Abstract: Wearable electrochemical sensors are driven by the user-friendly capability of continuous monitoring of key biomarkers for diagnostic or therapeutic operations. Particularly, microneedle (MN)-based sensors can access the interstitial fluid (ISF) in the dermis layer of skin to carry out on-body transdermal detection of analytes. Interestingly, 3D-printing technology allows for rapid and versatile prototyping reaching micrometer resolution. Herein, for the first time, we explore 3D-printed hollow MN patches (1 mm height x 1 mm base with 0.3 mm hole) which are modified with conductive inks to develop a potentiometric sensor for pH monitoring. First, the piercing capability of 3D-printed MN patches is demonstrated by using the parafilm model and their insertion in porcine skin. Subsequently, the hollow MNs are filled with conductive inks to engineer a set of microelectrodes. Thereafter, the working and reference electrodes are properly modified with polyaniline and polyvinyl butyral, respectively, toward a highly stable potentiometric cell. A full in vitro characterization is performed within a broad range of pH (i.e. pH 4 to pH 9). Besides, the MN sensor is analytically assessed in phantom gel and pierced on porcine skin to evaluate the resilience of the MN sensor. Finally, the MN sensor is pierced on the forearm of a subject and tested for its on-body monitoring capability. Overall, 3D-printed MN-based potentiometric sensing brings a versatile and affordable technology to minimally-invasively monitor key physiological parameters in the body.

Abstract: A wide variety of organic and inorganic compounds have been frequently released into the environment without control by industries and agricultural activities. This has caused significant impact on the development and health of living organisms, and biodiversity. Therefore, the challenge of monitoring and/or remediation of these contaminants remains. This thesis presents the development of three sensing strategies based on photoelectrochemical sensors to monitor phenolic contaminants. The well-known semiconductors TiO2 and ZnO were used for building novel setups for nM analysis of phenolic compounds in water. The setups employing TiO2-based photosensors were integrated into a photoelectrochemical flow cell. The flow system favored higher sensitivity of the method by periodic wash sequences of the electrode, significantly reducing the electrode fouling. Firstly, a straightforward method was developed based on the immobilization of TiO2 on screen printed graphite electrodes. Under UV light, the developed photosensor presented high performance for the detection of 4- aminophenol. A second study was developed by impregnating gold nanoparticles into TiO2 structure. The incorporation of gold nanoparticles can broaden the light absorption region of TiO2 and improve its photocatalytic activity for the detection of hydroquinone under visible light. In both systems, the detection was possible due to the presence of reactive oxygen species at the surface of TiO2 upon light, which participate in the oxidation process of the analyte. By applying a reductive potential, the oxidized form of the analyte gets reduced and a measurable amperometric response proportional to the initial analyte concentration is recorded. The third proposed setup is a ZnO-based photosensor for the quantification of 4-nitrophenol under UVA light. Nanostructured ZnO was electrochemically synthesized on FTO glass without the use of catalysts or seed layer. A post-growth annealing treatment significantly improved ZnO nanorods physicochemical properties. Subsequent modification of ZnO nanorods with a photosensitizer (perylene acid) increased the photocurrent response and the sensitivity. In this system, the detection mechanism is based on the decrease of the photocurrent response at the presence of an electron harvesting molecule, such as 4-nitrophenol. The decrease in photocurrent is proportional to the increase of 4-nitrophenol concentration in the solution. The applicability of the photoelectrochemical semiconductor-based sensing setups was verified to analyze phenolic compounds in natural and supply water samples. The proposed robust and sensitive approaches were designed for the on-site monitoring of phenolic compounds. The encouraging results confirm the potential of these photosensors as promising tools for tracelevel sensing purposes.

Abstract: The illicit drug precursor market for the manufacture of amphetamine-type stimulants (ATS), mainly amphetamine, methamphetamine and methylenedioxymethamphetamine (MDMA), has emerged quickly in the last years. The evidence of a more complex and sophisticated drug market underlines the pressing need for new on-site methods to quickly detect precursors of synthetic drugs, with electrochemical analysis as a promising technique. Herein, the electrochemical fingerprints of ten common ATS precursors-3-oxo-2-phenylbutanenitrile (APAAN), 3-oxo-2-phenylbutanamide (APAA), methyl 3-oxo-2-phenylbutanoate (MAPA), benzyl methyl ketone (BMK), 1-(1,3-benzodioxol-5-yl)propan-2-one (PMK), ephedrine, pseudoephedrine, safrole, sassafras oil and piperonal- are reported for the first time. The electrochemical screening disclosed the redox inactivity of BMK, which is an essential starting material for the production of ATS. Therefore, the local derivatization of BMK at an electrode surface by reductive amination is presented as a feasible solution to enrich its electrochemical fingerprint. To prove that, the resulting mixture was analyzed using a set of chromatographic techniques to understand the reaction mechanism and to identify possible electrochemical active products. Two reaction products (i.e. methamphetamine and 1-phenylpropan-2-ol) were found and characterized using mass spectrometry and electrochemical methods. Subsequently, the optimization of the reaction parameters was carefully addressed to set the portable electrochemical sensing strategy. Ultimately, the analysis concept was validated for the qualitative identification of ATS precursors in seizures from a forensic institute. Overall, the electrochemical approach demonstrates to be a useful and affordable analytical tool for the early identification of ATS precursors to prevent trafficking and drug manufacture in clandestine laboratories.

Abstract: There is an urgent need for sensing strategies to screen perfluoroalkyl substances (PFAS) in aqueous matrices. These strategies must be applicable in large-scale monitoring plans to face the ubiquitous use of PFAS, their wide global spread, and their fast evolution towards short-chain, branched molecules. To this aim, the changes in fluorinated self-assembled monolayers (SAM) with different architectures (pinholes/defects-free and with randomized pinholes/defects) were studied upon exposure to both long and short-chain PFAS. The applicability of fluorinated SAM in PFAS sensing was evaluated. Changes in the SAM structures were characterised combining electrochemical impedance spectroscopy and voltam-metric techniques. The experimental data interpretation was supported by molecular dynamics simu-lations to gain a more in-depth understanding of the interaction mechanisms involved. Pinhole/defect-free fluorinated SAM were found to be applicable to long-chain PFAS screening within switch-on sensing strategy, while a switch-off sensing strategy was reported for screening of both short/long-chain PFAS. These strategies confirmed the possibility to play on fluorophilic interactions when designing PFAS screening methods.(c) 2022 Elsevier B.V. All rights reserved.

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: 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.