Doctoral Theses / Doktorsavhandlingar Institutionen för fysik

Permanent URI for this collectionhttps://gupea-staging.ub.gu.se/handle/2077/17603

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Advanced and autonomous applications of optical tweezers
(2025-10-03) Selin, Martin; Selin, Martin; Selin, Martin
The invention of the microscope in the late 16th century revealed a hid den microscopic world, allowing direct observation of structures and or ganisms beyond the limit of human vision. Since then, the importance of this world for human health, materials science, and advanced technology has driven the development of increasingly sophisticated analysis methods. Among these, optical tweezers have become a central tool, using lasers to manipulate and probe objects with exceptional precision enabling single molecule, single-cell, and single-particle studies. Recent years have seen explosive growth in the use of artificial intelli gence (AI), particularly machine learning, across research. In Paper I, we examine how these methods are used in optical tweezers and the likely tra jectory of their integration and give some guidelines. Paper II presents an optical tweezers system with custom electronics, firmware, user interface which together makes the instrument capable of performing experiments fully autonomously. Using this system, we performed four representative experiments, demonstrating both reproducibility and variety of autonomous experiments. In Paper III, the system was applied to probe particle adsorp tion and desorption at liquid–liquid interfaces, revealing previously unseen dynamics of these processes. Finally, Paper IV addresses the challenge of force calibration under non-ideal conditions, in particular low sampling rates and long integration times, and proposes a framework to handle these chal lenges. Together, these works introduce several new techniques for optical tweez ers, spanning colloidal studies, automation, and trajectory analysis. Au tomation in particular will be crucial for the future of optical tweezers by bridging the gap between single-molecule, cell or particle studies and ensem ble measurements, enabling the application of deep learning for advanced modeling and unlocking the potential of optical tweezers for large, data driven studies.
Understanding machine learning through dynamical-systems methods
(2025-09-29) Storm, Ludvig; Storm, Ludvig
Machine learning has in the past decade been successfully applied to a vast range of tasks, ranging from classification, time-series prediction, and optimal navigation strategies. However, the internal mechanisms of many models are still difficult to interpret, and we lack a systematic understanding of when and why they perform successfully. Dynamical-systems theory has long been used to study complex, high-dimensional systems by focusing on their geometric and stability properties. In this thesis, methods from dynamical-systems theory are applied to machine learning models in order to gain new insights into their behaviour, with particular emphasis on finite-time Lyapunov exponents (FTLE) and Lagrangian coherent structures (LCS). In the first part, FTLEs are used to study how feed-forward neural networks organise sensitivity in input space, distinguishing regimes where networks align sensitivity with decision boundaries from regimes where embeddings appear random. In the second part, reservoir computing is analysed from a dynamical-systems perspective, and the maximal Lyapunov exponent of the driven reservoir is identified as the key parameter that controls prediction performance. In the third part, LCS theory is applied to the dynamics of active particles in flows, and it is shown how coherent structures determine when navigation strategies succeed or fail, in particular by explaining the trapping of swimmers in vortical regions. Overall, the thesis demonstrates that concepts originally developed to analyse complex physical systems can be fruitfully applied to machine learning. The use of FTLEs and LCS provides systematic tools for quantifying sensitivity and stability, offering a complementary perspective to existing approaches for analysing when and how machine-learning algorithms are able to learn.
Infrared Spectroscopy of Small Biomolecules in the Gas Phase
(2025-05-22) Andersson, Åke
Proteiner är makromolekyler som är nödvändiga för alla livformer. Deras biologiska funktion är kopplad till hur de veckas, vilket bestäms av deras primärsekvens, men det exakta sambandet är inte förstått. Fysikalisk modellering erbjuder en lösning från grunden i denna fråga, men modellerna behöver kalibreras med experimentella data från liknande biomolekyler. Infraröd (IR) spektroskopi är ett kraftfullt verktyg för att bestämma molekylär struktur och därmed validera fysikaliska modeller. Det infraröda området motsvarar molekylära vibrationer, som är nära relaterade till den geometriska strukturen. Genom att utföra IR-spektroskopi på biomolekyler är det sammantaget möjligt att bedöma fysikaliska modeller. Simuleringar av molekyler görs bäst i en isolerad miljö. För att matcha detta bör spektroskopiska experiment utföras i gasfas, där molekyler är isolerade och de inneboende egenskaperna kan studeras. Den låga densiteten i gasfasen kräver att särskilda tekniker för verkansspektroskopi används. Denna avhandling fokuserar på verkansspektroskopitekniker för biomolekyler i gasfas och all teori som krävs för att beskriva dessa. Fyra sådana tekniker tillämpas på små biomolekyler: IR flerfotondissociation (IRMPD) av joner, IRMPD av neutrala molekyler kombinerad med joniserande vakuumultraviolett strålning (IRMPD–VUV), ultraviolett (UV) resonansförstärkt flerfotonjonisering (REMPI), och konformer-specifik IR–UV-jon-dip-spektroskopi baserad på REMPI. Dessutom har en experimentell uppställning för de två sista teknikerna konstruerats. Den etablerade IRMPD-tekniken tillämpas på protonbundna dimerer av asparagin. Genom partiell isotopmärkning tilldelas IR-resonanserna vibrationsmoder. Motsvarande kvantkemiska beräkningar utförs för att förutsäga spektrumet och dra slutsatser om strukturen. Vidare tillämpas den nyligen utvecklade IRMPD–VUV-tekniken på peptiden pentaalanin, i tro om att den ska bilda en miniatyrhelix. Både experiment och teori indikerar flera förekommande konformer. Även om deras strukturer inte kan bestämmas, kan den kemiska miljön kring karboxylgruppen (COOH) avläsas. Slutligen tillämpas den konformerspecifika IR–UV-jon-dip-spektroskopitekniken på fenylerade polyalaninpeptider. Fenylgruppen möjliggör REMPI och jon-dip-spektroskopi. I motsats till pentaalanin visar experimentet endast en konformer, vilket stöds av teorin.
