Browsing by Subject "Magisterprogrammet i materialforskning"

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  • Lindblom, Otto (Helsingin yliopisto, 2020)
    Due to its exceptional thermal properties and irradiation resistance, tungsten is the material of choice for critical plasma-facing components in many leading thermonuclear fusion projects. Owing to the natural retention of hydrogen isotopes in materials such as tungsten, the safety of a fusion device depends heavily on the inventory of radioactive tritium in its plasma-facing components. The proposed methods of tritium removal typically include thermal treatment of massive metal structures for prolonged timescales. A novel way to either shorten the treatment times or lower the required temperatures is based performing the removal under an H-2 atmosphere, effectively exchanging the trapped tritium for non-radioactive protium. In this thesis, we employ molecular dynamics simulations to study the mechanism of hydrogen isotope exchange in vacancy, dislocation and grain boundary type defects in tungsten. By comparing the results to simulations of purely diffusion-based tritium removal methods, we establish that hydrogen isotope exchange indeed facilitates faster removal of tritium for all studied defect types at temperatures of 500 K and above. The fastest removal, when normalising based on the initial occupation of the defect, is shown to occur in vacancies and the slowest in grain boundaries. Through an atom level study of the mechanism, we are able to verify that tritium removal using isotope exchange depends on keeping the defect saturated with hydrogen. This study also works to show that molecular dynamics indeed is a valid tool for studying tritium removal and isotope exchange in general. Using small system sizes and spatially-parallelised simulation tools, we have managed to model isotope exchange for timescales extending from hundreds of nanoseconds up to several microseconds.
  • Laakso, Jarno (Helsingin yliopisto, 2021)
    Halide perovskites are a promising materials class for solar energy production. The photovoltaic efficiency of halide perovskites is remarkable but their toxicity and instability have prevented commercialization. These problems could be addressed through compositional engineering in the halide perovskite materials space but the number of different materials that would need to be considered is too large for conventional experimental and computational methods. Machine learning can be used to accelerate computations to the level that is required for this task. In this thesis I present a machine learning approach for compositional exploration and apply it to the composite halide perovskite CsPb(Cl, Br)3 . I used data from density functional theory (DFT) calculations to train a machine learning model based on kernel ridge regression with the many-body tensor representation for the atomic structure. The trained model was then applied to predict the decomposition energies of CsPb(Cl, Br)3 materials from their atomic structure. The main part of my work was to derive and implement gradients for the machine learning model to facilitate efficient structure optimization. I tested the machine learning model by comparing its decomposition energy predictions to DFT calculations. The prediction accuracy was under 0.12 meV per atom and the prediction time was five orders of magnitude faster than DFT. I also used the model to optimize CsPb(Cl, Br)3 structures. Reasonable structures were obtained, but the accuracy was qualitative. Analysis on the results of the structural optimizations exposed shortcomings in the approach, providing important insight for future improvements. Overall, this project makes a successful step towards the discovery of novel perovskite materials with designer properties for future solar cell applications.
  • Tavaststjerna, Miisa (Helsingin yliopisto, 2020)
    Further proof of the unique morphologies of water-soluble poly(2-isopropyl-2-oxazoline)-block-poly(DL-lactide) and poly(2-isopropyl-2-oxazoline)-block-poly(L-lactide) (PiPOx-b-PDLLA and PiPOx-b-PLLA) nanoparticles was obtained via Fluorescence Spectroscopy. Additionally, loading and release studies were carried out with hydrophobic curcumin molecules to outline the potential of the amphiphilic block copolymers in drug delivery applications. To study the morphology of the nanoparticles, absorption and emission spectra of pyrene were measured in water dispersions of the nanoparticles at several concentrations. The obtained I1/I3, I337/I333.5 and partitioning constant (Kv) values were compared to corresponding data from a control core/shell