Abstract: It remains a challenge to identify the active sites of bismuth catalysts in the electrochemical CO2 reduction reaction. Here we show through in situ characterization that the activation of bismuth oxyhalide electrocatalysts to metallic bismuth is guided by the halides. In situ X-ray diffraction results show that bromide promotes the selective exposure of planar bismuth surfaces, whereas chloride and iodide result in more disordered active sites. Furthermore, we find that bromide-activated bismuth catalysts outperform the chloride and iodide counterparts, achieving high current density (>100 mA cm(-2)) and formic acid selectivity (>90%), suggesting that planar bismuth surfaces are more active for the electrochemical CO2 reduction reaction. In addition, in situ X-ray absorption spectroscopy measurements reveal that the reconstruction proceeds rapidly in chloride-activated bismuth and gradually when bromide is present, facilitating the formation of ordered planar surfaces. These findings show the pivotal role of halogens on selective facet exposure in activated bismuth-based electrocatalysts during the electrochemical CO2 reduction reaction.

Abstract: In recent years, quantum dots (QDs) have emerged as bright,color-tunablelight sources for various applications such as light-emitting devices,lasing, and bioimaging. One important next step to advance their applicabilityis to reduce particle-to-particle variations of the emission propertiesas well as fluctuations of a single QD's emission spectrum,also known as spectral diffusion (SD). Characterizing SD is typicallyinefficient as it requires time-consuming measurements at the single-particlelevel. Here, however, we demonstrate multiparticle spectroscopy (MPS)as a high-throughput method to acquire statistically relevant informationabout both fluctuations at the single-particle level and variationsat the level of a synthesis batch. In MPS, we simultaneously measureemission spectra of many (20-100) QDs with a high time resolution.We obtain statistics on single-particle emission line broadening fora batch of traditional CdSe-based core-shell QDs and a batchof the less toxic InP-based core-shell QDs. The CdSe-basedQDs show significantly narrower homogeneous line widths, less SD,and less inhomogeneous broadening than the InP-based QDs. The timescales of SD are longer in the InP-based QDs than in the CdSe-basedQDs. Based on the distributions and correlations in single-particleproperties, we discuss the possible origins of line-width broadeningof the two types of QDs. Our experiments pave the way to large-scale,high-throughput characterization of single-QD emission propertiesand will ultimately contribute to facilitating rational design offuture QD structures.

Abstract: An unbiased photo-fuel cell (PFC) is a device that integrates the functions of a photoanode and a cathode to achieve simultaneous light-driven oxidation and dark reduction reactions. As such, it generates electricity while degrading pollutants like volatile organic compounds (VOCs). The photoanode is excited by light to generate electron-hole pairs, which give rise to a photocurrent, and are utilized to oxidise organic pollutants simultaneously. Here we have systematically studied various TiO2/WO3 photoanodes towards their photocatalytic soot degradation performance, PFC performance in the presence of VOCs, and the combination of both. The latter thus mimics an urban environment where VOCs and soot are present simultaneously. The formation of a type-II heterojunction after the addition of a thin TiO2 top layer over a dense WO3 bottom layer, improved both soot oxidation efficiency as well as photocurrent generation, thus paving the way towards low-cost PFC technology for energy recovery from real polluted air.

