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Improving permafrost dynamics in land surface models: insights from dual sensitivity experiments
(2024)
The thawing of permafrost and the subsequent release of greenhouse gases constitute one of the most significant and uncertain positive feedback loops in the context of climate change, making predictions regarding changes in permafrost coverage of paramount importance. To address these critical questions, climate scientists have developed Land Surface Models (LSMs) that encompass a multitude of physical soil processes. This thesis is committed to advancing our understanding and refining precise representations of permafrost dynamics within LSMs, with a specific focus on the accurate modeling of heat fluxes, an essential component for simulating permafrost physics.
The first research question overviews fundamental model prerequisites for the representation of permafrost soils within land surface modeling. It includes a first-of-its-kind comparison between LSMs in CMIP6 to reveal their differences and shortcomings in key permafrost physics parameters. Overall, each of these LSMs represents a unique approach to simulating soil processes and their interactions with the climate system. Choosing the most appropriate model for a particular application depends on factors such as the spatial and temporal scale of the simulation, the specific research question, and available computational resources.
The second research question evaluates the performance of the state-of-the-art Community Land Model (CLM5) in simulating Arctic permafrost regions. Our approach overcomes traditional evaluation limitations by individually addressing depth, seasonality, and regional variations, providing a comprehensive assessment of permafrost and soil temperature dynamics. I compare CLM5's results with three extensive datasets: (1) soil temperatures from 295 borehole stations, (2) active layer thickness (ALT) data from the Circumpolar Active Layer Monitoring Network (CALM), and (3) soil temperatures, ALT, and permafrost extent from the ESA Climate Change Initiative (ESA-CCI). The results show that CLM5 aligns well with ESA-CCI and CALM for permafrost extent and ALT but reveals a significant global cold temperature bias, notably over Siberia. These results echo a persistent challenge identified in numerous studies: the existence of a systematic 'cold bias' in soil temperature over permafrost regions. To address this challenge, the following research questions propose dual sensitivity experiments.
The third research question represents the first study to apply a Plant Functional Type (PFT)-based approach to derive soil texture and soil organic matter (SOM), departing from the conventional use of coarse-resolution global data in LSMs. This novel method results in a more uniform distribution of soil organic matter density (OMD) across the domain, characterized by reduced OMD values in most regions. However, changes in soil texture exhibit a more intricate spatial pattern. Comparing the results to observations reveals a significant reduction in the cold bias observed in the control run. This method shows noticeable improvements in permafrost extent, but at the cost of an overestimation in ALT. These findings emphasize the model's high sensitivity to variations in soil texture and SOM content, highlighting the crucial role of soil composition in governing heat transfer processes and shaping the seasonal variation of soil temperatures in permafrost regions.
Expanding upon a site experiment conducted in Trail Valley Creek by \citet{dutch_impact_2022}, the fourth research question extends the application of the snow scheme proposed by \citet{sturm_thermal_1997} to cover the entire Arctic domain. By employing a snow scheme better suited to the snow density profile observed over permafrost regions, this thesis seeks to assess its influence on simulated soil temperatures. Comparing this method to observational datasets reveals a significant reduction in the cold bias that was present in the control run. In most regions, the Sturm run exhibits a substantial decrease in the cold bias. However, there is a distinctive overshoot with a warm bias observed in mountainous areas. The Sturm experiment effectively addressed the overestimation of permafrost extent in the control run, albeit resulting in a substantial reduction in permafrost extent over mountainous areas. ALT results remain relatively consistent compared to the control run. These outcomes align with our initial hypothesis, which anticipated that the reduced snow insulation in the Sturm run would lead to higher winter soil temperatures and a more accurate representation of permafrost physics.
In summary, this thesis demonstrates significant advancements in understanding permafrost dynamics and its integration into LSMs. It has meticulously unraveled the intricacies involved in the interplay between heat transfer, soil properties, and snow dynamics in permafrost regions. These insights offer novel perspectives on model representation and performance.
