Abstract: We discuss the selection and assessment of alternative metals by a combination of ab initio computation of electronic properties, experimental resistivity assessments, and calibrated line resistance models. Pt-group metals as well as Nb are identified as the most promising elements, with Ru showing the best combination of material properties and process maturity. An experimental assessment of the resistivity of Ru, Ir, and Co lines down to similar to 30 nm(2) is then used to devise compact models for line and via resistance that can be compared to Cu predictions. The main advantage of alternative metals originates from the possibility for barrierless metallization.

Abstract: We introduce LPMLE3, a new 1-D approach to quantify vertical water flow components at streambeds using temperature data collected in different depths. LPMLE3 solves the partial differential equation for coupled water flow and heat transport in the frequency domain. Unlike other 1-D approaches it does not assume a semi-infinite halfspace with the location of the lower boundary condition approaching infinity. Instead, it uses local upper and lower boundary conditions. As such, the streambed can be divided into finite subdomains bound at the top and bottom by a temperature-time series. Information from a third temperature sensor within each subdomain is then used for parameter estimation. LPMLE3 applies a low order local polynomial to separate periodic and transient parts (including the noise contributions) of a temperature-time series and calculates the frequency response of each subdomain to a known temperature input at the streambed top. A maximum-likelihood estimator is used to estimate the vertical component of water flow, thermal diffusivity, and their uncertainties for each streambed subdomain and provides information regarding model quality. We tested the method on synthetic temperature data generated with the numerical model STRIVE and demonstrate how the vertical flow component can be quantified for field data collected in a Belgian stream. We show that by using the results in additional analyses, nonvertical flow components could be identified and by making certain assumptions they could be quantified for each subdomain. LPMLE3 performed well on both simulated and field data and can be considered a valuable addition to the existing 1-D methods.

Abstract: We investigate the diffusive electron-transport properties of charge-doped graphene ribbons and nanoribbons with imperfect edges. We consider different regimes of edge scattering, ranging from wide graphene ribbons with (partially) diffusive edge scattering to ribbons with large width variations and nanoribbons with atomistic edge roughness. For the latter, we introduce an approach based on pseudopotentials, allowing for an atomistic treatment of the band structure and the scattering potential, on the self-consistent solution of the Boltzmann transport equation within the relaxation-time approximation and taking into account the edge-roughness properties and statistics. The resulting resistivity depends strongly on the ribbon orientation, with zigzag (armchair) ribbons showing the smallest (largest) resistivity and intermediate ribbon orientations exhibiting intermediate resistivity values. The results also show clear resistivity peaks, corresponding to peaks in the density of states due to the confinement-induced subband quantization, except for armchair-edge ribbons that show a very strong width dependence because of their claromatic behavior. Furthermore, we identify a strong interplay between the relative position of the two valleys of graphene along the transport direction, the correlation profile of the atomistic edge roughness, and the chiral valley modes, leading to a peculiar strongly suppressed resistivity regime, most pronounced for the zigzag orientation.

Abstract: We model transport through two-dimensional topological insulator (TI) nanoribbons. To model the quantum transport, we employ the non-equilibrium Green's function approach. With the presented approach, we study the effect of lattice imperfections on the carrier transport. We observe that the topologically protected edge states of TIs are robust against a high percentage (2%) of vacancy defects. We also investigate tunneling of the edge states in two decoupled TI nanoribbons.

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: We present a computational model to model the magnetic structure of two-dimensional (2D) dilute-magnetic-semiconductors (DMS) both the monolayers and multilayers using first-principles density functional theory (DFT), as well as their magnetic phase transition as a function of temperature using Monte-Carlo simulations. Using our method, we model the magnetic structure of bulk, bilayer, and monolayer MoS2 substitutionally doped with Fe atoms. We find that the out-of-plane interaction in bilayer MoS2 is weakly ferromagnetic, whereas in bulk MoS2 it is strongly anti-ferromagnetic. Finally, we show that the magnetic order is more robust in bilayer Fe-doped MoS2 compared to the monolayer and results in a room-temperature FM at an atomic substitution of 14-16%.

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

Abstract: We present the growth and optimization of ultralow loss Si-based Al2O3 planar waveguides, which have a high potential to boost the performance of rare-earth ion doped waveguide devices operating at visible and C-band wavelength ranges. The planar waveguide structures are grown using thermal atomic layer deposition. Systematic characterization of the obtained thin films is performed by spectroscopic ellipsometry, X-ray diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analyses, and the optimum parameters are identified. The optical loss measurements for both transverse electric (TE) and transverse magnetic polarized light at 633, 829, and 1549 nm are performed. The lowest propagation loss value of 0.04 +/- 0.02 dB/cm for the Al2O3 waveguides for TE polarization at 1549 nm is demonstrated.

Abstract: We provide a comprehensive analysis of the prominent tight-binding (TB) models for transition metal dichalcogenides (TMDs) available in the literature. We inspect the construction of these TB models, discuss their parameterization used and conduct a thorough comparison of their effectiveness in capturing important electronic properties. Based on these insights, we propose a novel TB model for TMDs designed for enhanced computational efficiency. Utilizing MoS2 as a representative case, we explain why specific models offer a more accurate description. Our primary aim is to assist researchers in choosing the most appropriate TB model for their calculations on TMDs.