Abstract: Tidal marshes take up and release certain elements from and to the river water. Hence, they act as a filter, improving the estuarine water quality. This filtering function depends on the interaction between the marsh soil and water that infiltrates into the marsh platform at high tide and seeps out of the creek banks at low tide. In the past centuries, many tidal marshes disappeared due to large scale land reclamations, together with their associated ecosystem services. Nowadays, tidal marshes are increasingly restored on formerly embanked agricultural areas to regain these ecosystem services. Here, we studied the effect of historical agricultural land use on the contribution of restored tidal marshes to water quality improvement, and we investigated several solutions to stimulate the water quality improving function in newly restored tidal marshes. In restored tidal marshes, the soil is often compacted due to the historical agricultural land use, leading to a reduced organic matter content and micro- and macroporosity. In this compacted soil, groundwater flow is hindered, leading to a more waterlogged soil and reduced groundwater dynamics in the restored marsh as compared to a natural marsh. The depth of groundwater drainage and the groundwater flow velocity have important implications for the processes that contribute to water quality improvement, e.g. removal of nitrogen, phosphorus retention and silica cycling. Where groundwater drains deeper, i.e. in the vicinity of tidal creeks and in a more porous soil, these processes are promoted. We suggest that, in newly restored tidal marshes, the soil porosity can be increased by amending the soil (e.g. with organic matter), and the distance to the nearest tidal creek can be reduced by creek excavation. Numerical modelling showed that the largest gain in groundwater dynamics and seepage was attained when both measures were applied together. The effect of organic soil amendments on groundwater dynamics and nutrient cycling was further explored in a large scale in situ mesocosm experiment. Where the soil was amended, groundwater drained deeper and nitrogen removal increased. For new tidal marsh restoration projects, we advise to conduct an explorative soil study. When the soil is heavily compacted, design measures, such as creek initiation and organic soil amendments can be applied to jumpstart the contribution to water quality improvement of newly restored tidal marshes.

Abstract: Measuring the inequality of leaf area distribution per plant (ILAD) can provide a useful tool for quantifying the influences of intra- and interspecific competition, foraging behavior of herbivores, and environmental stress on plants’ above-ground architectural structures and survival strategies. Despite its importance, there has been limited research on this issue. This paper aims to fill this gap by comparing four inequality indices to measure ILAD, using indices for quantifying household income that are commonly used in economics, including the Gini index (which is based on the Lorenz curve), the coefficient of variation, the Theil index, and the mean log deviation index. We measured the area of all leaves for 240 individual plants of the species Shibataea chinensis Nakai, a drought-tolerant landscape plant found in southern China. A three-parameter performance equation was fitted to observations of the cumulative proportion of leaf area vs. the cumulative proportion of leaves per plant to calculate the Gini index for each individual specimen of S. chinensis. The performance equation was demonstrated to be valid in describing the rotated and right shifted Lorenz curve, given that >96% of root-mean-square error values were smaller than 0.004 for 240 individual plants. By examining the correlation between any of the six possible pairs of indices among the Gini index, the coefficient of variation, the Theil index, and the mean log deviation index, the data show that these indices are closely related and can be used interchangeably to quantify ILAD.

Abstract: Screening drugs on the street and biological samples pose a challenge to law enforcement agencies due to existing detection methods and instrument limitations. Herein we present a graphene-assisted molecularly imprinted polymer nanoparticle-based sensor for amphetamine. These nanoparticles are electroactive by incorporating ferrocene in their structure. These particles act as specific actuators in electrochemical sensors, and the presence of a ferrocene redox probe embedded in the structure allows the detection of non-electroactive amphetamine. In a control approach, nanoparticles were covalently immobilised onto electrochemical sensors by drop-casting using silanes. Alternatively, nanoparticles were immobilised employing 3D printing and a graphene ink composite. The electrochemical performance of both approaches was evaluated. As a result, 3D printed nanoMIPs/graphene sensors displayed the highest selectivity in spiked human plasma, with sensitivity at 73 nA nM-1, LOD of 68 nM (RSD 2.4%) when compared to the silane drop cast electrodes. The main advantage of the optimised 3D printing technology is that it allows quantitative determination of amphetamine, a nonelectroactive drug, challenging to detect with conventional electrochemical sensors. In addition, the costefficient 3D printing method makes these sensors easy to manufacture, leading to robust, highly selective and sensitive sensors. As proof of concept, sensors were evaluated on the street specimens and clinically relevant samples and successfully validated using UPLC-MS.

