Abstract: We investigate discharge contraction in a microwave plasma at sub-atmospheric pressure, operating in CO2 and CO2/CH4 mixtures. The rise of the electron number density with plasma contraction intensifies the gas heating in the core of the plasma. This, in turn, initiates fast core-periphery transport and defines the rate of thermal chemistry over plasma chemistry. In this context, power concentration describes the overall mechanism including plasma contraction and chemical kinetics. In a complex chemistry such as dry reforming of methane, transport of reactive species is essential to define the performance of the reactor and achieve the desired outputs. Thus, we couple experimental observations and thermodynamic calculations for model validation and understanding of reactor performance. Adding CH4 alters the thermodynamic properties of the mixture, especially the reactive component of the heat conductivity. The increase in reactive heat conductivity increases the pressure at which plasma contraction occurs, because higher rates of gas heating are required to reach the same temperature. In addition, we suggest that the predominance of heat conduction over convection is a key condition to observe the effect of heat conductivity on gas temperature.

Abstract: In this work, we report an anomalous phenomenon on superelastic cycling of NiTi shape memory alloys when deforming at the temperature close to the border of superelastic window. New unexpected effects are found-(i) critical stress for inducing martensite transformation during the second loading cycle is higher than that of the first cycle; ( ii ) the plateau stress of the second cycle decreases to the original level when the strain overcomes the limit of the first cycle; ( iii ) transition from good superelasticity in the first cycle to fully irreversible strain in the second. We propose that defects generated during the first superelastic cycle close to the border of superelastic window impede following stress-induced martensitic transformations, leading to the increase of critical stress beyond yield stress of the B2 matrix, and thus functional fatigue of NiTi alloys. (c) 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Abstract: The alpha-T3 lattice model interpolates a honeycomb (graphene-like) lattice and a T3 (also known as dice) lattice via the parameter alpha. These lattices are made up of three atoms per unit cell. This gives rise to an additional dispersionless flat band touching the conduction and valence bands. Electrons in this model are analogous to Dirac fermions with an enlarged pseudospin, which provides unusual tunneling features like omnidirectional Klein tunneling, also called super-Klein tunneling (SKT). However, it is unknown how small deviations in the equivalence between the atomic sites, i.e., variations in the alpha parameter, and the number of tunnel barriers changes the transmission properties. Moreover, it is interesting to learn how tunneling occurs through regions where the energy spectrum changes from linear with a middle flat band to a hyperbolic dispersion. In this paper we investigate these properties, its dependence on the number of square barriers and the alpha parameter for either gapped and gapless cases. Furthermore, we compare these results to the case where electrons tunnel from a region with linear dispersion to a region with a bandgap. In the latter case, contrary to tunneling through a potential barrier, the SKT is no longer observed. Finally, we find specific cases where transmission is allowed due to a symmetry breaking of sublattice equivalence.

Abstract: Recently, an extremely low lattice thermal conductivity value has been reported for the alkali-based telluride material BaIn2Te4. The value is comparable with low-thermal conductivity metal chalcogenides, and the glass limit is highly intriguing. Therefore, to shed light on this issue, we performed first-principles phonon thermal transport calculations. We predicted highly anisotropic lattice thermal conductivity along different directions via the solution of the linearized phonon Boltzmann transport equation. More importantly, we determined several different factors as the main sources of the predicted ultralow lattice thermal conductivity of this crystal, such as the strong interactions between low-frequency optical phonons and acoustic phonons, small phonon group velocities, and lattice anharmonicity indicated by large negative mode Gruneisen parameters. Along with thermal transport calculations, we also investigated the electronic transport properties by accurately calculating the scattering mechanisms, namely the acoustic deformation potential, ionized impurity, and polar optical scatterings. The inclusion of spin-orbit coupling (SOC) for electronic structure is found to strongly affect the p-type Seebeck coefficients. Finally, we calculated the thermoelectric properties accurately, and the optimal ZT value of p-type doping, which originated from high Seebeck coefficients, was predicted to exceed unity after 700 K and have a direction averaged value of 1.63 (1.76 in the y-direction) at 1000 K around 2 x 1020 cm-3 hole concentration. For n-type doping, a ZT around 3.2 x 1019 cm-3 concentration was predicted to be a direction-averaged value of 1.40 (1.76 in the z-direction) at 1000 K, mostly originating from its high electron mobility. With the experimental evidence of high thermal stability, we showed that the BaIn2Te4 compound has the potential to be a promising mid- to high-temperature thermoelectric material for both p-type and n-type systems with appropriate doping.

