Matemaattis-luonnontieteellinen tiedekunta
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(Helsingin yliopisto, 2020)The utilization of renewable biomass and development of greener technologies are in high demand. Especially, cellulose is a desired component in many applications, but the insolubility in most solvents limits its use. The currently used methods have also environmental issues and other concerns. To overcome these problems, ionic liquids were discovered to be able to dissolve cellulose and other renewable biomass fairly effectively. However, ionic liquids cannot be considered as “innocent” solvents, as their synthesis is not green, and they are often toxic and expensive. Deep eutectic solvents are recognized as promising green alternatives for ionic liquids and petroleum-based solvents, and are now widely used in biomass processing. Generally, deep eutectic solvents are binary mixtures formed by mixing cheap components together: a hydrogen bond donor with a hydrogen bond acceptor. The strong hydrogen bonding between the components is believed to be responsible of the reduced melting point of the mixture. As deep eutectic solvents were introduced quite recently, these novel solvents are still lacking the knowledge and understanding of their fundamentals. In this thesis, the introduction section covers the background on basic carbohydrate chemistry, ionic liquids which are able to dissolve cellulose, and deep eutectic solvents with their applications in biomass treatment. The characteristics of ionic liquids and deep eutectic solvents are compared. Generally, ionic liquids have better dissolution capability towards polysaccharides than deep eutectic solvents, but very little research has been done to explain why. The aim of this thesis is to understand and explain the fundamentals of deep eutectic solvents. Therefore, the properties affecting carbohydrate solubility, including cellulose (publication I), physicochemical properties of a new DES (publication III), solvent-solute interactions (publication I, II) and phase behavior of different alcoholic solutes in a DES (publication II) are studied in more detail. Furthermore, the unique properties of DESs are utilized in a novel way: as a green binder for Lithium-ion batteries (publication IV).
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(Helsingin yliopisto, 2020)The climate system responds to changes in the structure and physiology of vegetation. These changes can be induced by seasonal growing cycles, anthropogenic land cover changes (LCCs), and precipitation extremes. The extent to which vegetation changes impact the climate depends on the type of ecosystem, the season, and the intensity of perturbations from LCCs and precipitation extremes. Under the growing impacts of climate change and human modification of natural vegetation cover, understanding and monitoring the underlying biogeophysical processes through which vegetation affects the climate are central to the development and implementation of effective land use plans and mitigation measures. In Eastern Africa (EA) the vegetation is characterized by multiple growing cycles and affected by agricultural expansion as well as recurrent and severe drought events. Nonetheless, the degrees to which vegetation changes affect the surface energy budget and land surface temperature (LST) remain uncertain. Moreover, the relative contributions of various biogeophysical mechanisms to land surface warming or cooling across biomes, seasons, and scales (regional to local) are unknown. The objective of this thesis was to analyze and quantify the climatic impacts of land changes induced by vegetation seasonal dynamics, agricultural expansion, and precipitation extremes in EA. In particular, this thesis investigated these impacts across biomes and spatio-temporal scales. To address this objective, satellite observation and meteorological data were utilized along with empirical models, observation-based metrics, and statistical methods. The results showed that rainfall–vegetation interaction had a strong impact on LST seasonality across ecoregions and rainfall modality patterns. Furthermore, seasonal LST dynamics were largely controlled by evapotranspiration (ET) changes that offset the albedo impact on the surface radiation balance. Forest loss disturbed the LST dynamics and increased local LST consistently and notably during dry seasons, whereas during the wet season its impact was limited because of strong rainfall–vegetation interaction. Moreover, drought events affected LST anomalies; however, the impact of droughts on temperature anomalies was highly regulated by vegetation greening. In addition, the conversion of forest to cropland generated the highest net warming (1.3 K) compared with other conversion types (savanna, shrubland, grassland, and cropland). Warming from the reduction of ET and surface roughness was up to ~10 times stronger than the cooling effect from albedo increases (−0.12 K). Furthermore, large scale analysis revealed a comparable warming magnitude during bushland-to-cropland conversion associated with the dominant impact of latent heat (LE) flux reduction, which outweighed the albedo effect by up to ~5 times. A similar mechanism dominated the surface feedback during precipitation extremes; where LE flux anomalies dominated the energy exchange causing the strongest LST anomaly in grassland, followed by savanna. By contrast, the impact was negligible in forest ecosystems. In conclusion, the results of this thesis clarify the mechanics and magnitude of the impacts of vegetation dynamics on LST across biomes and seasons. These results are crucial for guiding land use planning and climate change mitigation efforts in EA. The methods and results of this thesis can assist in the development of ecosystem-based mitigation strategies that are tailored to EA biomes. Moreover, they can be used for assessing the performance of climate models and observation-based global scale studies that focus on the biogeophysical impacts of LCCs. Keywords: LST seasonality; Land cover change; Bushland (Acacia-Commiphora); Biophysical effects; Precipitation extremes; Satellite observation.
