Abstract: Topological properties of a double-stranded DNA (dsDNA) proximity-coupled by an s-wave superconductor are investigated, in which the energy spectra and the differential conductance are calculated within the framework of tight-binding approximation. Our results indicate that this dsDNA-superconductor system hosts Majorana zero modes (MZMs) when the Zeeman field is perpendicular to the helix axis, whereas no MZM could be observed when the Zeeman field is parallel to the helix axis, in sharp contrast to previous studies on nanowires including single-stranded DNA. In particular, two topological phase transitions could take place in the dsDNA-superconductor system by changing the Zeeman field, one from a topological trivial phase to a topological nontrivial phase with one pair of MZMs in small Zeeman field regime, and the other from a phase with one pair of MZMs to a phase with two pairs of MZMs by further increasing the Zeeman field. In the presence of a gate field normal to the helix axis, the topological nontrivial phase with two pairs of MZMs can transform into the phase with one pair of MZMs. The topological phase with one pair of MZMs is more stable and robust against Anderson disorder.

Abstract: Dirac plasmons in graphene hybridize with phonons of transition metal dichalcogenides (TMDs) when the materials are combined in so-called van der Waals heterostructures (vdWh), thus forming surface plasmon-phonon polaritons (SPPPs). The extend to which these modes are coupled depends on the TMD composition and structure, but also on the plasmons' properties. By performing realistic simulations that account for the contribution of each layer of the vdWh separately, we calculate how the strength of plasmon-phonon coupling depends on the number and composition of TMD layers, on the graphene Fermi energy and the specific phonon mode. From this, we present a semiclassical theory that is capable of capturing all relevant characteristics of the SPPPs. We find that it is possible to realize both strong and ultra-strong coupling regimes by tuning graphene's Fermi energy and changing TMD layer number.

Abstract: Motivated by the recent successful synthesis of highly crystalline ultrathin BiTeCl and BiTeBr layered sheets [Debarati Hajra et al., ACS Nano, 2020, 14, 15626], herein for the first time, we carry out a comprehensive study on the structural and electronic properties of BiTeCl and BiTeBr Janus monolayers using density functional theory (DFT) calculations. Different structural and electronic parameters including the lattice constant, bond lengths, layer thickness in the z-direction, different interatomic angles, work function, charge density difference, cohesive energy and Rashba coefficients are determined to acquire a deep understanding of these monolayers. The calculations show good stability of the studied single layers. BiTeCl and BiTeBr monolayers are semiconductors with electronic bandgaps of 0.83 and 0.80 eV, respectively. The results also show that the semiconductor-metal transformation can be induced by increasing the number of layers. In addition, the engineering of the electronic structure is also studied by applying an electric field, and mechanical uniaxial and biaxial strain. The results show a significant change of the bandgaps and that an indirect-direct band-gap transition can be induced. This study highlights the positive prospect for the application of BiTeCl and BiTeBr layered sheets in novel electronic and energy conversion systems.

Abstract: This work addresses the early stages (<= 1000 h) of the dissolution corrosion behavior of 316L and DIN 1.4970 austenitic stainless steels in contact with oxygen-poor (C-O < 10(-8) mass%), static liquid lead-bismuth eutectic (LBE) at 500 degrees C for 600-1000 h. The objective of this study was to determine the relative early-stage resistance of the uncoated steels to dissolution corrosion and to assess the protectiveness of select candidate coatings (Cr2AlC, Al2O3, V2AlxCy). The simultaneous exposure of steels with intended differences in microstructure and thermomechanical state showed the effects of steel grain size, density of annealing/deformation twins, and secondary precipitates on the steel dissolution corrosion behavior. The findings of this study provide recommendations on steel manufacturing with the aim of using the steels to construct Gen-IV lead-cooled fast reactors.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.

Abstract: The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimization of water resources management. Global challenges such as climate change, intensive agriculture and urbanization put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data for example on: the location of the measurement, upstream and downstream catchment characteristics, horizontal ellipsis are required. In this paper, we present a data management system to support flow-path related functionality for decision making and prediction modelling. Adding meta data sets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks.

Abstract: Theoretical studies on molecule-metal surface reactions have so far been limited to small surface unit cells due to computational costs. Here, for the first time molecular dynamics simulations on very large surface unit cells at the level of density functional theory are performed, allowing a direct comparison to experiments performed on a curved crystal. Specifically, the reaction of D-2 on a curved Pt crystal is investigated with a neural network potential (NNP). The developed NNP is also accurate for surface unit cells considerably larger than those that have been included in the training data, allowing dynamical simulations on very large surface unit cells that otherwise would have been intractable. Important and complex aspects of the reaction mechanism are discovered such as diffusion and a shadow effect of the step. Furthermore, conclusions from simulations on smaller surface unit cells cannot always be transfered to larger surface unit cells, limiting the applicability of theoretical studies of smaller surface unit cells to heterogeneous catalysts with small defect densities.