Abstract: Stone Matrix Asphalt (SMA) is a gap-graded mixture which requires high contents of asphalt binder. To prevent draindown, natural or synthetic fibres and polymer-modified asphalt binders are conventionally used in SMA. Banana agribusiness is one of the major sources of post-harvest residue in Brazil. Amongst those residues, fibres extracted from the pseudostem of the banana plant are resistant and used in diverse purposes. The present study assesses the incorporation of fibres from the pseudostem of the banana plant in an SMA mixture. The fibre contents and lengths capable to prevent binder draindown were evaluated from draindown tests. Mechanical properties of an SMA mixture stabilised with different banana fibre lengths were analysed through the tests of Marshall stability, modified Lottman, Indirect Tensile Strength and Cantabro. The results indicated that the fibres studied are a viable alternative for SMA, stabilising draindown and improving its mechanical performance, especially at the length of 20 mm.

Abstract: The quantum electrostatic heterostructure (QEH) model allows for efficient computation of the dielectric screening properties of layered van der Waals (vdW)-bonded heterostructures in terms of the dielectric functions of the individual two-dimensional (2D) layers. Here, we extend the QEH model by including (1) contributions to the dielectric function from infrared active phonons in the 2D layers, (2) screening from homogeneous bulk substrates, and (3) intraband screening from free carriers in doped 2D semiconductor layers. We demonstrate the potential of the extended QEH model by calculating the dispersion of coupled phonons in multilayer stacks of hexagonal boron-nitride (hBN), the strong hybridization of plasmons and optical phonons in graphene/hBN heterostructures, the effect of substrate screening on the exciton series of monolayer MoS2, and the properties of hyperbolic plasmons in a doped phosphorene sheet. The new QEH code is distributed as a Python package with a simple command line interface and a comprehensive library of dielectric building blocks for the most common 2D materials, providing an efficient open platform for dielectric modeling of realistic vdW heterostructures.

Abstract: We evaluate the importance of quantum effects on the sticking of H2 on Al(110) for conditions that are close to those of molecular beam experiments that have been done on this system. Calculations with the quasi-classical trajectory (QCT) method and with quantum dynamics (QD) are performed using a model in which only motion in the six molecular degrees of freedom is allowed. The potential energy surface used has a minimum barrier height close to the value recently obtained with the quantum Monte Carlo method. Monte Carlo averaging over the initial rovibrational states allowed the QD calculations to be done with an order of magnitude smaller computational expense. The sticking probability curve computed with QD is shifted to lower energies relative to the QCT curve by 0.21 to 0.05 kcal/mol, with the highest shift obtained for the lowest incidence energy. Quantum effects are therefore expected to play a small role in calculations that would evaluate the accuracy of electronic structure methods for determining the minimum barrier height to dissociative chemisorption for H2 + Al(110) on the basis of the standard procedure for comparing results of theory with molecular beam experiments.

Abstract: A wave-packet time evolution method, based on the split-operator technique, is developed to investigate the scattering of quasiparticles at a normal-superconductor interface of arbitrary profile and shape. As a practical application, we consider a system where low-energy electrons can be described as Dirac particles, which is the case for most two-dimensional materials, such as graphene and transition-metal dichalcogenides. However, the method is easily adapted for other cases such as electrons in few-layer black phosphorus or any Schrodinger quasiparticles within the effective mass approximation in semiconductors. We employ the method to revisit Andreev reflection in mono-, bi-, and trilayer graphene, where specular-and retro-reflection cases are observed for electrons scattered by a steplike superconducting region. The effect of opening a zero-gap channel across the superconducting region on the electron and hole scattering is also addressed, as an example of the versatility of the technique proposed here.

Abstract: Structural and electronic properties of silicon and germanium monolayers with two different diamondoid crystal structures are detailed ab initio. Our results show that, despite Si and Ge being well-known indirect gap semiconductors in their bulk form, their adamantane and diamantane monolayers can exhibit optically active direct gap in the visible frequency range, with highly anisotropic effective masses, depending on the monolayer crystal structure. Moreover, we reveal that gaps in these materials are highly tunable with applied strain. These stable monolayer forms of Si and Ge are therefore expected to help bridging the gap between the fast growing area of opto-electronics in two-dimensional materials and the established silicon-based technologies.

Abstract: In 2023, the European Union will vote on the reauthorization of glyphosate use, renewed in 2017 despite concern on impacts on the environment and public health. A ban is supported by several Member States but rejected by most farmers. What are citizens’ preferences to phase out glyphosate? To assess whether taxation could be an alternative to a ban, we conducted a discrete choice experiment in five European countries. Our results reveal that the general public is strongly willing to pay for a reduction in glyphosate use. However, while 75.5% of respondents stated to support a ban in the pre-experimental survey, experimental results reveal that in 73.35% of cases, earmarked taxation schemes are preferred when they lead to a strong reduction in glyphosate use for an increase in food price lower than that induced by a ban. When glyphosate reduction is balanced against its costs, a tax may be preferred.