Abstract: Methamphetamine (MA) is a synthetic psychoactive drug which is consumed both licitly and illicitly. In some countries it is prescribed for attention-deficit and hyperactivity disorder, and short-term treatment of obesity. More often though, it is abused for its psychostimulant properties. Unfortunately, the spread and abuse of this synthetic drug have increased globally, being reported as the most widely consumed synthetic psychoactive drug in the world in 2019. Attempting to overcome the shortcomings of the currently used on-site methods for MA detection in suspected cargos, the present study explores the potential of electrochemical identification of MA by means of square wave voltammetry on disposable graphite screen-printed electrodes. Hence, the analytical characterization of the method was evaluated under optimal conditions exhibiting a linear range between 50 mu M and 2.5 mM MA, a LOD of 16.7 mu M, a LOQ of 50.0 mu M and a sensitivity of 5.3 mu A mM-1. Interestingly, two zones in the potential window were identified for the detection of MA, depending on its concentration in solution. Furthermore, the oxidative pathway of MA was elucidated employing liquid chromatography – mass spectrometry to understand the change in the electrochemical profile. Thereafter, the selectivity of the method towards MA in mixtures with other drugs of abuse as well as common adulterants/cutting agents was evaluated. Finally, the described method was employed for the analysis of MA in confiscated samples and compared with forensic methods, displaying its potential as a fast and easy-to-use method for on-site analysis.

Abstract: Illicit drug consumption constitutes a great concern worldwide due to its increased spread and abuse, and the negative consequences exerted on society. For instance, 3,4-methylenedioxymethamphetamine (MDMA), a synthetic amphetamine-type substance, was abused by 20 million people worldwide in 2020. This psychoactive substance exerts a myriad of effects on the human body being dangerous for the consumer’s health. Besides, MDMA has been used in the treatment of some psychiatric conditions. Therefore, the development of wearable devices for MDMA sensing in biological fluids is of great importance for forensic toxicology (e.g., monitoring of patients with suspected or known MDMA consumption) as well as for therapeutic management of patients. Herein, we report the development of a wearable electrochemical platform based on a hollow microneedle (MN) array sensor for the monitoring of MDMA in the interstitial fluid by square-wave voltammetry. First, the holes of the MN array were modified with conductive pastes to devise a MN patch with a three-electrode system. Subsequently, the functionalization of the working electrode with nanomaterials enhanced MDMA detection. Thereafter, analytical parameters were evaluated exhibiting a slope of 0.05 µA µM−1 within a linear range from 1 to 50 µM and a limit of detection of 0.75 µM in artificial interstitial fluid. Importantly, critical parameters such as selectivity, piercing capability, temperature, reversibility and stability were assessed. Overall, the obtained MN sensor exhibited excellent analytical performance, making it a promising tool for MDMA tracking in interstitial fluid for individuals on probation or under therapeutic treatment.