Optical levitation and Mie Fano combs
(2025-05-11) Tello Marmolejo, Javier
The smallest component of light, the photon, carries a small amount of momentum. When matter refracts, reflects, or absorbs light, some of this momentum is transferred to it, meaning that light can push or even pull matter. This fact was used by Arthur Ashkin in his invention of optical levitation and optical tweezers, which merited him a Nobel prize in 2018. The range of applications of optical tweezers is huge, ranging from trapping cells and organelles to creating quantum-limited sensors. This thesis expands the applications of optical levitation through 6 new experiments using a vertical, single-beam trap and a horizontal, 2-beam counter-propagating trap. With the vertical trap, I created a fully-manipulable damped driven harmonic oscillator and visualized the spherical aberration of a lens. I also re-created the 116-year-old Millikan experiment with a single oil droplet, where single electrons can be observed by eye and measured with a school ruler. Then, with the counter-propagating trap, I studied the Mie scattering of evaporating water droplets, showing the existence of a comb structure made up of Fano resonances. I used this to propose a new pedagogical example to teach quantum mechanics, and to explore the effects of strong irradiative heating on evaporating droplets, where I find a turnover in the evaporation rate of droplets. The wide range of topics covered in this thesis shows the huge versatility of optical levitation as a research tool. It also provides an intuitive understanding of the structure of the resonances in Mie scattering and showcases how this spectrum can be applied to other fields such as droplet evaporation and physics education research.
A Fragile Hold on the Electron: Probing the Limits of Negative Ion Studies
(2025-04-25) Nichols, Miranda
Negativa joner är unika kvantsystem vars struktur och dynamik i hög grad påverkas av elektronkorrelationseffekter. På grund av deras svagt bundna natur och avsaknaden av långräckviddiga Coulomb-potentialer saknar dessa system ofta optiskt tillåtna övergångar, vilket försvårar högupplösta spektroskopiska studier i jämförelse med neutrala atomer. Forskningen har därför traditionellt fokuserat på mätningar mellan bundna tillstånd och kontinuerliga tillstånd, i synnerhet bestämning av elektronaffinitet (EA) med metoder såsom laserfotodetachement-tröskelspektroskopi (LPTS). För joner där elektroner lossnar till ett p-vågskontinuum har EA-bestämningen dock varit begränsad, till följd av den långsamma fotodetachementprocessen precis ovanför tröskeln och de generellt små tvärsnitten. I denna avhandling bemöts dessa utmaningar genom en vidareutveckling av en kombinerad metod baserad på LPTS och resonansjonisationsspektroskopi (RIS), vilket möjliggör tillståndsselektiva mätningar av partiella fotodetachementtvärsnitt. En ny detektionsstrategi har införts för att särskilja signal från bakgrund, vilket avsevärt förbättrar selektiviteten och den experimentella upplösningen. Den förbättrade LPTS-RIS-metoden demonstrerades genom förbättrade EA-mätningar av cesium (Cs) och rubidium (Rb), där noggrannheten överträffade tidigare resultat för p-vågströsklar och bekräftade metodens användbarhet för framtida studier av mer komplexa atomära system. För att möjliggöra experiment med radioaktiva negativa joner krävs effektiva produktionstekniker. I detta arbete visas att laddningsutbytesreaktioner med uran kan användas för att framställa sådana joner. En kompletterande teoretisk studie undersökte elektroninfångning och energiförlustprocesser vid atom-jon-kollisioner och identifierade avgörande faktorer som påverkar banstrukturer. Genom att inkludera elektron-nukleär koppling i modellen uppnåddes en fördjupad förståelse, vilket är centralt för vidare modellutveckling av produktionsprocesser. Ett av avhandlingens viktigaste resultat är introduktionen av en helt ny spektroskopisk metod för att studera tidigare otillgängliga förbjudna bundna-bundna övergångar i negativa joner. Genom att utnyttja möjligheterna hos en kryogen jonlagringsring har den första mätningen av isotopförskjutning (IS) för en elektriskt dipolförbjuden övergång genomförts, med tennanjonen Sn⁻ som testfall. Metodens känslighet för elektronkorrelationseffekter, särskilt via komponenten för specifik massförskjutning, understryker dess potential. Arbetet visar att en kombinerad experimentell och teoretisk metodik kan övervinna centrala begränsningar inom spektroskopi av negativa joner och möjliggöra studier av system och övergångar som tidigare varit otillgängliga. Genom att tillämpa högprecisionstekniker på tunga och radioaktiva joner, utveckla robusta men enkla teoretiska modeller och utforska förbjudna övergångar, utvidgar denna avhandling forskningsfältets räckvidd och öppnar nya vägar inom atomär, nukleär och kvantmekanisk mångkroppsfysik.
