Abstract: Ruddlesden-Popper La????Sr2−????MnO4−???? materials are interesting symmetric solid oxide
fuel cell electrodes due to their good redox stability, mixed ionic and electronic conducting behavior and thermal expansion that matches well with common electrolytes. In reducing environments – as at a solid oxide fuel cell anode – the x = 0.5 member, i.e. La0.5Sr1.5MnO4−????, has a much higher total conductivity than compounds with a different La/Sr ratio, although all those compositions have the same K2NiF4-type I4/mmm structure. The origin for this conductivity difference is not yet known in literature. Now, a combination of in-situ and ex-situ 3D electron diffraction, high-resolution imaging, energy-dispersive X-ray analysis and electron energy-loss spectroscopy uncovered clear differences between x=0.25 and x=0.5 in the pristine structure, as well as in the transformations upon high-temperature reduction. In La0.5Sr1.5MnO4−????, Ruddlesden-Popper n=2 layer defects and an amorphous surface layer are present, but not in La0.25Sr1.75MnO4−????. After annealing at 700°C in 5% H2/Ar, La0.25Sr1.75MnO4−???? transforms to a tetragonal 2D incommensurately modulated structure with modulation vectors ⃗????1 = 0.2848(1) · (⃗????* +⃗????*) and ⃗????2 =0.2848(1) · (⃗????* – ⃗????*), whereas La0.5Sr1.5MnO4−???? only partially transforms to an orthorhombic 1D incommensurately modulated structure,
with ⃗???? = 0.318(2) · ⃗????*. Perovskite domains grow at the crystal edge at 700°C in 5%
H2 or vacuum, due to the higher La concentration on the surface compared to the bulk, which leads to a different thermodynamic equilibrium. Since it is known that a lower degree of oxygen vacancy ordering and a higher amount of perovskite blocks enhance oxygen mobility, those differences in defect structure and structural transformation upon reduction, might all contribute to the higher conductivity of La0.5Sr1.5MnO4−???? in solid oxide fuel cell anode conditions compared to other La/Sr ratios.

Abstract: Warm plasma offers a promising route for CO2 splitting into valuable CO, yet recombination reactions of CO with oxygen, forming again CO2, have recently emerged as critical limitation. This study combines experiments and fluid dynamics + chemical kinetics modelling to comprehensively analyse the recombination reactions upon CO2 splitting in an atmospheric plasmatron. We introduce an innovative in-situ gas sampling technique, enabling 2D spatial mapping of gas product compositions and temperatures, experimentally confirming for the first time the substantial limiting effect of CO recombination reactions in the afterglow region. Our results show that the CO mole fraction at a 5 L/min flow rate drops significantly from 11.9 % at a vertical distance of z = 20 mm in the afterglow region to 8.6 % at z = 40 mm. We constructed a comprehensive 2D model that allows for spatial reaction rates analysis incorporating crucial reactions, and we validated it to kinetically elucidate this phenomenon. CO2 +M⇌O+CO+M and CO2 +O⇌CO+O2 are the dominant reactions, with the forward reactions prevailing in the plasma region and the backward reactions becoming prominent in the afterglow region. These results allow us to propose an afterglow quenching strategy for performance enhancement, which is further demonstrated through a meticulously developed plasmatron reactor with two-stage cooling. Our approach substantially increases the CO2 conversion (e.g., from 6.6 % to 19.5 % at 3 L/min flow rate) and energy efficiency (from 13.5 % to 28.5 %, again at 3 L/min) and significantly shortens the startup time (from ~ 150 s to 25 s). Our study underscores the critical role of inhibiting recombination reactions in plasma-based CO2 conversion and offers new avenues for performance enhancement.

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: Digital disconnection has emerged as a concept describing the actions people take to limit their digital connectivity to enhance their well-being. To date, evidence on its effectiveness is mixed, leading to calls for greater consideration of why, how, when, and for whom digital disconnection works. This article responds to these calls, presenting a framework that differentiates four key harms that contribute to experiences of digital ill-being (time displacement, interference, role blurring, and exposure effects). Using these four harms as a starting point, the framework explains: (1) why people are motivated to digitally disconnect; (2) how specific disconnection strategies (i.e., placing limits on time, access, channels, and contents, interactions and features) may help them; and for whom (3) and under which conditions (when) these strategies can be effective.

Abstract: The Night Watch, one of the most famous masterpieces by Rembrandt, is the subject of a large research and conservation project. For the conservation treatment, it is of great importance to understand its current condition. Correlated nano-tomography using x-ray fluorescence and ptychography revealed a-so far unknown-lead-containing “layer”, which likely acts as a protective impregnation layer applied on the canvas before the quartz-clay ground was applied. This layer might explain the presence of lead soap protrusions in areas where no other lead components are present. In addition to the three-dimensional elemental mapping, ptychography visualizes and quantifies components not detectable by hard x-ray fluorescence such as the organic fraction and quartz. The first-time use of this combination of synchrotron-based techniques on a historic paint micro-sample shows it to be an important tool to better interpret the results of noninvasive imaging techniques operating on the macroscale.

