Search NASA⌕ Search

SEARCH · Search NASA

Results for “Contact prediction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus

We perform a global sensitivity analysis of the binding energy and the charge radius of the nucleus 16 O to identify the most influential low-energy constants in the next-to-next-to-leading order chiral Hamiltonian with two- and three-nucleon forces. For this purpose, we develop a subspace-projected coupled-cluster method using eigenvector continuation [Frame D. et al., Phys. Rev. Lett. 121, 032501 (2018)]. With this method, we compute the binding energy and charge radius of 16 O at more than 106 different values of the 16 low-energy constants in one hour on a standard laptop computer. For relatively small subspace projections, the root-mean-square error is about 1% compared to full-space coupled-cluster results. We find that 58(1)% of the variance in energy can be apportioned to a single contact term in the 3 S 1 wave, whereas the radius depends sensitively on several low-energy constants and their higher-order correlations. The results identify the most important parameters for describing nuclear saturation and help prioritize efforts for uncertainty reduction of theoretical predictions. The achieved acceleration opens up an array of computational statistics analyses of the underlying description of the strong nuclear interaction in nuclei across the Segrè chart.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.

Crystal structure prediction↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2023

Language Barriers in Organismal Biology: What Can Journals Do Better?

Synopsis In the field of organismal biology, as in much of academia, there is a strong incentive to publish in internationally recognized, highly regarded, English-language journals to promote career advancement. This expectation has created a linguistic hegemony in scientific publishing, whereby scholars for whom English is an additional language face additional barriers to achieving the same scientific recognition as scholars who speak English as a first language. Here, we surveyed the author guidelines of 230 journals in organismal biology with impact factors of 1.5 or greater for linguistically inclusive and equitable practices and policies. We looked for efforts that reflect first steps toward reducing barriers to publication for authors globally, including the presence of statements that encouraged submissions from authors of diverse nationalities and backgrounds, policies regarding manuscript rejection based on perceived inadequacies of the English language, the existence of bias-conscious reviewer practices, whether translation and editing resources or services are available, allowance for non-English abstracts, summaries, or translations, and whether journals offer license options that would permit authors (or other scholars) to translate their work and publish it elsewhere. We also directly contacted a subset of journals to verify whether the information on their author guidelines page accurately reflects their policies and the accommodations they would make. We reveal that journals and publishers have made little progress toward beginning to recognize or reduce language barriers. Counter to our predictions, journals associated with scientific societies did not appear to have more inclusive policies compared to non-society journals. Many policies lacked transparency and clarity, which can generate uncertainty, result in avoidable manuscript rejections, and necessitate additional time and effort from both prospective authors and journal editors. We highlight examples of equitable policies and summarize actions that journals can take to begin to alleviate barriers to scientific publishing.

Nolde-Lopez, B.↗
DOE OSTI2021

Two- and three-nucleon contact interactions and ground-state energies of light- and medium-mass nuclei

Classes of two-nucleon (2N) contact interactions are developed in configuration space at leading order (LO), next-to-leading order (NLO), and next-to-next-to-next-to-leading order (N3LO) by fitting the experimental singlet np scattering length and deuteron binding energy at LO, and np and pp scattering data in the laboratory-energy ranges 0–15 MeV at NLO and 0–25 MeV at N3LO. These interactions are regularized by including two Gaussian cutoffs, one for T = 0 and the other for T = 1 channels. The cutoffs are taken to vary in the ranges R 0 = (1.5 –2.3) fm and R 1 = (1.5 –3.0) fm. The 780 (1100) data points up to 15 (25) MeV energy, primarily differential cross sections, are fitted by the NLO (N3LO) models with a χ 2 / datum about 1.7 or less (well below 1.5), when harder cutoff values are adopted. As a first application, we report results for the binding energies of nuclei with mass numbers A = 3 –6 and 16 obtained with selected LO and NLO 2N models both by themselves as well as in combination with a LO three-nucleon (3N) contact interaction. The latter is characterized by a single low-energy constant that is fixed to reproduce the experimental 3 H binding energy. The inclusion of the 3N interaction largely removes the sensitivity to cutoff variations in the few-nucleon systems and leads to predictions for the 3 He and 4 He binding energies that cluster around 7.8 and 30 MeV, respectively. However, in 16 O this cutoff sensitivity remains rather strong. Finally, predictions at LO only are also reported for medium-mass nuclei with A = 40 , 48, and 90.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Results on photon-mediated dark-matter–nucleus interactions from the PICO-60 C 3 F 8 bubble chamber

