is not capable of detecting the changes at sub-cellular level. The invention of super-resolution fluorescence optical microscopy (commonly referred to as opticalnanoscopy) has provided us with a glimpse of future impacts on cell-biology and medical diagnostics with the ability to visualize the structures even far beyond the diffraction limit. Until today, tissue and pathology slides are mostly explored using standard optical microscopy and have not been extensively studied using super-resolution microscopy methods which can provide abundant amount of useful information. AtUiTthenanoscopygroup have recently developed, patented, photonic chip-basednanoscopy, to be used in a standard optical microscope, where sample is placed on top of a photonic chip (PC) capable of both holding and illuminating the sample, enabling to acquire super-resolved imagesover extra-ordinary large areas.

Project setting

The group has received the Research Council of Norway project, BioTek2021 to develop, chip-based nanoscopy platform for pathology application. This master project is announced within the horizon of the BioTekl2021 project. The master student will have good support from the existing team of one post-doc and one PhD working in this project and it is anticipated that they will work closely. Moreover, there will be close co-operation with the hospitals from either UNNTromsøorRadiumhospitalOslo.

Research & Objectives

The focus of this master's thesis is to perform high-resolution imaging on tissue sections primarily using a homemade chip-based nanoscope. The focus will be on optimization of labeling and imaging of tissue biopsies. The conventional tissue biopsies are 4-8μm thick. We will investigate the use of chip-based microscopes to image different tissue sections. Both pre-labelled tissue sections and labeling tissues after hosting them on waveguide chip will be explored. Candidate will first optimize the labeling strategy suitable for chip platform and perform diffraction limited multi-color tissues imaging and based on the progress super-resolution imaging on tissues sectionscan also be investigated. Aberrations from tissue sections could be challenging for super-resolution imaging. To reduce such aberrations ultramicrotome will be used to section 200 nm thin tissue sections. These thin tissue sections (200 nm) can then be completely illuminated using the surface evanescent field generated from the waveguide surface. Work will be done in close collaboration with the medical faculty/UNN providing tissue samples.

Candidate will learn and develop the following skills:

a. Labeling of tissue section with primary and secondary anti-bodies

b. Operational skills on super-resolution bio-imaging

c. Imaging and handling of tissue sections

d. Data handling and manuscript writing

e. Possibility of publication and international working environment

f. Close co-operation and cross-disciplinary exposure

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Performance analysis and application study of MUSICAL

Supervisors: Krishna Agarwaland, Balpreet Singh Ahluwalia

Introduction

Fluorescence microscopy has enabled countless breakthroughs in medical and biological research. It combines the basic idea of ​​'seeing is believing' with molecular or biochemical specificity using specially-targeted fluorescent labelling. The opticalnanoscopygroup atUiThas expertise in and access to a variety of advancedmicroscopy techniques, including several that we have developed ourselves. Multiple Signal Classification Algorithm (MUSICAL) is one such technique; it uses an algorithm to super-resolve the distribution of fluorophores by utilizing fluctuations in fluorescence intensity[1]. It can support 100-50 nm resolution with short acquisition time (seconds), which makes it a valuable tool for imaging biological samples. In this project, the student will optimize the labeling and imaging protocols and benchmark the performance of MUSICAL.

Research & Objectives

The student will curate data from the current users of MUSICAL, including data on a variety of sample types, microscopes, and labeling conditions. In addition, the student will create benchmark data using DNA templates, DNA combs, and liposomes. The student will systematically categorize the raw microscopy and MUSICAL data into different pools of study for performance analysis and generate the MUSICAL results from the raw data with different control parameters. The student will also generate performance metrics regarding resolution, contrast, side lobes, artefacts, and threshold selection for MUSICAL results and also suitability metrics for the raw data in terms of signal to background ratio, fluctuations, sparsity of features, etc. The student will then write an application white paper that can be utilized by a user of MUSICAL for deriving insight about the optimal conditions of raw data for a given sample and label type, optimal MUSICAL parameters, and expected nature of performance. The student will then participate in data management for public use and scientific information sharing channels.