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(Helsingin yliopisto, 2020)How important are different uncertainty sources when simulating the future state of the forest ecosystem in Finland? In this thesis, we examine this question and provide some answers to this broad topic by simulating 21st century ecosystem conditions with a land-ecosystem model called JSBACH and compare the results to similar simulations performed by another model called PREBAS. We consider four different sources of uncertainty that are related to 1) the model that is used to generate the future ecosystem conditions; 2) climate used to drive the model, represented by an ensemble of CMIP5 simulations; 3) RCP scenarios that depict the rising atmospheric CO2 concentration and; 4) forest management actions. Before running the simulations described above, we calibrated and validated the JSBACH model extensively on different temporal resolutions and with multiple model modifications in order to improve the site-level model representation of transpiration, evaporation and carbon assimilation of boreal forests. These hindcasting calibrations were performed with two Bayesian approaches: the adaptive Metropolis algorithm and the adaptive population importance sampler. The resulting parameter values were compared to literature and the model performance was validated with distinct data sets and independent validation sites. We then generated a suitable model setup and parameter distributions to represent the JSBACH model uncertainty in the 21st century simulations. Canonical correlation analysis and redundancy indices were used to gleam the impact of the different uncertainty sources on multiple groups of ecosystem variables. Overall, forest management actions and RCP scenarios tend to dominate the uncertainties towards the end of the century, but the effect of climate models and parameters should not be overlooked especially since a more detailed examination revealed that their impact was not fully captured.
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(Helsingin yliopisto, 2020)Probabilistic graphical models provide a general framework for modeling relationships between multiple random variables. The main tool in this framework is a mathematical object called graph which visualizes the assertions of conditional independence between the variables. This thesis investigates methods for learning these graphs from observational data. Regarding undirected graphical models, we propose a new scoring criterion for learning a dependence structure of a Gaussian graphical model. The scoring criterion is derived as an approximation to often intractable Bayesian marginal likelihood. We prove that the scoring criterion is consistent and demonstrate its applicability to high-dimensional problems when combined with an efficient search algorithm. Secondly, we present a non-parametric method for learning undirected graphs from continuous data. The method combines a conditional mutual information estimator with a permutation test in order to perform conditional independence testing without assuming any specific parametric distributions for the involved random variables. Accompanying this test with a constraint-based structure learning algorithm creates a method which performs well in numerical experiments when the data generating mechanisms involve non-linearities. For directed graphical models, we propose a new scoring criterion for learning Bayesian network structures from discrete data. The criterion approximates a hard-to-compute quantity called the normalized maximum likelihood. We study the theoretical properties of the score and compare it experimentally to popular alternatives. Experiments show that the proposed criterion provides a robust and safe choice for structure learning and prediction over a wide variety of different settings. Finally, as an application of directed graphical models, we derive a closed form expression for Bayesian network Fisher kernel. This provides us with a similarity measure over discrete data vectors, capable of taking into account the dependence structure between the components. We illustrate the similarity measured by this kernel with an example where we use it to seek sets of observations that are important and representative of the underlying Bayesian network model.
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(Helsingin yliopisto, 2020)Cellulose, the major component of plant matter, has a complex hierarchical structure that extends from the scale of cells down to the molecular level. Knowledge of the structural fundamentals of cellulose is relevant, not only for an understanding of plant life, but also for numerous technologies that use it as a raw material. The methods of computational physics are increasingly used to support experimental efforts in cellulose research. This thesis reports molecular and fluid dynamics simulations that address questions related to the pyrolytic degradation of cellulose and the aggregation and deaggregation of cellulose microfibrils. Cellulose pyrolysis involves hundreds of chemical reactions and volatile products, the description of which remains a formidable challenge. Here, we demonstrate the use of reactive force field methods for predicting mechanisms and kinetics of cellulose pyrolysis. We show that reactive molecular dynamics simulations can reproduce essential features of the degradation process, most notably its onset via glycosidic bond cleavage, and thus offer a means to complement quantum chemistry methods and experimental analytics. The aggregation of microfibrils is fundamental to the structural hierarchy of native cellulose and has direct implications for its processing into nanostructured forms. Here, we use atomistic simulations to elaborate on the effects of chemical modification on microfibril interactions. Our simulations reveal the sensitivity of the interaction to non-uniform substitution patterns, a feature that is not captured by continuous theoretical models. Our findings suggest a connection between uneven charge distribution and heterogeneity observed in disintegration experiments. We also investigate the structure of microfibril bundles, and their relationship to the bound water of the cell wall, using molecular dynamics simulations. The simulations predict the spontaneous formation of a twisted ribbon-like bundle with a twist rate compatible with recent experimental evidence. This also leads to a reasonable prediction for the amount of bound water, which consists of molecular water layers surrounding the fibrils, along with several other experimental indicators. Microfibril interactions also manifest themselves in the rheology of aqueous cellulose nanofibril suspensions. Here, we demonstrate the coordinated use of rheometry, printing experiments and computational fluid dynamics simulations in the development of cellulose-based hydrogels for wound dressing applications. One of our key findings is the inadequacy of rotational rheometry as a basis for models of printer head flow, and the consequent need for an alternative model building strategy.