Learning from Data and Physics for Multiscale Modeling of Woven Composites
(2025-04-03) Ghane, Ehsan
Developing new composite materials with enhanced properties relied on a long trial-and-error process, requiring extensive mechanical testing and deep knowledge about fundamental phenomena and constituent interactions. While analytical micromechanical models have successfully predicted the effective properties of heterogeneous materials with idealized microstructures, computational methods and increased computing power have made it possible to overcome simplifying assumptions. This allows for considering realistic microstructures with complex behaviors and interactive effects of multiple scales on the effective composite behavior. Despite these advances, simulations of complex multiscale heterogeneous materials, like woven composites, and the transitions from microscale to macroscale still demand significant computational resources, making their integration into fast, practical user codes a persistent challenge. Data-driven surrogate models based on neural networks address the computationally demanding challenge but often suffer from high data requirements, limited interpretability, and poor extrapolation capabilities. This dissertation explores the intersection of multiscale material analysis and neural networks, aiming to develop a generalized model that can infer woven composites' meso- and macroscale behavior from general load conditions and micromechanical constitutive properties. Several neural network-based surrogate models are designed to serve as efficient alternatives to conventional homogenization techniques, enabling fast and scalable predictions across scales for both elastic and elasto-plastic conditions. A key focus of this work is to lower the barriers to applying deep learning in multiscale material modeling. To achieve this, strategies are investigated to reduce the required training data while maintaining high-fidelity representations of time-dependent material behavior. Additionally, efforts are made to embed fundamental material constitutive laws directly into neural network architectures. This approach not only follows computational homogenization for woven composites but also enables extrapolation beyond training data while enhancing the explainability of path-dependent network predictions. Given the interdisciplinary nature of these contributions, the thesis includes introductions that provide the necessary theoretical background for a deeper understanding of the appended papers.
Spintronic oscillator networks for unconventional computing accelerators
(2025-03-17) González, Victor H.
Networks of coupled oscillators are fundamental to various natural and technological processes. For instance, networks of biological neurons exhibiting oscillatory behavior are crucial for cognitive functions, memory, and perception. Brain waves, arising from the synchronized activity of neural populations, play a crucial role in decision-making and learning. Hence, oscillator networks have gained significant interest for their ability to perform complex computations through collective dynamics. This thesis explores spintronic and magnonics-based oscillators, which offer advantages over electronic counterparts by utilizing magnetization dynamics instead of charge currents. These oscillators are smaller, potentially more energy-efficient, and can be engineered into networks for unconventional computing architectures. Leveraging their nonlinear behavior, we advance two key approaches for implementing spintronic oscillator networks. First, we further develop spatially-resolved networks, where arrays of spin Hall nano-oscillators (SHNOs) have been shown to work as proof-of-concept spintronic Ising machines. Through experimental, theoretical, and computational studies, we demonstrate that voltage control over the local magnetic anisotropy can be used to fine-tune both individual SHNO behavior and coupling between SHNOs, enabling flexible and scalable architectures for spatially-resolved SHNO-based Ising machines. Second, we demonstrate a time-multiplexed oscillator network, where spin wave pulses propagate in a Yttrium Iron Garnet (YIG) delay line. This demonstration acts as a proof of concept for the potential that the implementation of magnonic systems holds. We further develop our approach, optimizing its performance through numerical modeling and expanding its computational complexity by adding a global biasing term to our experimental demonstration. The results presented in this thesis highlight the potential of spintronic oscillators as key building blocks for next-generation unconventional computing technologies.
Multi-Particle Coincidence Studies of Molecular Single-Photon Ionization Processes
(2025-03-10) Olsson, Emelie
In this thesis, single photon ionization processes are investigated by means of multi- particle coincidence measurements. The experimental method combines a magnetic bottle time of flight electron spectrometer with ion spectrometry, to efficiently measure the charged particles emerging from the ionization of a molecule in the gas phase. The excellent collection efficiency of the magnetic bottle-type electron spectrometer is vital for these kinds of experiments. In Papers I-IV of this thesis, the valence double ionization electron spectra and fragmentation of several doubly charged molecules are studied. Here, the multi- coincidence technique extracts ion specific final state ionization spectra revealing the dissociation mechanics for the molecules S2, CSe2, Fe(CO)5 and SF6. For S2 and CSe2, the valence double ionization electron spectra are characterized for the first time, and their dicationic state selected fragmentation pathways are identified. Both the double ionization spectra and fragmentation pathways show good agreement between experiment and theory, and provide a benchmark for quantum chemical computations. For the two latter molecules, Fe(CO)5 and SF6, the investigations focus on the fragmentation pattern and underlying dissociation mechanisms and energetics, for instance scrutinizing the applicability of pure statistical theory. In Paper V, the focus is on electron-only coincidences of the core-valence ionization of C3O2, where one electron is emitted from an innermost core shell of the molecule, and one from the valence shell. C3O2 has a cumulative double bond structure with one oxygen at each end, resulting in two chemically different carbon species in the molecule, each contributing differently to the electron spectra. Core-valence double ionization above the C 1s edge reveals several sharp features, which are described well by theoretical modelling. Upon core hole ionization, Auger decay is expected, and for C3O2 the Auger spectra from both core-valence and core ionization reveal an unusual energy relation, namely that the lower binding energy carbon core site is associated with higher energy Auger electrons. This suggests a strong selectivity in the final states for the different carbon core ionization sites. Paper VI considers the spatial distributions of fragments from two-body dissociations. The probability of ionization depends on the molecular orientation relative the polarization of the light, described by the anisotropy parameter b, and for two-body dissociations this is reflected in the fragment distribution. By measuring the ion flight time differences and applying the fit function derived in Paper VI, the anisotropy parameter can be estimated, without the need of a position sensitive detector or simulations of fragment distributions. The findings of this thesis are of importance for a basic understanding of ionization processes and mechanisms leading to fragmentation, to benchmark theoretical models which describe the electronic structure and the molecular dynamics following single photon ionization, and, for instance, in astrophysical context and plasma research.
