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At least 235 records · Page 13

Surface premelting of ice far below the triple point

Premelting of ice, a quasi-liquid layer (QLL) at the surface below the melting temperature, was first postulated by Michael Faraday 160 y ago. Since then, it has been extensively studied theoretically and experimentally through many techniques. Existing work has been performed predominantly on hexagonal ice, at conditions close to the triple point. Whether the same phenomenon can persist at much lower pressure and temperature, where stacking disordered ice sublimates directly into water vapor, remains unclear. Herein, we report direct observations of surface premelting on ice nanocrystals below the sublimation temperature using transmission electron microscopy (TEM). Similar to what has been reported on hexagonal ice, a QLL is found at the solid-vapor interface. It preferentially decorates certain facets, and its thickness increases as the phase transition temperature is approached. In situ TEM reveals strong diffusion of the QLL, while electron energy loss spectroscopy confirms its amorphous nature. More significantly, the premelting observed in this work is thought to be related to the metastable low-density ultraviscous water, instead of ambient liquid water as in the case of hexagonal ice. This opens a route to understand premelting and grassy liquid state, far away from the normal water triple point.

58 GEOSCIENCES↗

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows

The injection of multi-species fluids into high-pressure and high-temperature environments beyond the species' critical points is commonly found in engineering applications. At these conditions, for immiscible species, both subcritical interfacial dynamics and supercritical mixing can coexist due to variations in temperature around the mixture critical point. The modeling of these complex transcritical phenomena for large-scale configurations is so far not possible. To address this issue, we propose the Regularized-Interface Method (RIM) as a unified formulation that can describe both sub- and supercritical processes as well as the transition between them. The proposed method is derived via filtering of the nanoscale interface-resolving formulation based on van der Waals' linear gradient theory. Thus, this approach allows for the consistent modeling of interfacial dynamics that vanishes at supercritical conditions, while significantly reducing the temporal and spatial resolution constraints of the original nanoscale formulation. The resulting RIM formulation is examined in interface-capturing simulations of sub-, trans-, and supercritical fuel injection processes, involving droplets and jets. Furthermore, these results highlight the importance of resolving spatio-temporal transitions from subcritical interfacial dynamics to supercritical mixing in high-pressure multiphase simulations, in contrast to commonly employed diffused-interface methods, where interfacial dynamics are often neglected.

Interface capturing↗

Evolution of magnetism, valence, and crystal lattice in EuCd 2 As 2 under pressure

EuCd 2 As 2 has been proposed to be one of the ideal platforms as an intrinsic topological magnetic system, potentially hosting a single pair of Weyl points when it is tuned into the ferromagnetic state with spins aligned out of plane by either external pressure or chemical doping. To investigate the possible realization of an ideal topological state, we have systematically investigated pressure control of the magnetic state, valence, and crystal structure using synchrotron-based time-domain Mössbauer spectroscopy, x-ray absorption spectroscopy, and powder x-ray diffraction. Our experimental results show that the magnetic configuration remains mostly in plane under pressure up to 42.8 GPa and pressure effectively enhances the magnetic ordering temperature. Meanwhile, Eu ions remain divalent when subjected to pressure up to 35.9 GPa, and the trigonal crystal lattice is maintained up to 34.6 GPa. Our work provides valuable experimental data to benchmark future theoretical studies in magnetic topological materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Isotope effects in supercooled H 2 O and D 2 O and a corresponding-states-like rescaling of the temperature and pressure

Water shows anomalous properties that are enhanced upon supercooling. The unusual behavior is observed in both H 2 O and D 2 O, however, with different temperature dependences for the two isotopes. It is often noted that comparing the properties of the isotopes at two different temperatures (i.e., a temperature shift) approximately accounts for many of the observations—with a temperature shift of 7.2 K in the temperature of maximum density being the most well-known example. However, the physical justification for such a shift is unclear. Motivated by recent work demonstrating a “corresponding-states-like” rescaling for water properties in three classical water models that all exhibit a liquid–liquid transition and critical point, the applicability of this approach for reconciling the differences in the temperature- and pressure-dependent thermodynamic properties of H 2 O and D 2 O is investigated here. Utilizing previously published data and equations-of-state for H 2 O and D 2 O, we show that the available data and models for these isotopes are consistent with such a low temperature correspondence. These observations provide support for the hypothesis that a liquid–liquid critical point, which is predicted to occur at low temperatures and high pressures, is the origin of many of water’s anomalies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Simulations of the churning mode: Toroidally symmetric plasma convection and turbulence around the X-points in a snowflake divertor

