Abstract: Synthetic cathinones (SCs) are a group of new psychoactive substances often referred to as “legal highs” or “bath salts”, being characterized by a dynamic change, new compounds continuously emerging on the market. This creates a lack of fast screening tests, making SCs a constant concern for law enforcement agencies. Herein, we present a fast and simple method for the detection of four SCs (alpha-pyrrolidinovalerophenone, N-ethylhexedrone, 4-chloroethcathinone, and 3-chloromethcathinone) based on their electrochemical profiles in a decentralized manner. In this regard, the voltametric characterization of the SCs was performed by cyclic and square wave voltammetry. The elucidation of the SCs redox pathways was successfully achieved using liquid chromatography coupled to (tandem) mass spectrometry. For the rational identification of the ideal experimental conditions, chemometric data processing was employed, considering two critical qualitative and quantitative variables: the type of the electrochemical platform and the pH of the electrolyte. The analytical figures of merit were determined on standard working solutions using the optimized method, which exhibited wide linear ranges and LODs suitable for confiscated sample screening. Finally, the performance of the method was evaluated on real confiscated samples, the resulting validation parameters being similar to those obtained with another portable device (i.e., Raman spectrometer).

Abstract: The goal of this study is to provide a rigorous tool to quantify the inequality of the leaf size distribution of an individual plant, thereby serving as a reference trait for quantifying plant adaptations to local environmental conditions. The tool to be presented and tested employs three components: (1) a performance equation (PE), which can produce flexible asymmetrical and symmetrical bell-shaped curves, (2) the Lorenz curve (i.e., the cumulative proportion of leaf size vs. the cumulative proportion of number of leaves), which is the basis for calculating, and (3) the Gini index, which measures the inequality of leaf size distribution. We sampled 12 individual plants of a dwarf bamboo and measured the area and dry mass of each leaf of each plant. We then developed a generalized performance equation (GPE) of which the PE is a special case and fitted the Lorenz curve to leaf size distribution using the GPE and PE. The GPE performed better than the PE in fitting the Lorenz curve. We compared the Gini index of leaf area distribution with that of leaf dry mass distribution and found that there was a significant difference between the two indices that might emerge from the scaling relationship between leaf dry mass and area. Nevertheless, there was a strong correlation between the two Gini indices (r2 = 0.9846). This study provides a promising tool based on the GPE for quantifying the inequality of leaf size distributions across individual plants and can be used to quantify plant adaptations to local environmental conditions.

Abstract: The haem group is a promising redox probe for the design of albumin-based voltammetric sensors. Among the endogenous ligands carried by human serum albumin (hSA), haem is characterised by a reversible redox behaviour and its binding kinetics strongly depend on hSA’s conformation, which, in turn, depends on the presence of other ligands. In this work, the potential applicability of haem, especially hemin, as a redox probe was first tested in a proof-of-concept study using perfluorooctanoic acid (PFOA) as model analyte. PFOA is known to bind hSA by occupying Sudlow’s I site (FA7) which is spatially related to the haem-binding site (FA1). The latter undergoes a conformational change, which is expected to affect hemin’s binding kinetics. To verify this hypothesis, hemin:albumin complexes in the presence/absence of PFOA were first screened by UV–Vis spectroscopy. Once the complex formation was verified, haem was further characterised via electrochemical methods to estimate its electron transfer kinetics. The hemin:albumin:PFOA system was studied in solution, with the aim of describing the multiple equilibria at stake and designing an electrochemical assay for PFOA monitoring. This latter could be integrated with protein-based bioremediation approaches for the treatment of per- and polyfluoroalkyl substances polluted waters. Overall, our preliminary results show how hemin can be applied as a redox probe in albumin-based voltammetric sensing strategies.

