Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image

Abstract: Spin-orbit torques (SOT) allow ultrafast, energy-efficient toggling of magnetization state by an in-plane charge current for applications such as magnetic random-access memory (SOT-MRAM). Tailoring the SOT vector comprising of antidamping (T-AD) and fieldlike (T-FL) torques could lead to faster, more reliable, and low-power SOT-MRAM. Here, we establish a method to quantify the longitudinal (T-AD) and transverse (T-FL) components of the SOT vector and its efficiency chi(AD) and chi(FL), respectively, in nanoscale three-terminal SOT magnetic tunnel junctions (SOT-MTJ). Modulation of nucleation or switching field (B-SF) for magnetization reversal by SOT effective fields (B-SOT) leads to the modification of SOT-MTJ hysteresis loop behavior from which chi(AD) and chi(FL) are quantified. Surprisingly, in nanoscale W/CoFeB SOT-MTJ, we find chi(FL) to be (i) twice as large as chi(AD) and (ii) 6 times as large as chi(FL) in micrometer-sized W/CoFeB Hall-bar devices. Our quantification is supported by micromagnetic and macrospin simulations which reproduce experimental SOT-MTJ Stoner-Wohlfarth astroid behavior only for chi(FL) > chi(AD). Additionally, from the threshold current for current-induced magnetization switching with a transverse magnetic field, we show that in SOT-MTJ, T-FL plays a more prominent role in magnetization dynamics than T-AD. Due to SOT-MRAM geometry and nanodimensionality, the potential role of nonlocal spin Hall spin current accumulated adjacent to the SOT-MTJ in the mediation of T-FL and chi(FL) amplification merits to be explored.

Abstract: With the growth of the world population, the agricultural sector is required to meet an increasing demand for nutrients and currently relies on industrially produced fertilizers. Among them, nitrogen-based fertilizers are the most common choice and require N2 to be converted into more reactive molecules in a process called “nitrogen fixation”. This is mainly performed through the Haber-Bosch process, which, is not ideal since it requires large-scale facilities to be economical and is associated with a high energy cost and high CO2 emissions, resulting in an environmental impact that is pushing for the study of greener alternatives. Among these, plasma-based nitrogen fixation into NOx is promising, and gliding arc plasma, specifically, proved to be suitable for nitrogen fixation. This thesis aims to study plasma-based nitrogen fixation focusing on an atmospheric pressure gliding arc plasma on three different levels. On a fundamental level, an approach dealing with laser-based excitation of separate rotational lines was successfully developed. This method can be implemented on atmospheric discharges that produce rather high NOx densities and, thus, can impose essential restrictions for the use of “classical” laser-induced fluorescence methods. The approach is then implemented, providing a discussion on the two-dimensional distributions of both the gas temperature and the NO ground state density. A clear correlation between these quantities is found and the effects of both the gas temperature and the plasma power on NO and NO2 concentrations are discussed, revealing how the NO oxidation is already significant in the plasma afterglow region and how the gas flow rate is a crucial parameter affecting the temperature gradients. >From a technological level, the conventional approach of introducing external resistors to stabilize the arc is challenged by studying both its performance and its stability replacing the external resistor with an inductor. We conclude that similar stabilization results can be obtained while significantly lowering the overall energy cost, which decreased from up to a maximum of 7.9 MJ/mol N to 3 MJ/mol N. Finally, we study whether a small-scale fertilizer production facility based on a gliding arc plasma can be a local competitive alternative. This is done by proposing a comparative model to understand how capital, operative expenditures and transport costs affect the production costs. The model highlights how, with the current best available technology, plasma-based nitrogen fixation, while being an interesting alternative for NOx synthesis, still requires a more efficient use of H2 for direct NH3 production.

Abstract: Transport measurements are readily used to probe different phases in disordered topological insulators (TIs), where determining topological invariants explicitly is challenging. On that note, universal conductance fluctuations (UCF) theory asserts the conductance G for an ensemble has a Gaussian distribution, and that standard deviation 8G depends solely on the symmetries and dimensions of the system. Using a real-space tight -binding Hamiltonian on a system with Anderson disorder, we explore conductance fluctuations in a thin Bi2Se3 film and demonstrate the agreement of their behavior with UCF hypotheses. We further show that magnetic field applied out-of-plane breaks the time -reversal symmetry and transforms the system's Wigner-Dyson class from root symplectic to unitary, increasing 8G by 2. Finally, we reveal that while Bi2Se3 is a strong TI, weak TI and metallic phases can be stabilized in presence of strain and disorder, and detected by monitoring the conductance fluctuations.

