Masteruppsatser
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Item Light-Controlled Self-Organisation of Active Molecules(2025-04-25) Klint, John; Klint, Niphredil; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikActive matter systems can be found on many different length- and time-scales in nature. Tiny molecular machines, colonies of bacteria and swarming insects are all examples of such systems. What they all have in common is that they are composed of agents that convert energy into different types of directed motion. This activity occurs only when the agents are in a non-equilibrium state. Often, the interactions give rise to emergent behaviours otherwise not observed for single individuals. A key aspect of active matter systems is that without an energy source, the agents do not exhibit any directed motion and therefore no emergence. The energy source may consist of, for example, light, heat, chemical reactions or vibrations. Research into active matter often involves laboratory experiments and these can be both expensive and time-consuming to set up. In this project, we explore a simple yet powerful numerical method designed to be efficient but still capable of capturing essential phenomena of light-activated systems. We consider two distinct types of colloidal particles, one that absorbs light and one that does not absorb light. When light is absorbed by one of the particle species, a temperature gradient is generated. Both types of particles are attracted to higher temperatures, and this phoretic attraction is the only interaction at a distance considered between the particles. Since the only particles that generate a temperature gradient are the ones that absorb light, there is an effective non-reciprocal phoretic interaction, which is directed from centre to centre. To avoid unphysical overlaps in the simulation, we implemented a volume exclusion scheme to account for the finite size and hard-core nature of the particles. Through simulations, we examined and catalogued emergent properties for clusters of particles and statistically determined their speed and rotational frequency. We also investigated cluster lifetimes and categorised different formations of active colloidal molecules. Furthermore, we implemented a number of different ways to simulate the illumination of the agents, from homogeneous light to square and Gaussian light pulses. We successfully induced several emergent properties, such as cluster disintegration immediately followed by regeneration (in a cellular automata-like fashion), as well as speed and rotational frequency modulation and orientation of clusters in the direction of the wavefront. The results obtained from our simulations are in agreement with previous experimental research on similar non-reciprocal systems governed by phoretic interactions. Our model and its implementation is capable of capturing a wide range of emergent behaviours. We have confirmed that the model can be used to explore how various light environments influence the behaviour of light-activated agents. The minimalistic approach of our work can be seen as a vantage point for further numerical studies of active matter systems.Item Dynamiken hos guldstavar i optiska fällor(2025-01-20) Bloom Rolewska, Julia; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikGold nanorods, with their unique plasmonic properties, have a wide range of applications in nanotechnology. When trapped in an optical tweezer, they can function as nanomotors, converting energy into motion at the nanoscale, serving as highly sensitive detector of movement, driven by scattering-induced optical torques. The development of artificial nanomotors hold promise for applications in DNA manipulation, nanolithography and environmental remediation to name a few. This thesis aims to investigate the dynamics of gold nanorods within an optical trap as a function of position along the optical axis of a laser beam in various media using video microscopy and analysis of back-scattered light. The nanorods ability to act as highly sensitive sensors for detecting nanoscale motion, particularly in single bacteria, is investigated. Since their motion is influenced by interactions within the optical trap and their rotational speed varies with height in the beam profile, calibrating the rotational speed variations allows for the detection of subtle fluctuations caused by bacterial interactions. When bacteria interacts with the nanorods, they shift the nanorods’ position within the beam, enabling precise detection of the nanomotion. The optical setup included circularly polarized laser tweezers, dark-field illumination, a photon multiplier tube collecting the scattered light and video microscopy for real-time measurements of the dynamics of the gold nanorods. The study revealed that the surrounding medium affects nanoparticle behavior in different ways: in lysogeny broth (LB), biomolecule binding increases the effective particle size and friction, slowing rotation motion. In phosphate buffered saline (PBS), ions confine particles closer to the surface, while milli-Q water (MQ), lacking ions, allows for greater freedom of movement relative to the interacting surface. Moreover, the higher stiffness measured in MQ is associated with the absence of ions and Coulomb screening, enhancing movement in the z-direction and indicating a more stable trapping environment in the xy-plane. Additionally, unexpected results present how the temperature rises gradually as the focal point is approached, likely due to reflections from the laser on the glass surface. The system demonstrates high sensitivity for detecting bacterial motion, with a sensitivity ranging of 0.5 Hznm−1 to 0.6 Hznm−1, exceeding the standard deviations of the fluctuations. The results