nanoparticle poly(ethylene glycol)-block-poly(DL-lactide) (PEG-b-PDLLA). Of the three different amphiphilic polymers, PEG-b-PDLLA showed the smallest and PiPOx-b-PDLLA the highest Kv value. This indicates, that PiPOx-b-PDLLA core is less hydrophobic and looser compared to the dense cores of PEG-b-PDLLA and PiPOx-b-PLLA, making it capable of encapsulating the greatest amount of pyrene. In the loading and release studies, the nanoparticles were loaded with curcumin and placed in dialysis against PBS Tween® 80 solution. Curcumin content of the samples was monitored over a week by measuring the emission spectra of curcumin. PiPOx-b-PDLLA showed greater potential as a drug delivery agent: It formed more stable nanoparticles, showed higher loading capacities, higher encapsulation efficiencies and slower release rates. Flash nanoprecipitation method (FNP) was also used to prepare the same nanoparticles with and without encapsulated curcumin. In addition to the encapsulation efficiencies, sizes of the nanoparticles were determined via dynamic light scattering (DLS). PiPOx-b-PLLA forms the smallest nanoparticles with lowest encapsulation efficiencies, thus agreeing well with the higher density of PLLA core. All three investigated amphiphilic copolymers formed stable nanoparticles in water at room temperature. On the contrary, stability of the nanoparticles was found to be poor in saline solutions at body temperature. Mixing PEG-b-PDLLA with PiPOx-b-PLA in a ratio of 20:80 w-% increased the stability of the nanoparticles in physiological conditions simultaneously uncovering the thermoresponsive character of the PiPOx-blocks. Turbidity measurements of PEG-b-PDLLA mixed with PiPOx-b-PDLLA in ratio of 20:80 w-% showed slight decrease in transmittance at the 30 °C, which corresponds to the cloud point of PiPOx-b-PDLLA in PBS solution. However, it remains unclear, whether the increased stability is due to the PEG-b-PDLLA mixing in the same micelles with PiPOx-b-PDLLA, thus hindering the aggregation of the nanoparticles upon the cloud point of the PiPOx-blocks.
  • Lasonen, Valtteri (Helsingin yliopisto, 2022)
    Modern semiconductor devices require sophisticated patterning techniques that not only offer excellent resolution but also high throughput, low cost, and low number of errors. And because these devices require several patterning steps, even a slight improvement in a patterning technique can have a huge impact. New patterning technique that has a great potential to be used in many of these patterning steps is area-selective etching of polymers by catalytic decomposition. The catalytic effect can either be an intrinsic property of the underlying material, or materials can be catalytically activated/deactivated to achieve the desired pattern. This new technique is self-aligning and extremely simple, and therefore has a potential to significantly reduce the number of errors and cost, while having excellent resolution and throughput. In the literature review part of this thesis, we will have an overview of different aspects that must be considered when using polymers as thermocatalytically decomposable resists. Polymers are already widely used as resists in several patterning techniques due to an immense number of different polymers available, allowing almost endless possibilities to adjust the properties of the resist. Important polymer properties to consider include adequate gas permeability for the etching gases and the decomposition products, decomposition and degradation mechanisms, reflow, integrity during the patterning and the deposition processes, and adhesion to the substrate. Different catalysts and catalytic decomposition mechanisms of polymers as well as other carbon-containing compounds in different atmospheres are reviewed. Because area-selective etching of polymers is a new technique many challenges are still unknown. Therefore, this thesis is mainly aimed to give ideas and directions for the future research. In the experimental part, several metals and metal oxides were tested for their catalytic effect for decomposing poly(methyl methacrylate) (PMMA) in air and H2-atmosphere. Pt, Ti, and CeO2 were confirmed to have a catalytic effect in air, whereas SiO2 and Al2O3 showed no catalytic effect. In the H2-atmosphere, only Ti and Cu showed some promising catalytic effect, whereas SiO2, Al2O3, CeO2, Pt, W, Ni, and Co did not. Additionally, experiments were conducted to find out how thin CeO2 film has an adequate catalytic effect. And finally, the area-selectivity of this patterning technique was tested in the air atmosphere using CeO2 as a catalytic surface and Al2O3 as a non-catalytic surface.