Abstract: We study the electronic spectral properties at zero temperature of the one-dimensional (1D) version of the degenerate two-orbital Kanamori-Hubbard model, one of the well-established frameworks to study transition metal compounds, using state-of-the-art numerical techniques based on the density matrix renormalization group. While the system is Mott insulating for the half-filled case, as expected for an interacting 1D system, we find interesting and rich structures in the single-particle density of states (DOS) for the hole-doped system. In particular, we find the existence of in-gap states which are pulled down to lower energies from the upper Hubbard band with increasing the interorbital Coulomb interaction V. We analyze the composition of the DOS by projecting it onto different local excitations, and we observe that for large dopings these in-gap excitations are formed mainly by interorbital holon-doublon (HD) states and their energies follow approximately the HD states in the atomic limit. We observe that the Hund interaction J increases the width of the in-gap band, as expected from the two-particle fluctuations in the Hamiltonian. The observation of a finite density of states within the gap between the Hubbard bands for this extended 1D model indicates that these systems present a rich excitation spectra which could help us understand the microscopic physics behind multiorbital compounds.

Abstract: We theoretically investigate the effect of intercalation of third row transition metals (Co, Cr, Fe, Mn, Ti and V) in the layers of WSe2. Using density functional theory (DFT), we investigate the structural stability. We also compute the DFT energies of various magnetic spin configurations. Using these energies, we construct a Heisenberg Hamiltonian and perform a Monte Carlo study on each WSe2 + intercalant system to estimate the Curie or Neel temperature. We find ferromagnetic ground states for Ti and Cr intercalation, with Curie temperatures of 31K and 225K, respectively. In Fe-intercalated WSe2, we predict that antiferromagnetic ordering is present up to 564K. For V intercalation, we find that the system exhibits a double phase transition.

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: When applying hydrological models, different sources of uncertainty are present, and evaluations of model performances should take these into account to assess model outcomes correctly. Furthermore, uncertainty in the discharge observations complicates the model identification, both in terms of model structure and parameterization. In this paper, the authors compare two different lumped model structures (PDM and NAM) considering uncertainty coming from the rating curve. Limits of acceptability for the model simulations were determined based on derived uncertainty bounds of the discharge observations. The authors applied the DYNamic Identifiability Approach (DYNIA) to identify structural failure of both models and to evaluate the configuration of their structures. In general, similar model performances are observed. However, the model structures tend to behave differently in the course of time, as revealed by the DYNIA approach. Based on the analyses performed, the probability based soil storage representation of the PDM model outperforms the NAM structure. The incorporation of the observation error did not prevent the DYNIA analysis to identify potential model structural deficiencies that are limiting the representation of the seasonal variation, primarily indicated by shifting regions of parameter identifiability. As such, the proposed approach is able to indicate where deficiencies are found and model improvement is needed.

Abstract: When macrophytes are growing in the river, the vegetation induces substantial changes to the water quality. Some effects are the result of direct interactions, such as photosynthetic activity or nutrient uptake, whereas others may be attributed to indirect effects of the water plants on hydrodynamics and river processes. This research focused on the direct effect of macrophytes on oxygen dynamics and nutrient cycling. Discharge, macrophyte biomass density, basic water quality, dissolved oxygen and nutrient concentrations were in situ monitored throughout the year in a lowland river (Nete catchment, Belgium). In addition, various processes were investigated in more detail in multiple ex situ experiments. The field and aquaria measurement results clearly demonstrated that aquatic plants can exert considerable impact on dissolved oxygen dynamics in a lowland river. When the river was dominated by macrophytes, dissolved oxygen concentrations varied from 5 to 10 mg l−1. Considering nutrient retention, it was shown that the investigated in-stream macrophytes could take up dissolved inorganic nitrogen (DIN) from the water column at rates of View the MathML source. And DIN fluxes towards the vegetation were found to vary from 0.03 to 0.19 g N ha−1 h−1 in spring and summer. Compared to the measured changes in DIN load over the river stretch, it means that about 313% of the DIN retention could be attributed to direct nitrogen uptake from the water by macrophytes. Yet, the role of macrophytes in rivers should not be underrated as aquatic vegetation also exerts considerable indirect effects that may have a greater impact than the direct fixation of nutrients into the plant biomass.

Abstract: While deammonification of high-strength wastewater in the sludge line of sewage treatment plants has become well established, the potential cost savings spur the development of this technology for mainstream applications. This study aimed at identifying the effect of aeration and organic carbon on the deammonification process. Two 10 L sequencing bath reactors with different aeration frequencies were operated at 25 degrees C. Real wastewater effluents from chemically enhanced primary treatment and high-rate activated sludge process were fed into the reactors with biodegradable chemical oxygen demand/nitrogen (bCOD/N) of 2.0 and 0.6, respectively. It was found that shorter aerobic solids retention time (SRT) and higher aeration frequency gave more advantages for aerobic ammonium-oxidizing bacteria (AerAOB) than nitrite oxidizing bacteria (NOB) in the system. From the kinetics study, it is shown that the affinity for oxygen is higher for NOB than for AerAOB, and higher dissolved oxygen set-point could decrease the affinity of both AerAOB and NOB communities. After 514 days of operation, it was concluded that lower organic carbon levels enhanced the activity of anoxic ammonium-oxidizing bacteria (AnAOB) over denitrifiers. As a result, the contribution of AnAOB to nitrogen removal increased from 40 to 70%. Overall, a reasonably good total removal efficiency of 66% was reached under a low bCOD/N ratio of 2.0 after adaptation.