Freeze-thaw cycling accelerates corrosion damage in high-strength aluminum alloys, yet its real-time mechanisms remain unexplored. Using lab-based x-ray tomography, we have captured the in situ 3D evolution of corrosion damage in AA7075-T651 under freeze-thaw cycling. We developed an in situ cooling stage that can be integrated with x-ray computed tomography at sub-zero temperature. We have elucidated the process of corrosion initiated by metallic particles, which subsequently induces the formation of cracks due to the freezing and expansion of water. This corrosion propagates along the microstructural defects between grains. Upon melting, chloride ions infiltrate these regions, exacerbating the growth of the fissures. These insights provide a mechanistic understanding of freeze-thaw-induced degradation in aerospace materials.

Hillock formation in metallic thin films is commonly attributed to stress relaxation; however, in phase-separating alloy systems, surface instabilities can emerge purely from diffusion-limited processes during vapor deposition. While phase field modeling has previously captured such morphological evolution, quantitative classification and predictive mapping of diffusion-limited hillock modes remain unresolved. In this work, we introduce a spectral characterization framework based on structure factor analysis derived from Fast Fourier Transform to rigorously distinguish vertically aligned, partially ordered, and disordered hillock morphologies that cannot be reliably separated using real-space metrics. Building on this physics-based classification, a supervised neural network is developed to map deposition rate and diffusion kinetics directly to hillock growth modes, generating probability density maps that reveal clear transitions in kinetic parameter space. Phase field simulations are used to system atically vary deposition rate and atomic diffusion coefficients, confirming that hillock morphology is governed by the competition between atomic diffusion and deposition flux, with lower deposition rates enabling diffusion-limited surface reorganization. The integrated spectral and machine learning framework provides a quantitative and predictive pathway for classifying and controlling diffusion-driven surface instabilities in vapor-deposited immiscible alloy thin films.

The skeleton of Tubipora musica, also commonly known as the organ pipe coral, is made up of calcium carbonate and serves as a habitat for small sea creatures called polyps. The present paper provides a comprehensive study on the hierarchical structure and micromechanical properties of the organ pipe coral skeleton. The hierarchical structure of the coral skeleton was probed across multiple length scales using a combination of X-ray microcomputed tomography and scanning electron microscopy. At the macroscale, the structure of the coral consisted of vertical tubes connected by horizontal platforms. On the other hand, the microstructure comprises spherulites and an assembly of cells that were formed through a unique arrangement of plates of calcite. This unique arrangement of fibres and plates resulted in varying microstructural morphologies on the surface of the coral skeleton. Nanoindentation was conducted at multiple load regimes to investigate mechanical properties of coral's hierarchical structure. At smaller indentation depths, Young's modulus and hardness increased with indentation depth due to densification of the porous structure. At larger indentation depths, multiple damage mechanisms were observed, such as crack deflection and secondary crack formation.

The reduction of hematite to metallic iron using hydrogen (H2) as a reducing agent presents a promising pathway for decarbonizing steel production. In this study, we employ a combination of in situ confocal scanning laser microscopy (CSLM) and advanced computer vision techniques to quantitatively analyze dendritic growth of ferrite during H2-based reduction of iron oxide at high temperatures. A workflow integrating Watershed Image Segmentation (WIS) and Lucas-Kanade Optical Flow (LKOF) is developed to extract both global and local kinetic information from time-resolved micrograph sequences. H2 reduction experiments conducted 1400 degrees C and 1500 degrees C demonstrate a clear correlation between temperature and reduction rate, as evidenced by accuracy of fitted Johnson-Mehl-Avrami-Kolmogorov (JMAK) parameters. Optical flow analysis further elucidates the anisotropic and branched nature of dendritic growth, providing spatially resolved velocity fields that correlate well with global transformation kinetics. The proposed methodology demonstrates strong agreement with experimental measurements and literature values, offering a robust framework for automated image-based analysis to study kinetics through microstructural evolution in the reduction of iron ore, and likely other reaction-diffusion phenomena.