  • Toijala, Risto (Helsingin yliopisto, 2019)
    Ion beams have been the subject of significant industry interest since the 1950s. They have gained usage in many fields for their ability to modify material properties in a controlled manner. Most important has been the application to semiconductor devices such as diodes and transistors, where the necessary doping is commonly achieved by irradiation with appropriate ions, allowing the development of the technology that we see in everyday use. With the ongoing transition to ever smaller semiconductor devices, the precision required of the manufacturing process correspondingly increases. A strong suite of modeling tools is therefore needed to advance the understanding and application of ion beam methods. The binary collision approximation (BCA) as a simulation tool was first introduced in the 1950s. It allows the prediction of many radiation-related phenomena for single collision cascades, and has been adopted in many experimental laboratories and industries due to its efficiency. However, it fails to describe chemical and thermodynamic effects, limiting its usefulness where ballistic effects are not a sufficient description. Parallel to BCA, the molecular dynamics (MD) simulation algorithm was developed. It allows a more accurate and precise description of interatomic forces and therefore chemical effects. It is, however, orders of magnitude slower than the BCA method. In this work, a new variant of the MD algorithm is developed to combine the advantages of both the MD and the BCA methods. The activation and deactivation of atoms involved in atomic cascades is introduced as a way to save computational effort, concentrating the performed computations in the region of interest around the cascade and ignoring surrounding equilibrium regions. By combining this algorithm with a speedup scheme limiting the number of necessary relaxation simulations, a speedup of one order of magnitude is reached for covalent materials such as Si and Ge, for which the algorithm was validated. The developed algorithm is used to explain the behavior of Ge nanowires under Xe ion irradiation. The nanowires were shown experimentally to bend towards or away from the ion beam, and computational simulations might help with the understanding of the underlying physical processes. In this thesis, the high-fluence irradiation of a Ge nanowire is simulated and the resulting defect structure analyzed to study the bending, doubling as a second test of the developed algorithm.
  • Paulamäki, Henri (Helsingin yliopisto, 2019)
    Tailoring a hybrid surface or any complex material to have functional properties that meet the needs of an advanced device or drug requires knowledge and control of the atomic level structure of the material. The atomistic configuration can often be the decisive factor in whether the device works as intended, because the materials' macroscopic properties - such as electrical and thermal conductivity - stem from the atomic level. However, such systems are difficult to study experimentally and have so far been infeasible to study computationally due to costly simulations. I describe the theory and practical implementation of a 'building block'-based Bayesian Optimization Structure Search (BOSS) method to efficiently address heterogeneous interface optimization problems. This machine learning method is based on accelerating the identification of a material's energy landscape with respect to the number of quantum mechanical (QM) simulations executed. The acceleration is realized by applying likelihood-free Bayesian inference scheme to evolve a Gaussian process (GP) surrogate model of the target landscape. During this active learning, various atomic configurations are iteratively sampled by running static QM simulations. An approximation of using chemical building blocks reduces the search phase space to manageable dimensions. This way the most favored structures can be located with as little computation as possible. Thus it is feasible to do structure search with large simulation cells, while still maintaining high chemical accuracy. The BOSS method was implemented as a python code called aalto-boss between 2016-2019, where I was the main author in co-operation with Milica Todorović and Patrick Rinke. I conducted a dimensional scaling study using analytic functions, which quantified the scaling of BOSS efficiency for fundamentally different functions when dimension increases. The results revealed the target function's derivative's important role to the optimization efficiency. The outcome will help people with choosing the simulation variables so that they are efficient to optimize, as well as help them estimate roughly how many BOSS iterations are potentially needed until convergence. The predictive efficiency and accuracy of BOSS was showcased in the conformer search of the alanine dipeptide molecule. The two most stable conformers and the characteristic 2D potential energy map was found with greatly reduced effort compared to alternative methods. The value of BOSS in novel materials research was showcased in the surface adsorption study of bifenyldicarboxylic acid on CoO thin film using DFT simulations. We found two adsorption configurations which had a lower energy than previous calculations and approximately supported the experimental data on the system. The three applications showed that BOSS can significantly reduce the computational load of atomistic structure search while maintaining predictive accuracy. It allows material scientists to study novel materials more efficiently, and thus help tailor the materials' properties to better suit the needs of modern devices.