Many compelling models predict dark matter coupling to the electromagnetic current through higher multipole interactions, while remaining electrically neutral. Different multipole couplings have been studied, among them anapole moment, electric and magnetic dipole moments, and millicharge. This study sets limits on the couplings for these photon-mediated interactions using nonrelativistic contact operators in an effective field theory framework. Using data from the PICO-60 bubble chamber leading limits for dark matter masses between 2.7 and 24 GeV/c 2 and above 265 GeV/c 2 (anapole moment), 2.7 and 11.7 GeV/c 2 (electric moment), 3 and 9.5 GeV/c 2 (magnetic moment), and 2.7 and 12 GeV/c 2 (millicharged) are reported for the coupling of these photon-mediated dark matter-nucleus interactions. The detector was filled with 52 kg of C 3 F 8 operating at thermodynamic thresholds of 2.45 keV and 3.29 keV, reaching exposures of 1404 kg-day and 1167 kg-day, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Influence of soluble oligomeric aluminum on precipitation in the Al-KOH-H2O system

The role of oligomeric aluminate (Al(OH)4-) species in the precipitation of aluminum phases such as gibbsite (a-Al(OH)3) from aqueous hydroxide solutions remains unclear and difficult to probe directly, despite its importance to developing accurate predictions of Al solubility in highly alkaline systems. Precipitation in this system entails a transition from predominantly tetrahedrally coordinated Al species in solution to octahedrally coordinated Al in gibbsite. Here we report a quantitative study of dissolved Al in the Al-KOH-H2O system using a combination of molecular spectroscopies. We established a relationship between changes in 27Al NMR chemical shifts and the relative intensity of Raman vibrational bands, indicative of variations in the ensemble speciation of Al in solution, and the formation of unique contact ion pair interactions with the aluminate dimer, Al2O(OH)62 . A strong correlation between the extent of Al oligomerization and the amount of solvated Al was demonstrated by systematically varying the KOH:Al molar ratio. The concentration of dissolved oligomeric Al in solution also directly impacted the particle size and morphology of gibbsite; high concentrations of dimeric Al2O(OH)62-, yielded smaller and more numerous anhedral to subhedral gibbsite particles, while low concentrations yielded fewer and larger euhedral gibbsite platelets. The collective observations suggest a key role of the Al2O(OH)62- dimer in promoting gibbsite precipitation from solution, where the potassium ion-paired dimer possibly catalyzes a more rapid transformation of Al coordination from tetrahedral in solution to octahedral in gibbsite.

Dembowski, Mateusz↗
DOE OSTI2026

Multiscale modeling of packed-bed microwave reactors and estimation of intrinsic materials' permittivity

Modeling of packed-bed microwave reactors relies on an accurate representation of particle size, shape, and distribution within the bed, as well as the particles' dielectric properties. The measured permittivity of microwave susceptors (powders or structured materials) depends on the geometric features of the particles and the porosity of the bed, as well as the specific form factor of a structured material. These are effective properties and cannot be used to analyze other reactor configurations unless the geometric effects are removed. Therefore, we introduce a methodology for extracting the intrinsic particle permittivity from experimentally measured effective permittivity by combining cavity-based measurements with multiscale simulations and machine learning. Further, we develop the first multiscale model of packed-bed microwave reactors that incorporate particle effects (geometric features, random packing, and particle contact). This approach bridges macroscopic observables with mesoscopic physics, enabling analysis of local hotspots, arcing, and contact effects that control reactor performance. Using polymer-based spherical activated carbon (PBSAC) and silicon carbide (SiC) as examples, we demonstrate that the inferred particle permittivity is consistent with independent experimental heating profiles we collect from microwave reactors without adjustable parameters. Finally, this methodology establishes a foundation for predictive, multiscale design of microwave packed-bed reactors that explicitly accounts for particle-scale effects, enabling the estimation of intrinsic permittivity for the first time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum simulation of an exotic quantum critical point in a two-site charge Kondo circuit