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(Helsingin yliopisto, 2020)Peralkaline syenites and granites form a small yet significant group of rocks within the A-type granite association worldwide. Although the Mid-Proterozoic Finnish rapakivi (A-type) granite complexes are voluminous in southern Finland, they only host minor quantities of peralkaline and marginally metaluminous syenitic rocks. Within the southeastern part of the subalkaline 1644-Ma Suomenniemi rapakivi granite complex (SE Finland), these alkali-feldspar rich syenitic rocks form numerous NW-oriented dike- and pod-like bodies, up to 5 meters in width and 100 meters in length. The Suomenniemi complex is thought to have formed by melting of granodioritic lower crust, whereas peralkaline syenites generally form by melting or fractionation of alkaline and transitional basalts, sourced in the subcontinental lithospheric mantle. Because of extensive fractionation and significant late fluid processes, peralkaline rocks are often associated with important mineralization. The origin and ore-forming potential of these rocks were studied using isotope geochemistry (single-grain zircon U-Pb and O, whole-rock Sm-Nd), mineral chemistry, whole-rock geochemistry, optical petrography and cathodoluminescence petrography. The data imply that these syenitic rocks formed in situ from the rapakivi granite either in post-magmatic (episyenites) or late-magmatic stage in the presence of a sodic fluid, and do not require a distinct magmatic source. Varying temperatures and differing fluid compositions produced geochemically and mineralogically diverse syenitic rocks, connected by relatively sodic and oxidized mineralogy and significant Si-depletion. The mechanism for the loss of Si from the rapakivi granite (or granite magma) is an elusive issue. In some, if not all, of the syenitic rocks it likely results from quartz dissolution and transport (episyenitization) after the transition from lithostatic to hydrostatically pressurized magmatic-hydrothermal system. Some of the syenitic rocks include hypersolvus feldspar and record ductile deformation, suggesting relatively high alteration temperature and pressure; if these rocks did not form in the subsolidus, a late-magmatic filter-pressing process during magmatic shearing resulted in loss of interstitial Si-rich magma and caused accumulation of K-feldspar. The ambiguous (magmatic/post-magmatic) textures of these rocks emphasize the nontrivial distinction between magmatic and (high-temperature) metasomatic processes. While episyenites are related to significant uranium and tin deposits worldwide, the economic potential of the syenitic rocks found in the Suomenniemi complex area is probably insignificant. The possible occurrence of these rocks beyond the Suomenniemi rapakivi complex, as well as the exact timing of alteration, should be constrained by further field work and radiogenic isotope dating.
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(Helsingin yliopisto, 2020)This thesis explores novel applications which utilize ionic liquids and electrolyte solutions in treatment of biomass for obtaining high-quality value-added products, such as cellulose, hemicelluloses and lignin as relatively pure fractions, which can be further processed into useful materials and chemicals. Albeit ionic liquids have been described as chemically and thermally highly stable solvents, the potential drawbacks of ionic liquids and electrolyte solutions regarding to their possible degradation in common processing temperatures are discussed in detail in this thesis. The potential of a highly hydrophilic aqueous organic electrolyte solution tetra-n-butylphosphonium hydroxide [P4444][OH] was studied in fractionation of wheat straw. Lignin from the biomass was extracted with varying concentations of [P4444][OH] in water. The carbohydrate rich fractions were isolated and finally all collected fractions were analyzed. The results showed that 40 w/w% aqueous solutions of [P4444][OH] were found stable. The irreversible decomposition kinetics were assessed at for 60 w/w% solution and it was not possible to obtain 70 w/w% concentrations or higher because of the decomposition at during evaporation at 25 °C. The next project was to assess the hydrolytic stability of 1,5-diazabicyclo[4.3.0]non-5-enium acetate [DBNH][OAc] using HPLC. The [DBNH][OAc] can rapidly dissolve large quantities of cellulose and can be utilized in the IONCELL-F process to convert cellulose pulp into strong cellulosic man-made fibers. In addition, synthesis routes were studied for the superbase precursors 9-methyl-1,5-diazabicyclo(4.3.0)non-5-ene (9-mDBN) which was assumed to lead to increased hydrolytic stabilities of the final ionic liquid. In the last research, aqueous solution of triethylammonium hydrogensulphate [TEAH][HSO4] was studied for fractionation of wheat straw and aspen in comparison with non-sulfate NaOH pulping and various recently discovered pulping methods utilizing a microwave reactor in all procedures. All fractions were analyzed with GPC and spectroscopic methods in order to evaluate their potential for further refining.
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(Helsingin yliopisto, 2020)Many pharmaceuticals, corrosion inhibitors, colorants, materials for LEDs and OLEDs, as well pH and fluorescence indicators contain as a key core structure the quinoline or quinolone motive. The chemical modifications of the quinoline and quinolone core are as important as their synthesis for the development of new materials. In order to achieve the desirable diversity the protection group chemistry, as well the functional group transformations, play a significant role in the quinoline and quinolone chemical modifications. Additionally, in many cases the tautomeric state of the hydroxyquinoline is of paramount importance for the correct functioning of the corresponding pharmaceuticals. The literature review part of the thesis deals with the quinoline and quinolone syntheses and their chemical modifications. In addition to this, the rudimentary concept of tautomerism is introduced, together with the tautomerism in hydroxyquinolines. Moreover, the basic concept of visible-light photocatalysis is presented with highlights of the reductive quenching cycles. The experimental results presented in this thesis have been published in three peer-reviewed journals. The first one covers tautomeric locking and switching and the second and third ones report visible-light photoreductions of functionalized quinolines. The design and synthesis of hydroxyquinolines equipped with two side arms with hydrogen bond accepting/donating properties and or metal chelating properties, allow us to achieve a fragile tautomeric equilibrium. This equilibrium was subject to controllable adjustment to one or the other side by metal chelation and locking or solvent addition. Deprotection of O-benzylated quinolines to the corresponding quinolones was achieved by visible-light photoreduction. The described method merges the tautomeric abilities of the masked quinolones and modern photoredox catalysis, allowing us to achieve regio- and chemoselectivity. Reduction of nitroquinolines to the corresponding aminoquinolines was achieved by visible-light photoreduction. The described method uses ascorbic acid as a sacrificial reductant and hydrogen source, which make it particularly useful. The method is green and has broad functional group tolerance.