  • Kontinen, Joona (Helsingin yliopisto, 2020)
    Tutkielman kirjallisuusosuudessa on käyty läpi erilaisia kaupallisia biopolymeerejä, niiden synteesiä, käyttöä ja biohajoamista. Tutkielman pääpaino on erilaisten materiaalien biohajoamisessa ja näiden materiaalien kaupallisessa käytössä. Biohajoamisen evaluointiin tarkoitettuja standardeja, tutkimusmenetelmiä ja hyväksyntäkriteerejä on esitelty laajasti. Tutkimusosuudessa on valmistettu PLA:n ja PBAT:n seoksesta puukomposiitti ja materiaalin termomekaaniset ominaisuudet on karakterisoitu. Tavoitteena oli luoda biohajoava materiaali, jonka ominaisuudet ovat sellaisia, että sen kaupallinen hyödyntäminen kertakäyttömuovin korvikkeena on järkevää. Materiaalin mekaaniset ominaisuudet karakterisoitiin lopputuotteen kestävyyden, ja sulaominaisuudet kaupallisen tuotannon mahdollistamisen takia. Termomekaanisia analyysejä tehtiin materiaalin säilyvyyden ja lämpöominaisuuksien karakterisoimiseksi. Työssä on tutkittu myös puhtaan PLA/puukomposiitin biohajoamista meriympäristössä. Tutkimuksen tuloksena saatiin luotua riittävällä nopeudella biohajoava puukomposiitti, jonka mekaaniset ominaisuudet ovat riittäviä korvaamaan erilaisia kertakäyttöisiä muovituotteita ja joka on prosesoitavissa nykyisillä ekstruusiolaitteistoilla.
  • Halkoaho, Johannes (Helsingin yliopisto, 2022)
    MRS or magnetic resonance spectroscopy is an imagining technique which can be used to gain information about the metabolite concentration within a certain volume of interest. This can be used for example in brain imagining. The brain consists of three main types of tissue: cerebrospinal fluid, white and gray matter. It is important to know the different volume fractions of these tissues as the resolution in MRS is significantly lower than that of magnetic resonance imagining (MRI). The tissues all have different metabolite profiles and in order to get meaningful data the volume fractions need to be taken into account. This information can be gained from the segmentation of an image formed by using MRI. In this work a software tool was created to find these volume fractions with the input of a .rda file that is created by the scanner and Nifti file. The Nifti file is the image formed by using MRI and the .rda file is the manufacturers raw data format for spectroscopy data which has the relevant information about the volumes of interest. The software tool was created using Python and JavaScript programming languages and different functions of FSL. FSL is a comprehensive library of analysis tools used in brain imaging data processing. The steps for the software tool are: determining the coordinates of the volume of interest in FSL voxel coordinates, creating a mask in the correct orientation and location, removing non-brain tissue from the image using FSL’s tool tailored for that purpose (BET), segmenting the image using FSL’s segmenting tool (FAST), registering the mask on the segmented images and calculating the volume fractions. The software tool was tested on imaging data that was obtained at Meilahti Kolmiosairaala for the purpose of the testing. The testing data set included five different spectroscopy volumes from different parts of the brain and a T1 weighted image. The software tool was given the relevant information about the volume of interest in the form of a .rda file and the T1 weighted image in the form of a Nifti file. The software tool then determined the different volume fractions from all of the five volumes of interest. There is variation on the volume fraction of different brain areas within different brains and it is not possible to have an absolute reference value. The results of the test corresponded to the possible volume fractions that can be expected from the volumes in question.
  • Mäkelä, Ville (Helsingin yliopisto, 2022)
    With their ability to convert chemical energy to electrical energy through electrochemical reactions, rechargeable batteries are widely used to store energy in various applications such as electronic mobile equipment, aerospace aviation, road transportation, power grid, and national defense industry Numerous battery types are available commercially. Lithium ion-based batteries stand out due to several key advantages such as high operating voltage, high specific energy, and long cycle life. They also have a market dominance in a wide range of electric vehicles. However, like all battery technologies, lithium ion-based ones suffer from the effects of aging-induced degradation which can lead to reduced capacity, lifetime, and in some cases even safety hazards. One method of preventing/slowing down these aging reactions is to modify the standard battery materials by using dopants and additives. They are specific impurities purposely introduced into the battery during the manufacturing process. In this master’s thesis, the effect of additives (Mg/Al) on the aging of Li-ion cells was examined by using X-ray absorption spectroscopy, more specifically x-ray absorption near edge structure (XANES). For the experiment, 7 different cells, all containing lithium cobalt oxide as the major component (with 4 having a stoichiometric ration of Li/Co, and 3 being Li-rich), with 5 of them containing Mg/Al as dopants, and 2 containing no dopants were examined using XANES as a function of aging in terms of charge/discharge cycles. The dopants were introduced at different stages of the material preparation, either at the lithiation step or at the synthesis of the precursor. This thesis focuses on the XANES experiment and the data analysis, with extensive literature review on the topic of using additives and dopants. The cells were prepared by the Aalto University. The results showed that of the cells with dopant materials, the cells doped during lithiation stage aged slightly better after cycling than the undoped ones, whereas the cells doped during precursor stage aged worse than the undoped cells. This would suggest that doping might be more effective when done during the lithiation stage.