Aluminum alloys benefit from the formation of a passivating layer for protection against atmospheric corrosion. However, mechanical damage to this layer can expose the underlying material, initiating localized corrosion and compromising structural integrity. Despite its critical role, the evolution of the mechanical properties of the corrosion product layer under dynamic environmental conditions remains poorly understood. This study investigates the microstructural and micromechanical behavior of the corrosion product layer formed in an AA7075-T651 alloy in an aqueous chloride solution. In situ nanoindentation experiments were performed to measure mechanical properties, while chemical composition and phase evolution were analyzed using electron microscopy and Raman spectroscopy. The results reveal that prolonged immersion leads to a reduction in the Young's modulus and hardness of the corrosion layer attributed to chloride ion infiltration in the oxide layer inducing microporosity. Furthermore, transitioning the corrosion layer from an aqueous to a dry atmospheric environment induces microcracking due to dehydration and the loss of crystallinity in the AlOOH phase. This transition may also involve a phase transformation from AlOOH to Al(OH)s, attributed to structural changes within the corrosion products, reducing their mechanical integrity. These findings provide critical insights into the degradation mechanisms of aluminum alloys corrosion layer in chloride-rich environments, supporting the development of advanced materials and coatings with enhanced mechanical and corrosion resistance.

The present study investigates the effects of laser resolidification on AlSi10Mg alloys modified with Nickel (Ni), describing the evolution of microstructure and nanohardness due to rapid solidification and growth of AlNi intermetallic phases. The AlSi10Mg alloy has an excellent strength-to-weight ratio and is widely used in automotive and aerospace parts. Adding Ni helps increase its hardness and improve performance. Further, rapid solidification via laser surface remelting (LSR) promotes microstructural refinement and strengthening. Thus, LSR treatment was applied to AlSi10Mg with 1 to 3 wt pct Ni using two different energy densities: 100 and 400 J/mm2. Microstructural evolution was quantified using optical microscopy, scanning electron microscopy, and image analysis, then correlated to models generated using Computer Coupling of Phase Diagrams and Thermochemistry (CALPHAD). The phase clusters identified in the molten pools included α-Al, Al + Si and Al + Si + Al3Ni constituents. Rapid solidification led to the formation of α-Al cells at the top and very fine eutectic Si fibers, while Al3Ni formed complex fishbone-like structures. Measurements of secondary dendritic spacing agreed well with theoretical (Bouchard–Kirkaldy) predictions. However, the equilibrium and Scheil models did not match the experimental phase fractions. The reasonable to good agreement with the Bouchard–Kirkaldy (BK) predictions supported its applicability, despite limitations related to the thermal gradient. Such predictions demonstrated positive correlations with depth in the melt pool (i.e., top to bottom) and with increasing solidification velocity. Nanoindentation measurements of hardness were taken with high spatial resolution, showing that local hardness increased with Ni content, correlating with the Al3Ni fraction and the refinement of Si particles. These results support the effectiveness of Ni alloying and LSR to improve the performance of castable aluminum alloys and guide the selection of parameters to maximize hardness.

Aluminum aircraft structures experience severe corrosion from exposure to aggressive chloride environments, including cyclic freezing and thawing of residual water during ascent and descent, introducing a cyclic freeze-thaw component to the corrosion process. While corrosion mechanisms in aircraft structures are well studied at constant temperatures, the microstructural and mechanistic behavior under freeze-and-thaw conditions remains unclear. To understand transformations induced by cyclic temperature, we used three-dimensional (3D) x-ray computed tomography (XCT) with scanning electron microscopy (SEM) to study the behavior of AA7075-T651 in a simulated seawater environment undergoing freezing and thawing cycles. Rods immersed in saltwater were thermally cycled above and below freezing, and structural changes were intermittently characterized in 3D. Under freeze-thaw conditions, cracks initiated within corrosion pits through ice expansion, causing progressive crevice growth and spalling along inclusions and grain boundaries with intermediate misorientation angles. Damage mechanisms in freeze-thaw and conventional corrosion environments are compared, with correlations to microstructural evolution.

The self-induced formation of core-shell InAlN nanorods (NRs) is addressed at the mesoscopic scale by density functional theory (DFT)-resulting parameters to develop phase field modeling (PFM). Accounting for the structural, bonding, and electronic features of immiscible semiconductor systems at the nanometer scale, we advance DFT-based procedures for computation of the parameters necessary for PFM simulation runs, namely, interfacial energies and diffusion coefficients. The developed DFT procedures conform to experimental self-induced InAlN NRs' concerning phase-separation, core/shell interface, morphology, and composition. Finally, we infer the prospects for the transferability of the coupled DFT-PFM simulation approach to a wider range of nanostructured semiconductor materials.