Using a reduced MHD model, extended to include field-aligned thermal conduction, we present numerical simulations of the churning mode (CM): a toroidally symmetric, non-linear plasma vortex in the vicinity of the null points in a snowflake (SF) divertor [D. D. Ryutov et al., Phys. Scr. 89, 088002 (2014)]. Simulations are carried out across a range of inter-null separations, d xx ⁠, and inter-null orientations, θ⁠, primarily in conditions relevant to the MAST-U tokamak. We find that, when d xx is small, the CM induces additional transport across the X-points when β pm ≳8%, where β pm is the ratio of the plasma pressure in the null region to poloidal magnetic pressure at the midplane. This transport also increases approximately linearly as d xx is reduced. A diffusive model of this transport is shown to predict the total transport across the null points, where diffusion coefficients of up to ∼ 10 2 m 2 s −1 centered on a small region around the X-points are used. However, the CM also results in significant changes to the flux surfaces in the null region, which is not captured by this diffusive model. The changes in magnetic geometry mean the fractional exhaust power delivered to each divertor leg is highly sensitive to β pm ⁠, d xx ⁠, and θ⁠. For small values of θ⁠, the CM can induce a change in topology, redirecting exhaust power from a secondary divertor leg on the high field side to one on the low field side. Similar behavior is found in the fraction of exhaust power going to the inner and outer divertor. Such changes in the flux surfaces may not be captured by Grad–Shafranov solvers and so may be a source of error in the magnetic reconstruction of SF experiments. We consistently find that the fractional exhaust power going to a secondary divertor leg on the high field side is small, consistent with SF experiments.

Computer simulation↗

Enhancing ZFP: A Statistical Approach to Understanding and Reducing Error Bias in a Lossy Floating-Point Compression Algorithm

The amount of data generated and gathered in scientific simulations and data collection applications is continuously growing, putting mounting pressure on storage and bandwidth concerns. A means of reducing such issues is data compression; but, lossless data compression is typically ineffective when applied to floating-point data. Thus, users tend to apply a lossy data compressor, which allows for small deviations from the original data. It is essential to understand how the error from lossy compression impacts the accuracy of the data analytics. Thus, we must analyze not only the compression properties but the error as well. In this paper, we provide a statistical analysis of the error caused by ZFP compression, a state-of-the-art, lossy compression algorithm explicitly designed for floating-point data. We show that the error is indeed biased and propose simple modifications to the algorithm to neutralize the bias and further reduce the resulting error.

97 MATHEMATICS AND COMPUTING↗

Melting diamond in the diamond cell by laser-flash heating

The phase behavior of carbon at high pressure and the search for carbon structures denser than diamond has been explored for decades showing large discrepancies, with many fundamental questions remaining unresolved. Here we show evidence of melting above the graphite-diamond-liquid (GDL) triple point (~13 GPa, 4000 K) up to 50 GPa on samples recovered from single flash-heating events using spectroscopic and electron microscopic methods. The results show that for all pressures, diamond melts below the triple point temperature contradicting previous studies, most of which predict a positive slope of the melting curve.

36 MATERIALS SCIENCE↗

Calculation of neutron flux spectra of the VVER-1000 mock-up shielding benchmark with Monte Carlo code MCS utilizing mesh-based weight window

The measurements of neutron spectra compiled inside the NEA-1517/82 package from the Shielding Integral Benchmark Archive and Database (SINBAD) are chosen as benchmark cases to validate the variance reduction technique based on weight window in Monte Carlo Code MCS. A full 3D model for fixed source mode calculation with hexagonal lattice source definition is developed to simulate total of 6 points of measurements at the vicinity of the reactor and the reactor pressure vessel region. A code/code comparison against MCNP6 code is first conducted as verification element for the mesh-based weight window capability in MCS. Finally, the validation results are presented against measurements. The verification against MCNP6 code gives good agreement in addition of the insight to the importance of user understanding to determine proper reference point and reference lower weight bound for scaling which is not required in MCS code due to its capability of automatic scaling. The comparison of neutron spectra between MCS and measurements shows good agreement within 3 standard deviations for all of six detector positions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Coupling Between Electrons and Charge Density Wave Fluctuation and its Possible Role in Superconductivity