In order to perform this work, the student will be trained in scientific data curation, benchmark data creation, scientific data assessment and characterization, big data management, scientific writing, and application/case creation. The student will be trained in nanoscopy and microscopy data analysis and qualification. The student will gain medium to advance expertise in image processing using FIJI. The student may also opportunistically learn to acquire microscopy data.

The project requires meticulous data experimentation and interest in microscopy, image analysis, and scientific study design. It is preferred that the student has taken a course in microscopy or nanoscopy (or is taking it this semester). There is a publication component and potential to participate in business plan development of MUSICAL.

References

[J1] K. Agarwal and R. Machan. "Multiple Signal Classification Algorithm for super-resolution fluorescence microscopy." Nature Communications(2016).

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Raman spectroscopy of biological particles

Supervisor: Professor Olav Gaute Hellesø, olav.gaute.helleso@uit.no

Required course: FYS-3009 Photonics

Recommended courses: 'FYS-2008 Measurement techniques' and signal-processing courses

Background

Extracellular vesicles (EVs) are bilayer membrane vesicles released from various cells into their surroundings, with diameter 30-1000 nm. EVs are considered a mechanism for intercellular communications, allowing cells to exchange proteins, lipids and genetic material [1, 2]. EVs can provide important information about the cell it originates from, and therefore can have applications in clinical settings. New methods to characterize and analyze EVs are essential to understand the physiological and pathological functions of these vesicles, and to develop new clinical methods involving their use and/or analysis.

Resistance of bacteria to antibiotics is predicted to become a global problem. New antibiotics are under development, and there is a need for fast methods to predict which antibiotics affect a small sample of a given bacteria. Currently a lengthy process is used, involving culturing of the bacteria.

Raman spectroscopy can reveal the chemical composition, or 'chemical fingerprint', of a sample. However, owing to the small EV size, acquisition of Raman signals from EVs is very demanding. By combining optical trapping with Raman spectroscopy, it is possible to collect the Raman spectrum of a few EVs [3]. Similarly, Raman spectroscopy can be used for bacteria, possibly indicating if and the percentage of a given bacteria that is affected by an antibiotic.

 

Project description

The aim of the project is to use advanced Raman microscopy to reveal the chemical composition of biological particles, notably EVs and bacteria, and link this information to clinical properties. The student will use several set-ups and approaches, depending on what is found suitable. This includes a commercial Raman microscope, a combined set-up for optical trapping and Raman spectroscopy, and finally methods based on the use of an optical chip. Data analysis of the acquired spectra is necessary to find a link between the spectra and clinical properties of the EVs or bacteria.

The project will include using and improving advanced optical set-ups. The student will learn how to handle and prepare the biological samples, and how to do measurements with several techniques. The project can include data analysis, where advanced machine learning can be applied. It is up to the student how much experimental optics, biology and data analysis is included, and numerical simulations can also be included if the student wishes.

 

References

1. van Niel, G., G. D'Angelo, and G. Raposo, Nature Reviews Molecular Cell Biology, 2018. 19(4): p. 213-228.

2. Jamaly, S., et al., Scientific Reports, 2018. 8.

3. Kruglik, SG, et al., Nanoscale, 2019. 11(4): p. 1661-1679.

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Correlative QPI and fluorescence microscopy for segmentation of biological cells

Supervisors: Krishna Agarwal and Ankit Butola

Introduction

Quantitative phase imaging (QPI) is a label-free technique that enables monitoring of morphological changes at the subcellular level. QPI measures the path length shift associated with a specimen which contains the information about both refractive index and local thickness of the structure. The combined information can be used in various biological applications such as classification of sperm cells, dynamics of red blood cells, cell growth and among others.

On the other hand, fluorescence microscopy is a widely popular technique that offers high sensitivity and chemical specificity. For example, histopathology is a gold standard diagnostic technique to identify the cancer margin. However, the sample labeling process is extremely challenging and involves laborious sample preparations.