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(Helsingin yliopisto, 2020)Clouds play a vital role in Earth's energy balance by modulating atmospheric processes, thus it is crucial to have accurate information on their spatial and temporal variability. Furthermore, clouds are relevant in those processes involved in aerosol-cloud-radiation interactions. The work conducted and presented herein concentrates on the retrievals of cloud properties, as well as their application for climate studies. While remote sensing observation systems have been used to analyze the atmosphere and observe its changes for the last decades, climate models predict how climate will change in the future. Altogether, these sources of observations are needed to better understand cloud processes and their impact on climate. In this thesis aerosol and cloud properties from the three above mentioned sources are applied to evaluate their potential in representing cloud properties and applicability in climate studies on local, regional and global scales. One aim of this thesis focuses on evaluating cloud parameters from ground-based remote-sensing sensors and from climate models using the MODerate Imaging Spectroradiometer (MODIS) data as a reference dataset. It is found that ground-based measurements of liquid clouds are in good agreement with MODIS cloud droplet size while poor correlation is found in the amount of cloud liquid water due to the management of drizzle. The comparison of the cloud diagnostic from three climate models with MODIS data, enabled through the application of a satellite simulator, helped to understand discrepancies among models as well as discover deficiencies in their simulation processes. These findings are important to further improve the parametrization of atmospheric constituents in climate models, therefore enhancing the accuracy of climate projections. In this thesis it is also assessed the impact of aerosol particles on clouds. Satellite data can be used to derive climatically crucial quantities that are otherwise not directly retrieved (such as aerosol index and cloud droplet number concentration) which can be used to infer the sensitivity of clouds to aerosols changes. Results on the local and regional scales show that contrasting aerosol backgrounds indicate a higher sensitivity of clouds to aerosol changes in cleaner ambient air and a lower sensitivity in polluted areas, further corroborating the notion that anthropogenic emission modify clouds. On the global scale, the estimates of the aerosol-cloud interactions present, overall, a good agreement between the satellite- and model-based values which are in line with the results from other models.
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(Helsingin yliopisto, 2020)Waves are important for both the leisure and safety of the human population. Open-sea waves have been studied since the 1940’s and their central properties are known. The wave field is described by the so called wave spectrum, which is a decomposition of the wave energy with respect to the wave frequency. In practice, the wave field is still often reduced to a few parameters, most importantly the dominant frequency (so called peak frequency) and the significant wave height. These parameters, however, does not sufficiently describe an archipelago wave field, but waves in archipelagos have still received relatively little attention from the scientific community. This thesis focuses on waves in archipelagos, and the study was carried out by using both numerical models and instrumental observations from the Helsinki archipelago and the Archipelago Sea in the Baltic Sea. Waves in archipelagos are heavily affected by the numerous small islands; they attenuate long waves arriving from the open sea, while also defining new fetches for local waves. As a result, the wave spectrum has a wide frequency range where the energy is practically constant. The existence of this energy carrying range is in contrast to open sea measurements where the energy is concentrated around one dominant frequency. This study proposed a characteristic frequency that quantified the centre of the energy carrying range. For a traditional open sea spectrum the characteristic frequency closely resembled the dominant frequency, thus making it suitable for a wide range of wave conditions. The height of single waves in the archipelago were lower relative to the significant wave height. As a consequence, there was a large (10-15%) discrepancy between two definitions of the significant wave height; in the open sea this discrepancy is typically only 7-8%. The three numerical models of this study simulated the archipelago wave field well. The largest discrepancy with the observations was found in an area just outside the archipelago that was sheltered by a peninsula. Inside the archipelago the models disagreed slightly on the energy distribution within the energy carrying range. These small differences strongly affected the dominant frequency in a way that was not representative of the good model performance. The differences were inconsequential for the significant wave height. During certain conditions the energy of the shortest waves were underestimated when using more advanced methods to calculate the energy transfer from the wind to the waves, most probably because a too small friction velocity. A simple older method to determine the friction velocity reproduced the shorter waves well. Coarse operational wind products were sufficient to force the high-resolution coastal wave models. Providing wind data only every third hour reduced the variability in the modelled wave field in the time scales between 2 and 10 hours. An hourly wind product captured all variations well, except for the statistical sampling variability in the measurements. Spatial properties of the wave field were inferred from high-frequency wave staff measurements taken by R/V Aranda. These measurements were used to form a new wave spectrum where the waves are decomposed according to their inverse phase-speed. The new spectrum agreed well with the spatial wavenumber spectrum for the shortest waves, while the frequency spectrum did not. The good agreement between the inverse phase-speed spectrum and the wavenumber spectrum meant that the effect of the Doppler shift was small. The reason for the disparate results of the frequency domain were attributed to wave non-linearities. Using direct measurements to determining the waves as a function of their phase speed can be useful when studying the interaction between the wind and the waves, since no additional current measurements are needed to quantify the real wave speed relative to the wind.