In most charge density wave (CDW) systems of different material classes, ranging from traditional correlated systems in low-dimension to recent topological systems with Kagome lattice, superconductivity emerges when the system is driven toward the quantum critical point (QCP) of CDW via external parameters of doping and pressure. Despite this rather universal trend, the essential hinge between CDW and superconductivity has not been established yet. Here, the evidence of coupling between electron and CDW fluctuation is reported, based on a temperature- and intercalation-dependent kink in the angle-resolved photoemission spectra of 2H-Pd x TaSe 2 . Kinks are observed only when the system is in the CDW phase, regardless of whether a long- or short-range order is established. Notably, the coupling strength is enhanced upon long-range CDW suppression, albeit the coupling energy scale is reduced. Interestingly, the estimation of the superconducting critical temperature by incorporating the observed coupling characteristics into McMillan's equation yields results closely resembling the known values of the superconducting dome. The results thus highlight a compelling possibility that this new coupling mediates Cooper pairs, which provides new insights into the competing relationship not only for CDW but also for other competing orders.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Iron, magnesium, and titanium isotopic fractionations between garnet, ilmenite, f ayalite, biotite, and tourmaline: Results from NRIXS, ab initio , and study of mineral separates from the Moosilauke metapelite

Interpreting isotopic signatures documented in natural rocks requires knowledge of equilibrium isotopic fractionation factors. Here, we determine equilibrium Fe isotope fractionation factors between several common rock-forming minerals using a comparative approach involving three independent methods: (i) isotopic analyses of natural minerals from a metapelite from Mt. Moosilauke, New Hampshire, for which equilibration temperature and pressure are well constrained to be near the aluminosilicate triple point (T ≃ 500 °C, P ≃ 4 kbar), (ii) Nuclear Resonant Inelastic X-ray Scattering (NRIXS) measurements of Fe force constants of minerals, and (iii) Density Functional Theory (DFT) ab initio calculations of Fe force constants of minerals. The minerals studied for Fe isotopes include, in increasing order of their β-factors: garnet < ilmenite ≈ fayalite < biotite < tourmaline < muscovite ≈ plagioclase. Some of this ordering is affected by the presence of Fe 3+ in the minerals, which tends to form stiffer bonds and be associated with heavy Fe isotope enrichments relative to Fe 2+ . We are, however, able to assess the magnitude of the effect of the ratio Fe 3+ /ΣFe on equilibrium fractionation factors, notably on the ilmenite-hematite solid solution. Equilibrium Fe isotopic fractionation factors between garnet, ilmenite, biotite, tourmaline and fayalite are determined. We also report Mg and Ti isotopic compositions of selected Moosilauke minerals that allow us to better constrain the equilibrium fractionation factors for garnet-biotite-tourmaline (Mg isotopes) and biotite-ilmenite (Ti isotopes). We show how the newly determined equilibrium fractionation factors can be used to address diverse problems in Earth and planetary sciences, notably (i) Fe and Mg isotopic fractionation during anatexis, (ii) Fe isotopic fractionation in lunar ilmenite, and (iii) Ti isotopic fractionation during fluvial transport of minerals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

One-Pot Coating of Ceramic Powders by Exfoliated Boron Nitride Layers with a Dense CO 2 Medium and Ultrasound-Aided Mixing

The coating or doping of substrates by two-dimensional materials to impart superior functional properties (such as improved thermal conductivity, bacterial resistance, reduced friction) is receiving increased attention. Environmentally benign, rapid and scalable coating techniques are desirable for this purpose. Here, we report a novel process for coating alumina, silicon carbide and boron carbide substrates with hexagonal boron nitride (h-BN) layers. The process consists of two sequential steps. First, h-BN layers are exfoliated from bulk h-BN in supercritical carbon dioxide (scCO 2 ) using ultrasound-aided mixing. This step is followed by self-assembly (i.e., coating) of the exfoliated h-BN on the substrates in liquid CO 2 also aided by ultrasound. The liquid CO 2 state is achieved by simply lowering the pressure and temperature of the first step below the critical point of CO 2 (P c = 72.8 atm; T c = 31.1 °C). Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) micrographs clearly reveal the exfoliation of h-BN under scCO 2 conditions as well as the h-BN assembly on various substrates under liquid CO 2 conditions. X-ray diffraction patterns confirm the structural integrity of the coated h-BN layers. It was also confirmed that without a transition to the liquid CO 2 phase following exfoliation in scCO 2 , there was negligible coating of h-BN on the substrates. Researchers in the field could consider this benign process to rapidly coat or dope materials with h-BN and other 2D materials to impart improved functional properties in myriad applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

Carbon dioxide (CO 2 ) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, in this study, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO 2 in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO 2 solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO 2 solubility (ln(x CO 2 )) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO 2 solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO 2 capture and utilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Pantropical Tree Leaf Hydraulic Properties, 1986 - 2015