Abstract The phase-field method is an attractive computational tool for simulating microstructural evolution during phase separation, including solidification and spinodal decomposition. However, the high computational cost associated with solving phase-field equations currently limits our ability to comprehend phase transformations. This article reports a novel phase-field emulator based on the tensor decomposition of the evolving microstructures and their corresponding two-point correlation functions to predict microstructural evolution at arbitrarily small time scales that are otherwise nontrivial to achieve using traditional phase-field approaches. The reported technique is based on obtaining a low-dimensional representation of the microstructures via tensor decomposition, and subsequently, predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once we obtain the microstructure prediction in the low-dimensional space, we employ a hybrid input–output phase-retrieval algorithm to reconstruct the microstructures. As proof of concept, we present the results on microstructure prediction for spinodal decomposition, although the method itself is agnostic of the material parameters. Results show that we are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of $$6.78 \times 10^{-7}$$ 6.78 × 10 - 7 ) at time scales half of that employed in obtaining training data. Our data-driven microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity. Impact statement The phase-field method is an attractive computational tool for simulating microstructural evolution during phase separation, including solidification and spinodal decomposition. However, they remain computationally intensive due to the strict limits on the maximum time and length scales imposed by the numerical methods. This research presents a phase-field emulator that will predict the microstructural evolution observed in phase separating materials by leveraging the already existing data sets obtained from traditional phase-field modeling approaches. The phase-field emulator, which for the first time, couples a tensor representation and nonparametric tensor methods with microstructure modeling and representation, will enable high-throughput and facile prediction of the microstructural evolution, as opposed to solving computationally intensive phase-field simulations whenever a new simulation needs to be performed. Our data-driven microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity. Graphical Abstract

Electromigration (EM)-induced diffusional transport of metal atoms, which can manifest as the defects of grain boundary slits and voids in a metal line, often fail an entire electronic component. Formulating preventive strategies and their efficient implementation involves the analysis of failure mechanisms in 4D microstructures via tedious in situ x-ray tomography characterization as well as large-scale phase-field simulations, both of which are resource-intensive. Given this limitation, we report a data-driven emulation (DDE) technique, which couples machine learning with microstructure modeling, to enable a high-throughput and accurate prediction of grain boundary slit evolution in progressively degrading Cu interconnects under EM. In this context, the effectiveness of the stepwise linear regression approach which is the cornerstone of DDE has been quantified. We also analyze the importance of training dataset choice that significantly impacts the convergence between emulated and the phase-field simulated slit evolution dynamics. Finally, we also discuss the DDE-based insights related to the predominance of one or more descriptors in determining the slit evolution dynamics, which cannot be otherwise obtained directly from phase-field simulations.

The spicules of the deep-sea sponge Euplectella aspergillum show exceptional mechanical properties due to their unique layered structure. In the present study, a systematic study utilizing in situ nanoindentation was conducted to understand the influence of water on the micromechanical properties of these spicules. The properties of the spicules were investigated in three conditions—dry state, after 24 h of exposure, and after 1 week of exposure with water. To isolate the effect of the architecture of the spicule, properties of the central core and layered regions of the spicule were probed separately. The results of nanoindentation revealed that the layered regions were significantly softer and more compliant than the central core of the spicule. The properties of the central core remained unchanged when exposed to water. On the other hand, layered regions showed a decrease in their stiffness and hardness. These effects were attributed to the hydrated nature of silica and plasticization of the organic interlayers, respectively. The present results provide insight into the role of the architecture of the spicule and its organic-inorganic constituents in determining its mechanical behavior in water environment.

Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatiotemporal information is lost in the pursuit of dimensional reduction. Given these limitations, we present a novel data-driven emulator (DDE) for predicting microstructural evolution, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, we also compare the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states. Finally, we discuss the effectiveness of our microstructure emulation technique in the context of runtime speed-up while also highlighting its trade-off with accuracy.