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(Helsingin yliopisto, 2019)Information on snow water equivalent (SWE) of seasonal snow is used for various purposes, including long-term climate monitoring and river discharge forecasting. Global monitoring of SWE is made feasible through remote sensing. Currently, passive microwave observations are utilized for SWE retrievals. The main challenges in the interpretation of microwave observations include the spatial variability of snow characteristics and the inaccurate characterization of snow microstructure in retrieval algorithms. Even a minor variability in snow microstructure has a notable impact to microwave emission from the snowpack. This thesis work aims to improve snow microstructure modelling and measurement methods, and understanding the influence of snow microstructure to passive microwave observations, in order to enable a more accurate SWE estimation from remote sensing observations. The thesis work applies two types of models: physical snow models and radiative transfer models that simulate microwave emission. The physical snow models use meteorological driving data to simulate physical snow characteristics, such as SWE and snow microstructure. Models are used for different purposes such as hydrological simulations and avalanche forecasting. On the other hand, microwave emission models use physical snow characteristics for predicting microwave emission from a snowpack. Microwave emission models are applied for the interpretation of spaceborne passive microwave remote sensing observations, for example. In this study, physical snow model simulations and microwave emission model simulations are compared with field observations to investigate problems in characterizing snow for microwave emission models. An extensive set of manual field measurements of snow characteristics is used for the comparisons. The measurements are collected from taiga snow in Sodankylä, northern Finland. The representativeness of the measurements is defined by investigating the spatial and temporal variability of snow characteristics. The work includes studies on microwave emission modelling from natural snowpacks and from excavated snow slabs. Radiometric observations of microwave emission from natural snowpacks are compared with simulations from three microwave emission models coupled with three physical snow models. Additionally, homogenous snow samples are excavated from the natural snowpack during the Arctic Snow Microstructure Experiment, and the incident snow characteristics and microwave emission characteristics are measured with an experimental set-up developed for this study. Predictions of two microwave emission models are compared with the radiometric observations of collected snow samples. The results indicate that none of the model configurations can accurately simulate the microwave emission from natural snowpack or snow samples. The results also suggest that the characterization of microstructure in the applied microwave emission models is not adequate.
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(Helsingin yliopisto, 2019)As the world is constantly changing, and there are concerns over a sustainable future, educating teachers for sustainability is crucial, as education is one of the most effective means to improve sustainability. Science, such as chemistry, plays a significant role in addressing sustainability issues, because chemistry can contribute to both solving as well as causing the challenges through knowledge and products that chemistry produces. Science and sustainability are inherently connected, as are the discussions over their education. On both these fields, discussions over the role of the students have emerged. In science education there has been a growing interest to educate scientifically literate students who can use scientific thinking in their own lives and in the society. This requires active participation of the students in their own learning. Sustainability education has been advocating transformative learning so that students could take action in their own lives towards sustainability. Moreover, teacher education could be developed in a direction in which the student teachers would be given possibilities to make decisions concerning the learning and teaching methods used and contents chosen, and develop their actioncompetence through active participation. However, in order to reach sustainability, all citizens should be considered as learners, not only students in schools and universities. Discussion over the learners’ roles has led to the using of terms, such as learner-centred and learner-driven learning. What these terms actually entail is, however, not always clear. In science education, learner-driven approaches are usually practiced in the form of open inquiry – an inquiry that starts with the students’ questions. Addressing and using the students’ questions is important in science education, but also in sustainability education to activate learners to think and act for sustainability. The aim of this thesis is to understand the possibilities and challenges of learner-centred and learner-driven science teacher education for sustainability. The research questions are: i) Which possibilities do learner-centred and learner-driven science teacher education for sustainability offer? and ii) What are the challenges for learner-centred and learner-driven science teacher education for sustainability? For this purpose, two types of approaches are studied: inquiry-based education as a typical approach in science teacher education from the point of view of learner-centred and learner-driven inquiry, and sustainability education as a part of science teacher education for sustainability from the viewpoint of learnercentred and learner-driven sustainability education. This is a qualitative multi-method research with one systematic review and three case studies applying grounded theory and discourse analysis. The thesis consists of four articles: i) Inquiry as a context-based practice – A case study of pre-service teachers’ beliefs and implementation of inquiry in contextbased science teaching ii) Student-question-based inquiry in science education, iii) From learner-centred to learner-driven sustainability education, and iv) Challenges and tensions in collaborative planning of a student-led course on sustainability education. Data for the studies was derived from three sources including higher education student groups and peer-reviewed articles. Study I utilised data from five student teachers who participated in a course “inquiry-based chemistry teaching” in 2015. Their beliefs about inquiry were studied by interviewing them, and their implementations of inquiry were studied from their reports. Data in study II consisted of 30 articles reviewed using systematic review. In studies III and IV, the research data consisted of a planning process of higher education students (student teachers and students interested in teaching) who planned and ran a course “sustainable development in education” in 2015. Their planning meetings and two semi-structured interviews were analysed using discourse analysis and grounded theory. As a result, understanding on the differences between learner-centred and learner-driven sustainability education was obtained. This thesis reveals that learner-driven and learnercentred education are different constructs, especially related to the learners’ roles. Studentled planning on sustainability education was studied to be challenging, as the students had to discuss several interrelated issues on sustainability and sustainability education, as well as their own roles and ways to work as a group. However, the challenges in learner-driven approaches can sometimes be viewed as part of the process. In addition, possibilities for learner-centred and learner-driven practices were revealed on how to use students’ questions in inquiries and contexts-based inquiry as a humanistic approach. For science education, a student-question-based inquiry model was created, which the teacher can use to support students in their question asking. The study also revealed challenges related to the ownership of students’ questions. The results from this thesis are relevant when planning teacher education for sustainability. This thesis points out that especially higher education has the potential to involve the students more in teaching by promoting action-competence among students through learner-driven education. Science teacher education could be focusing more on using learner-centred and learner-driven approaches, because the studied higher education students could plan and carry out teaching that mirror central aspects of science and sustainability education. Moreover, in order to be able to use learner-driven approaches, there is a need to use extra-situational knowledge, to improve students’ ownership of their own questions, to redefine expertise, and to work with non-predefined goals and with the whole community.