Abstract: Defect-free graphene is impermeable to all atoms(1-5) and ions(6,7) under ambient conditions. Experiments that can resolve gas flows of a few atoms per hour through micrometre-sized membranes found that monocrystalline graphene is completely impermeable to helium, the smallest atom(2,5). Such membranes were also shown to be impermeable to all ions, including the smallest one, lithium(6,7). By contrast, graphene was reported to be highly permeable to protons, nuclei of hydrogen atoms(8,9). There is no consensus, however, either on the mechanism behind the unexpectedly high proton permeability(10-14) or even on whether it requires defects in graphene's crystal lattice(6,8,15-17). Here, using high-resolution scanning electrochemical cell microscopy, we show that, although proton permeation through mechanically exfoliated monolayers of graphene and hexagonal boron nitride cannot be attributed to any structural defects, nanoscale non-flatness of two-dimensional membranes greatly facilitates proton transport. The spatial distribution of proton currents visualized by scanning electrochemical cell microscopy reveals marked inhomogeneities that are strongly correlated with nanoscale wrinkles and other features where strain is accumulated. Our results highlight nanoscale morphology as an important parameter enabling proton transport through two-dimensional crystals, mostly considered and modelled as flat, and indicate that strain and curvature can be used as additional degrees of freedom to control the proton permeability of two-dimensional materials. A study using high-resolution scanning electrochemical cell microscopy attributes proton permeation through defect-free graphene and hexagonal boron nitride to transport across areas of the structure that are under strain.

Abstract: Oxygen loss is a serious problem of lithium-rich layered oxide (LLO) cathodes, as the high capacity of LLO relies on reversible oxygen redox. Oxygen release can occur at the surface leading to the formation of spinel or rock salt structures. Also, the lattice oxygen will usually become unstable after long cycling, which remains a major roadblock in the application of LLO. Here, it is shown that Zr doping is an effective strategy to retain lattice oxygen in LLO due to the high affinity between Zr and O. A simple sol-gel method is used to dope Zr4+ into the LLOs to adjust the local electronic structure and inhibit the diffusion of oxygen anions to the surface during cycling. Compared with untreated LLOs, LLO-Zr cathodes exhibit a higher cycling stability, with 94% capacity retention after 100 cycles at 0.4 C, up to 223 mAh g-1 at 1 C, and 88% capacity retention after 300 cycles. Theoretical calculations show that due to the strong Zr-O covalent bonding, the formation energy of oxygen vacancies has effectively increased and the loss of lattice oxygen under high voltage can be suppressed. This study provides a simple method for developing high-capacity and cyclability Li-rich cathode materials for lithium-ion batteries. Oxygen release can occur at the cathode surface leading to the formation of spinel or rock salt structures. Here, it is shown that Zr doping is an effective strategy to retain lattice oxygen in lithium-rich layered oxides (LLO) due to the high affinity between Zr and O. LLO-Zr exhibit higher cycling stability, with 88% capacity retention after 300 cycles at 1 C. image

Abstract: The objective of this paper is to examine the recycling rates for mechanical and thermochemical recycling of postconsumer polyethylene flexible packaging after the implementation of different policy instruments. The study uses a supply chain equilibrium model that incorporates market data and techno-economic assessments to simulate market equilibrium. It combines this with a life cycle assessment to explore the environmental implications of implementing different policy instruments. The results show that instruments that do not target a specific technology are more likely to increase thermochemical recycling than mechanical recycling. Furthermore, a higher recycling rate is not equivalent to a better environmental outcome. An increased collection target that ensures a supply of plastic waste would increase the overall recycling rates the most. A recycled content standard for mechanical recycling would lead to the highest increase in mechanical recycling, with top results for environmental indicators, but low results for economic indicators.

