Abstract: This study investigates the impact of lower temperature on short term and long term (down to 10 degrees C) on a completely anoxic anammox granular sludge process. This is the first time granular sludge Anammox is operated in pure anoxic condition in SBR and at low temperature. Conversion performance, kinetic parameters, sludge characteristics and microbial community were analyzed.

Abstract: In this work, a nonlinear dispersive multiphysic model based on Maxwell and asymptotic Smoluchowsky equations has been developed to analyze the electroporation phenomenon induced by pulsed electric field on biological cells. The irregular plasma membrane geometry has been modeled by incorporating in the numerical algorithm the Gielis superformula as well as the dielectric dispersion of the plasma membrane has been modeled using the multi-relaxation Debye-based relationship. The study has been carried out with the aim to compare our model implementing a thin plasma membrane with the simplified model in which the plasma membrane is modeled as a distributed impedance boundary condition. The numerical analysis has been performed exposing the cell to external electric pulses having rectangular shapes. By an inspection of the obtained results, significant differences can be highlighted between the two models confirming the need to incorporate the effective thin membrane into the numerical algorithm to well predict the cell response to the pulsed electric fields in terms of transmembrane voltages and pore densities, especially when the cell is exposed to external nanosecond pulses.

Abstract: For the first time, we demonstrate, using an atomistic description of a 30nm diameter spin-transfer-torque magnetic random access memories (STT-MRAM), that the difference in mechanical properties of its sub-nanometer layers induces a high compressive strain in the magnetic tunnel junction (MTJ) and leads to a detrimental magnetostrictive effect. Our model explains the issues met in engineering the electrical and magnetic performances in scaled STT-MRAM devices. The resulting high compressive strain built in the stack, particularly in the MgO tunnel barrier (t-MgO), and its associated non-uniform atomic displacements, impacts on the quality of the MTJ interface and leads to strain relieve mechanisms such as surface roughness and adhesion issues. We illustrate that the strain gradient induced by the different materials and their thicknesses in the stacks has a negative impact on the tunnel magneto-resistance (TMR), on the magnetic nucleation process and on the STT-MRAM performance.

Abstract: The fine balance between dipole-field energy and anion drift force defines the switching mechanism during polarization reversal: for the first time we show that only Pbcm mechanism obeys the ferroelectric switching physics, whereas P4(2)/nmc (or any other) mechanism does not. However, with lower energy barrier, it represents an important antiferroelectric mechanism. Constraints relaxation can lead to 90 degrees polarization rotation (domain deactivation). Intrinsically, the Si/VO-doping can switch faster than undoped HfO2 or HfZrOx. Theoretical Arrhenius model / intrinsic material switching (DFT) overestimates the switching speed extracted from experiments.

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: Ag nanocluster-doped glasses have been prepared by a conventional melt-quenching method. The effect of melt temperature and dwell time on the formation of Ag nanoclusters and Ag nanoparticles in simple host oxyfluoride glasses has been studied. The increase of melt temperature and dwell time results in the dissolution of Ag nanoparticles and substantial red-shift of absorption and photoluminescence spectra of the prepared glasses. The quantum yield of the glasses is similar to 5% and does not depend on melt temperature and dwell time. The prepared glasses may be used as red phosphors or down-conversion layers for solar-cells.

Abstract: Epitaxially grown ultra-thin Si layers are often used to passivate Ge surfaces in the high-k gate module of (strained) Ge FinFET devices. We use Si4H10 as Si precursor as it enables epitaxial Si growth at temperatures down to 330 degrees C. C-V characteristics of blanket capacitors made on Ge virtual substrates point to the presence of an optimal Si thickness. In case of compressively strained Ge fin structures, the Si growth results in non-uniform and high strain levels in the strained Ge fin. These strain levels have been calculated for different shapes of the Ge fin and in function of the grown Si thickness. The high strain is the driving force for potential (unwanted) Ge surface reflow during the Si deposition. The Ge surface reflow is strongly affected by the strength of the H-passivation during Si-capping and can be avoided by carefully selected process conditions.

Abstract: As the field of perovskite optoelectronics developed, a plethora of strategies has arisen to control their electronic and morphological characteristics for the purpose of producing high efficiency devices. Unfortunately, despite this wealth of deposition approaches, the community experiences a great deal of irreproducibility between different laboratories, batches and preparation methods. Aiming to address this issue, we developed a simple deposition method based on gas quenching that yields smooth films for a wide range of perovskite compositions, in single, double, triple and quadruple cation varieties, and produces planar heterojunction devices with competitive efficiencies, so far up to 20%.

Abstract: Non-charge-based logic devices are promising candidates for future logic circuits. Interest in studying and developing these devices has grown dramatically in the past decade as they possess key advantages over conventional CMOS technology. Due to their novel designs, a large number of micromagnetic simulations are required to fully characterize the behavior of these devices. The number and complexity of these simulations place large computational requirements on device development. We use state-of-the-art machine learning techniques to expedite identification of their behavior. Several intelligent sampling strategies are combined with machine learning multi-class classification models. These techniques are applied to a recently developed exchange-driven magnetic logic scheme that utilizes direct exchange coupling as the main driver.

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: In the present work, multiphysics modeling was used to investigate the feasibility of a photocatalysis-based outdoor air purifying solution that could be used in high polluted streets, especially street canyons. The article focuses on the use of a semi-active photocatalysis in the surfaces of the street as a solution to remove anthropogenic pollutants from the air. The solution is based on lamellae arranged horizontally on the wall of the street, coated with a photocatalyst (TiO2), lightened with UV light, with a dimension of 8 cm × 48 cm × 1 m. Fans were used in the system to create airflow. A high purification percentage was obtained. An artificial neural network (ANN) was used to predict the optimal purification method based on previous simulations, to design purification strategies considering the energy cost. The ANN was used to forecast the amount of purified with a feed-forward neural network and a backpropagation algorithm to train the model.