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: We investigate the coupling factor phi( mu) that quantifies the magnetic flux phi per magnetic moment mu of a point-like magnetic dipole that couples to a superconducting quantum interference device (SQUID). Representing the dipole by a tiny current-carrying (Amperian) loop, the reciprocity of mutual inductances of SQUID and Amperian loop provides an elegant way of calculating phi(mu)(r,e(mu)) vs. position r and orientation e(mu) of the dipole anywhere in space from the magnetic field B-J(r) produced by a supercurrent circulating in the SQUID loop. We use numerical simulations based on London and Ginzburg-Landau theory to calculate phi (mu) from the supercurrent density distributions in various superconducting loop geometries. We treat the far-field regime ( r greater than or similar to a= inner size of the SQUID loop) with the dipole placed on (oriented along) the symmetry axis of circular or square shaped loops. We compare expressions for phi (mu) from simple filamentary loop models with simulation results for loops with finite width w (outer size A > alpha), thickness d and London penetration depth lambda(L )and show that for thin ( d << alpha ) and narrow (w < alpha) loops the introduction of an effective loop size a(eff) in the filamentary loop-model expressions results in good agreement with simulations. For a dipole placed right in the center of the loop, simulations provide an expression phi(mu)(a,A,d,lambda(L)) that covers a wide parameter range. In the near-field regime (dipole centered at small distance z above one SQUID arm) only coupling to a single strip representing the SQUID arm has to be considered. For this case, we compare simulations with an analytical expression derived for a homogeneous current density distribution, which yields excellent agreement for lambda(L)>w,d . Moreover, we analyze the improvement of phi(mu) provided by the introduction of a narrow constriction in the SQUID arm below the magnetic dipole.

Abstract: Immobilization of two single transition metal (TM) atoms on a substrate host opens numerous possibilities for catalyst design. If the substrate contains more than one vacancy site, the combination of TMs along with their distribution patterns becomes a design parameter potentially complementary to the substrate itself and the bi-metal composition. By means of DFT calculations, we modeled three dissimilar bi-metal atoms (Ti, Mn, and Cu) doped into the six porphyrin-like cavities of porous C24N24 fullerene, considering different bi-metal distribution patterns for each binary complex, viz. TixCuz@C24N24, TixMny@C24N24, and MnyCuz@C24N24 (with x, y, z = 0-6). We elucidate whether controlling the distribution of bi-metal atoms into the C24N24 cavities can alter their catalytic activity toward CO2, NO2, H-2, and N-2 gas capture. Interestingly, Ti2Mn4@C24N24 and Ti2Cu4@C24N24 complexes showed the highest activity and selectively toward gas capture. Our findings provide useful information for further design of novel few-atom carbon-nitride-based catalysts.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: Biomass fast pyrolysis has been considered one of the best alternatives for the thermal conversion of biomass into bio-oil. This work introduces a new reactor technology for biomass fast pyrolysis, the Gas-Solid Vortex Reactor (GSVR), to obtain high bio-oil yields. The GSVR was designed to decrease the residence time of the pyrolysis vapors; thus, the secondary cracking reactions are reduced, to enhance the segregation of the char and the unreacted biomass and to improve the heat transfer rate. Biomass fast pyrolysis experiments have been carried out for the first time in a Gas-Solid Vortex Reactor (GSVR) at 773 K, using softwood (pine) and hardwood (poplar) as feedstock. Char yields as low as 10 wt. % in the GSVR were comparable to those reported for the same feedstocks processed in conventional fluidized bed reactors. The yields of non-condensable gases in the range of 15–17 wt. % were significantly lower than those reported for other commonly used biomass fast pyrolysis reactors. Two-dimensional gas chromatography (GC × GC) revealed noticeable differences at the molecular level between the bio-oils from the GSVR and bio-oils from other reactors. The aromatics in the pine bio-oil consist almost entirely (85 wt. %) of guaiacols. For poplar bio-oils no predominant group of aromatics was found, but phenolics, syringols, and catechols were the most pronounced. The experimental results highlight the advantages of the GSVR for biomass pyrolysis, reaching stable operation in around 60 s, removing the formed char selectively during operation, and enabling fast entrainment of pyrolysis vapors. Results indicate a great potential for increasing yield and selectivity towards guaiacols in softwood (e.g., pine) bio-oil. Likewise, decreasing pyrolysis temperature could increase the yield of guaiacols and syringols in hardwood (e.g., poplar) bio-oil.