Abstract: We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification.  The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to  – read the simulated data – transform HDF5 format to TFRecord format – train and evaluate neural networks using the simulated data – use the trained networks for automated element identification is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at:                    Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. Sci Rep 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585 [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)

Abstract: Urine nitrification is pH-sensitive due to limited alkalinity and high residual ammonium concentrations. This study aimed to investigate how the pH affects nitrogen conversion and the microbial community of urine nitrification with a pH-based feeding strategy. First, kinetic parameters for NH3, HNO2, and NO2– limitation and inhibition were determined for nitrifiers from a urine nitrification reactor. The turning point for ammonia-oxidizing bacteria (AOB), i.e., the substrate concentration at which a further increase would lead to a decrease in activity due to inhibitory effects, was at an NH3 concentration of 12 mg-N L–1, which was reached only at pH values above 7. The total nitrite turning point for nitrite-oxidizing bacteria (NOB) was pH-dependent, e.g., 18 mg-N L–1 at pH 6.3. Second, four years of data from two 120 L reactors were analyzed, showing that stable nitrification with low nitrite was most likely between pH 5.8 and 6.7. And third, six 12 L urine nitrification reactors were operated at total nitrogen concentrations of 1300 and 3600 mg-N L–1 and pH values between 2.5 and 8.5. At pH 6, the AOB Nitrosomonas europaea was found, and the NOB belonged to the genus Nitrobacter. At pH 7, nitrite accumulated, and Nitrosomonas halophila was the dominant AOB. NOB were inhibited by HNO2 accumulation. At pH 8.5, the AOB Nitrosomonas stercoris became dominant, and NH3 inhibited NOB. Without influent, the pH dropped to 2.5 due to the growth of the acid-tolerant AOB “Candidatus Nitrosacidococcus urinae”. In conclusion, pH is a decisive process control parameter for urine nitrification by influencing the selection and kinetics of nitrifiers.

Abstract: Plant health monitoring is devised as a new concept to elucidate in situ physiological processes. The need for increased food production to nourish the growing global population is inconsistent with the dramatic impact of climate change, which hinders crop health and exacerbates plant stress. In this context, wearable sensors play a crucial role in assessing plant stress. Herein, we present a low-cost 3D-printed hollow microneedle array (HMA) patch as a sampling device coupled with biosensors based on screen-printing technology, leading to affordable analysis of biomarkers in the plant fluid of a leaf. First, a refinement of the 3D-printing method showed a tip diameter of 25.9 ± 3.7 μm with a side hole diameter on the microneedle of 228.2 ± 18.6 μm using an affordable 3D printer (<500 EUR). Notably, the HMA patch withstanded the forces exerted by thumb pressing (i.e. 20-40 N). Subsequently, the holes of the HMA enabled the fluid extraction tested in vitro and in vivo in plant leaves (i.e. 13.5 ± 1.1 μL). A paper-based sampling strategy adapted to the HMA allowed the collection of plant fluid. Finally, integrating the sampling device onto biosensors facilitated the in situ electrochemical analysis of plant health biomarkers (i.e. H2O2, glucose, and pH) and the electrochemical profiling of plants in five plant species. Overall, this electrochemical platform advances precise and versatile sensors for plant health monitoring. The wearable device can potentially improve precision farming practices, addressing the critical need for sustainable and resilient agriculture in changing environmental conditions.

Abstract: Today, illicit drugs are omnipresent in society. Clandestine markets are growing faster than ever before, record amounts of cocaine are seized in seaports and airports, while the associated violence is spiralling out of control. In addition, drug monitoring centres worldwide are warning for the increasing complexity of the drug markets, as the traditionally popular drugs are joined by countless new synthetic variants, while medical drugs are also increasingly being abused. In order to provide services confronted with illicit drug samples (police, customs, forensic scientists, first responders, …) with important information on the identity of an unknown sample, suitable analytical tests are required. While these exist for laboratory environments, on-site applicable tests are important to accelerate the decision-making process. Electrochemical sensors have all the advantages required for such on-site tests: they are fast, portable, easy-to-use and reliable. Furthermore, they are not influenced by colours, which are frequently added to drug samples to deceive the existing tests. Previous work has mainly focussed on the detection of a single drug per analysis. However, many drugs could be encountered due to the diversity of the drug markets. Therefore, this project developed electrochemical strategies for the detection of multiple drugs simultaneously. First, the electrochemical behaviour of the individual drugs was studied in different measuring conditions (assessing the influence of pH, concentration and temperature). Then, all findings and strategies were combined to detect multiple targets simultaneously. An electrochemical sensor was developed for the four most popular drugs at music festivals: cocaine, MDMA, amphetamine and ketamine. This sensor generates a so-called ‘superfingerprint’ of the sample, which is then automatically interpreted by a developed algorithm in order to produce a straightforward output. Finally, a pill analysis sensor was developed in the context of drug checking services, where a consumer can anonymously have a sample chemically analysed to obtain information on the composition, dose and potentially harmful additives. The sensor achieved an outstanding accuracy in identifying the main component and provided the option to quantify, as well as an indication on the presence of other substances in the sample. The project’s findings demonstrate the potential for electrochemistry in illicit drug detection and provide a basis for the development of new sensors, targeting other drug combinations.