A comprehensive study on seismic hazard and earthquake triggering is crucial for effective mitigation of earthquake risks. The destructive nature of earthquakes motivates researchers to work on forecasting despite the apparent randomness of the earthquake occurrences. Understanding their underlying mechanisms and patterns is vital, given their potential for widespread devastation and loss of life. This thesis combines methodologies, including Coulomb stress calculations and aftershock analysis, to shed light on earthquake complexities, ultimately enhancing seismic hazard assessment.
The Coulomb failure stress (CFS) criterion is widely used to predict the spatial distributions of aftershocks following large earthquakes. However, uncertainties associated with CFS calculations arise from non-unique slip inversions and unknown fault networks, particularly due to the choice of the assumed aftershocks (receiver) mechanisms. Recent studies have proposed alternative stress quantities and deep neural network approaches as superior to CFS with predefined receiver mechanisms. To challenge these propositions, I utilized 289 slip inversions from the SRCMOD database to calculate more realistic CFS values for a layered-half space and variable receiver mechanisms. The analysis also investigates the impact of magnitude cutoff, grid size variation, and aftershock duration on the ranking of stress metrics using receiver operating characteristic (ROC) analysis. Results reveal the performance of stress metrics significantly improves after accounting for receiver variability and for larger aftershocks and shorter time periods, without altering the relative ranking of the different stress metrics.
To corroborate Coulomb stress calculations with the findings of earthquake source studies in more detail, I studied the source properties of the 2005 Kashmir earthquake and its aftershocks, aiming to unravel the seismotectonics of the NW Himalayan syntaxis. I simultaneously relocated the mainshock and its largest aftershocks using phase data, followed by a comprehensive analysis of Coulomb stress changes on the aftershock planes. By computing the Coulomb failure stress changes on the aftershock faults, I found that all large aftershocks lie in regions of positive stress change, indicating triggering by either co-seismic or post-seismic slip on the mainshock fault.
Finally, I investigated the relationship between mainshock-induced stress changes and associated seismicity parameters, in particular those of the frequency-magnitude (Gutenberg-Richter) distribution and the temporal aftershock decay (Omori-Utsu law). For that purpose, I used my global data set of 127 mainshock-aftershock sequences with the calculated Coulomb Stress (ΔCFS) and the alternative receiver-independent stress metrics in the vicinity of the mainshocks and analyzed the aftershocks properties depend on the stress values. Surprisingly, the results show a clear positive correlation between the Gutenberg-Richter b-value and induced stress, contrary to expectations from laboratory experiments. This observation highlights the significance of structural heterogeneity and strength variations in seismicity patterns. Furthermore, the study demonstrates that aftershock productivity increases nonlinearly with stress, while the Omori-Utsu parameters c and p systematically decrease with increasing stress changes. These partly unexpected findings have significant implications for future estimations of aftershock hazard.
The findings in this thesis provides valuable insights into earthquake triggering mechanisms by examining the relationship between stress changes and aftershock occurrence. The results contribute to improved understanding of earthquake behavior and can aid in the development of more accurate probabilistic-seismic hazard forecasts and risk reduction strategies.
Column-oriented database systems can efficiently process transactional and analytical queries on a single node. However, increasing or peak analytical loads can quickly saturate single-node database systems. Then, a common scale-out option is using a database cluster with a single primary node for transaction processing and read-only replicas. Using (the naive) full replication, queries are distributed among nodes independently of the accessed data. This approach is relatively expensive because all nodes must store all data and apply all data modifications caused by inserts, deletes, or updates.
In contrast to full replication, partial replication is a more cost-efficient implementation: Instead of duplicating all data to all replica nodes, partial replicas store only a subset of the data while being able to process a large workload share. Besides lower storage costs, partial replicas enable (i) better scaling because replicas must potentially synchronize only subsets of the data modifications and thus have more capacity for read-only queries and (ii) better elasticity because replicas have to load less data and can be set up faster. However, splitting the overall workload evenly among the replica nodes while optimizing the data allocation is a challenging assignment problem.
The calculation of optimized data allocations in a partially replicated database cluster can be modeled using integer linear programming (ILP). ILP is a common approach for solving assignment problems, also in the context of database systems. Because ILP is not scalable, existing approaches (also for calculating partial allocations) often fall back to simple (e.g., greedy) heuristics for larger problem instances. Simple heuristics may work well but can lose optimization potential.
