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At least 37 records · Page 2

Sensitivity of Thermal Predictions to Uncertain Surface Tension Data in Laser Additive Manufacturing

To understand the process-microstructure relationships in additive manufacturing (AM), it is necessary to predict the solidification characteristics in the melt pool. This study investigates the influence of Marangoni driven fluid flow on the predicted melt pool geometry and solidification conditions using a continuum finite volume model. A calibrated laser absorptivity was determined by comparing the model predictions (neglecting fluid flow) against melt pool dimensions obtained from single laser melt experiments on a nickel super alloy 625 (IN625) plate. Using this calibrated efficiency, predicted melt pool geometries agree well with experiments across a range of process conditions. When fluid mechanics is considered, a surface tension gradient recommended for IN625 tends to overpredict the influence of convective heat transfer, but the use of an intermediate value reported from experimental measurements of a similar nickel super alloy produces excellent experimental agreement. Despite its significant effect on the melt pool geometry predictions, fluid flow was found to have a small effect on the predicted solidification conditions compared to processing conditions. We find that this result suggests that under certain circumstances, a model only considering conductive heat transfer is sufficient for approximating process-microstructure relationships in laser AM. Extending the model to multiple laser passes further showed that fluid flow also has a small effect on the solidification conditions compared to the transient variations in the process. Furthermore, limitations of the current model and areas of improvement, including uncertainties associated with the phenomenological model inputs are discussed.

36 MATERIALS SCIENCE↗

Direct observation of anisotropic growth of water films on minerals driven by defects and surface tension

Knowledge of the occurrences of water films on minerals is critical for global biogeochemical and atmospheric processes, including element cycling and ice nucleation. The underlying mechanisms controlling water film growth are, however, misunderstood. Using infrared nanospectroscopy, amplitude-modulated atomic force microscopy, and molecular simulations, we show how water films grow from water vapor on hydrophilic mineral nanoparticles. We imaged films with up to four water layers that grow anisotropically over a single face. Growth usually begins from the near edges of a face where defects preferentially capture water vapor. Thicker films produced by condensation cooling completely engulf nanoparticles and form thicker menisci over defects. The high surface tension of water smooths film surfaces and produces films of inhomogeneous thickness. Nanoscale topography and film surface energy thereby control anisotropic distributions and thicknesses of water films on hydrophilic mineral nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗

Altering the Supply of Shielding Gases to Fabricate Distinct Geometry in GMA Additive Manufacturing

Wire arc additive manufacturing (WAAM) is the process by which large, metallic structures are built, layer-by-layer, using a welding arc to melt wire feedstock. In this process, the proper selection of the shielding gas plays a vital role in the achievement of structurally acceptable part geometries and quality surface finishes. In this study, the authors used either a ternary mix (He, Ar and CO 2 ) or a binary mix (Ar and CO 2 ) of shielding gases to deposit wall geometries using an open loop-controlled WAAM system developed at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility. The binary blend produced a wider and shorter geometry, while the ternary blend resulted in a narrower build that was more equivalent to the CAD geometry. The data indicated that the binary blend provided a higher oxygen concentration in the weld as compared to that of the ternary blend. The results imply that the arc characteristics and heat input had a significantly higher impact on the weld penetration than the surface tension effect of surface active elements. This was further verified by developing and applying a high-fidelity computational fluid dynamics (CFD) model of the thermophysical properties of gas mixtures. The results from the model showed that, while the influence of increased oxygen concentration on the surface tension for the binary blend led to a deeper penetration, the ternary blend gave rise to heat flux to the workpiece.

36 MATERIALS SCIENCE↗

Stemflow Hydrodynamics

Stemflow hydrodynamics is the study of water movement along the exterior surface area of plants. Its primary goal is to describe water velocity and water depth along the stem surface area. Its significance in enriching the rhizosphere with water and nutrients is not in dispute. Yet, the hydrodynamics of stemflow have been entirely overlooked. This review seeks to fill this knowledge gap by drawing from thin film theories to seek outcomes at the tree scale. The depth‐averaged conservation equations of water and solute mass are derived at a point. These equations are then supplemented with the conservation of momentum that is required to describe water velocities or relations between water velocities and water depth. Relevant forces pertinent to momentum conservation are covered and include body forces (gravitational effects), surface forces (wall friction), line forces (surface tension), and inertial effects. The inclusion of surface tension opens new vistas into the richness and complexity of stemflow hydrodynamics. Flow instabilities such as fingering, pinching of water columns into droplets, accumulation of water within fissures due to surface tension and their sudden release are prime examples that link observed spatial patterns of stemflow fronts and morphological characteristics of the bark. Aggregating these effects at the tree‐ and storm‐ scales are featured using published experiments. The review discusses outstanding challenges pertaining to stemflow hydrodynamics, the use of dynamic similarity and 3D printing to enable the interplay between field studies and controlled laboratory experiments.