and conclusions presented in this thesis provide insights that could contribute to future applications like detecting bacterial activity.Item Reconstructing Transmission Trees in Healthcare Setting using Bayesian Inference(2024-11-25) Kumbhar, Minal; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikOutbreaks of multidrug-resistant bacteria, such as Klebsiella oxytoca, present critical challenges to healthcare systems worldwide. Such bacteria can cause severe infections in immune-suppressed patients, spreading through contact with infected individuals, equipment, or contaminated environments. This thesis focuses on reconstructing transmission trees in healthcare systems using Bayesian modeling, focusing on the significance of data integration for effective infection control strategies. First, the study examines how patient, and contact data generated in hospitals contribute to understanding transmission trees. Second, it explores the incremental impact on inferred transmission trees’ accuracy by incorporating different data sources, such as temporal, contact, diagnostic, and genetic data. Lastly, the study evaluates the effects of varying sampling on transmission inference accuracy. The results indicate that integrating temporal, contact, reporting, and genetic data enhances the accuracy of transmission tree reconstructions. Furthermore, our investigation into the impact of sampling revealed that increased sampling improves accuracy and reduces variability in transmission tree structure. Overall, this research emphasizes the importance of comprehensive data integration for effective infection control strategies and provides insights for managing outbreaks of multidrug-resistant organisms in hospital environments.Item Fabrication and Characterization of Au/TiO2 Catalysts for Low Temperature CO Oxidation(2024-10-07) Olesen, Allan; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikCO oxidation is the reaction between carbon monoxide and oxygen forming carbon dioxide, as described by 2CO + O2 → 2CO2. Gold (Au) being chemically highly inert is not expected to be a good catalyst but exhibits an unexpectedly high activity towards CO oxidation even at room temperature. Au nanoparticles (NPs) with a diameter of 3 nm supported on TiO2 exhibit the highest activity towards CO oxidation. In this work, a method of fabricating such a catalyst in a clean room facility, relying on solid state dewetting, was developed. AuNPs were be formed by annealing 5 Å thin films of Au deposited on ~10 nm films of TiO2 produced by reactive sputtering onto fused silica. The performance towards CO oxidation was evaluated by employing mass spectrometry in combination with a gas reactor to measure the production of CO2. Comparing the CO2 signal pre- and post annealing, an increase of one order of magnitude was observed which could be attributed to the formation of highly active AuNPs. The influence of varying parameters, such as annealing time and temperature, were investigated. It was found that the activity of the catalyst greatly depends on the temperature at which the reaction is carried out and the annealing time. Short annealing times (900 s) was preferred. The catalyst suffered from CO poisoning at reaction temperatures of 40, 100 and 130 ◦C, but the effects of this was reduced at 100 ◦C and almost disappeared completely at 130 ◦C. There was no clear trend between annealing temperature and activity, although temperatures below 450 ◦C seem to be favorable. The localized surface plasmon resonance (LSPR) of the AuNPs was studied in-operando, finding a correlation between spectral shift and activity, most likely as a result of the formation of surface oxygen vacancies. To gain a better understanding of the morphology of the samples, scanning electron microscopy (SEM) was used, finding that particles were randomly dispersed. The portion of particles exhibiting high activity towards CO Oxidation, meaning being close to 3 nm in diameter, was only 22%. It is unclear how the distribution changes under different annealing conditions. The surface chemistry was studied using X-ray photoelectron spectroscopy (XPS), finding that annealing in an oxidizing environment results in a chemical shift of Ti. This could be linked to a decreased catalytic activity caused by the loss of surface oxygen vacancies in the TiO2 support.Item Simulation of light-absorbing microparticles in an optical landscape(2024-06-10) Lech, Alex; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikSimulating the dynamics of active particles play a key role in understanding the many behaviours active matter can exhibit. Experimental studies are more costly than simulations in this regard, as there is much work that needs to be performed with setups and observation time. Computer simulations are a powerful and costeffective supplements to experiments. One topic of study within active matter is light-absorbing microparticles which are commonly made of silica with a lightabsorbing metallic compound such as iron oxide or gold. One such microparticle is the Janus particle, a silica particle with a hemispherical coating of gold as the absorbing compound. When illuminated with a laser, the coating absorbs the light and heats up rapidly, generating a temperature gradient which allows the Janus particle to exhibit self-propulsion and clustering with other Janus particles due to thermophoresis and Brownian motion. In this thesis, I introduce a model which simulates light-absorbing microparticles with a desired distribution of iron oxide in an optical landscape. In particular, the case of an optical landscape characterized by a periodical sinusoidal intensity profile of a given spatial periodicity will be considered. The results show that for a hemispherical distribution (Janus particle) there is selfpropulsion originating at the side of the cap, with super-diffusive characteristics. When the laser periodicity is similar to the particle radius, it becomes confined between two high intensity peaks. A particle with uniform distribution diffuses with Brownian motion, with no self-propulsion. Clustering behaviour arises when multiple particles are in close proximity to each other, as observed in experiments. The agreement with experimental results opens up for the opportunity to simulate other light-absorbing particles with different distributions of absorbing compounds.Item Effectiveness of Iterative Algorithms for Recovering Phase in the Presence of Noise for Coherent Diffractive Imaging(2023-11-29) Wittler, Henry; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikMethods of coherent diffractive imaging (CDI) rely on iterative algorithms to reconstruct the complex exit-surface wave (ESW) of the object being imaged from the measured diffraction intensity only. In this thesis we investigate by simulation the artifacts on reconstruction when noise are present in the measurement. We first confrmed the results obtained by Williams et al. [1, 2, 3] for plane-wave CDI, for reconstructions from simulated measurement data with various amount of shot-noise, non-sample beam scatter and background levels. Two kinds of iterative reconstruction algorithms were tested, error-reduction (ER) and hybrid-input output (HIO). An analogous examination of the effects of noise for Fresnel coherent diffractive imaging (FCDI) was then undertaken. The technique of FCDI requires a separate algorithm to recover the phase of the illumination, prior to the use of ER or HIO algorithm for obtaining the ESW of the object. Thus we simulated measurements of both the illumination and the diffracted intensity, with a certain amount of shot-noise and additionally equal or different amounts of background noise. This resulted in distinct artifacts on the reconstruction for the different noise types. A wide range of different error metrics was investigated for each noise type and level, for the reconstructed ESW and it's derived transmission function. Our results show that certain error metrics are very useful for identifying a good estimate to the generally unknown true solution, in any amount and type of noise tested. These observations will help to design FCDI experiments for optimal use of the available signal and to design new algorithms for iterative phase retrieval that can be applied to noisy data.Item Decoding the surface code using graph neural networks(2023-10-17) Lange, Moritz; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikQuantum error correction is essential to achieve fault-tolerant quantum computation in the presence of noisy qubits. Among the most promising approaches to quantum error correction is the surface code, thanks to a scalable two-dimensional architecture, only nearest-neighbor interactions, and a high error threshold. Decoding the surface code, i.e. finding the most likely error chain given a syndrome measurement outcome is a computationally complex task. Traditional decoders rely on classical algorithms, which, especially for larger systems, can be slow and may not always converge to the optimal solution. This thesis presents a novel approach to decoding the surface code using graph neural networks. By mapping the syndrome measurements to a graph and performing graph classification, we find that the graph neural networks can predict the most likely error configuration with high accuracy. Our results show that the GNN-based decoder outperforms the classic minimum weight perfect matching (MWPM) decoder in terms of accuracy. With a phenomenological noise model with depolarizing noise and perfect syndrome measurements, our networks beat MWPM up to code-size 15 across all relevant error rates. Furthermore, the GNN is capable of surpassing MWPM under circuit-level noise up to code size 7. We also show that training the network on repetition code data from a recent experiment [Google Quantum AI, Nature 614, 676 (2023)] produces per-step error rates comparable to those achieved with a matching decoder specifically adapted to the error rates of the physical qubits. This indicates that graph neural network decoders are capable of learning the underlying error distribution on the qubits. Our findings advance the field of quantum error correction and provide a promising new direction for the development of efficient decoding algorithms.Item Development of a DEM-FEM framework for infrastructure simulations(2022-09-20) Ullrich, Anita; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikThis thesis presents a coupling algorithm of the discrete element method (DEM) and finite element method (FEM). The algorithm formulates an explicit coupling of transient simulations of particle systems interacting with elastic bodies. To lay a foundation for the requirements in terms of stability and temporal and spatial resolution, the DEM and FEM methods are introduced. The coupling algorithm is implemented in a Python framework, using the FCC in-house solvers Demify® and LaStFEM. The combined tool is applied to three different main scenarios. As a first case, the solver exchange of forces between the DEM and FEM solver is verified using a fixed elastic beam simulation with uniform load, comparing the deflection under the load of particles to an analytical condition. Second, the dynamic accuracy and stability of the coupling method is proven on a simulation of a steel sheet deflection under the load of particles flowing on the elastic object. The simulations are compared to experimental results and show good agreement with a measured sheet deflection. Finally, the coupled solver is used to simulate