  • Mäntysaari, Matti (Helsingin yliopisto, 2021)
    This thesis describes the data and data analysis concerning Compton scattering experiments to obtain the Compton profiles of metallic sodium (Na) as a function of temperature. The temperatures used in the experiment were 6 K and 300 K. The purpose of the work was to visualize the effect of temperature in the electron momentum density in a free electron gas. The effects of temperature were expected to be manifested through changes to the Fermi momentum according to the free-electron theory, but also more subtle changes could have been possible owing to possible deviations from the free electron theory. The measurements were done at the European Synchrotron Radiation Facility (Grenoble, France) beamline ID20. The data was analyzed with a help of a program written with Matlab, and it converted the measured Compton spectra from photon energy space to electron momentum space, while applying self-absorption corrections to the data, subtracting background, and normalizing the data using trapezoidal numerical integral to yield final Compton profiles. Results were obtained as valence Compton profiles and their differences between 300 K and 6 K, and compared with the prediction from free-electron gas theory. The Compton profiles followed the predictions of the free-electron gas theory well, although the theoretical profiles had a higher amplitude than the measured profiles. This is a commonly found phenomenon in Compton spectroscopy and assigned to originate from electron-electron correlations. The effect of the temperature in the Compton profiles is in good agreement with the free-electron theory.
  • Zaka, Ayesha (Helsingin yliopisto, 2021)
    X-ray absorption spectroscopy(XAS) measures the absorption response of the system as a function of incident X-ray photon energy. XAS can be a great tool for material characterization due to its ability to reveal characteristic information specific to chemical state of element by using the core level electrons as a probe for empty electronic states just above the Fermi level of the material (XANES) or for the neighboring atoms (EXAFS). For years, the highly brilliant synchrotron light sources remained the center of attention for these XAS experiments, but the increasing competition for available beamtime at these facilities led to an increased interest in laboratory scale X-ray spectroscopy instruments. However, the energy resolution of laboratory scale instruments still remains sometimes limited as compared to their synchrotron counterparts. When operating at low Bragg angles, the finite source size can greatly reduce the energy resolution by introducing the effects of dispersion in the beam focus at the detector. One method to overcome this loss in resolution can be to use a position sensitive detector and use the 'pixel compensation correction' method in the post-processing of the experimental data. The main focus of this study was to improve the energy resolution of a wavelength dispersive laboratory-scale X-ray absorption spectrometer installed at the University of Helsinki Center for X-ray Spectroscopy. The project focuses on the case of Fe K-absorption edge at 7.112 keV energy and a Bragg angle of 71.74 degrees when using Silicon (5 3 1) monochromator crystal. Our results showed that the data that had been corrected using this method showed sharper spectral features with reduced effects of broadening. Moreover, contribution of other geometrical factors to the energy resolution of this laboratory X-ray spectrometer were also estimated using ray-tracing simulation and an expected improvement in resolution due to pixel compensation correction was calculated. The same technique can be extended to other X-ray absorption edges where a combination of a large deviation of Bragg angle from 90 degrees and a large source size contributes a dominant factor to the energy resolution of the instrument.