In this thesis, we present optimal and ILP-based heuristic programming models for calculating data fragment allocations for partially replicated database clusters. Using ILP, we are flexible to extend our models to (i) consider data modifications and reallocations and (ii) increase the robustness of allocations to compensate for node failures and workload uncertainty. We evaluate our approaches for TPC-H, TPC-DS, and a real-world accounting workload and compare the results to state-of-the-art allocation approaches. Our evaluations show significant improvements for varied allocation’s properties: Compared to existing approaches, we can, for example, (i) almost halve the amount of allocated data, (ii) improve the throughput in case of node failures and workload uncertainty while using even less memory, (iii) halve the costs of data modifications, and (iv) reallocate less than 90% of data when adding a node to the cluster. Importantly, we can calculate the corresponding ILP-based heuristic solutions within a few seconds. Finally, we demonstrate that the ideas of our ILP-based heuristics are also applicable to the index selection problem.
Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial.
In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic.
To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.
Moss-microbe associations are often characterised by syntrophic interactions between the microorganisms and their hosts, but the structure of the microbial consortia and their role in peatland development remain unknown.
In order to study microbial communities of dominant peatland mosses, Sphagnum and brown mosses, and the respective environmental drivers, four study sites representing different successional stages of natural northern peatlands were chosen on a large geographical scale: two brown moss-dominated, circumneutral peatlands from the Arctic and two Sphagnum-dominated, acidic peat bogs from subarctic and temperate zones.
The family Acetobacteraceae represented the dominant bacterial taxon of Sphagnum mosses from various geographical origins and displayed an integral part of the moss core community. This core community was shared among all investigated bryophytes and consisted of few but highly abundant prokaryotes, of which many appear as endophytes of Sphagnum mosses. Moreover, brown mosses and Sphagnum mosses represent habitats for archaea which were not studied in association with peatland mosses so far. Euryarchaeota that are capable of methane production (methanogens) displayed the majority of the moss-associated archaeal communities. Moss-associated methanogenesis was detected for the first time, but it was mostly negligible under laboratory conditions. Contrarily, substantial moss-associated methane oxidation was measured on both, brown mosses and Sphagnum mosses, supporting that methanotrophic bacteria as part of the moss microbiome may contribute to the reduction of methane emissions from pristine and rewetted peatlands of the northern hemisphere.
Among the investigated abiotic and biotic environmental parameters, the peatland type and the host moss taxon were identified to have a major impact on the structure of moss-associated bacterial communities, contrarily to archaeal communities whose structures were similar among the investigated bryophytes. For the first time it was shown that different bog development stages harbour distinct bacterial communities, while at the same time a small core community is shared among all investigated bryophytes independent of geography and peatland type.
The present thesis displays the first large-scale, systematic assessment of bacterial and archaeal communities associated both with brown mosses and Sphagnum mosses. It suggests that some host-specific moss taxa have the potential to play a key role in host moss establishment and peatland development.
This dissertation examines the integration of incongruent visual-scene and morphological-case information (“cues”) in building thematic-role representations of spoken relative clauses in German.
Addressing the mutual influence of visual and linguistic processing, the Coordinated Interplay Account (CIA) describes a mechanism in two steps supporting visuo-linguistic integration (Knoeferle & Crocker, 2006, Cog Sci). However, the outcomes and dynamics of integrating incongruent thematic-role representations from distinct sources have been investigated scarcely. Further, there is evidence that both second-language (L2) and older speakers may rely on non-syntactic cues relatively more than first-language (L1)/young speakers. Yet, the role of visual information for thematic-role comprehension has not been measured in L2 speakers, and only limitedly across the adult lifespan.
Thematically unambiguous canonically ordered (subject-extracted) and noncanonically ordered (object-extracted) spoken relative clauses in German (see 1a-b) were presented in isolation and alongside visual scenes conveying either the same (congruent) or the opposite (incongruent) thematic relations as the sentence did.