54 ENVIRONMENTAL SCIENCES↗

X-ray photoelectron spectroscopy of surfactants on sub-micron aqueous aerosols

Organic molecules at the aqueous interfaces of droplets and aerosols can influence multiphase chemistry and cloud condensation nuclei activity. While surface tension measurements on flat surfaces are commonly used to quantify organic partitioning and structure, extending these measurements to more realistic aerosols that might exist in metastable, supercooled and supersaturated states remains a challenge. Here, we use aerosol velocity map imaging X-ray photoelectron spectroscopy (A-VMI-XPS), a surface-sensitive and in situ technique, to study the partitioning and structure of a model surfactant (n-octyl β-D-thioglucopyranoside, OTG) in submicron aqueous aerosols with a mean radius of 116 nm. After accounting for finite size effects and evaporative cooling, we find the bulk concentrations of OTG in the aerosol are depleted 600-fold relative to macroscopic solutions. At the low temperatures of the experiment (ca. ∼188 K), we obtain a Langmuir equilibrium constant of 700 m3 mol-1, which corresponds to 4.9 m3 mol-1 at room temperature; a value that is consistent with prior literature reports. Analysis of photoelectron angular distributions, peak areas, and secondary electron escape barriers indicates a transition from a disordered surfactant layer at low [OTG] to a more structured layer at high [OTG]. These results link macroscopic surface tension measurements to nanoparticle surfactant behavior and demonstrate the utility of A-VMI-XPS in probing surfactant coating structure on free aerosols, with implications for understanding the impact of organic coatings on cloud formation and atmospheric chemistry.

Deal, Alexandra M↗

Compiled properties of nucleonic matter and nuclear and neutron star models from nonrelativistic and relativistic interactions

Here, this paper compiles the model parameters and zero-temperature properties of an extensive collection of published theoretical nuclear interactions, including 255 nonrelativistic (Skyrme-like) forces, 270 relativistic mean field (RMF) and point-coupling (RMF-PC) forces, and 13 Gogny-like forces. This forms the most exhaustive tabulation of model parameters to date. The properties of uniform symmetric matter and pure neutron matter at the saturation density are determined. Symmetry properties found from the second-order term of a Taylor expansion in neutron excess are compared with the energy difference of pure neutron and symmetric nuclear matter at the saturation density. Selected liquid-droplet model parameters, including the surface tension and surface symmetry energy, are determined for semi-infinite surfaces. Theoretical liquid droplet model neutron skin thicknesses of the neutron-rich closed-shell nuclei 48 Ca and 208 Pb are compared with published theoretical Hartree-Fock and experimental results. A similar comparison is made between theoretical liquid-droplet and experimental values of dipole polarizabilities. In addition, radii, binding energies, moments of inertia and tidal deformabilities of 1.2⁢M ⊙ , 1.4⁢M ⊙ and 1.6⁢M ⊙ neutron stars are computed. An extensive correlation analysis of bulk matter, nuclear structure, and low-mass neutron star properties is performed and compared with nuclear experiments and astrophysical observations.

nuclear astrophysics↗

Non-Equilibrium Nucleation of Rare Earth Metals at Aqueous Interfaces

We conducted mechanical and electrical perturbation experiments on several systems of NdCl 3 aqueous solutions with two phosphonic acids, one as a stable surfactant with high surface activity and another that was fully soluble in aqueous solution. We also conducted experiments with solely the salt in aqueous solution. Our goal was to stimulate surface aggregation at the air/aqueous solution interface that was observable by Brewster angle microscopy, surface tension, and/or surface spectroscopy. We observed changes only with the surface active phosphonic acid. Additional work is needed to further explore confined environments to stimulate surface aggregation events.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