  • Spönla, Elisa (Helsingin yliopisto, 2020)
    The aim of the thesis was to study enzymatic treatment as a way to modify paper grade pulp to be a suitable raw material for the future textile industry. Wood as a raw material is an environmentally friendly option for textile production but its sustainable exploitation is not easy. Currently, ionic liquids are assumed to enable a safe and sustainable process for the production of wood-based regenerated fibres. These processes commonly use dissolving pulp as their raw material but replacing dissolving pulp with a paper grade kraft pulp would decrease environmental impact and production expenses. In this work, molar mass distribution of softwood paper grade kraft pulp was selectively modified using enzymes. Enzymes were utilized instead of acids because of their favourable abilities to selectively modify targeted polymers and to increase fibre porosity. Enzymatic modifications of softwood kraft pulp were performed to decrease degree of polymerization of cellulose and lower the quantity of hemicellulose. Hydrolysis of cellulose was catalysed with endo-1,4-β-glucanase (endoglucanase) and hemicellulose was degraded using endo-1,4-β-mannanase and endo-1,4-β-xylanase. The treatments were carried out both at high (20%) and low (3%) pulp consistency to examine the synergistic effect of enzymatic and mechanical action arising in the high consistency treatment. Additionally, influence of different enzyme combinations on the pulp properties was studied. The modified pulp samples were characterized by determining intrinsic viscosity, molar mass distribution, yield loss, and its composition. The fibres were imaged with light microscopy. The degree of polymerization of the pulp cellulose was successfully decreased with a relatively small endoglucanase dose. The amount of hemicellulose was reduced by removing 11% of the total galactoglucomannan and 40% of the total arabinoglucuronoxylan. The high consistency treatments decreased intrinsic viscosity 1.9 times more on average than the low consistency treatments. The high consistency treatments were effective with low enzyme doses, easy to control, and reliably repeated. Therefore, enzymatic pulp treatment at high consistency seems to be a compatible way to modify paper grade kraft pulp to suitable raw material for textile production. Further studies related to pulp dissolution in ionic liquids, fibre spinning, and fibre regeneration should be concluded to confirm applicability of the modified fibres.
  • Bäckroos, Sami (Helsingin yliopisto, 2021)
    High pressure inside e.g. blood vessels or other biological cavities is a major risk factor for many preventable diseases. Most of the measuring methods require physical contact or other kinds of projected forces. Both variants can be unpleasant for the patient and additionally physical contact might warrant for either continuous disinfecting or single-use probes, depending on the measurement method and the target body part. We have been experimenting with handheld non-contacting pressure measuring devices based on acoustic waves. These excite mechanical waves, whose velocity varies with pressure, on the surface of a biological cavity. The tried excitation methods are nearly unnoticeable for the patient, allowing for more pleasant and waste free measurements. Using the data from the latest clinical trial, a new analysis algorithm was devised to improve the accuracy of the pressure estimates. Instead of the time-of-flight (TOF) of the main mechanical wave (MMW), the new algorithm estimates the pressure using the MMW and a previously unseen feature, improving the R^2 from 0.60 to 0.72.
  • Hyvönen, Jere (Helsingin yliopisto, 2021)
    High-intensity and -amplitude focused ultrasound has been used to induce cavitation for decades. Well known applications are medical (lithotripsy and histotripsy) and industrial ones (particle cleaning, erosion, sonochemistry). These applications often use low frequencies (0.1-5 MHz), which limits the spatial precision of the actuation, and the chaotic nature of inertial cavitation is rarely monitored or compensated for, constituting a source of uncertainty. We demonstrate the use of high-frequency (12 MHz) high-intensity (ISPTA=90 W/cm2 ) focused-ultrasound- induced cavitation to locally remove solid material (pits with a diameter of 20 µm to 200 µm) for non- contact sampling. We demonstrate breaking cohesion (aluminium) and adhesion (thin film on a substrate, i.e. marker ink on microscope glass). The eroded surfaces were analyzed with a scanning acoustic microscope (SAM). We present the assembly and the characterization of a focused ultrasound transducer and show quantification of the effect of different sonication parameters (amplitude, cycle count, burst count, defocus) on the size and shape of the resulting erosion pits. The quantitative precision of this method is achieved by systematic calibration measurements, linking the resulting erosion to acoustic parameters to ensure repeatability (sufficient probability of cavitation), and inertial cavitation monitoring of the focal echoes. We discuss the usability of this method for localized non-contact sampling.