This data package contains leaf pressure-volume parameters (osmotic potential at full turgor, turgor loss point, bulk elastic modulus, and others). Data was obtained by combining new data I gleaned from original publications not already in the following compilations (after removing the extratropical data): Bartlett, M. K., Zhang, Y., Kreidler, N., Sun, S., Ardy, R., Cao, K., and Sack, L.: Global analysis of plasticity in turgor loss point, a key drought tolerance trait, Ecol Lett, 17, 1580-1590, 2014. Bartlett, M. K., Scoffoni, C., and Sack, L.: The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global meta-analysis, Ecology Letters, 15, 393-405, 2012. Maréchaux, I., Bartlett, M. K., Sack, L., Baraloto, C., Engel, J., Joetzjer, E., Chave, J., and Kitajima, K.: Drought tolerance as predicted by leaf water potential at turgor loss point varies strongly across species within an Amazonian forest, Functional Ecology, 29, 1268-1277, 2015. Data are from natural tropical and subtropical forests or savannas and span rainfall gradients from deciduous dry tropical forests to everwet tropical forests, and come from countries all throughout the pantropics. The attached zip file contains data organized into three CSV files.

54 ENVIRONMENTAL SCIENCES↗

Liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential

We simulate high-pressure hydrogen in its liquid phase close to molecular dissociation using a machine-learned interatomic potential. The model is trained with density functional theory (DFT) forces and energies, with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. We show that an accurate NequIP model, an E(3)-equivariant neural network potential, accurately reproduces the phase transition present in PBE. Moreover, the computational efficiency of this model allows for substantially longer molecular dynamics trajectories, enabling us to perform a finite-size scaling (FSS) analysis to distinguish between a crossover and a true first-order phase transition. Here, we locate the critical point of this transition, the liquid-liquid phase transition (LLPT), at 1200-1300 K and 155-160 GPa, a temperature lower than most previous estimates and close to the melting transition.

08 HYDROGEN↗

Band Engineering of Large-Twist-Angle Graphene/h-BN Moiré Superlattices with Pressure

Graphene interfacing hexagonal boron nitride (h-BN) forms lateral moiré superlattices that host a wide range of new physical effects such as the creation of secondary Dirac points and band gap opening. A delicate control of the twist angle between the two layers is required as the effects weaken or disappear at large twist angles. In this Letter, we show that these effects can be reinstated in large-angle (~1.8°) graphene/h-BN moiré superlattices under high pressures. Additionally, a graphene/h-BN moiré superlattice microdevice is fabricated directly on the diamond culet of a diamond anvil cell, where pressure up to 8.3 GPa is applied. The band gap at the primary Dirac point is opened by 40-60 meV, and fingerprints of the second Dirac band gap are also observed in the valence band. Theoretical calculations confirm the band engineering with pressure in large-angle graphene/h-BN bilayers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Overview of the MAST Upgrade physics programme: testing novel concepts at low aspect ratio to inform future devices

The research programme performed on the Mega Amp Spherical Tokamak (MAST) Upgrade experiment has made significant advances in developing the physics understanding of low aspect ratio tokamaks in support of the operation of ITER and design of fusion powerplants. High performance plasma scenarios have been developed to facilitate a broad programme of experiments, in which confinement is constrained by the presence of m/n = 2/1 modes that cause substantial losses of fast ions. The onset of these modes coincides with the q = 2 surface residing in a local minimum in the toroidal current density profile. The maximum electron temperature at the pedestal top, T e,ped is limited with gas fuelling to ∼350 eV to maintain regular ELMs; higher T e,ped results in a transition to a non-stationary ELM-free regime. The operational space of spherical tokamaks has been expanded into small and ELM-free regimes. Strong shaping of the last closed flux surface can induce a transition from large to small ELMs, and ELM suppression with resonant magnetic perturbations has been observed for the first time in a low aspect ratio tokamak. Negative triangularity shaping has induced a transition from ELMy H-mode to a high-performance L-mode regime for the first time in a low aspect ratio tokamak. In studies of fast ion confinement, losses of fast particles due to Global Alfvén Eigenmodes have been identified. Interactions between fast ions generated by off-axis neutral beam injection and thermal neutrals can result in significant losses of fast ions. Experiments with on- and off-axis neutral beam injection exhibit a flux pumping mechanism, where the central safety factor is held to ∼1 in the absence of sawteeth. In studies of pedestal physics, it has been found that elevated main chamber neutral pressures result in an increase in the electron density and reduction in the temperature at the pedestal top. Advances in understanding plasma exhaust include the integration of a high-performance plasma core with detached outer divertors in the X-point target configuration. A newly commissioned lower divertor cryopump reduces the lower divertor neutral pressure by up to 50%, with minimal effect on the main chamber or upper divertor. New measurements and SOLPS-ITER simulations emphasise the importance of plasma–neutral interactions on divertor detachment in the conditions accessible in experiments. Real-time control of the ionisation front location in both divertor chambers independently has been demonstrated in double null experiments, enabled by the tightly baffled divertor chambers.

MAST Upgrade↗

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