This overview begins with observations of capillary-mediated effects on crystal-melt interfaces in microgravity and attempts to interpret them using the LeChâtelier–Braun effect and Kelvin’s equation—both local equilibrium requirements applicable at curved interfaces. Numerical studies of interfacial kinetics followed, using Greens function distributions that simulated evolving sharp solid–liquid interfaces undergoing pattern formation. Those methods exposed a need for more incisive steady-state techniques. Grain boundary grooves were used as constrained, stationary, microstructures. Their steady-state profiles derive from variational calculus and were analyzed for their imputed Gibbs–Thomson thermopotentials for comparative thermodynamic studies. Variational profiles, however, have unrealistic zero-thickness transitions between phases, and thus lack fluxes of energy or solute that occur on real interfaces. The exact formulation of variational profiles, however, advantageously supports field-theoretic calculations of their first-order formation free energy, thermodynamic stability, capillary-mediated chemical potentials, interfacial gradients and scalar divergences. These linked fields all depend on an interface’s curvature distribution, i.e., its geometry, but others, for example tangential fluxes of energy and solute, also depend on interface thickness and structure, i.e., thermodynamics. We comparatively analyzed capillary-mediated fields up to 4th-order, including the surface Laplacian of a profile’s chemical potential. This Laplacian is proportional to scaled divergences of fluxes that appear on counterpart real or simulated microstructures with congruent shapes. Divergent energy fluxes manifest as cooling distributions, which cause depression of the thermochemical potential measured along diffuse-interfaces simulated with phase-field. Cooling distributions are visualized to explain qualitative and quantitative features of a microstructure’s steady-state thermal maps. Evidence is included of how thickness and shape of crystal-melt interfaces co-determine whether, and to what extent, interfacial transport occurs. Understanding the origin and actions of interfacial capillary fields might offer improved control of microstructure at mesoscopic levels, accessible with these deterministic fields through physical and chemical means.

We use a phase-field method to elucidate grain-boundary grooving as a mechanism of genesis and subsequent propagation of intergranular slit in metal interconnects under concurrent surface and grain boundary diffusion. Surface diffusion is assumed to be the rate limiting transport mechanism. Accelerated grain-boundary grooving induced by electromigration is shown to ensue a narrow channel-like slit which advances at a steady state along the grain-boundary preserving its shape near the tip region. The slit characteristics namely the width and velocity dependence on electric field derived are shown to be in excellent agreement with the sharp interface calculations which treat the tip region of the slit independently. Furthermore, the simulations reveal that for the same magnitude of electric field, a slit conceived from a smaller grain exhibits a slower kinetics and delay damage dissemination. The apparent discrepancy from the sharp interface description is resolved on the basis of curvature gradient and electromigration-induced surface flux which heal the root during groove to slit transition and is predominant in smaller grains. Finally, drawing analogy from the nucleation and growth models of electromigration voids, failure due to intergranular slit is divided into serial process of initial grooving followed by slit propagation stage. The lifetime analysis suggests the grooving stage to be the rate determining step in the failure process, yielding an exponent of 1.33 in Black's law.

We devise reduced-dimension metrics for effectively measuring the distance between two points (i.e., microstructures) in the microstructure space and quantifying the pathway associated with microstructural evolution, based on a recently introduced set of hierarchical n-point polytope functions P_{n}. The P_{n} functions provide the probability of finding particular n-point configurations associated with regular n polytopes in the material system, and are a special subset of the standard n-point correlation functions S_{n} that effectively decompose the structural features in the system into regular polyhedral basis with different symmetries. The nth order metric Ω_{n} is defined as the L_{1} norm associated with the P_{n} functions of two distinct microstructures. By choosing a reference initial state (i.e., a microstructure associated with t_{0}=0), the Ω_{n}(t) metrics quantify the evolution of distinct polyhedral symmetries and can in principle capture emerging polyhedral symmetries that are not apparent in the initial state. To demonstrate their utility, we apply the Ω_{n} metrics to a two-dimensional binary system undergoing spinodal decomposition to extract the phase separation dynamics via the temporal scaling behavior of the corresponding Ω_{n}(t), which reveals mechanisms governing the evolution. Moreover, we employ Ω_{n}(t) to analyze pattern evolution during vapor deposition of phase-separating alloy films with different surface contact angles, which exhibit rich evolution dynamics including both unstable and oscillating patterns. The Ω_{n} metrics have potential applications in establishing quantitative processing-structure-property relationships, as well as real-time processing control and optimization of complex heterogeneous material systems.

Analytic profiles for periodic grain boundary grooves (PGBGs) were determined from variational theory. Variational profiles represent stationary solid-liquid profiles with abrupt, zero-thickness, transitions between adjoining phases. Variational PGBGs consequently lack tangential interfacial fluxes, the existence of which requires more realistic (non-zero) interfacial thicknesses that allow energy and solute transport. Variational profiles, however, permit field-theoretic calculations of their scaled formation free energy and thermodynamic stability, capillary-mediated chemical potentials, and their associated vector gradient distributions, all of which depend on a profile's geometry, not its thickness. Despite the fact that variational profiles are denied interface fluxes, one may, nevertheless, impute shape-dependent interface transport in the form of a profile's surface Laplacian of its presumptive chemical potential distribution due to capillarity. We compare variational surface Laplacians with residuals of the thermochemical potential measured along counterpart diffuse-interface PGBGs, simulated via phase-field with metrically-proportional profiles. Fundamentally, it is the thickness of a microstructure's interfaces and its shape that co-determine whether, and to what extent, gradients of the chemical potential excite fluxes that transport energy and/or solute. PGBGs, both variational and simulated, greatly expand the limited universe of solid-liquid microstructures suitable for steady-state thermodynamic analysis. Understanding the origin and action of these capillary-mediated interfacial fields opens a pathway for estimating and, eventually, measuring how solid-liquid interface thickness modifies the transport of energy and solute during solidification and crystal growth, and influences microstructure.