Abstract: Oxocarbons, known for over two centuries, have recently revealed a long-awaited facet: two-dimensional crystalline structures. Employing an intelligent global optimization algorithm (IGOA) alongside densityfunctional calculations, we unearthed a quasi -flat oxocarbon (C 6 0 6 ), featuring an oxygen -decorated hole, and a novel 3D-borocarbon. Comparative analyses with recently synthesized isostructures, such as 2D -porous carbon nitride (C 6 N 6 ) and 2D -porous boroxine (B 6 0 6 ), highlight the unique attributes of these compounds. All structures share a common stoichiometry of X 6 Y 6 (which we call COF-66), where X = B, C, and Y = B, N, O (with X not equal Y), exhibiting a 2D -crystalline structure, except for borocarbon C 6 B 6 , which forms a 3D crystal. In our comprehensive study, we conducted a detailed exploration of the electronic structure of X 6 Y 6 compounds, scrutinizing their thermodynamic properties and systematically evaluating phonon stability criteria. With expansive surface areas, diverse pore sizes, biocompatibility, pi-conjugation, and distinctive photoelectric properties, these structures, belonging to the covalent organic framework (COF) family, present enticing prospects for fundamental research and hold potential for biosensing applications.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: Background: Currently, nanoliposomal irinotecan (nal-IRI) + 5-fluorouracil/folinic acid (5-FU/ LV) is the only approved second-line treatment for patients suffering from metastatic pancreatic ductal adenocarcinoma (mPDAC). However, also other chemotherapeutic regimens are used in this setting and due to the lack of clear real-world data on the efficacy of the different regimens, there is no consensus on the optimal treatment sequence for mPDAC patients. Objectives: To provide information on the safe and efficacious use of nal-IRI + 5-FU/LV in clinical practice in Belgium, which is needed for healthcare professionals to estimate the risk-benefit ratio of the intervention. Methods: Medical data of adult patients with mPDAC who were treated with nal-IRI + 5-FU/ LV in one of the participating Belgian hospitals were retrospectively collected. Kaplan-Meier analysis was performed to obtain survival curves to estimate the median overall survival (OS) and progression-free survival (PFS). All other results were presented descriptively. Results: A total of 56 patients [median age at diagnosis: 69 years (range 43 years), 57.1% male] were included. Patients received a median of 5 (range 49 cycles) nal-IRI + 5-FU/LV cycles, extended over 10 weeks (range 130.8 weeks). The median start dose for nal-IRI was 70 mg/ m(2) (range 49.24 mg/m(2)) and chemotherapy dose reduction and delay occurred in, respectively, 42.8% and 37.5% of the patients. The median OS was 6.8 months (95% CI: 5.6-8.4 months) with a 6-month survival rate of 57.4% and a 1-year survival rate of 27.8% in the overall study population. The median OS for patients treated with nal-IRI as second-line therapy or as laterline treatment was, respectively, 6.8 months (95% CI: 5.9-7.0 months) and 5.6 months (95% CI: 4.2-no upper limit). In the overall study population, a median PFS of 3.1 months (95% CI: 2.4-4.6 months) and a disease control rate of 48.3%, comprising 30.4% stable disease, 16.1% partial and 1.8% complete response, was observed. The median PFS for patients treated with nal-IRI as second-line therapy was 3.9 months (95% CI: 2.8-4.8 months) while this was 2.4 months (95% CI: 1.9-9.1 months) for those that received nal-IRI in a later-line treatment. In terms of safety, gastrointestinal problems occurred most (64.3% of the patients) and from all reported treatment emergent adverse events, 39.2% were grade 3 or 4. Conclusion: Nal-IRI + 5-FU/LV is a valuable, effective, and safe sequential treatment option following gemcitabine-based therapy in patients with mPDAC.