Deep Learning Enhanced Optical Methods for Single-Plankton Studies
(2025-03-05) Bachimanchi, Harshith
Among Earth’s earliest life forms, cyanobacteria reshaped the planet by oxygenating the atmosphere during the Great Oxidation Event 2.4 billion years ago. This process, which led to ozone formation and UV protection, paved the way for more complex photosynthetic organisms—phytoplankton, the eukaryotic descendants of cyanobacteria. Today, phytoplankton drive the global carbon cycle, producing 50–80% of Earth’s oxygen and fueling the marine food web. Microzooplankton consume nearly two-thirds of the organic carbon generated, yet despite their ecological significance, tracking biomass flow at the single-cell level remains a major challenge. This thesis presents novel methodologies that integrate advanced optical techniques, deep learning, and simulated datasets to analyze microplankton dynamics with unprecedented resolution. A key contribution is a deep-learning-enhanced holographic microscopy approach that quantifies microplankton biomass at the single-cell level while simultaneously capturing their three-dimensional swimming behavior. This method overcomes computational bottlenecks in traditional holography, enabling high-throughput analysis across diverse species and size ranges. Expanding on this, I demonstrate its application in mixed-species experiments to examine feeding interactions between phytoplankton and microzooplankton, capturing biomass transfer and behavioral shifts during predation. Beyond direct imaging, this thesis leverages synthetic data to advance microscopy-based research. Neural networks trained on simulated microscopy datasets are used to detect, segment, and classify plankton species while reconstructing motion dynamics. To showcase the versatility of this approach, I present its application in a non-biological setting—detecting bubble-propelled artificial micromotors within complex experimental backgrounds. In addition to object detection, these methods also enable motion characterization of microscopic entities. To demonstrate this, I introduce synthetic microscopy videos that model microscopic organisms undergoing various anomalous diffusion behaviors. This framework is then used to develop a method that extracts motion characteristics without explicit trajectory linking, broadening its applications beyond plankton ecology. Finally, I investigate how zooplankton—key players in the marine food web—respond to ocean wave-induced light patterns using an LED matrix. The results suggest that zooplankton use steady light sources, such as celestial objects, to ascend more rapidly during favorable low-turbulent conditions, offering new insights into their migratory strategies. Collectively, this thesis bridges marine ecology, microscopy, artificial intelligence, and biophysics to provide new tools for exploring the unseen dynamics that shape our planet.
Neural Networks for Complex Systems: From Epidemic Modeling to Swarm Robotics
(2025-03-04) Laura, Natali
Machine learning refers to data-driven approaches first introduced from the 1950s, drawing inspiration from developments in neurosciences. An artificial neuron is the smallest computational unit in machine learning. Neural networks are obtained by combining multiple neurons into layers that progressively process input information to determine an output. Neural networks are trained to find complex relationships in large amounts of data, and over the last decades, they have enhanced several fields of research including physics. In this thesis, I work at the intersection of complex systems and neural networks. In the first part, I focus on applications in which neural networks are a tool for analyzing data generated by complex systems. In Paper I, I employ neural network in agent-based simulations of epidemics. In Paper II, I look at one of the main issues in real-world applications, handling incomplete datasets. In the second part, I shift to use physical systems to build neural networks. I employ robots to play the role of artificial neurons within a swarm. In Paper III, I introduce physical constraints and use movement to restructure the neural network. In Paper IV, I use a robotic swarm to realize a generative machine learning model that reconstructs light patterns. These robotic experiments can be seen as an intermediate step between artificial and biological neural networks, still keeping the programmable abilities of the former, but including some of the physical constraints of the latter.