Tuning a material to the cusp between two distinct ground states can produce physical properties that are unlike those in either of the neighbouring phases. Advances in the fabrication and control of quantum systems have raised the tantalizing prospect of artificial quantum simulators that can capture such behaviour. A tunable array of coupled qubits should have an appropriately rich phase diagram, but realizing such a system with either tunnel-coupled semiconductor quantum dots or metal nanostructures has proven difficult. The challenge for scaling up to clusters or lattices is to ensure that each element behaves essentially identically and that the coupling between elements is uniform, while also maintaining tunability of the interactions. Here we study a nanoelectronic circuit comprising two coupled hybrid metal–semiconductor islands, combining the strengths of both materials to form a potentially scalable platform. The semiconductor component allows for controllable inter-site couplings at quantum point contacts, while the metal component’s effective continuum of states means that different sites can be made equivalent by tuning local potentials. The couplings afforded by this architecture can realize an unusual quantum critical point resulting from frustrated Kondo interactions. In this study, the observed critical behaviour matches theoretical predictions, verifying the success of our experimental quantum simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Temperature effects on adsorption and capillarity water retention mechanisms in constrained unsaturated soils

Abstract This paper focuses on the impact of elevated temperatures on the adsorptive and capillarity water retention mechanisms of unsaturated soils under constrained (constant volume) conditions. This topic is critical for simulating the thermo-hydraulic behavior of soils in hydrogeological or geotechnical applications, including climate change effects on near surface soils, energy piles or soil borehole thermal energy storage systems in unsaturated soil layers, and buffers for geological nuclear waste repositories. A nonisothermal soil water retention curve (SWRC) that separately considers the temperature-dependency of the key parameters governing adsorptive and capillarity water retention mechanisms and soil physical parameters (e.g., surface tension, contact angle, adsorption capacity, cation exchange capacity, mean cavitation suction, air entry value and equilibrium film thickness) was developed to provide insights into the impact of temperature on water retention over the full suction range. The nonisothermal SWRC was validated using experimental data on high plasticity clays, with a good prediction of temperature effects on adsorption and capillarity water retention mechanisms in constrained unsaturated soils.

Lu, Yu↗
DOE OSTI2022

Modeling and Analysis of a Novel Offshore Binary Species Free-Floating Longline Macroalgal Farming System

The investigation of innovative macroalgal cultivation is important and needed to optimize farming operations, increase biomass production, reduce the impact on the ecosystem, and lower system and operational costs. However, most macroalgal farming systems (MFSs) are stationary, which need to occupy a substantial coastal area, require extensive investment in farm infrastructure, and cost high fertilizer and anchoring expenses. This study aims to model, analyze, and support a novel binary species free-floating longline macroalgal cultivation concept. The expected outcomes could provide a basis for the design and application of the novel MFS to improve biomass production, decrease costs, and reduce the impact on the local ecosystem. In this paper, Saccharina latissima and Nereocystis luetkeana were modeled and validated, and coupled with longline to simulate the binary species MFS free float in various growth periods and associated locations along the US west coast. Further, the numerical predictions indicated the possibility of failure on the longline and breakage at the kelp holdfasts is low. However, the large forces due to an instantaneous change in dynamic loads caused by loss of hydrostatic buoyancy when the longline stretches out of the water would damage the kelps. Buoy-longline contact interactions could damage the buoy, resulting in the loss of the system by sinking. Furthermore, the kelp-longline and kelp-kelp entanglements could potentially cause kelp damage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

classLog: Logistic regression for the classification of genetic sequences

Introduction Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. Methods We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. Results When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Discussion Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .

Zeller, Michael A.↗
DOE OSTI2022

Results on photon-mediated dark matter-nucleus interactions from the PICO-60 C$_{3}$F$_{8}$ bubble chamber

Many compelling models predict dark matter coupling to the electromagnetic current through higher multipole interactions, while remaining electrically neutral. Different multipole couplings have been studied, among them anapole moment, electric and magnetic dipole moments, and millicharge. This study sets limits on the couplings for these photon-mediated interactions using non-relativistic contact operators in an effective field theory framework. Using data from the PICO-60 bubble chamber leading limits for dark matter masses between 2.7 GeV/c$^2$ and 24 GeV/c$^2$ are reported for the coupling of these photon-mediated dark matter-nucleus interactions. The detector was filled with 52 kg of C$_3$F$_8$ operating at thermodynamic thresholds of 2.45 keV and 3.29 keV, reaching exposures of 1404 kg-day and 1167 kg-day, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗
DOE OSTI2025

Adsorptive behavior of poly (vinylidene fluoride) membranes for the recovery of lignin-derived hydrophobic deep eutectic solvents

Abstract Recently, membrane technology has gained significant traction as an energy-efficient alternative to traditional thermal processes for solvent recovery. Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional organic solvents, yet a systematic methodology for selecting compatible membrane materials for their recovery remains underdeveloped. This study established a predictive framework for membrane material selection in hydrophobic DES applications using Hansen Solubility Parameters (HSP) with inverted criteria targeting materials with relative energy difference (RED) values greater than 1.0. Flat sheet membranes were fabricated via the non-solvent induced phase separation (NIPS) technique. Four NIPS fabricated polymer membranes were evaluated: polysulfone, cellulose acetate, polyvinylidene fluoride (PVDF) fabricated with polyethylene glycol (PEG) as a pore-forming agent, and polybenzimidazole (PBI). The HSP approach successfully predicted membrane-solvent compatibility, with polysulfone (RED = 0.6) and cellulose acetate (RED = 0.9) dissolving completely within 24 h, while PVDF (RED = 1.9) and PBI (RED = 1.1) maintained structural integrity throughout a 7-day exposure period. Furthermore, PVDF demonstrated superior performance with minimal weight gain (3.0%), hydrophobic surface characteristics (122° water contact angle), and enhanced mechanical properties following DES exposure. Comprehensive chemical and morphological characterization confirmed PVDF’s chemical stability and revealed a surface-selective interaction mechanism involving simultaneous PEG (pore-forming agent) extraction and DES component adsorption. Adsorption kinetics followed pseudo-first-order behavior with reversible characteristics, best described by the Temkin isotherm model (R² = 0.9987). PVDF membranes-maintained separation functionality with average lignin rejection (75.2 ± 7.69%) and demonstrated filtration permeability of 2.0 ± 0.34 LMH/bar. This methodology provides a rational approach for membrane selection in emerging solvent systems, contributing to the advancement of sustainable separation technologies for DES-based biomass processing applications.

Science & Technology - Other Topics↗
DOE OSTI2021

Toward Complete Leading-Order Predictions for Neutrinoless Double β Decay

The amplitude for the neutrinoless double β (0νββ) decay of the two-neutron system nn → ppe – e – constitutes a key building block for nuclear-structure calculations of heavy nuclei employed in large-scale 0νββ searches. Assuming that the 0νββ process is mediated by a light-Majorana-neutrino exchange, a systematic analysis in chiral effective field theory shows that already at leading order a contact operator is required to ensure renormalizability. Here, we develop a method to estimate the numerical value of its coefficient (in analogy to the Cottingham formula for electromagnetic contributions to hadron masses) and validate the result by reproducing the charge-independence-breaking contribution to the nucleon-nucleon scattering lengths. Our central result, while derived in dimensional regularization, is given in terms of the renormalized amplitude Aν(|p|, |p'|) , matching to which will allow one to determine the contact-term contribution in regularization schemes employed in nuclear-structure calculations. Our results thus greatly reduce a crucial uncertainty in the interpretation of searches for 0νββ decay.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Spontaneous Imbibition Velocities in Porous Metal Layers on Ceramic Substrates Calculated Using Microstructural Analyses

In this paper, the imbibition velocity of molten silver into a wettable porous nickel interlayer on a non-wetting sapphire substrate is investigated. Several imbibition models for porous media that use bulk characteristics and material properties are employed to predict the imbibition front location as a function of time. The model predictions are compared to experimental observations. It is found that pore size distribution is a better predictor of imbibition velocity than correlations based on permeability and porosity. The tortuosity of the microstructure is also found to have a significant effect and should be considered. A new model accounting for the different liquid contact angles on the underlying substrate and the porous interlayer material is proposed and achieves better agreement with the experimental observations.

36 MATERIALS SCIENCE↗