Abstract: Nutrient circularity is an emerging concept that seeks to address the environmental problems and nutrient losses caused by agriculture and food consumption. The implementation of circular nutrient technologies and practices (CNTPs), that recover, reuse, and recycle nutrients from agricultural and urban waste is an important policy objective. Yet, which policies govern the adoption of CNTPs is not well defined. This study presents the first systematic review of impact evaluations of policy measures that aim to improve nutrient circularity regulating components of the biological cycle of the circular economy, particularly focusing on bioaccessible nutrients. The key CNTPs that were subject to existing impact evaluations were identified. CNTPs were categorized into nutrient circularity themes, with manure management emerging as a prominent focus. The reviewed studies implemented several methods to assess the impacts of policies on various dependent variables, associated with nutrient circularity. Economic simulation models and linear programming were the most prevalent methods for impact evaluation. Policy measures were labeled as either enabling or not-enabling nutrient circularity based on whether they sufficiently promoted nutrient circularity through facilitating the adoption of CNTPs, and controlling for soil, water and air health, preventing its contamination. It is concluded that incentive policies, harmonizing market support, tax incentives, and technological advancements, as well as coherence of local, national and cross-country legislation prove indispensable in steering the economic feasibility and sustainability of CNTPs, offering a promising avenue for progress and a transformative shift towards nutrient circularity.

Abstract: Climate models suggest that the persistence of summer precipitation regimes (PRs) is on the rise, characterized by both longer dry and longer wet durations. These PR changes may alter plant biochemical composition and thereby their economic and ecological characteristics. However, impacts of PR persistence have primarily been studied at the community level, largely ignoring the biochemistry of individual species. Here, we analyzed biochemical components of four grassland species with varying sensitivity to PR persistence (Holcus lanatus, Phleum pratense, Lychnis flos-cuculi, Plantago lanceolata) along a range of increasingly persistent PRs (longer consecutive dry and wet periods) in a mesocosm experiment. The more persistent PRs decreased nonstructural sugars, whereas they increased lignin in all species, possibly reducing plant quality. The most sensitive species Lychnis seemed less capable of altering its biochemical composition in response to altered PRs, which may partly explain its higher sensitivity. The more tolerant species may have a more robust and dynamic biochemical network, which buffers the effects of changes in individual biochemical components on biomass. We conclude that the biochemical composition changes are important determinants for plant performance under increasingly persistent precipitation regimes.

Abstract: Hydrogen (H2) is expected to become a key molecule in the transition towards a society running on renewable energy. It can be used to store excess renewable energy at peak production moments and release this energy at a later stage when renewable energy production is less. However, storing H2 is challenging due to the low density of this gas. As a solution, Liquid Organic Hydrogen Carriers or LOHC molecules have been proposed in the passed to increase volumetric energy density of H2. LOHC are a class of molecules that have storage sites available, to which the H2 gas can be chemically bounded. The LOHC molecule under investigation was dibenzyltoluene (DBT), which is an oil like liquid, that is easy to transport and poses little fire or explosion risks. To release the H2 from the DBT carrier, via a so-called dehydrogenation reaction, efficient mass and heat transfer is required during the process, since a large volume increase is expected from H2 release and the reaction is endothermic, i.e., a self – cooling process that takes place at temperatures around 300 C. The heat has to be supplied specifically to the active sites of catalyst particles that are present inside the reactor and which enable the dehydrogenation to proceed. For heat transfer limited processes fluidized bed reactors are often used, which is a type of reactor where the particle phase is being agitated by the fluid flow. The research proposed in this work, was to explore via computational fluid dynamics (CFD) simulations the possibilities and challenges of using fluidized bed reactors for the dehydrogenation of LOHC. The model selection required for CFD simulations of a three-phase system was investigated in this work, with a main emphasis on the drag model selection. The CFD modelling study was focused on the use of swirling fluidized bed reactors, since it was hypothesised that the swirling effect could also aid in increased removal of the gas phase from the reaction medium to increase the efficiency of the process. Ultimately, it was shown that the main challenges in the design of fluidized bed reactors will be to create uniform particle distribution inside the reactor. A new design for a dehydrogenation reactor is proposed based on the insights gained in this thesis.