Abstract: Exposure to air pollution, such as particulate matter (PM), causes adverse health effects, particularly to the respiratory tract and cardiovascular system. PM is the collective name for all kinds of particles ranging from small particles and liquid droplets, which contain organic compounds, acids and metals, to soil or dust particles. One distinguishes PM10, PM2.5 and PM0.1, which have aerodynamic particle sizes smaller than 10, 2.5 and 0.1 µm, respectively. It is mainly the latter that is the most harmful, as PM0.1 penetrates deep into the respiratory system and carries relatively more toxic substances than the other PM fractions. Over a 15-year period, PM concentrations in European member states have fallen by about 30%. Nevertheless, the World Health Organisation (WHO) air quality guidelines, which became stricter in 2021, are exceeded in most places around the world. Particularly in cities, excessive levels of PM are measured and it is here that PM mitigation should be investigated. For this, the implementation of urban green infrastructure, including trees, shrubs, green roofs and green walls, is being looked at. Plants hinder airflow and remove PM from the air by deposition on their leaves and branches. This process is known as dry deposition. Plants can capture PM very efficiently, due to their complex structure of leaves and branches. Green walls offer significant advantages over other types of urban green infrastructure because they can grow on the huge available wall area and, because they do not hinder air circulation, as we sometimes see with trees. Green walls are believed to have a much greater, untapped potential to reduce PM pollution. However, a literature review showed that we do not know the quantitative impact of green walls and lack the tools and/or general methodology to do so. The objective of this thesis is therefore to develop a method for assessing PM removal by green walls, based on predictive models and based on relevant parameters that are experimentally determined. Computational fluid dynamics (CFD) is a numerical method to simulate airflow in complex environments such as cities. These models can also simulate the vegetation-wind interaction in detail and are interesting tools to assess the effect of green walls on PM concentrations in real environments. It is important to first study the aerodynamic effect of green walls and parameterise it correctly in CFD models. Plants decrease the wind speed and create turbulence through a combination of viscous and form drag, which are determined by the permeability (K) and drag coefficient (Cd), respectively. Wind tunnel experiments were conducted with three commonly found climbers (Hedera helix, Parthenocissus tricuspidata and Parthenocissus quinquefolia) and the variation of leaf area density was investigated for two of them. It was observed that the air resistance depended on plant species, leaf area density and wind speed. The difference between the plant species was assigned to the functional leaf size (FLS), the ratio of the largest circle within the boundaries of the leaf to the total leaf area. FLS is likely associated with other morphological characteristics of plants that, when considered collectively, provide a more comprehensive representation of leaf complexity. The pressure and velocity measurements obtained were used to optimise the permeability and drag coefficient in a CFD model. At wind speeds below 0.6 m s-1, the resistance was mainly determined by viscous drag and a larger leaf size resulted in a higher viscous drag. At wind speeds above 1.5 m s-1, form drag was dominant and the parameterised Cd decreased with increasing wind speed due to the sheltering effect of successive plant elements. The leaf area density had a significant effect on K and Cd and, is therefore an important plant parameters in CFD models. The main conclusion here is that the common practice of using a constant Cd to model the influence of plants on the air flow leads to deviations from reality. Wind tunnels are highly suitable to study the impact of green walls on PM concentration under controlled environmental conditions. For this purpose, a new wind tunnel setup was built and great attention was paid to obtaining a uniform air flow. Thus, based on CFD models, appropriate flow controllers were chosen, consisting of honeycombs and screens with different mesh sizes. New PM generation devices and measuring equipment were installed and set up appropriately. Devices were available for generating and measuring ultrafine dust (<0.1 µm, i.e. PM0.1) and fine dust (<0.3 µm, i.e. PM0.3) consisting of soot particles, and, on the other hand, fine dust with particle sizes smaller than 2.5 (PM2.5) and 10 µm (PM10) consisting of 'Arizona fine test dust'. With the new wind tunnel setup, it was possible to measure the influence of Hedera helix (common ivy), grown in a planter against a climbing aid, on the PM concentration and this was expressed by a collection efficiency, i.e. the difference in concentration in front and behind the plants normalised for the incoming concentration. The collection efficiency of H. helix depended on the particle size of the PM and wind speed. The collection efficiency decreased when the particle size increased from 0.02 to 0.2 µm and increased again for particle sizes above 0.3 µm. The collection efficiency also increased with increasing wind speed, especially for particle sizes > 0.03 µm. On the other hand, relative humidity and the type of PM (soot or dust) did not significantly affect the collection efficiency. The main objective of this study was to obtain an optimised size-resolved deposition model. Dry deposition occurs through several mechanisms, in particular gravity, diffusion, impaction and interception, and the subsequent resuspension of deposited PM back to the environment. The modelling of these mechanisms