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(Helsingin yliopisto, 2019)Atmospheric aerosols are small liquid or solid particles suspended in the air. These small particles have significant effects to our climate and health. Approximately half of the particles that grow into cloud condensation nuclei −size are primary particles and emitted directly into the atmosphere, whereas the other half are secondary particles which are formed in the atmosphere. In new particle formation, molecular clusters form from atmospheric low-volatility vapors by condensation and/or chemical reactions. Atmospheric oxidation is a key phenomenon enhancing atmospheric particle formation since oxidized compounds condense easier due to their lower vapor pressure. So far two oxidation processes have been identified as relevant for new particle formation: the oxidation of sulfur dioxide to sulfuric acid and oxidation of volatile organic compounds to highly oxygenated compounds. The most significant atmospheric oxidants have previously thought to be ozone, hydroxyl radical and nitrate radical. Recently the importance of stabilized Criegee intermediates in atmospheric oxidation has been brought into discussion. In this thesis, we used Chemical Ionization Atmospheric Pressure interface Time of Flight mass spectrometer together with different particle measurements in order to widen the understanding of the first steps of new particle formation. We also developed new mass spectrometric measurement techniques to fill the gaps in our current methods. We developed an indirect method to measure non- OH oxidants of sulfur dioxide to better understand the role of stabilized Criegee intermediates and other non-OH oxidants of sulfur dioxide in sulfuric acid formation. We also developed a new technique to determine concentration of ambient dimethylamine at sub-pptV-level. We used both of these new techniques to measure the ambient concentrations in Boreal forest, at SMEAR II station (Station for Measuring Ecosystem-Atmosphere Relations II, Hyytiälä, Finland). Furthermore, we measured new particle formation in different environments and in a chamber study and tried to identify the condensing vapors. We studied the ozonolysis of α-pinene, the most abundant monoterpene in the atmosphere, in controlled chamber measurements in order to be able to follow the formation of highly oxygenated organics and oxidation of sulfur dioxide purely by stabilized Criegee intermediates and compare the results with kinetic model results. We studied the new particle formation near an oil refinery and found that significantly large fraction of the growth during the new particle formation events was due to sulfuric acid condensation. In our studies at the Atlantic coast, we identified the molecular steps involved in new particle formation at iodine-rich environment and could follow the growth of molecular clusters by subsequent addition of iodic acid molecules. We also did field measurements in Arctic and Antarctic sites and showed that the occurrence of high iodic acid concentration is not limited only to coastal areas with macro algae beds. Keywords: mass spectrometry, atmospheric aerosols, low-volatility vapors, ozonolysis, new particle formation
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(Helsingin yliopisto, 2019)Computationally hard optimization problems are commonplace not only in theory but also in practice in many real-world domains. Even determining whether a solution exists can be NP-complete or harder. Good, ideally globally optimal, solutions to instances of such problems can save money, time, or other resources. We focus on a particular generic framework for solving constraint optimization problems, the so-called implicit hitting set (IHS) approach. The approach is based on a theory of duality between solutions and sets of mutually conflicting constraints underlying a problem. Recent years have seen a number of new instantiations of the IHS approach for various problems and constraint languages. As the main contributions, we present novel instantiations of this generic algorithmic approach to four different NP-hard problem domains: maximum satisfiability (MaxSAT), learning optimal causal graphs, propositional abduction, and answer set programming (ASP). For MaxSAT, we build on an existing IHS algorithm with a fresh implementation and new methods for integrating preprocessing. We study a specific application of this IHS approach to MaxSAT for learning optimal causal graphs. In particular we develop a number of domain-specific search techniques to specialize the IHS algorithm for the problem. Furthermore, we consider two optimization settings where the corresponding decision problem is beyond NP, in these cases Σᴾ₂-hard. In the first, we compute optimal explanations for propositional abduction problems. In the second, we solve optimization problems expressed as answer set programs with disjunctive rules. For each problem domain, we empirically evaluate the resulting algorithm and contribute an open-source implementation. These implementations improve or complement the state of the art in their respective domains.
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(Helsingin yliopisto, 2019)This thesis consists of four research papers and an introduction covering the most important concepts appearing in the papers. The papers deal with applications of gauge/gravity dualities in the study of various physical quantities and systems. Gauge/gravity dualities are equivalences between certain quantum field theories and classical theories of gravity. These dualities can be used as computational tools in a wide range of applications across different fields of physics, and as such they have garnered much attention in the last two decades. The great promise of these new tools is the ability to tackle difficult problems in strongly interacting quantum field theories by translating them to problems in classical gravity, where progress is much easier to make. Quantum information theory studies the information contained in quantum systems. Entanglement is the fundamental property of quantum mechanics that sets it apart from classical theories of physics. Entanglement is commonly quantified by entanglement entropy, a quantity which is difficult to compute in interacting quantum field theories. Gauge/gravity dualities provide a practical way for computing the entanglement entropy via the Ryu-Takayanagi formula. The primary focus of this thesis is to use this formula for computing various entanglement measures in strongly interacting quantum field theories via their gravity duals. The purpose of this thesis is to introduce quantum information theory concepts that have been important in our research. When applicable, quantities of interest are first introduced in the classical setting in order to build intuition about their behaviour. Quantum properties of entanglement measures are discussed in detail, along with their holographic counterparts, and remarks are made concerning their applications.