Abstract: Resource recovery and reuse from domestic wastewater has become an important subject for the current development of sanitation technologies and infrastructures. Different technologies are available and combined into sanitation concepts, with different performances. This study provides a methodological approach to evaluate the sustainability of these sanitation concepts with focus on resource recovery and reuse. St. Eustatius, a small tropical island in the Caribbean, was used as a case study for the evaluation. Three source separation-communityon-site and two combined sewerage island-scale concepts were selected and compared in terms of environmental (net energy use, nutrient recovery/reuse, BOD/COD, pathogens, and GHG emission, land use), economic (CAPEX and OPEX), social cultural (acceptance, required competences and education), and technological (flexibility/ adaptability, reliability/continuity of service) indicators. The best performing concept, is the application of Upflow Anaerobic Sludge Bed (UASB) and Trickling Filter (TF) at island level for combined domestic wastewater treatment with subsequent reuse in agriculture. Its overall average normalised score across the four categories (i. e., average of average per category) is about 15% (0.85) higher than the values of the remaining systems and with a score of 0.73 (conventional activated sludge – centralised level), 0.77 (UASB-septic tank (ST)), 0.76 (UASB-TF – community level), and 0.75 (ST – household level). The higher score of the UASB-TF at community level is mainly due to much better performance in the environmental and economic categories. In conclusion, the case study provides a methodological approach that can support urban planning and decision-making in selecting more sustainable sanitation concepts, allowing resource recovery and reuse in small island context or in other contexts.

Abstract: The correct prediction of air pollutants dispersed in urban areas is of paramount importance to safety, public health and a sustainable environment. Vehicular traffic is one of the main sources of nitrogen oxides (NO ) and particulate matter (PM), strongly related to human morbidity and mortality. In this study, the pollutant level and distribution in a section of one of the main road arteries of Antwerp (Belgium, Europe) are analyzed. The assessment is performed through computational fluid dynamics (CFD), acknowledged as a powerful tool to predict and study dispersion phenomena in complex atmospheric environments. The two main traffic lanes are modeled as emitting sources and the surrounding area is explicitly depicted. A Reynolds-averaged Navier–Stokes (RANS) approach specific for Atmospheric Boundary Layer (ABL) simulations is employed. After a validation on a wind tunnel urban canyon test case, the dispersion within the canopy of two relevant urban pollutants, nitrogen dioxide (NO) and particulate matter with an aerodynamic diameter smaller than 10 m (PM10), is studied. An experimental field campaign led to the availability of wind velocity and direction data, as well as PM10 concentrations in some key locations within the urban canyon. To accurately predict the concentration field, a relevant dispersion parameter, the turbulent Schmidt number, , is prescribed as a locally variable quantity. The pollutant distributions in the area of interest – exhibiting strong heterogeneity – are finally demonstrated, considering one of the most frequent and concerning wind directions. Possible local remedial measures are conceptualized, investigated and implemented and their outcomes are directly compared. A major goal is, by realistically reproducing the district of interest, to identify the locations inside this intricate urban canyon where the pollutants are stagnating and to analyze which solution acts as best mitigation measure. It is demonstrated that removal by electrostatic precipitation (ESP), an active measure, and by enhancing the dilution process through wind catchers, a passive measure, are effective for local pollutant removal in a realistic urban canyon. It is also demonstrated that the applied ABL methodology resolves some well known problems in ABL dispersion modeling.

Abstract: Lignosulfonate-based bioactive coatings as soil improvers for lawns were developed using laccase as a biocatalyst. Incorporation of glycerol, xylitol and sorbitol as plasticizers considerably reduced the brittleness of the synthesized coatings of marine carbonate granules while thermal enzyme inactivation at 100 degrees C enabled the production of stable coatings. Heat inactivation produced stable coatings with a molecular weight of 2000 kDa and a viscosity of 4.5 x 10(-3) Pas. The desired plasticity for the spray coating of soil improver granules was achieved by the addition of 2.7% of xylitol. Agriculture beneficial microorganisms (four different Bacillus species) were integrated into the coatings. The stable coatings protected the marine calcium carbonate granules, maintained the viability of the microorganisms and showed no toxic effects on the germination and growth of model plants including corn, wheat, salad, and tomato despite a slight delay in germination. Moreover, the coatings reduced the dust formation of soil improvers by 70%. CO2 emission analysis showed potential for the reduction of up to 3.4 kg CO2-eq. kg(-1) product, making it a viable alternative to fossil-based coatings.