the interaction between a timber sleeper and a rock particle ballast bed. The particles are in the third case represented by a polyhedron particle model. The system is studied for variations of both material properties as well as different simulation parameters. The coupled solver is shown to capture dynamic effects in the ballast bed under a dynamic load cycle. The simulation results are compared to experimental results of the pressure distribution in the bed from the open literature and demonstrate good qualitative and quantitative agreement with the experiments. The overall performance of the different parts of the solver is presented and it is shown that the developed tool is capable of simulating large scenarios with very good performance on desktop computers with a single GPU.Item Quantum Error Correction Using Graph Neural Networks(2021-06-17) Bergentall, Valdemar; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikA graph neural network (GNN) is constructed and trained with a purpose of using it as a quantum error correction decoder for depolarized noise on the surface code. Since associating syndromes on the surface code with graphs instead of grid-like data seemed promising, a previous decoder based on the Markov Chain Monte Carlo method was used to generate data to create graphs. In this thesis the emphasis has been on error probabilities, p = 0.05, 0.1 and surface code sizes d = 5, 7, 9. Two specific network architectures have been tested using various graph convolutional layers. While training the networks, evenly distributed datasets were used and the highest reached test accuracy for p = 0.05 was 97% and for p = 0.1 it was 81.4%. Utilizing the trained network as a quantum error correction decoder for p = 0.05 the performance did not achieve an error correction rate equal to the reference algorithm Minimum Weight Perfect Matching. Further research could be done to create a custom-made graph convolutional layer designed with intent to make the contribution of edge attributes more pivotal.Item Topological Band Theory, An Overview(2021-06-14) Roderus, Jens; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikTopological insulators, superconductors and semi-metals are states of ma er with unique features such as quantized macroscopic observables and robust, gapless edge states. ese states can not be explained by standard quantum mechanics, but require also the framework of topology to be properly characterized. Topology is a branch of mathematics having to do with properties that are conserved under continuous deformations of spaces. is review presents some of the ways in which topology and condensed ma er physics come together, with a focus on non-interacting models which can be described with a band theory approach. Furthermore, the focus is on insulating systems but the discussions may sometimes be applied to superconductors and semi-metals. e eld of topological phases of ma er is not all together new, yet it lacks elementary introductions to newcomers. is review is meant for those with basic condensed ma er physics background and aims at providing a self-consistent overview of the central concepts in the eld of topological ma er. e structure of the review is as follows: In Chapter 1, a brief historical background is given. Also, a basic introduction to topology is presented, with focus on how it is used in condensed ma er physics. Following this, Chapter 2 introduces three important discrete symmetries which are key in characterizing topological phases of ma er. In particular, the e ect that these symmetries have on a general Bloch Hamiltonian is shown. In Chapter 3, the e ect of discrete symmetries on certain models is investigated. e well-known Su-Schrie er-Heeger model is discussed because it is the simplest models known to exhibit a topological phase and a topological invariant. Chapter 4 broadens the discussion of this topological invariant which is a winding number. Chapter 5 introduces the geometric phase (Berry phase) which is used to describe another topological invariant, the Chern number, the subject of Chapter 6. ere the alternative interpretations of the Chern number are discussed. A erwards, in Chapter 7, the quantum Hall e ect is presented. Following this, a general classi cation scheme for topological phases of fermionic, non-interacting systems will be presented in Chapter 8. It will be shown how it can be determined whether a system could possibly host a topological phase or not based on the symmetries of the Hamiltonian. Chapter 9 focuses on the concepts pertaining to the physics of the gapless edge states which appear between the interface of a (non-interacting) topological insulator and a topologically trivially insulator. Among the concepts discussed here is the bulk boundary correspondence and topological protection. Lastly, Chapter 10 contains a brief recap of what has been established in the review and some conclusionary remarks.Item Opto-vibrational coupling in molecular solar thermal storage materials: Electronic structure calculations and neural-networkbased analysis Giannis Kostaras Degree project(2021-01-26) Kostaras, Giannis; University of Gothenburg/Department of Physics; Göteborgs universitet / Institutionen för fysikMolecular solar thermal storage materials are proposed as a clean, renewable energy solution for a world with ever increasing energy needs. Norbornadiene is an organic compound suitable for molecular solar thermal storage systems. Computational methods such as density functional theory offer solutions for improvement of norbornadiene-based molecular solar thermal storage systems via theoretical spectroscopy. Machine learning methods, such as artificial neural networks may offer useful insights to improve theoretical spectroscopy methods.