Abstract: Optical hyperspectral cameras capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, the relationship between spectral reflectance and material composition is very complex. Quantitative validation of spectral unmixing algorithms requires high-quality ground truth fractional abundance data, which are very difficult to obtain. In this work, we generated a comprehensive laboratory ground truth dataset of intimately mixed mineral powders. For this, five clay powders (Kaolin, Roof clay, Red clay, mixed clay, and Calcium hydroxide) were mixed homogeneously to prepare 325 samples of 60 binary, 150 ternary, 100 quaternary, and 15 quinary mixtures. Thirteen different hyperspectral sensors have been used to acquire the reflectance spectra of these mixtures in the visible, near, short, mid, and long-wavelength infrared regions (350-15385) nm. Overlaps in wavelength regions due to the operational ranges of each sensor and variations in acquisition conditions resulted in a large amount of spectral variability. Ground truth composition is given by construction, but to verify that the generated samples are sufficiently homogeneous, XRD and XRF elemental analysis is performed. We believe these data will be beneficial for validating advanced methods for nonlinear unmixing and material composition estimation, including studying spectral variability and training supervised unmixing approaches. The datasets can be downloaded from the following link: https://github.com/VisionlabHyperspectral/Multisensor_datasets.

Abstract: Multi-objective optimization is an important decision-making tool for energy processes, as multiple targets need to be achieved. These objectives are usually conflicting since a single solution cannot be optimal for all objectives, resulting in a set of Pareto-optimal solutions. Multiple indicators might be available to describe a sustainability objective, such as the environmental impact which is commonly evaluated by performing a life cycle assessment. In this study, Pareto aggregation is proposed as a method which employs a novel multi-objective optimization-based approach as an alternative to the classically used aggregation in life cycle assessment. This method identifies conflicting environmental indicators and performs an aggregation among those that require a trade-off. An environmental-economic optimization of a second-generation bioethanol plant is used to illustrate and evaluate the proposed method. Process parameters from a biochemical conversion pathway flowsheet simulation model are chosen as optimization variables. To reduce the computational time, surrogate models, based on artificial neural networks, are used. Out of the eighteen ReCiPe Midpoint environmental indicators, five were identified as conflicting, resulting in an aggregated environmental objective, which was then traded off with the economic objective function, chosen as the levelized cost of ethanol. Comparison with the widely used single-score EcoIndicator99 showed that the Pareto aggregation method can reduce most of the environmental indicators by up to 6.5%. This research provides an insight on non-redundant objective functions, aiming at reducing the dimensionality of multi-objective optimization problems, while taking into consideration decision-makers’ preferences.

Abstract: During the past decades, metallic nanoparticles (NPs) have attracted great attention in materials science due to their specific optical properties based on surface plasmon resonances. Because of these phenomena, plasmonic NPs (or nanoplasmonics) are very promising for application in biosensing, photocatalysts, medicine, data storage, solar energy conversion, etc. Currently, colloidal synthesis techniques enable scientists to routinely produce mono and bimetallic NPs of various shapes, sizes, composition, and elemental distribution, with superior properties for plasmonic applications. Two primary directions for further advancing nanoplasmonic-based technologies include synthesizing novel morphologies, such as highly asymmetric chiral NPs, and gaining deeper insights into the factors affecting the stability of produced nanoplasmonics. With the increasing complexity of nanoplasmonics morphologies and higher stability requirements, there is a pressing need for thorough investigations into their 3D structures and their evolution under different conditions, with high resolution. Electron tomography (ET) emerges as an ideal tool to retrieve shape and element-sensitive information about individual nanoparticles in 3D, achieving resolutions down to the atomic level. Moreover, ET techniques can be combined with in situ holders, enabling detailed studies of processes mimicking real applications of nanoplasmonic-based devices. The first part of this thesis will focus on detailed studies of chiral Au NPs, promising for spectroscopy techniques based on the differential absorption of left- and right-handed circularly polarized light. Specifically, I will discuss the primary strategies for wet-colloidal growth of the various types of intrinsically chiral Au NPs. Advanced ET methods will be demonstrated as powerful tools for characterizing the final helical morphologies of the produced Au NPs and for studying the chiral growth mechanisms by examining intermediate structures obtained during chiral growth. The second part will focus on the heat-induced stability of various Au@Ag core-shell NPs. Operating in real conditions, such as elevated temperatures, may cause particle reshaping and redistribution of metals between the core and shell, gradually altering nanoplasmonics properties. Hence, a thorough understanding of the influence of size, shape, and defects on these processes is crucial for further developments. Recently developed techniques, combining fast ET with in-situ heating holders, have allowed me to evaluate the influence of various parameters (size, shape, defect structure) on heat-induced elemental redistribution in Au@Ag core-shell nanoparticles qualitatively and quantitatively. Additionally, I will discuss the prospects of high-resolution ET for visualizing the diffusion of individual atoms within complex nanostructures.