1 a Das ist der Koch, der die Braut verfolgt.
This is the.NOM cook who.NOM the.ACC bride follows
This is the cook who is following the bride.
b Das ist der Koch, den die Braut verfolgt.
This is the.NOM cook whom.ACC the.NOM bride follows
This is the cook whom the bride is following.
The relative contribution of each cue to thematic-role representations was assessed with agent identification. Accuracy and latency data were collected post-sentence from a sample of L1 and L2 speakers (Zona & Felser, 2023), and from a sample of L1 speakers from across the adult lifespan (Zona & Reifegerste, under review). In addition, the moment-by-moment dynamics of thematic-role assignment were investigated with mouse tracking in a young L1 sample (Zona, under review).
The following questions were addressed: (1) How do visual scenes influence thematic-role representations of canonical and noncanonical sentences? (2) How does reliance on visual-scene, case, and word-order cues vary in L1 and L2 speakers? (3) How does reliance on visual-scene, case, and word-order cues change across the lifespan?
The results showed reliable effects of incongruence of visually and linguistically conveyed thematic relations on thematic-role representations. Incongruent (vs. congruent) scenes yielded slower and less accurate responses to agent-identification probes presented post-sentence. The recently inspected agent was considered as the most likely agent ~300ms after trial onset, and the convergence of visual scenes and word order enabled comprehenders to assign thematic roles predictively.
L2 (vs. L1) participants relied more on word order overall. In response to noncanonical clauses presented with incongruent visual scenes, sensitivity to case predicted the size of incongruence effects better than L1-L2 grouping. These results suggest that the individual’s ability to exploit specific cues might predict their weighting.
Sensitivity to case was stable throughout the lifespan, while visual effects increased with increasing age and were modulated by individual interference-inhibition levels. Thus, age-related changes in comprehension may stem from stronger reliance on visually (vs. linguistically) conveyed meaning.
These patterns represent evidence for a recent-role preference – i.e., a tendency to re-assign visually conveyed thematic roles to the same referents in temporally coordinated utterances. The findings (i) extend the generalizability of CIA predictions across stimuli, tasks, populations, and measures of interest, (ii) contribute to specifying the outcomes and mechanisms of detecting and indexing incongruent representations within the CIA, and (iii) speak to current efforts to understand the sources of variability in sentence comprehension.
Diglossic translanguaging
(2024)
This book examines how German-speaking Jews living in Berlin make sense and make use of their multilingual repertoire. With a focus on lexical variation, the book demonstrates how speakers integrate Yiddish and Hebrew elements into German for indexing belonging and for positioning themselves within the Jewish community. Linguistic choices are shaped by language ideologies (e.g., authenticity, prescriptivism, nostalgia). Speakers translanguage when using their multilingual repertoire, but do so in a diglossic way, using elements from different languages for specific domains
Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties.
Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult – if not impossible – to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates (‘higher export in warmer years’) that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes.
Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine Ötztal valley in Tyrol, Austria, over decadal timescales in the past and future – i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest.
The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper Ötztal, Vent, Sölden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 % of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors.
The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.
The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed – unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology.
This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves – especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.
Global warming, driven primarily by the excessive emission of greenhouse gases such as carbon dioxide into the atmosphere, has led to severe and detrimental environmental impacts. Rising global temperatures have triggered a cascade of adverse effects, including melting glaciers and polar ice caps, more frequent and intense heat waves disrupted weather patterns, and the acidification of oceans. These changes adversely affect ecosystems, biodiversity, and human societies, threatening food security, water availability, and livelihoods. One promising solution to mitigate the harmful effects of global warming is the widespread adoption of solar cells, also known as photovoltaic cells. Solar cells harness sunlight to generate electricity without emitting greenhouse gases or other pollutants. By replacing fossil fuel-based energy sources, solar cells can significantly reduce CO2 emissions, a significant contributor to global warming. This transition to clean, renewable energy can help curb the increasing concentration of greenhouse gases in the atmosphere, thereby slowing down the rate of global temperature rise.