Research and Objectives

We have demonstrated both proof-of-principle of both QPI and fluorescence microscopy at UiT. The intelligent integration of QPI and fluorescence microscopy is important for label-free identification of cells and tissue. Additionally, label-free segmentation of the specimens reduces the aforementioned challenges such as lengthy sample preparations.

The student will work on the integrated platform of QPI + fluorescence microscopy and a computational technique such as semantic segmentation, deep learning. Image registration between different imaging modalities and label free segmentation of different parts of the cell will increase the practicality of the system for disease classification.

The candidate will learn and develop the following skills:

a) Overview of QPI

b) Independent user of QPI + fluorescence microscopy

c) Computational technique for label-free segmentation of cells

The candidate will mainly work in the Marker-free Lab (U1.023) at IFT and will get assistance from the Clinical research group.

 

Analysis for label-free measurement of mechanical tension in biological cells from 3D live microscopic image data

Supervisors:  Krishna Agarwal, Biswajoy Ghosh

Introduction: Mechanical forces are crucial for cells to move in the body in all 3 dimensions. Different cells have different tension eg..skin cells bs beating heart cells.   Furthermore, cells in healthy conditions can have a different tension than cells in diseases. The current methods of determining mechanical stiffness are based on the use of suitable markers/labels. The labels provide only limited information about the mechanical tension and are often not very compatible with studying cells in live conditions. Here we investigate a very novel way of determining the cell's tension fully without the use of any labels. The outcome of the project will lead to the development of a process that can measure the change in cellular stiffness when they move around the body. This will impact classifying healthy and disease cells leading to improved understanding of our body as well as improving medical diagnosis of several diseases. 

Research & Objectives: The student will develop a user-friendly analysis toolkit on Java (FIJI)/python/MATLAB for the analysis. The following tasks are expected from the candidate.

  1. Generate a database for the images/videos.
  2. Identify the mechanical parameters to be measured.
  3. Identify suitable mathematical models to measure the said parameters.
  4. Develop an analysis and quantification pipeline to measure the cell's mechanical tension. 
  5. Create a user-friendly toolkit on Java, MATLAB, or Python to execute the action. 
  6. Disseminate the results in academic journals/conferences.

Pre-requisites: The applicant needs to have skills in image processing and analysis. Should be able to work in coding platforms or image analysis platforms. Basic knowledge of mechanical forces is desirable. 

Analysis of cell motility and plasticity in 3D live microscopic image data. 

Supervisors:  Krishna Agarwal, Biswajoy Ghosh

Introduction: Cells in our body are always on the move traveling from one place to another. This has implications in the development of a baby in the fetus, wound healing, and cancer progression to name a few. However, the ability of the cells to bend, turn, and move forward can be quantified. The existing cell tracking methods track the cell's trajectory from the centroid only. However, with high-resolution microscopes, it is now possible to image specific parts of the cells that initiate the decision for them to move in a certain direction.   In this project, we quantify these motility features and track the cells while they are moving in a 3D environment. The success of the project will have an impact on basic research, clinical diagnosis and the pharmaceutical industry. 

Research & Objectives: The student will develop a user-friendly analysis toolkit on Java (FIJI)/python/MATLAB for the analysis. The following tasks are expected from the candidate.

  1. Generate a database for the images/videos.
  2. Identify the motility/plasticity parameters to be measured.
  3. Develop an analysis and quantification pipeline to measure the cell's motility parameters.
  4. Create a user-friendly toolkit on Java, MATLAB, or Python to execute the action. 
  5. Disseminate the results in academic journals/conferences.

Pre-requisites: The applicant needs to have skills in image processing and analysis. Should be able to work in coding platforms or image analysis platforms.

