Browsing by master's degree program "Materiaalitutkimuksen maisteriohjelma"

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.

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.

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.

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.

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.

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.

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.

Röntgenabsorptiospektroskopia (X-ray absorption spectroscopy, XAS) kuvaa röntgensäteilyn absorboitumistodennäköisyyttä tutkittavaan materiaaliin röntgenfotonien energian funktiona. XAS-spektroskopiaa hyödynnetään monilla tieteenaloilla kuten fysiikassa, materiaalitutkimuksessa, kemiassa ja biologiassa. Menetelmällä saadaan yksityiskohtaista tietoa valitun alkuaineen ympäristöstä ja materiaalin rakenteesta atomitasolla. Kun röntgenfotonin energia vastaa tutkittavan atomin sidosenergiaa, absorptio kasvaa jyrkästi. Tämän nousun eli absorptioreunan paikkaa ei ole yksiselitteisesti määritelty, vaan kirjallisuudessa esiintyy yhä toisistaan poikkeavia menetelmiä absorptioreunan paikan määritykseen XAS-spektristä. Tutkielmassa hyödynnetään XANES-spektroskopiaa (X-ray Absorption Near Edge Structure), joka kuvaa absorptioreunan läheistä aluetta XAS-spektrissä, ja keskitytään koboltin K-kuoren XANES-spektroskopiaan. Työssä tutkitaan absorptioreunan paikan eri määritysmenetelmiä kobolttioksidien CoO, Co2O3, Co3O4 ja LiCoO2 XANES-spektreistä. Käytetyt reunan määritysmenetelmät ovat 1) absorptioreunan puolivälikorkeutta vastaavan energian etsiminen, 2) keskiarvon laskeminen niistä energioista, joilla reunan korkeus saavuttaa 20% ja 80% maksimiabsorptiosta, 3) reunan ensimmäisen käännepisteen etsiminen ja 4) reunan jyrkimmän käännepisteen etsiminen. Tutkielman kirjallisuuskatsauksessa käsitellään röntgenabsorptiospektroskopiaa keskittyen XANES-spektroskopiaan. Kirjallisuuskatsauksessa esitellään eri menetelmiä absorptioreunan määritykseen. Kokeellisessa osiossa mitataan kobolttioksidien spektrit ja verrataan eri menetelmillä määritettyjä absorptioreunojen paikkoja tunnettuihin hapetustiloihin ja pyritään etsimään lineaarista riippuvuutta reunan paikan ja hapetustilan välille. Pienin epätarkkuus, noin 10%, suoran sovituksessa saatiin hapetustilojen ja jyrkimmän käännepisteen menetelmällä määritettyjen reunan paikkojen välille. Tällä menetelmällä sovitussuoran kulmakerroin oli myös suurin, joten voidaan todeta tämän menetelmän herkkyyden olevan suurin. Toisaalta kulmakerroin poikkeaa myös selvästi muilla menetelmillä määritetyistä kulmakertoimista.

Several nuclear power plants in the European Union are approaching the ends of their originally planned lifetimes. Extensions to the lifetimes are made to secure the supply of nuclear power in the coming decades. To ensure the safe long-term operation of a nuclear power plant, the neutron-induced embrittlement of the reactor pressure vessel (RPV) must be assessed periodically. The embrittlement of RPV steel alloys is determined by measuring the ductile-to-brittle transition temperature (DBTT) and upper-shelf energy (USE) of the material. Traditionally, a destructive Charpy impact test is used to determine the DBTT and USE. This thesis contributes to the NOMAD project. The goal of the NOMAD project is to develop a tool that uses nondestructively measured parameters to estimate the DBTT and USE of RPV steel alloys. The NOMAD Database combines data measured using six nondestructive methods with destructively measured DBTT and USE data. Several non-irradiated and irradiated samples made out of four different steel alloys have been measured. As nondestructively measured parameters do not directly describe material embrittlement, their relationship with the DBTT and USE needs to be determined. A machine learning regression algorithm can be used to build a model that describes the relationship. In this thesis, six models are built using six different algorithms, and their use is studied in predicting the DBTT and USE based on the nondestructively measured parameters in the NOMAD Database. The models estimate the embrittlement with sufficient accuracy. All models predict the DBTT and USE based on unseen input data with mean absolute errors of approximately 20 °C and 10 J, respectively. Two of the models can be used to evaluate the importance of the nondestructively measured parameters. In the future, machine learning algorithms could be used to build a tool that uses nondestructively measured parameters to estimate the neutron-induced embrittlement of RPVs on site.

Ultrasonic transducers convert electric energy into mechanical energy at ultrasonic frequencies. High-power ultrasound is widely used in the industry and in laboratories e.g. in cleaning, sonochemistry and welding solutions. To be effective in these cases, a piezoelectric transducer must deliver maximal power to the medium. Most of these systems rely on having the power delivery maximized during long driving sequences where stable performance is critical. Power ultrasonic transducers are typically narrowband, featuring high Q-value, that are finely tuned to a specific resonance frequency. The resonance frequency can vary during driving due to temperature, mechanical loading and nonlinear effects. When the transducers resonance frequency changes, drastic changes in its impedance (resonance to anti-resonance) can lead quickly to damage or failure of the driving electronics or the transducers themselves. In this work we developed a multi-channel high-power ultrasonic system with a software-based resonance frequency tracking and driving frequency control. The implementation features a feedback loop to maximize power delivery during long driving sequences in an ultrasonic cleaning vessel. The achieved total real power increased from 6.5 kW to almost 10 kW in peak with our feedback loop. The feedback loop also protected the electronics and transducers from breaking due to heating and varying impedance.