Accumulation of intermetallic compounds (IMC), such as Cu6Sn5, adversely impacts the solder joint toughness. Therefore, a basic understanding of the factors that determine the growth kinetics of IMC under distinct operating conditions is warranted to improve the reliability of microelectronic devices. However, predicting the growth of IMC involves tedious experiments or large-scale 3D phase-field simulations, both of which are labor- and resource-intensive techniques. Here, we present simple approaches that couple material thermodynamics with diffusional kinetics for predicting the isothermal growth characteristics of Cu6Sn5 in Cu-Sn alloy for temperatures ranging from 145°C to 230°C. While our calculations can reproduce the kinetics of growth obtained from experiments, they also indicate the limited role of IMC/Sn interfacial curvature in determining the rate at which the IMC layer thickens. The reported parametric study, while contrasting the uni- and bidirectional Cu6Sn5 growth rates, highlights the utility of combined thermodynamic-kinetic 1D approaches in predicting steady-state growth velocity of IMCs in Cu-Sn alloys.

Physical vapor deposition of phase-separating alloy films yields a rich variety of distinct self-assembled nanostructures depending on the deposition rate and temperature. However, the role of grain boundaries, elastic imhomogeneity, and anisotropy, and surface tension, in the formation of such nanostructures is currently not well understood. Here, we employ a phase-field approach that couples the multiphysics of elemental diffusion, elastic misfit and anisotropy, surface tension, and grain boundaries with processing parameters, namely deposition rate and temperature, to investigate phase separation and grain boundary evolution in binary alloy films. We develop phase-field deposition models of increasing complexity to isolate and analyze the influence of processing parameters on nanostructural transitions in vapor co-deposited films. While it is found that such transitions are primarily guided by a minimization of total free energy, our simulation-based insights strongly indicate the phenomena of nanostructure selection at faster deposition rates. It is anticipated that the insights gained from this study will provide the much-required knowledgebase for establishing nanostructure-level control in the physical vapor deposition of alloy films.

Steady-state solid-liquid interfaces allow both analytic description as sharp-interface profiles, and numerical simulation via phase-field modeling as stationary diffuse-interface microstructures. Profiles for sharp interfaces reveal their exact shapes and allow identification of the thermodynamic origin of all interfacial capillary fields, including distributions of curvature, thermochemical potential, gradients, fluxes, and surface Laplacians. By contrast, simulated diffuse interface images allow thermodynamic evolution and measurement of interfacial temperatures and fluxes. Quantitative results using both approaches verify these capillary fields and their divergent heat flow, to provide insights into interface energy balances, dynamic pattern formation, and novel methods for microstructure control. The microgravity environment of low-Earth orbit was proven useful in past studies of solidification phenomena. We suggest that NASA’s ISS National Lab can uniquely accommodate aspects of experimental research needed to explore these novel topics.

The formation of surface features, such as grooves, protruding grains, or hillocks, in vapor-deposited phase-separating films is typically attributed to internal residual stresses arising due to a difference in thermal expansion coefficients of the film and the substrate. Even though such protuberances are typically observed on the film’s surface, the current understanding of how interfacial energies and surface contact angles influence this nanostructural evolution is very limited. In view of this knowledge gap, we adopt a three-dimensional phase-field approach to numerically investigate the role of seed morphology and contact angles on the morphological evolution of surface protuberances in phase-separating alloy films. Film nanostructures are quantified using a statistical morphological descriptor, namely, n-point polytope functions, which provides a host of insights into the kinetic pathways while unraveling a hidden length scale correlation present at all contact angles. Finally, we also apply this characterization technique on previously reported micrographs of Cu–Ta and Cu–Mo–Ag films to highlight similarities between our simulation-based findings with those obtained from co-deposition experiments.