  • Tommiska, Oskari (Helsingin yliopisto, 2021)
    Työssäni tutkin mahdollisuutta käyttää akustista ajankääntömenetelmää (time-reversal) teollisen ultraäänipuhdistimen puhdistustehon kohdentamiseen. Akustisella ajankääntömenetelmällä pystytään kohdistamaan painekenttä takaisin alkuperäiseen pisteeseen, tallentamalla ko. pisteestä lähetetyt painesignaalit akustisilla antureilla (etusuunta) ja lähettämällä ne takaisin ajassa käännettyinä (takasuunta). Tässä työssä tutkitun kohdentamismenetelmän perusteena toimii elementtimenetelmällä toteutettu simulaatiomalli, jossa sekä ultraäänipuhdistin, että puhdistettava järjestelmä oli mallinnettu tarkasti. Simulaatiomallin avulla voitiin puhdistettavasta alueesta valita mielivaltainen piste johon halutaan kohdentaa puhdistustehoa. Simuloidun etusuuntaisen ajon tuloksena tuotetut signaalit tuotiin ulos mallista ja takasuuntainen ajo suoritettiin kokeellisessa ympäristössä käyttäen simuloituja signaaleja. Työssä esitetään vertailu simuloidun ja kokeellisen ajankääntömenetelmään perustuvan kohdentamisen tuloksista ja osoitetaan, että simuloiduilla signaaleilla on mahdollista kohdentaa akustista tehoa ennalta valittuun mielivaltaiseen pisteeseen. Lisäksi työssä esitetään analyysi anturien määrän vaikutuksesta kohdentamiskykyyn, tarkastellaan ultraäänipuhdistimen avaruudellista kohdentamiskykyä sekä vahvistetaan simulaatioissa tehdyn lineaarisen oletuksen paikkansapitävyys.
  • Heczko, Vilma (Helsingin yliopisto, 2021)
    Plasmonic catalysis utilises light energy to drive chemical reactions. Compared to conventional catalytic processes, which are run by high temperatures and pressures, light-driven processes can lower energy consumption and increase selectivity. Conventional plasmonic nanoparticles (Ag, Au) are relatively scarce and expensive, and therefore the use of materials with earth-abundant elements in plasmonic catalysis is widely pursued. Despite their good optical properties, plasmonic nanoparticles are often unsuitable catalysts. Hybrid catalysts, structures consisting of a light-harvesting plasmonic part and a catalytical centre of different material, have emerged as an opportunity to address these challenges and obtain desired properties. This thesis consists of two parts: In the first part, properties of plasmonic materials are described, and previous studies of hybrid catalysts with earth-abundant plasmonic materials are reviewed. Experimental work on plasmonic-catalytic nanohybrids, with TiN as the plasmonic part and Pd as the catalytic entity, is described in the second part. In this context, a Pd/TiN (Pd nanoparticles supported into TiN) catalyst was synthesised, characterised and applied to test catalytical reactions. Contrary to the hypothesis, light-induced rate enhancement was not observed in our current catalytical studies. These results call for further optimisation of synthesis and reaction conditions to prepare an earth-abundant, light-active catalyst.
  • Kurki, Lauri (Helsingin yliopisto, 2021)
    Atomic force microscopy (AFM) is a widely utilized characterization method capable of capturing atomic level detail in individual organic molecules. However, an AFM image contains relatively little information about the deeper atoms in a molecule and thus interpretation of AFM images of non-planar molecules offers significant challenges for human experts. An end-to-end solution starting from an AFM imaging system ending in an automated image interpreter would be a valuable asset for all research utilizing AFM. Machine learning has become a ubiquitous tool in all areas of science. Artificial neural networks (ANNs), a specific machine learning tool, have also arisen as a popular method many fields including medical imaging, self-driving cars and facial recognition systems. In recent years, progress towards interpreting AFM images from more complicated samples has been made utilizing ANNs. In this thesis, we aim to predict sample structures from AFM images by modeling the molecule as a graph and using a generative model to build the molecular structure atom-by-atom and bond-by-bond. The generative model uses two types of ANNs, a convolutional attention mechanism to process the AFM images and a graph neural network to process the generated molecule. The model is trained and tested using simulated AFM images. The results of the thesis show that the model has the capability to learn even slight details from complicated AFM images, especially when the model only adds a single atom to the molecule. However, there are challenges to overcome in the generative model for it to become a part of a fully capable end-to-end AFM process.