Solar energy’s positive impact extends beyond emission reduction. As solar panels become more efficient and affordable, they empower individuals, communities, and even entire nations to generate electricity and become less dependent on fossil fuels. This decentralized energy generation can enhance resilience in the face of climate-related challenges. Moreover, implementing solar cells creates green jobs and stimulates technological innovation, further promoting sustainable economic growth. As solar technology advances, its integration with energy storage systems and smart grids can ensure a stable and reliable energy supply, reducing the need for backup fossil fuel power plants that exacerbate environmental degradation.
The market-dominant solar cell technology is silicon-based, highly matured technology with a highly systematic production procedure. However, it suffers from several drawbacks, such as: 1) Cost: still relatively high due to high energy consumption due to the need to melt and purify silicon, and the use of silver as an electrode, which hinders their widespread availability, especially in low-income countries. 2) Efficiency: theoretically, it should deliver around 29%; however, the efficiency of most of the commercially available silicon-based solar cells ranges from 18 – 22%. 3) Temperature sensitivity: The efficiency decreases with the increase in the temperature, affecting their output. 4) Resource constraints: silicon as a raw material is unavailable in all countries, creating supply chain challenges.
Perovskite solar cells arose in 2011 and matured very rapidly in the last decade as a highly efficient and versatile solar cell technology. With an efficiency of 26%, high absorption coefficients, solution processability, and tunable band gap, it attracted the attention of the solar cells community. It represented a hope for cheap, efficient, and easily processable next-generation solar cells. However, lead toxicity might be the block stone hindering perovskite solar cells’ market reach. Lead is a heavy and bioavailable element that makes perovskite solar cells environmentally unfriendly technology. As a result, scientists try to replace lead with a more environmentally friendly element. Among several possible alternatives, tin was the most suitable element due to its electronic and atomic structure similarity to lead.
Tin perovskites were developed to alleviate the challenge of lead toxicity. Theoretically, it shows very high absorption coefficients, an optimum band gap of 1.35 eV for FASnI3, and a very high short circuit current, which nominates it to deliver the highest possible efficiency of a single junction solar cell, which is around 30.1% according to Schockly-Quisser limit. However, tin perovskites’ efficiency still lags below 15% and is irreproducible, especially from lab to lab. This humble performance could be attributed to three reasons: 1) Tin (II) oxidation to tin (IV), which would happen due to oxygen, water, or even by the effect of the solvent, as was discovered recently. 2) fast crystallization dynamics, which occurs due to the lateral exposure of the P-orbitals of the tin atom, which enhances its reactivity and increases the crystallization pace. 3) Energy band misalignment: The energy bands at the interfaces between the perovskite absorber material and the charge selective layers are not aligned, leading to high interfacial charge recombination, which devastates the photovoltaic performance. To solve these issues, we implemented several techniques and approaches that enhanced the efficiency of tin halide perovskites, providing new chemically safe solvents and antisolvents. In addition, we studied the energy band alignment between the charge transport layers and the tin perovskite absorber.
Recent research has shown that the principal source of tin oxidation is the solvent known as dimethylsulfoxide, which also happens to be one of the most effective solvents for processing perovskite. The search for a stable solvent might prove to be the factor that makes all the difference in the stability of tin-based perovskites. We started with a database of over 2,000 solvents and narrowed it down to a series of 12 new solvents that are suitable for processing FASnI3 experimentally. This was accomplished by looking into 1) the solubility of the precursor chemicals FAI and SnI2, 2) the thermal stability of the precursor solution, and 3) the potential to form perovskite. Finally, we show that it is possible to manufacture solar cells using a novel solvent system that outperforms those produced using DMSO. The results of our research give some suggestions that may be used in the search for novel solvents or mixes of solvents that can be used to manufacture stable tin-based perovskites.