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(Helsingin yliopisto, 2020)Nanostructures are structures where at least one dimension is in nanoscale which ranges typically from 1 to 100 nm. 1D nanostructure is an object where two dimensions are in the nanometer scale and one dimension in a larger scale, for example carbon nanotubes and electrospun fibers. Due to a very small size, nanostructured materials have different properties than what they have in bulk form, for example chemical reactivity is increased when the size comes smaller. Electrospinning is a very simple but versatile and scalable method for preparing micro- and nanosized fibers. In an electrospinning process an electrical charge is used to spin very fine fibers from a polymer solution or melt. By changing electrospinning parameters, for example voltage and spinneret-collector distance, fibers of different diameters can be obtained. With different electrospinning setups it is also possible to prepare hollow fibers, and even macroscopic objects with fiber walls can be obtained. This work was concentrated on A) constructing different electrospinning setups and verifying their operation by electrospinning various materials, and B) preparing 1D nanostructures like inorganic nanofibers directly by electrospinning and nanotubes by combining electrospinning and atomic layer deposition, ALD. This is so called Tubes by Fiber Template (TUFT) –process. The electrospinning setup was constructed successfully, and its operation was verified. Several materials were electrospun. Polymers (PVP, PVA, PVAc, PEO, PMMA and PVB, Chitosan) were electrospun directly from polymer/solvent solution, and ceramic materials like TiO2, BaTiO3, SnO2, CuO, IrO2, ZnO, Fe2O3, NiFe2O4, CoFe2O4, SiO2 and Al2O3 were electrospun from polymer solutions containing the corresponding metal precursor(s). In the case of the ceramic fibers, the electrospinning was followed by calcination to remove the polymer part of the fibers. Metallic fibers were obtained by a reduction treatment of the corresponding oxides, for example Ir fibers were prepared by reducing IrO2 fibers. Combination of electrospinning and ALD was used for TUFT processing of ceramic nanotubes. In the TUFT process, electrospun template fibers were coated with the desired material (Al2O3, TiO2, IrO2, Ir, PtOx and Pt) and after coating the template fibers were removed by calcination. The inner diameter of the resulting tubes was determined by the template fiber and the tube wall thickness by the thickness of the ALD deposited film. Promising results were obtained in searching for new applications for electrospun fibers. For the first time, by combining electrospinning and ALD, the TUFT process was used to prepare reusable magnetic photocatalyst fibers. These fibers have a magnetic core fiber and a photocatalytic shell around it. After a photocatalyst purification was completed, the fibers could be collected from the solution by a strong magnet and reused in cleaning the next solution. In this study, the most commercially and environmentally valuable application invented was to use electrospun ion selective sodium titanate nanofibers for purification of radioactive wastewater. These fibers were found to be more efficient than commercial granular products, and they need much less space in final disposal.
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(Helsingin yliopisto, 2019)A search for a pseudoscalar Higgs boson A is performed, focusing on its decay into a standard model-like Higgs boson h and a Z boson. Decays of the h boson into a pair of tau leptons are considered along with Z boson decays into a pair of light leptons (electrons or muons). A data sample of proton-proton collisions collected by the CMS experiment at the LHC at √s= 13 TeV is used, corresponding to an integrated luminosity of 35 inverse femtobarns. The search uses the reconstructed mass distribution of the A boson as the discriminating variable. This analysis is the first of its kind to utilise the svFit algorithm while exploiting the possibility to apply a mass constraint of 125 GeV in the h→ττ four-vector reconstruction. The resolution of the reconstructed mass of the A boson is improved compared to the mass resolution obtained in previous analyses targeting the same final state. No excess above the standard model expectation is observed in data. Model-independent as well as model-dependent upper limits in the mA – tanβ plane for two minimal supersymmetric standard model benchmark scenarios are set at 95% confidence level. The model-independent upper limit on the product of the gluon fusion production cross section and the branching fraction for the A→Zh→llττ decay ranges from 27 fb at 220 GeV to 5 fb at 400 GeV. The observed model-dependent limits on the process σ(gg→A+bbA)B(A→Zh→llττ) in case of the hMSSM (low-tb-high) scenario exclude tanβ values from 1.6 (1.8) at mA= 220 GeV to 3.7 (3.8) at mA = 300 GeV, respectively.