Microfabrication Technique Applications: From Passive Particle Manipulation to Active Microswimmers, Micromachines, and Fluidic Control
(2024-12-10) Wang, Gan
Overcoming Brownian motion at the micro- and nanoscale to achieve precise control of objects is crucial for fields such as materials science and biology. Significant progress has been made in trapping and manipulating micro- and nanoscale objects, either by generating gradients through external physical fields or by engineering systems that can harvest energy from their environment for autonomous motion. These techniques rely on the precise application of forces, such as optical and electromagnetic forces, and have found extensive applications across various scientific disciplines. Recent advances in micro- and nanofabrication technologies have greatly enhanced the generation and regulation of these forces, offering new possibilities for manipulating micro- and nanoscale objects. This thesis applies traditional micro- and nanofabrication techniques, typically used in semiconductor manufacturing, to construct micro- and nanostructures for manipulating forces, primarily critical Casimir forces and optical forces, to achieve precise control over microscale object movement. I first show the fabrication of periodic micropatterns on a substrate, followed by chemical functionalization to impart hydrophilic and hydrophobic properties. Near the critical temperature of a binary liquid, attractive and repulsive critical Casimir forces are generated between the micropatterns and microparticles. These forces allow the stable trapping of the microparticles on the substrate and the manipulation of their configuration and movement. Then, my research transitions from passive control to active motion by fabricating metasurfaces capable of modulating optical fields and embedding them within micro-particles (microswimmers). This enables light-momentum exchange under planar laser illumination, resulting in autonomous movement of the microswimmers. By varying the metasurface design as well as the intensity and polarization of the light, complex behaviors can emerge within these microswimmers. Subsequently, My research focused on using these microfabrication techniques to build micromotors integrated on a chip surface. These micromotors couple with other objects through gear structures, creating miniature machines that can execute functional tasks. Finally, by altering the configuration of these machines and the distances between them, I acheived precise, multifunctional control over fluid dynamics, facilitating the transport of micro- and nanoscale objects. Insights gained from this research suggest innovative manufacturing approaches for scalable manipulation of particles, more intelligent microrobots, and powerful miniaturized on-chip machines, with applications across various fields.
Annotation-free deep learning for quantitative microscopy
(2024-12-05) Midtvedt, Benjamin
Quantitative microscopy is an essential tool for studying and understanding microscopic structures. However, analyzing the large and complex datasets generated by modern microscopes presents significant challenges. Manual analysis is time-intensive and subjective, rendering it impractical for large datasets. While automated algorithms offer faster and more consistent results, they often require careful parameter tuning to achieve acceptable performance, and struggle to interpret the more complex data produced by modern microscopes. As such, there is a pressing need to develop new, scalable analysis methods for quantitative microscopy. In recent years, deep learning has transformed the field of computer vision, achieving superhuman performance in tasks ranging from image classification to object detection. However, this success depends on large, annotated datasets, which are often unavailable in microscopy. As such, to successfully and efficiently apply deep learning to microscopy, new strategies that bypass the dependency on extensive annotations are required. In this dissertation, I aim to lower the barrier for applying deep learning in microscopy by developing methods that do not rely on manual annotations and by providing resources to assist researchers in using deep learning to analyze their own microscopy data. First, I present two cases where training annotations are generated through alternative means that bypass the need for human effort. Second, I introduce a deep learning method that leverages symmetries in both the data and the task structure to train a statistically optimal model for object detection without any annotations. Third, I propose a method based on contrastive learning to estimate nanoparticle sizes in diffraction-limited microscopy images, without requiring annotations or prior knowledge of the optical system. Finally, I deliver a suite of resources that empower researchers in applying deep learning to microscopy. Through these developments, I aim to demonstrate that deep learning is not merely a "black box" tool. Instead, effective deep learning models should be designed with careful consideration of the data, assumptions, task structure, and model architecture, encoding as much prior knowledge as possible. By structuring these interactions with care, we can develop models that are more efficient, interpretable, and generalizable, enabling them to tackle a wider range of microscopy tasks.
High-threshold error-correcting codes for biased noise with advanced decoding strategies
(2024-11-18) Srivastava, Basudha
Quantum computers are highly sensitive to noise due to interactions with the environment, which severely limits the utility of near-term devices. Quantum error correction addresses this challenge by encoding logical information over multiple qubits in order to preserve information in the presence of noise. This facilitates the realization of the theoretical advantages of quantum computing such as exponential speed-up compared to classical computers for certain algorithms. Physical realizations of qubits may have different noise profiles, including biased noise, where phase-flip errors may be more likely than bit-flip errors. We propose a quantum error-correcting code, the XYZ² code, which is tailored to biased noise. The code has high error thresholds against biased noise, below which logical errors are exponentially suppressed with the linear dimension of the code. The important classical task of deriving corrections given partial information about errors is called decoding. We investigate several decoding strategies tailored to specific challenges, ranging from a novel maximum-likelihood sampling decoder, to a model-free, data-driven, neural-network-based decoder, to an efficient multi-step decoder for concatenated codes. We also study the link between quantum error-correcting codes and their statistical-mechanical counterparts by mapping to generalized random-bond Ising models and subsequently deriving the exact results for finite-size corrections under biased noise for moderate code sizes. Quantum error correction is still in its developing stages, and this work provides a useful contribution to the field by bridging a gap between theory and practice. By adapting well-studied topological codes to error models that are closer to reality, this work paves the way for the realization of a fault-tolerant quantum architecture.