Abstract: The short exciton lifetime and strong exciton-exciton interaction in transition metal dichalcogenides limit the efficiency of exciton emission. Here, the authors show that exciton-exciton interaction in monolayer WS2 can be screened using proximal metal plates, leading to an improved quantum yield. Monolayer transition metal dichalcogenides (1L-TMDs) have tremendous potential as atomically thin, direct bandgap semiconductors that can be used as convenient building blocks for quantum photonic devices. However, the short exciton lifetime due to the defect traps and the strong exciton-exciton interaction in TMDs has significantly limited the efficiency of exciton emission from this class of materials. Here, we show that exciton-exciton interaction in 1L-WS2 can be effectively screened using an ultra-flat Au film substrate separated by multilayers of hexagonal boron nitride. Under this geometry, induced dipolar exciton-exciton interaction becomes quadrupole-quadrupole interaction because of effective image dipoles formed within the metal. The suppressed exciton-exciton interaction leads to a significantly improved quantum yield by an order of magnitude, which is also accompanied by a reduction in the exciton-exciton annihilation (EEA) rate, as confirmed by time-resolved optical measurements. A theoretical model accounting for the screening of the dipole-dipole interaction is in a good agreement with the dependence of EEA on exciton densities. Our results suggest that fundamental EEA processes in the TMD can be engineered through proximal metallic screening, which represents a practical approach towards high-efficiency 2D light emitters.

Abstract: We have developed an innovative methodology to overcome the lack of techniques for real-time assessment of degradable biomedical polymers at physiological conditions. The methodology was established by combining polymer characterization techniques with electrochemical sensors. The in vitro hydrolytic degradation of a series of aliphatic polyesters was evaluated by following the molar mass decrease and the mass loss at different incubation times while tracing pH and l-lactate released into the incubation media with customized miniaturized electrochemical sensors. The combination of different analytical approaches provided new insights into the mechanistic and kinetics aspects of the degradation of these biomedical materials. Although molar mass had to reach threshold values for soluble oligomers to be formed and specimens’ resorption to occur, the pH variation and l-lactate concentration were direct evidence of the resorption of the polymers and indicative of the extent of chain scission. Linear models were found for pH and released l-lactate as a function of mass loss for the l-lactide-based copolymers. The methodology should enable the sequential screening of degradable polymers at physiological conditions and has potential to be used for preclinical material’s evaluation aiming at reducing animal tests.

Abstract: Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this study, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.

Abstract: Amphetamine (AMP) is posing critical issues in our society being one of the most encountered drugs-of-abuse in the current illicit market. The continuous drug production in Europe urges the development of new tools for the rapid on-site determination of illicit drugs such as AMP. However, the direct electrochemical detection of AMP is a challenge because the molecule is non-electroactive at the potential window of conventional graphite SPEs. For this reason, a derivatization step is needed to convert the primary amine into an electroactive oxidizable group. Herein, the rapid electrochemical detection of AMP in seized samples based on the derivatization by 1,2-naphthoquinone-4-sulfonate (NQS) is presented by using square wave voltammetry (SWV) at graphite screen-printed electrodes (SPEs). First, a detailed optimization of the key parameters and the analytical performance is provided. The method showed a sensitivity of 7.9 µA mM-1 within a linear range from 50 to 500 µM, a limit of detection of 22.2 µM, and excellent reproducibility (RSD = 4.3%, n = 5 at 500 µM). Subsequently, the effect of NQS on common cutting agents for the selective detection of AMP is addressed. The comparison of the method with drugs-of-abuse containing secondary and tertiary amines confirms the selectivity of the method. Finally, the concept is applied to quantify AMP in 20 seized samples provided by forensic laboratories, exhibiting an accuracy of 97.3 ± 10.5%. Overall, the fast analysis of samples with the electrochemical profiling of derivatized AMP exhibits a straightforward on-site screening aiming to facilitate the tasks of law enforcement agents in the field.

Abstract: Immobilization of single metal atoms on a solid host opens numerous possibilities for catalyst designs. If that host is a two-dimensional sheet, sheet curvature becomes a design parameter potentially complementary to host and metal composition. Here, we use a combination of density functional theory calculations and microkinetic modeling to compare the mechanisms and kinetics of formic acid decomposition and formation, chosen for their relevance as a potential hydrogen storage medium, over single Ru atoms anchored to pyridinic nitrogen in a planar graphene flake (RuN4-G) and curved carbon nanotube (RuN4-CNT). Activation barriers are lowered and the predicted turnover frequencies are increased over RuN4-CNT relative to RuN4-CNT. The results highlight the potential of curvature control as a means to achieve high performance and robust catalysts.