Abstract: Sr2FeO3+x F1-x (x = 0.08, 0.2), an n = 1 Ruddlesden-Popperphase, was synthesized from the oxidationof Sr2FeO3F in air at high temperature followinga fluorine for oxygen substitution and Fe3+ to Fe4+ oxidation. A structural investigation of both compounds was performedusing complementary and high-resolution techniques (Synchrotron X-rayand electron diffraction, Mo''ssbauer spectroscopy, HR-STEM)coupled to DFT calculation. This study reveals that oxidation leadsto a high degree of apical anion disorder coupled to antiphase boundaries. Sr2FeO3F, an oxyfluoride compoundwith an n = 1 Ruddlesden-Popper structure,was identifiedas a potential interesting mixed ionic and electronic conductor (MIEC).The phase can be synthesized under a range of different pO(2) atmospheres, leading to various degrees of fluorinefor oxygen substitution and Fe4+ content. A structuralinvestigation and thorough comparison of both argon- and air-synthesizedcompounds were performed by combining high-resolution X-ray and electrondiffraction, high-resolution scanning transmission electron microscopy,Mo''ssbauer spectroscopy, and DFT calculations. While the argon-synthesizedphase shows a well-behaved O/F ordered structure, this study revealedthat oxidation leads to averaged large-scale anionic disorder on theapical site. In the more oxidized Sr2FeO3.2F0.8 oxyfluoride, containing 20% of Fe4+, two differentFe positions can be identified with a 32%/68% occupancy (P4/nmm space group). This originates due to the presenceof antiphase boundaries between ordered domains within the grains.Relations between site distortion and valence states as well as stabilityof apical anionic sites (O vs F) are discussed. This study paves theway for further studies on both ionic and electronic transport propertiesof Sr2FeO3.2F0.8 and its use in MIEC-baseddevices, such as solid oxide fuel cells.

Abstract: Pretreatment is a critical step in the conversion of lignocellulose into biofuels and biochemicals. During pretreatment, the recalcitrance of lignocellulose is reduced, e.g. by removing lignin, thereby making the carbohydrates more accessible for enzymatic saccharification. Fungal delignification by white-rot fungi is a biotechnological alternative to chemical/physicochemical methods, which is carried out in solid-state fermentation with mild reaction conditions and without the formation of microbial inhibitors. However, fungal pretreatment presents some challenges, such as long pretreatment time, non-selective and low delignification, low enzymatic digestibility and feedstock sterilisation requirement, making its commercial implementation challenging compared to conventional methods. This study investigates the possibility of improving and characterising the solid-state fungal pretreatment of poplar wood by Phanerochaete chrysosporium. The individual and combined effects of MnSO4 and CuSO4 supplements on the delignification of sterilised wood are investigated using response surface methodology to improve the degree and selectivity of fungal delignification. Spore-inoculated solid-state fermentations are carried out for 4 weeks in sterile vented bottles. The mechanism of the concerted action of the metal ions on lignin degradation is then elucidated by relating fungal growth and ligninolytic enzyme activities to lignocellulose degradation as a function of pretreatment time. The optimised metal-supplemented system is then applied to the pretreatment of non-sterilised wood using different inoculation techniques (spores and pre-colonised substrate), nutrients (metal ions with or without glucose and sodium nitrate) and cultivation environments (sterile aerated bottles and open trays). The fermentations are then characterised using infrared spectroscopy, in particular NIR and ATR-FTIR spectroscopy, with the aim of developing rapid lignin quantification methods as an alternative to conventional wet chemical methods. Finally, the feasibility of producing fermentable sugars from sterilised and non-sterilised poplar wood using fungal pretreatment is evaluated through a techno-economic analysis. Supplementing the pretreatment system with 2.01 µmol CuSO4 and 0.77 µmol MnSO4 g-1 wood resulted in 1.9-fold higher lignin degradation, 2.3-fold higher delignification selectivity value and 2.9-fold higher glucose yield. The improved delignification could be explained by the concerted action of Mn2+ and Cu2+ ions, with Mn2+ ions inducing and Cu2+ prolonging manganese peroxidase production responsible for delignification. Fungal pretreatment at non-sterile conditions was obtained using trays in a simple solid-state fermentation set-up without sterile aeration. A 1:3 ratio of pre-colonised and untreated wood was applied for inoculation and only Cu2+, Mn2+ and sodium nitrate as supplements. Remarkably, this technology resulted in a comparably high glucose yield (28.51 ± 0.28%) to the traditional method using sterilised wood, sterile aeration and spores as inoculum, while reducing the amount of wood to be sterilised by 71.2%. Infrared spectroscopy-based methods with high coefficients of determination (R_CV

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.