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(Helsingin yliopisto, 2019)Each nuclear energy country has their strategy for handling the spent nuclear fuel: direct disposal, recycling or a combination of both. The advances in nuclear fuel partitioning enhance the safety of both of these approaches. The spent fuel contains fissionable material that could be used in modern and future reactors. The re-use, however, requires a separation of the fissionable material from the neutron poisoners that are also present in the spent fuel. Time-proven separation technologies exist for the recovery of uranium and plutonium, but for the trivalent actinides americium and curium, such technologies are still young. The majority of current separation technologies in nuclear fuel partitioning, such as the solvent extraction, are based on the recovery of target nuclides from liquids by organic extractants. Their application can be limited by the high radiation doses during the separation process. Ion exchange with inorganic materials offers a robust supportive role in the separation challenges for radionuclides. The materials are stable in high temperatures, high acidity and under extreme radiation, and are ion selective. By altering their structure, the desired ion selectivity can be further improved. Throughout the dissertation, a solid inorganic ion exchanger, α-zirconium phosphate, was investigated, developed and applied in column operation with one goal in mind: the application of ion exchange in the column separation of trivalent actinides from lanthanides. The α-zirconium phosphate proved suitable for americium-europium separation. The material was modified and the connections between synthesis, properties and ion selectivity between various products were investigated and discussed. Numerous characterization techniques were applied in the investigation of material properties. Radioactive materials and radiochemical methods were used in the investigation of ion selectivities for europium and americium. The materials were up to 400 times more selective towards europium over americium. For an application in nuclear fuel management, this order of preference is preferred, as americium can be readily recovered from the material for its fissioning, while europium is retained in the solid, a suitable matrix for nuclear waste disposal. In column operation, highly pure americium, up to 99.999 mol-%, could be separated from europium with high recovery in low pH. The effects of multiple factors on the separation, such as europium concentration, salt concentration and pH were investigated throughout the dissertation. Ion exchange can excel in such specific and demanding jobs, as the structures of the materials can be engineered to enhance the desirable separation properties. Whereas the well-established solvent extraction based separation processes are already applied in many areas of nuclear fuel management, I believe that ion exchange can have a supportive role in their shortcomings.
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(Helsingin yliopisto, 2019)Optical snow monitoring methods have tendency to underestimate snow cover beneath the evergreen forest canopy due to the masking effect of trees. There is need to develop method for providing more reliable snow products and enhance their use e.g. in hydrological and climatological models. The main objective of this thesis is to provide information to improve the accuracy of snow mapping by algorithm development and its regional parameterization. This thesis exploits reflectance data derived from ground-based, mast-borne, airborne and space-borne sensors. Each datatype with different ground resolutions has specific strengths and weaknesses. Together this dataset provides valuable information to advance knowledge of reflectance properties of snow-covered forests and supports the interpretation of satellite-borne reflectance observations. Improvement of satellite-based snow cover mapping is essential because it is the only way to monitor snow cover spatially, temporally and economically effectively. To obtain information about certain geophysical variable using satellite data, a model for interpreting the satellite signal must be developed. The feasibility of satellite-borne observations in describing geophysical variables depends on the reliability of the model used. Here simple reflectance models based on the zeroth order radiative transfer equation and lineal mixing models are investigated. They are found to reliably describe the observed surface reflectances from snow-covered terrain, both in forests and in open areas. Additionally, to improve methods for seasonal snow cover monitoring in forests, the high spatial resolution observations are required to describe spectral properties and their temporal behaviour of different targets inside the investigated scene. It is also important to combine these target-specific reflectances with the in situ data to describe the characteristics of the target area. In this thesis the datasets complement each other so that while mast-borne data provides information on the temporal behaviour of the scene reflectance of the specific location where measurement conditions are well known, the airborne data provides information during a very short time (~1 hour) on the spatial variation of scene reflectance from the areas where land cover, forest characteristics and snow conditions are well defined. The results demonstrate the notable effect of forest on observed reflectance in both the temporal (changes in illumination geometry) and on the spatial (changes in forest structure) scale. The presence of tree canopy also weakens the capability of the Normalized Difference Snow Index (NDSI) to detect snow-covered areas. Additionally, the effect of melting snow cover on reflectances and NDSI is significant in all land covers producing high variation inside individual land cover types too.
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(Helsingin yliopisto, 2019)Data collected from the learning process of students can be used to improve education in many ways. Such data can benefit multiple stakeholders of a programming course. Data about students’ performance can be used to detect struggling students who can then be given additional support benefiting the student. If data shows that students have to read a certain section of the material multiple times, it could indicate either that that section is possibly more important than others, or it might be unclear and could be improved, which benefits the teacher. Data collected through surveys can yield insight into students’ motivations for studying. Ultimately, data can increase our knowledge of how students learn benefiting educational researchers. Different kinds of data can be collected in online courses. In programming courses, data is typically collected from tools that are specifically made for learning programming. These tools include Integrated Development Environments (IDEs), program visualization tools, automatic assessment tools, and online learning materials. The granularity of data collected from such tools varies. Fine-grained data is data that is collected frequently, while coarse-grained data is collected less frequently. In a programming course, coarse-grained data might include students’ submissions to exercises, whereas fine-grained data might include students’ actions within the IDE such as editing source code. An example of extremely fine-grained data is keystroke data, which typically includes each key pressed while typing together with a timestamp that tells when exactly the key was pressed. In this work, we study what benefits there are to collecting keystroke data in programming courses. We explore different aspects of keystroke data that could be useful for research and to students and educators. This is studied by conducting multiple quantitative experiments where information about students’ learning or the students themselves is inferred from keystroke data. Most of the experiments are based on examining how fast students are at typing specific character pairs. The results of this thesis show that students can be uniquely identified solely based on their typing whilst they are programming. This information could be used in online courses to verify that the same student completes all the assignments. Excessive collaboration can also be detected automatically based on the processes students take to reach a solution. Additionally, students’ programming experience and future performance in an exam can be inferred from typing, which could be used to detect struggling students. Inferring students’ programming experience is possible even when data is made less accurate so that identifying individuals is no longer feasible.