Abstract: Environmental circumstances shaping soil microbial communities have been studied extensively. However, due to disparate study designs, it has been difficult to resolve whether a globally consistent set of predictors exists, or context‐dependency prevails. Here, we used a network of 18 grassland sites (11 of those containing regional plant productivity gradients) to examine (i) if similar abiotic or biotic factors predict both large‐scale (across sites) and regional‐scale (within sites) patterns in bacterial and fungal community composition, and (ii) if microbial community composition differs consistently at two levels of regional plant productivity (low vs. high). Our results revealed that bacteria were associated with particular soil properties (such as base saturation) and both bacteria and fungi were associated with plant community composition across sites and within the majority of sites. Moreover, a discernible microbial community signal emerged, clearly distinguishing high and low‐productivity soils across different grasslands independent of their location in the world. Hence, regional productivity differences may be typified by characteristic soil microbial communities across the grassland biome. These results could encourage future research aiming to predict the general effects of global changes on soil microbial community composition in grasslands and to discriminate fertile from infertile systems using generally applicable microbial indicators.

Abstract: New Cinema History broadened film studies by emphasising the complexity of cinema as a multifaceted phenomenon that includes the socio-economic context in which films were made, circulated, shown and received. As part of the digital turn, the discipline adopted computational methods and created quantitative research data to research this socio-economic context at scale. However, not all datasets created in this context adhere to FAIR principles, decreasing their reusability. By reconciling 14 cinema-related datasets, Cinema Belgica facilitates research into the history of Belgian cinema. This research paper documents and critically reflects on the choices made when selecting, modelling and reconciling information for the Cinema Belgica database.

Abstract: Aim: We aimed to assess the impact of road disturbances on the dominant mycorrhizal types in ecosystems at the global level and how this mechanism can potentially lead to lasting plant community changes. Location: Globally distributed mountain regions Time Period: 2007-2018 Taxa studied: Plants (linked to their associated mycorrhizal fungi) Methods: We used a database of coordinated plant community surveys following mountain roads from 894 plots in 11 mountain regions across the globe in combination with an existing database of mycorrhizal-plant associations in order to approximate the relative abundance of mycorrhizal types in natural and disturbed environments. Results: Our findings show that roadside disturbance promotes the cover of plants associated with arbuscular mycorrhizal (AM) fungi. This effect is especially strong in colder mountain environments and in mountain regions where plant communities are dominated by ectomycorrhizal (EcM) or ericoid-mycorrhizal (ErM) associations. Furthermore, non-native plant species, which we confirmed to be mostly AM plants, are more successful in environments dominated by AM associations. Main Conclusions: These biogeographical patterns suggest that changes in mycorrhizal types could be a crucial factor in the worldwide impact of anthropogenic disturbances on mountain ecosystems. Indeed, roadsides foster AM-dominated systems, where AM-fungi might aid AM-associated plant species while potentially reducing the biotic resistance against invasive non-native species, often also associated with AM networks. Restoration efforts in mountain ecosystems will have to contend with changes in the fundamental make-up of EcM- and ErM plant communities induced by roadside disturbance.

Abstract: Land-tenure security is integral to local communities' socioeconomic development. It has been a center of debate in academia and for legislators and advocates to implement reforms to enhance efficient and sustainable development in land management. Yet, knowledge gaps remain in how various contract-farming regimes contribute to land-improvement investment and technical efficiency. This study used a data set of 650 farm households collected through a two-stage stratified sampling to investigate the influence of three contract-farming regimes: long-term, medium-term, and short-term contracts, on the land-improvement investment, productivity, and technical efficiency of contract farmers in Punjab, Pakistan. The study used multivariate probit and ordinary least square regression models to examine the posit relationships. The findings highlight that farmers with long-term land contracts have higher per hectare yield, income and profit than those with medium-term and short-term contracts. The results confirm that farmers with medium- and long-term contracts tend to invest more in land-improvement measures, i.e., organic and green manure. Further, the study findings demonstrate that long-term land tenures are more effective when farmers make decisions regarding the on-farm infrastructure, like tube-well installation, tractor ownership, and holding farm logistics. Last, the study results confirm that long-term contracts are more robust regarding technical efficiency. Moreover, the findings support the Marshallian inefficiency hypothesis and extend the literature on contract farming, land-improvement investment, and land use policy, and offer coherent policy actions for stakeholders to improve farmers' productivity, technical efficiency, and income.