Ultrafast magnetodynamics of self-localized spin textures driven by spin current
(2024-11-07) Ovcharov, Roman
Spintronics is a developing field of electronics that utilizes electron spin. This additional degree of freedom opens up new horizons for the design of non-volatile memory, logic, signal, and data processing devices. In particular, a spin-polarized charge current or a "pure" spin current can locally excite the magnetic order of the material, which is the basis for the operation of spintronic oscillators. Meeting the nanometer size requirements needed to compete with the achievements of semiconductor technology, these nano-oscillators are promising candidates for building unconventional computing schemes, such as neuromorphic computing or Ising machines. This thesis examines the dynamics of self-localized spin structures, dynamically or topologically stable configurations of magnetic order localized in space, for their application in spintronic devices. The thesis starts with an introduction to the theoretical framework and an overview of spintronic nano-oscillators — a major focus of this work. Subsequent chapters are organized based on the type of magnetic order examined. Chapter 2 presents an experiment on injection-locking of field-localized and self-localized spin wave modes, which are easily reconfigurable in a ferromagnetic spin Hall nano-oscillator. A theoretical model of synchronization in the presence of noise that explains this experiment is presented. Chapters 3 and 4 focus on antiferromagnets, investigating quasi-1D and 2D spin textures, respectively. Most of the results cover domain walls, whose internal dynamics offer a pathway for constructing a nano-oscillator with high (sub-THz) frequency and overcoming challenges related to uniformly ordered antiferromagnets. A long domain wall can act as a transmission line, with information carried by another localized inhomogeneity within it: a Bloch line. The scheme for such a transmission is explored at the beginning of Chapter 4. The antiferromagnetic section concludes with a study of a dynamic droplet-like texture formed by the mutual attraction of elementary excitations, magnons, and excited by a spin current. Chapter 5 shifts focus to the unique properties of ferrimagnets, revisiting domain wall dynamics. Although ferrimagnets combine the advantages of the two previous ordering types, we show that the forced dynamics of domain walls in ferrimagnets do not always reduce to these limiting cases and exhibit distinctive behavior - periodic "explosive" instabilities. Chapter 6 concludes the thesis by discussing possible future research directions based on the obtained results.
Microscopic approaches for bacterial collective behaviour studies
(2024-07-26) Antunez Dominguez, Jesus Manuel; Antunez Dominguez, Jesus Manuel
Bacteria significantly impact our lives, from their beneficial role as probiotics to their involvement in infection environments. Their widespread presence is largely due to their ability to adapt to diverse conditions through collective behavior, which enables the devel- opment of complex strategies from the contributions of simple individual entities. However the understanding of these systems is limited by the reach of current study techniques. This work presents the development of three platforms designed to perform microscopic studies and characterise bacterial collective behaviors in situ, profiting the advantages of microfluidics over traditional culture techniques. The first platform integrates bacterial culture on solid agar directly on the microscope stage, allowing for extended observation periods of up to a week. The agar is housed within an elastomer structure sealed with glass, ensuring environmental isolation while maintaining optical accessibility. This platform was used to document the complex social strategies of Myxococcus xanthus, including motility mechanisms, predation organisation, and fruiting body formation. The second platform is an automated testing system for quantifying bacterial viability under various conditions. Using microfluidic technology, this platform streamlines and parallelise the process. It adapts the Ames genotoxicity test to a miniaturized version, using microscopy imaging as the readout. This approach reduces experimental turnaround time and minimizes the handling of hazardous substances. The third platform is a microfluidic system designed for the microscopy observation of bacteria within stabilised droplets. This approach enhances throughput and allows for the production of various types of droplets on the same chip. Bacillus subtilis bacteria were encapsulated in these droplets, and their entire biofilm formation life cycle was observed in detail. Parallel to this, custom software was developed specifically for analysing microscopy images to automatically quantify biofilm formation. Each of these platforms provides a unique perspectives in the study of bacterial collec- tive behavior to offer a comprehensive toolkit for researchers. complementing one another. This work will equip researchers with the tools to address the mysteries of bacterial col- lective behavior and opens up new possibilities for application and investigation
Microswimmer Navigation in Turbulence
(2024-04-26) Mousavi, Navid
This thesis provides a summary of our exploration of navigation strategies for microswimmers with limited control and local sensing capabilities, drawing inspiration from copepods. Our research concentrates on two critical biologically inspired tasks essential for the survival of planktonic swimmers. First, we delve into the optimal vertical navigation for these swimmers. Many planktonic swimmers perform daily and seasonal vertical migrations quite efficiently in the turbulent pelagic environment. Given their abilities to navigate, we uncover active reorientation strategies that enable such microswimmers to double their vertical migration compared to those relying only on passive strategies such as gravitaxis. Moreover, we demonstrate the potential for these swimmers to translate even faster than their propulsion speed by leveraging the background flow. Second, we investigate optimal navigation strategies to avoid high strain rates in turbulent flows. This is important since the most crucial source of information for these species to survive is the flow disturbances. Here, we uncover intriguing phenomena such as emergent counter-current swimming behavior, which allows the swimmers to persist in low-strain regions of complex turbulent flow fields for extended periods. Our findings contribute to a deeper understanding of planktonic microswimmers; organisms that play vital roles in sustaining life on Earth by regulating climate, participating in the carbon cycle, influencing oceanic albedo, and serving as the foundation of aquatic food webs. Moreover, they have implications for developing optimal autonomous navigation policies for biologically inspired micro and nanorobots, with applications such as directed drug delivery. Given the interdisciplinary relevance of our results, the thesis includes a comprehensive introduction providing the necessary background knowledge for a deeper understanding of the appended papers.