Due to the quick crystallization of tin, it is more difficult to deposit tin-based perovskite films from a solution than manufacturing lead-based perovskite films since lead perovskite is more often utilized. The most efficient way to get high efficiencies is to deposit perovskite from dimethyl sulfoxide (DMSO), which slows down the quick construction of the tin-iodine network that is responsible for perovskite synthesis. This is the most successful approach for achieving high efficiencies. Dimethyl sulfoxide, which is used in the processing, is responsible for the oxidation of tin, which is a disadvantage of this method. This research presents a potentially fruitful alternative in which 4-(tert-butyl) pyridine can substitute dimethyl sulfoxide in the process of regulating crystallization without causing tin oxidation to take place. Perovskite films that have been formed from pyridine have been shown to have a much-reduced defect density. This has resulted in increased charge mobility and better photovoltaic performance, making pyridine a desirable alternative for use in the deposition of tin perovskite films.
The precise control of perovskite precursor crystallization inside a thin film is of utmost importance for optimizing the efficiency and manufacturing of solar cells. The deposition process of tin-based perovskite films from a solution presents difficulties due to the quick crystallization of tin compared to the more often employed lead perovskite. The optimal approach for attaining elevated efficiencies entails using dimethyl sulfoxide (DMSO) as a medium for depositing perovskite. This choice of solvent impedes the tin-iodine network’s fast aggregation, which plays a crucial role in the production of perovskite. Nevertheless, this methodology is limited since the utilization of dimethyl sulfoxide leads to the oxidation of tin throughout the processing stage. In this thesis, we present a potentially advantageous alternative approach wherein 4-(tert-butyl) pyridine is proposed as a substitute for dimethyl sulfoxide in regulating crystallization processes while avoiding the undesired consequence of tin oxidation. Films of perovskite formed using pyridine as a solvent have a notably reduced density of defects, resulting in higher mobility of charges and improved performance in solar applications. Consequently, the utilization of pyridine for the deposition of tin perovskite films is considered advantageous.
Tin perovskites are suffering from an apparent energy band misalignment. However, the band diagrams published in the current body of research display contradictions, resulting in a dearth of unanimity. Moreover, comprehensive information about the dynamics connected with charge extraction is lacking. This thesis aims to ascertain the energy band locations of tin perovskites by employing the kelvin probe and Photoelectron yield spectroscopy methods. This thesis aims to construct a precise band diagram for the often-utilized device stack. Moreover, a comprehensive analysis is performed to assess the energy deficits inherent in the current energetic structure of tin halide perovskites. In addition, we investigate the influence of BCP on the improvement of electron extraction in C60/BCP systems, with a specific emphasis on the energy factors involved. Furthermore, transient surface photovoltage was utilized to investigate the charge extraction kinetics of frequently studied charge transport layers, such as NiOx and PEDOT as hole transport layers and C60, ICBA, and PCBM as electron transport layers. The Hall effect, KP, and TRPL approaches accurately ascertain the p-doping concentration in FASnI3. The results consistently demonstrated a value of 1.5 * 1017 cm-3. Our research findings highlight the imperative nature of autonomously constructing the charge extraction layers for tin halide perovskites, apart from those used for lead perovskites.
The crystallization of perovskite precursors relies mainly on the utilization of two solvents. The first one dissolves the perovskite powder to form the precursor solution, usually called the solvent. The second one precipitates the perovskite precursor, forming the wet film, which is a supersaturated solution of perovskite precursor and in the remains of the solvent and the antisolvent. Later, this wet film crystallizes upon annealing into a full perovskite crystallized film. In our research context, we proposed new solvents to dissolve FASnI3, but when we tried to form a film, most of them did not crystallize. This is attributed to the high coordination strength between the metal halide and the solvent molecules, which is unbreakable by the traditionally used antisolvents such as Toluene and Chlorobenzene. To solve this issue, we introduce a high-throughput antisolvent screening in which we screened around 73 selected antisolvents against 15 solvents that can form a 1M FASnI3 solution. We used for the first time in tin perovskites machine learning algorithm to understand and predict the effect of an antisolvent on the crystallization of a precursor solution in a particular solvent. We relied on film darkness as a primary criterion to judge the efficacy of a solvent-antisolvent pair. We found that the relative polarity between solvent and antisolvent is the primary factor that affects the solvent-antisolvent interaction. Based on our findings, we prepared several high-quality tin perovskite films free from DMSO and achieved an efficiency of 9%, which is the highest DMSO tin perovskite device so far.