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NANOSPECTRUM: Development of an open-source software tool for spectral particle classification in nanofluidic confinement 

Supervisor:  Oliver Vanderpoorten

Introduction:

Nanofluidic devices have great potential for applications ranging from human health to single molecule metrology. However, their broad application potential remains largely untouched as microfluidic equipment with pumps, setups and complex chip handling limit their usability. The research community is in urgent need of a standardized way of sizing biomacromolecules and particles in solution in an easy manner but would also greatly benefit from measuring the absorption/emission spectra of these on a single particle level. Recently, we have demonstrated the usage of nanofluidic capillaries (NANOSPACERS) for single particle tracking and sizing of nanoscopic specimen and are currently upgrading this technology with spectral fingerprinting capabilities. A spectral analysis system which allows to distinguish (i) the size and (ii) the chemical composition of individual nanoparticles upon light illumination would be a powerful tool contributing to many scientific disciplines using nanofluidic metrology such as (bio-)nanotechnology, pharmacology, cancer research and colloidal science.

Research & Objectives:

In this master project, the student will develop a multi-spectral imaging system with additional principal component analysis (PCA) software to classify individual nanoparticles and biomolecules in a nanofluidic confinement. The project will be executed in several structured phases:

  1. Optical System Assembly: Implementation of a dark field/ transmission light microscope system with wide-field spectral readout of individual point emitters using grating/prism optics. (Similar to technology known from telescopes and space physics)
  2. Integration with nanofluidic technology: Fabrication of nanofluidic capillaries (NANOSPACERS) compatible with the imaging system. Access to UiT labs/clean room and basic introduction to nanolithography and microfabrication techniques.
  3. Experimental Validation and Optimization: Simulation of diffusing particles and Brownian motion using the Einstein-Stokes relation and conduction of sizing and spectrum acquisition experiments using table-top microscopy. Comparison between simulated values and real experiments of nanocolloids in confinement. After validation on passive colloids, experiments will be conducted on biological samples relevant to the research of the group with higher complexity in composition and number of particle subpopulations.
  4. Data Analysis and Evaluation: Development of an open-source imaging software package for spectral analysis and mapping of individually tracked particles. PCA on active and passive colloidal specimens using acquired spectra and tracking data. The software tool should provide immediate readout and classification of particles in the solution working towards a Nano-Total-Analysis-System (NTAS) of liquid samples of any kind.
  5. Documentation and Scientific Communication: The student will learn how to present in this scientific discipline and will document the findings in form of a master thesis. Contributions of the student to scientific discoveries can potentially result in a publication or contribute to ongoing projects. Attendance at minimum one scientific conference/symposium is encouraged.

Training and Learning Outcomes

Throughout the project, the student will receive hands-on training in:

  1. Building a spectroscopic imaging system
  2. Advanced image processing and analysis
  3. Scientific experimentation and data handling
  4. Presentation in an academic environment

The student will gain practical experience in microscopy systems and gain academic excellence in advanced image analysis. This project will also enhance the student's skills in scientific communication, both written and oral, since attendance on UiT research days/ outreach events is highly encouraged and supported by the supervisor.

Project Requirements

The ideal candidate for this project should have a background in optics, physics, or a related field, with an interest in hands-on optical instrumentation development and image analysis. Prior experience in laboratory settings and familiarity with optical instrumentation will be advantageous but is not required. The project is research-intensive and requires a commitment to meticulous experimental procedures and data analysis.

References

Vanderpoorten, O., Peter, Q., Challa, P.K. et al. Scalable integration of nano-, and microfluidics with hybrid two-photon lithography. Microsyst Nanoeng 5, 40 (2019). https://doi.org/10.1038/s41378-019-0080-3

Raphaël P. B. Jacquat, Georg Krainer, Quentin A. E. Peter, Ali Nawaz Babar, Oliver Vanderpoorten, Catherine K. Xu, Timothy J. Welsh, Clemens F. Kaminski, Ulrich F. Keyser, Jeremy J. Baumberg, and Tuomas P. J. Knowles, Single-Molecule Sizing through Nanocavity Confinement, Nano Letters 2023 23 (5), 1629-1636, DOI: 10.1021/acs.nanolett.1c04830

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