was described by \citet{Zhang2001} and \citet{Petroff2010}. The data obtained from the wind tunnel experiments allowed validating these deposition models. It was for the first time that deposition of real PM on green walls was studied. The different PM deposition mechanisms were found to be strongly dependent on particle size and wind speed. The models of \citet{Zhang2001} and \citet{Petroff2010} each matched PM concentration measurements for only certain particle sizes. Therefore, a combination of the two models was investigated and the root mean square error was lower by on average 3.5% (PM < 0.03 µm) and 46% (PM > 0.03 µm) compared to the original models at wind speeds greater than 1.5 m s-1. For wind speeds less than 1.5 m s-1, the optimised model did not differ from the original models. The optimised model was able to meet the imposed criteria for air quality models, where a correct model exhibits low deviation from measurements ('normalised mean square error' < 1.5), low bias ('fractional bias' between -0.3 and 0.3) and high R2. In comparison, the R$2$ of the optimised model was 0.57, while that of Zhang et al. (2001) and Petroff et al. (2010) was 0.23 and 0.31, respectively. The optimised model was however characterised by a high scatter, with the fraction of modeled results located within a factor of two of the measurements being lower than 50. A model study with a green façade oriented parallel to the incoming airflow showed that deposition by interception and impaction reduced remarkably, but that the orientation had no effect on deposition by Brownian diffusion. A promising green wall form for PM mitigation is the living wall system (LWS). LWS consist of supporting structures with substrate to grow plants in and can be planted with a variety of plant species. This allows to select plant species with optimal characteristics to achieve PM deposition. These characteristics refer to the macro- and microstructure of the leaves, and research has been conducted mainly on these. On the other hand, the influence of the supporting structure and substrate on PM concentrations has rarely been studied. With the new wind tunnel setup, LWS from different manufacturers were tested for their ability to capture PM. The setups were subjected for three hours to an air flow with a low PM concentration (resuspension phase) and then for three hours to an air flow to which additional PM was added (deposition phase). Some setups were able to decrease the PM concentration during both phases, while others just caused the concentration to increase. Some systems were able to reduce particulate matter concentration during both phases, namely LWS consisting of planters (-2% and -4% for PM0.1 and PM2.5, respectively) and textile cloths (-23% and -5% for PM0.1 and PM2.5, respectively). While other systems actually resulted in an increase in concentration especially LWS existing textile fabrics consisting of geotextiles (+11% for both PM fractions) and with moss as substrate (+2% and +5% for PM0.1 and PM2.5, respectively). This highlights the importance of careful selection of suspension systems to reduce particulate matter concentrations. Further research is therefore needed on the materials used in these systems in relation to their particulate content, as well as on plant development in these systems. In addition to air measurements, measurements were taken of the amount of PM deposited on the leaves and suspension system of LWS. This allowed the difference in PM resuspension and deposition between plant species to be investigated. The amount of deposited particulate matter was determined based on 'saturation isothermal remanent magnetisation' (SIRM), a measure of magnetisable particulate matter. This was possible because the added 'Arizona fine test dust' contained iron oxide. However, no significant difference was observed between the SIRM values measured before the wind tunnel experiment, after resuspension and after deposition. This suggested that the iron oxide content in the Arizona fine test dust was too low to measure a significant difference in the SIRM values on leaves after three hours. The plant species did give rise to different SIRM values ranging between 5 and 260 µ A. In particular, SIRM values above 26 µ A were observed for the plant species that were grouped due to their significantly higher accumulation of PM. 'Specific leaf area' (SLA), specifically the ratio of the one-sided 'fresh' leaf area to its dry mass, was the significant leaf characteristic. SLA correlated with leaf complexity. In particular, plant species with elongated leaves were characterized by low SLA, high FLS and high complexity and showed significantly higher SIRM values. Finally, the optimised size-resolved deposition model was also tested in an urban model to get an idea of the impact of a green wall on PM concentrations in a so-called 'street canyon'. These are narrow streets with high buildings on both sides, making air pollution more persistent. To this end, an ideal scenario was tested in which a green wall was introduced along both sides of the street over a length of about 270 m. The model result showed a decrease in PM2.5 and PM10 of 46 ± 12% and 52 ± 14%. This result is of course for a very optimal scenario where the green wall covers the entire building façades. Since this is not feasible in reality, other ways of promoting contact between green walls and polluted air can be explored. The insights obtained illustrate that the use of climbing plants can be a cost-effective and environmentally friendly solution to reduce PM concentrations. Moreover, the findings showed that models can be used to investigate the impact of green walls on PM levels. These findings fit within the broader context of designing healthy and sustainable urban environments and developing innovative solutions based on solid scientific knowledge.