Formation and Decay of Multiply-Charged Molecules upon Single-Photon Excitation
(2024-03-25) Wallner, Måns
This thesis presents the investigation of dissociative multi-ionisation processes in gas-phase molecules through single photon absorption, employing a combination of ion spectrometry and electron spectroscopy, relying on a magnetic bottle time-of-flight principle. This method is pivotal for capturing all electrons emitted during ionisation, enabled by its unique electric and magnetic field configuration. The initial part of the thesis (Paper I) explores Coulomb explosion in CD3I, induced by deep inner-shell ionisation with hard X-rays. The study examines core vacancy relaxation through Auger-Meitner cascades. Using a a comparatively simple model, the relaxation and subsequent dynamics of Coulomb explosions are simulated, yielding results that align well with experimental data for medium charge states, although limitations are noted for higher charge states. The research provides insights into the distribution of final charge states following L-shell ionisation, including the involvement of rapid Coster-Kronig transitions. Papers II-V focus on the dissociation mechanisms of small molecules (SO2, OCS, and HNCS) upon single photon double valence ionisation, employing the same experimental approach. Paper II discusses an unconventional dissociation mechanism in SO2 leading to the unexpected charge retaining SO2+ species via autoionisation of the super-excited 2h-1p Rydberg state molecular oxygen, O2, in an ionic pathway via a newly revealed metastable state of O-O-S2+. Paper IV examines bond rearrangement upon double ionisation in OCS, establishing a newly identified metastable isomer COS++. Paper V details the dissociation pathways of the main fragmentation channels of HNCS. Each paper contributes to a broader understanding of molecular behaviour under double ionisation. Paper VI focuses on the sequential three-body dissociation mechanisms leading to unusual shapes, that also deviate from the expected limiting slope of -mB/mBC. Two isomers of perflouro-propyl iodide are found to sequentially break into three fragments, ABC++ → A+ + BC+ followed by BC+ → B+ + C, where simulations suggest that the bow-tie and twisted shapes arise from femtosecond secondary lifetimes. At these short time scales the BC+ is placed well within the effective Coulomb range of A+. Overall, this thesis sheds light on the complex dynamics of dissociative multi-ionisation in several molecules, paving the way for future research in molecular dynamics and chemical reactions in more complex environments.
Binding Energies and Lifetimes in Negative Ions
(2024-03-15) Karls, Julia
Negative ions hold significant interest because of their importance in understanding electron correlation. Further, they capture substantial focus for their role in, for example: stellar environments, medical applications, antimatter research, and accelerator mass spectrometry (AMS). This thesis covers experimental studies of both the structure and dynamics of negative ions at the ion beam facilities DESIREE (Double ElectroStatic Ion Ring ExpEriment), CERN-ISOLDE (Isotope Separator OnLine DEvice) and GUNILLA (Gothenburg University Negative Ion and Laser LAboratory). At DESIREE, the electron affinites (EA) for the three stable isotopes of silicon have been measured with high precision, using lasermanipulation of quantum-state populations followed by laser photodetachment threshold (LPT) spectroscopy. The corresponding isotope shifts in the EA have been calculated. Additionally, the hyperfine splitting of the ground state in 29Si− was measured. The EA for two radioactive isotopes, 128I and 211At, have been determined using LPT spectroscopy with the GANDALPH (Gothenburg ANion Detector for Affinity measurements by Laser PHotodetachment) detector at ISOLDE. These are the first ever EA measurements of radioactive isotopes. This opens up a whole new field of experiments where the EA of even heavier ions can be measured, and giving the possibility of measuring isotope shifts in radioactive isotopes. At the ion beam facility GUNILLA at the University of Gothenburg, the EA for rubidium has been measured using a state selective detection of the residual atom in the LPT processes. If this selective measurement technique is combined with the possibility of studying radioactive beams at ISOLDE, it can be applied to studies of rare anions, like francium. In terms of dynamical properties, the radiative lifetimes of excited states in several atomic and molecular anions have been measured at DESIREE and some previously unobserved energy states have been detected. The methods used for detailed studies of the structure of negative ions used in this work lay the foundation for the ultimate goal to map out the lifetimes of all the excited states in negative ions. The research presented in here shows that it is only by combining structural and dynamical experimental results that it is possible to obtain a complete picture of negative ions. The results shown so far have been used to benchmark theoretical methods, but there are still quite a few discrepancies. By applying the methods used in this work to the full range of elements in the periodic table, and comparing them with theoretical results, it will be possible to enhance our understanding of electron correlation. Of particular interest will be to study the very heavy systems, where relativistic effects play a decisive role.
Advanced methods for the calibration of optical tweezers
(2023-09-08) Pérez García, Laura; Pérez García, Laura; Pérez García, Laura
Optiska pincetter har sedan Arthur Ashkin och hans kollegors uppfinning på 1980-talet möjliggjort noggrann manipulation av mikroskopiska partiklar. Denna teknik har haft betydande inverkan inom flera områden, inklusive biologi, fysik, nanoteknik, spektroskopi, mjuka material och nanotermodynamik. För att utföra experiment som kräver exakta mätningar av krafter med optiska pincetter måste pincetterna kalibreras, vilket innebär att deras styvhet måste bestämmas. I denna avhandling presenterar jag resultaten av min kalibrering av optiska pincetter med hjälp av probabilistiska metoder. Målet med dessa metoder är att effektivt använda tillgänglig data och samtidigt uppskatta fel som kan vara förknippade med kalibreringen. Detta är särskilt viktigt när det finns begränsad data, vilket är vanligt förekommande i system som är ur jämvikt, har låga signal-brus-förhållanden och där förutsättningarna ändras snabbt över tid. Denna avhandling är uppdelad i två huvudproblem. Det första problemet jag stötte på var bristen på en generisk metod för att mäta kraftfält i utsträckta, icke-konservativa och instabila jämviktspunkter. För att lösa detta problem använde jag bayesiansk inferens i form av en maximum likelihood-estimator. Denna metod gjorde det möjligt för mig att karakterisera kraftfältet även under förhållanden som tidigare var svåra att hantera. Metoden, som kallas FORMA, visade sig vara mer exakt, noggrann, snabbare och mindre datorkrävande än de tidigare konventionella metoderna, såsom equipartition, MSD, ACF och PSF. Utöver detta, möjliggjordes karakteriseringen av de kraftfält genererade av både Laguerre-Gaussiska strålar med olika orbital/spin-impuls, en dubbelbrunnspotential samt ett specklemönster. Det andra problemet jag tacklade var fel i uppskattningarna på grund av begränsad bandbredd och ändlig integrationstid. För detta ändamål utvecklade vi en gemensam sannolikhetsdensitetsfunktion för att observera partikeln vid olika positioner och tider. Vi härledde generella formler för kalibreringsmetoderna, vilka framgångsrikt korrigerar överestimeringen av styvheten och underestimeringen av diffusionskoefficienten som orsakas av ändlig integrationstid. Dessa formler tar även hänsyn till begränsad samplingsfrekvens och längden på partikelns bana. Sammanfattningsvis visar denna avhandling på möjligheten att använda en probabilistisk och inferensbaserad metod för att härleda de parametrar som karakteriserar Langevin-ekvationen för en partikels rörelse från en tidssekvens av dess position. Lösningen på detta problem har breda tillämpningar, inte bara inom kalibrering av optiska pincetter, utan också inom områden som mikroreologi, beteende hos enskilda molekyler inuti celler och djurmigration.
Spectroscopy of Stable and Radioactive Negative Ions
(2023-04-03) Ringvall Moberg, Annie
Negative ions are unique quantum systems which are of fundamental interest within the field of atomic physics due to the importance of the electron correlation in their creation, stability, and existence. Properties of these ions, such as the electron affinity, i.e. the binding energy of the additional electron (EA), are of importance for example within theoretical calculations, astrophysics, medical applications, antimatter studies, and accelerator mass spectrometry. This work aims to improve the understanding of negative ions and electron correlation by measuring previously not, or less precisely, known binding energies and lifetimes of both stable and radioactive negative ions by using laser photodetachment spectroscopy. In order to achieve this, several technical developments were carried out and their feasibility was demonstrated. The measurements were performed at several different facilities: ISOLDE at CERN, the Gothenburg University Negative Ion and Laser LAboratory (GUNILLA), the Double ElectroStatic Ion Ring ExpEriment (DESIREE). The results of this thesis can be divided into different categories, the first being the determinations of the EAs of 128-iodine of 3.059 052(38) eV and of 211-astatine to be 2.415 78(7) eV, marking the first ever measurements of EAs of radioactive isotopes, and thereby paving the way to future EA measurements of other elements of the periodic table. Second, high precision measurements of the EAs of cesium and rubidium have been performed at GUNILLA, utilizing a new technique: state selected detection of the residual atom in the photodetachment processes. The measured EAs were determined to be 471.5987(6) meV for cesium and 485.898(4) meV for rubidium, which are both improvements of previously determined values. This measurement technique can, if combined with the radioactive beam technique at CERN, be applied to study francium. Third, the radiative lifetimes of the excited states in rhodium have been investigated at the DESIREE facility. In addition to the two previously known bound states, a highly mixed bound state and an autodetaching state were observed. By measuring radiative lifetimes of negative ions, theoretical models within this field can be benchmarked. Fourth, the molecular dissociation process within the Radio-Frequency Quadrupole cooler and buncher (RFQcb), ISCOOL, at ISOLDE, CERN, has been investigated to determine the efficiency of producing atomic beams from molecular beams. Finally, a new non-radioactive mass separator beamline has been designed, built and commissioned at ISOLDE, CERN. This new facility will be dedicated to ion source developments, beam optics and beam manipulation studies aiming to improve ion beam quality and the performance of targetion sources during on-line operation at the ISOLDE facility.

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