  • Heikkilä, Jesse (Helsingin yliopisto, 2022)
    Nanoformation of an active pharmaceutical ingredient (API) in controlled expansion of supercritical solution (CESS®) was studied in situ with a schlieren imaging technique in wide range of pre-expansion and expansion conditions, involving pressures up to 1000 bars. The optical methods allowed measurements on solvent state in the first micro and milliseconds of the expansion. Quantitative values on jet shape and solvent thermodynamic state were obtained for different nozzle configurations. These values, combined with mathematical modelling, enabled tracking the nanoparticle formation along the flow. We also report on the importance of solvent phase behavior demonstrated by three fundamentally different expansion schemes: supercritical (SC) to liquid/gas, SC to gas/liquid, and SC to SC. Scanning electron microscope images of the nanosized API are presented. This shows that one can have controlled particle formation by altering the thermodynamic conditions at the nozzle and changing the expansion path. The results guide the in-house process optimization and offer insight into the physics of supercritical fluid processing.
  • Kauppala, Juuso (Helsingin yliopisto, 2021)
    The rapidly increasing global energy demand has led to the necessity of finding sustainable alternatives for energy production. Fusion power is seen as a promising candidate for efficient and environmentally friendly energy production. One of the main challenges in the development of fusion power plants is finding suitable materials for the plasma-facing components in the fusion reactor. The plasma-facing components must endure extreme environments with high heat fluxes and exposure to highly energetic ions and neutral particles. So far the most promising materials for the plasma-facing components are tungsten (W) and tungsten-based alloys. A promising class of materials for the plasma-facing components is high-entropy alloys. Many high-entropy alloys have been shown to exhibit high resistance to radiation and other wanted properties for many industrial and high-energy applications. In materials research, both experimental and computational methods can be used to study the materials’ properties and characteristics. Computational methods can be either quantum mechanical calculations, that produce accurate results while being computationally extremely heavy, or more efficient atomistic simulations such as classical molecular dynamics simulations. In molecular dynamics simulations, interatomic potentials are used to describe the interactions between particles and are often analytical functions that can be fitted to the properties of the material. Instead of fixed functional forms, interatomic potentials based on machine learning methods have also been developed. One such framework is the Gaussian approximation potential, which uses Gaussian process regression to estimate the energies of the simulation system. In this thesis, the current state of fusion reactor development and the research of high-entropy alloys is presented and an overview of the interatomic potentials is given. Gaussian approximation potentials for WMoTa concentrated alloys are developed using different number of sparse training points. A detailed description of the training database is given and the potentials are validated. The developed potentials are shown to give physically reasonable results in terms of certain bulk and surface properties and could be used in atomistic simulations.
  • Keränen, Laura (Helsingin yliopisto, 2021)
    Tutkielman kirjallisuusosassa tarkastellaan johtavien metalli-, oksidi- ja nitridikalvojen kasvattamista epitaksiaalisesti strontiumtitanaatille. Epitaksiaalisia kalvoja on kasvatettu fysikaalisilla kasvatusmenetelmillä, kuten laserpulssikasvatuksella, elektronisuihkuhöyrystyksellä ja sputteroimalla, sekä kemiallisilla kasvatusmenetelmillä, kuten atomikerroskasvatuksella, sooli-geeli-menetelmällä sekä metalliorgaanisella kemiallisella kaasufaasikasvatuksella. Useiden tekijöiden, kuten substraattien lämpötilan ja esikäsittelyn todettiin vaikuttavan kalvojen orientaatioon. Kokeellisessa osassa iridium- ja platinaohutkalvoja kasvatettiin (100)-orientoiduille strontiumtitanaattisubstraateille atomikerroskasvatuksella. Iridiumkalvojen lähtöaineina käytettiin iridiumasetyyliasetonaattia sekä happea tai otsonia ja vetyä. Platinakalvojen lähtöaineina käytettiin platina-asetyyliasetonaattia, otsonia ja vetyä tai metyylisyklopentadienyylitrimetyyliplatinaa ja happea. Kalvojen rakennetta ja tekstuuria tutkittiin θ-2θ- ja in plane -röntgendiffraktiolla. Osaa iridiumkalvojen poikkileikkauksista tutkittiin myös läpäisyelektronimikroskopialla. Iridiumkalvojen todettiin olevan vahvasti (100)-orientoituneita, mutta monikiteisiä. Platinan (h00)-piikkejä ei kyetty erottamaan substraatin (h00)-piikeistä, mutta vahvojen (111)-piikkien perusteella kalvot eivät olleet epitaksiaalisia. Kalvojen kuumentaminen lisäsi (111)-orientaatiota molemmissa metalleissa.