Abstract: A highly sensitive and portable electrochemical sensor based on molecularly imprinted nanoparticles (nanoMIPs) was developed. NanoMIPs were computationally designed for specific recognition of amphetamine, and then synthetized using solid phase synthesis. NanoMIPs were immobilized onto screen-printed carbon electrodes using a composite film comprising chitosan, nanoMIPs, and graphene oxide.Ferrocenylmethyl methacrylate was incorporated in nanoMIPs allowing electrochemical detection. The signal recorded for the electrochemical oxidation of ferrocene has proven to be dependent on the presence of amphetamine interacting with nanMIPs. The sensor was tested successfully with street samples, with high sensitivity and satisfactory recoveries (from 100.9% to 107.6%). These results were validated with UPL-MS/MS. The present technology is suitable for forensic applications in selective determination of amphetamine in street samples.

Abstract: Purple phototrophic bacteria (PPB) show an underexplored potential for resource recovery from wastewater. Raceway reactors offer a more affordable full-scale solution on wastewater and enable useful additional aerobic processes. Current mathematical models of PPB systems provide useful mechanistic insights, but do not represent the full metabolic versatility of PPB and thus require further advancement to simulate the process for technology development and control. In this study, a new modelling approach for PPB that integrates the photoheterotrophic, and both anaerobic and aerobic chemoheterotrophic metabolic pathways through an empirical parallel metabolic growth constant was proposed. It aimed the modelling of microbial selection dynamics in competition with aerobic and anaerobic microbial community under different operational scenarios. A sensitivity analysis was carried out to identify the most influential parameters within the model and calibrate them based on experimental data. Process perturbation scenarios were simulated, which showed a good performance of the model.

Abstract: Nitrification is well-suited for urine stabilization. No base dosage is required if the pH is controlled within an appropriate operating range by urine feeding, producing an ammonium-nitrate fertilizer. However, the process is highly dependent on the selected pH set-points and is susceptible to process failures such as nitrite accumulation or the growth of acid-tolerant ammonia-oxidizing bacteria. To address the need for a robust and reliable process in decentralized applications, two different strategies were tested: operating a two-position pH controller (inflow on/off) with a narrow pH control band at 6.20/6.25 (∆pH = 0.05, narrow-pH) vs. a wider pH control band at 6.00/6.50 (∆pH = 0.50, wide-pH). These variations in pH also cause variations in the chemical speciation of ammonia and nitrite and, as shown, the microbial production of nitrite. It was hypothesized that the higher fluctuations would result in greater microbial diversity and, thus, a more robust process. The diversity of nitrifiers was higher in the wide-pH reactor, while the diversity of the entire microbiome was similar in both systems. However, the wide-pH reactor was more susceptible to tested process disturbances caused by increasing pH or temperature, decreasing dissolved oxygen, or an influent stop. In addition, with an emission factor of 0.47%, the nitrous oxide (N2O) emissions from the wide-pH reactor were twice as high as the N2O emissions from the narrow-pH reactor, most likely due to the nitrite fluctuations. Based on these results, a narrow control band is recommended for pH control in urine nitrification.

Abstract: In this study, we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently, a corresponding patch of the phase object is recovered using the phase object approximation. Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full-phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation, and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on the thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness, and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM.