The thermal stability of GaAsSb/InP is known to be compromised by group-V volatility and intermixing at the heterojunction that adversely impact the performance of subsequently fabricated optoelectronic or high-speed devices. We interrogate the GaAsSb/InP interface and trace its degradation during extended annealing, where we observe significant intermixing and nanostructure formation. Scanning transmission electron microscopy reveals the formation of pyramidal nanostructures that extend from the epitaxial layer into the substrate. Energy-dispersive x-ray spectroscopy and geometric phase analysis show migration of Sb from the GaAsSb epilayer to the InP substrate and migration of P from the InP substrate to the epilayer. The pronounced migration of Sb and P leads to the formation of InSb-rich facets and tips of the pyramidal nanostructures. The interdiffusion also leads to InGaAsP replacing the epitaxial GaAsSb. These results are consistent with bulk characterization by high-resolution x-ray diffraction and Raman spectroscopy. The intermixing appears to be driven by simultaneous phase separation and melting of InSb that enhances atomic mobility, providing an alternative mechanism to previously proposed phase separation by spinodal decomposition.

When formed in vivo, murine hemostatic thrombi exhibit a heterogeneous architecture comprised of distinct regions of densely and sparsely packed platelets. In this study, we utilize high-resolution electron microscopy alongside machine learning and physics-based simulations to investigate how such clot microstructure impacts molecular diffusivity. We used Serial Block Face - Scanning Electron Microscopy (SBF-SEM) to image select volumes of hemostatic masses formed in a mouse jugular vein, producing high-resolution 2D images. Images were segmented using machine learning software (Cellpose), whose training was augmented by manually segmented images. The segmented images were then utilized as 2D computational domains for Lattice Kinetic Monte-Carlo (LKMC) simulations. This process constitutes a computational pipeline that combines purely data-derived biological domains with physics-driven simulations to estimate how molecular movement is hindered in a hemostatic platelet mass. Using our pipeline, we estimated that the 2D hindered diffusion rates of a globular protein range from 2% to 40% of the unhindered rate, with denser packing regions lending to lower molecular diffusivity. These data suggest that coagulation reactions rates, thrombin generation and activity, as well as platelet releasate activity may be drastically impacted by the internal geometry of a hemostatic thrombus.

During thrombosis, platelets rapidly deposit and activate on the vessel wall, driving conditions such as myocardial infarction and stroke. The complexity of thrombus formation in pathological flow geometries, along with patient-specific pharmacological responses, presents an opportunity for computational modeling to help deliver novel diagnostic and therapeutic insights. In the present study, we employed a multiscale 3D computational model that incorporates unique donor-derived neural networks (NNs) trained with platelet calcium mobilization traces under combinatorial exposure to 6 agonists (n = 10 donors). The 3D model comprises four modules: a donor-specific NN model for platelet calcium mobilization, a lattice kinetic Monte Carlo solver for tracking platelet motion and bonding, a finite volume method solver for modeling soluble agonist release and convective-diffusive transport, and a lattice Boltzmann method solver for predicting the blood velocity field. Simulations were conducted for platelets from individual blood donors under venous and arterial flow conditions on a defined collagen surface, examining the effects of inhibiting ADP and TXA 2 , as well as the influence of nitric oxide and prostacyclin. The results reveal significant individual variability in platelet responses, influencing simulated thrombus growth dynamics and emphasizing the importance of personalized models for predicting thrombotic behavior. This approach enables consideration of patient-specific platelet signaling, drug responses, and vascular geometry for predicting thrombotic episodes, essential for advancing precision medicine and improving patient outcomes in thrombotic conditions.

The multiparticle collision dynamics (MPCD) simulation method is an attractive technique for studying the effects of hydrodynamic interactions in colloidal suspensions because of its flexibility, computational efficiency, and ease of implementation. Here, we analyze an extension of the basic MPCD method in which colloidal particles are discretized with a surface mesh of sensor nodes/particles that interact with solvent particles (MPCD + Discrete Particle or MPCD + DP). We use several situations that have been described analytically to probe the impact of colloidal particle mesh resolution on the ability of the MPCD + DP method to resolve short-ranged hydrodynamic interactions, which are important in crowded suspensions and especially in self-assembling systems that create high volume fraction phases. Specifically, we consider (A) hard-sphere diffusion near a wall, (B) two-particle diffusion, (C) hard-sphere diffusion in crowded suspensions, and (D) the dynamics of aggregation in an attractive colloidal suspension. We show that in each case, the density of sensor nodes plays a significant role in the accuracy of the simulation and that a surprisingly high number of surface nodes are needed to fully capture hydrodynamic interactions.

Stochastic and dynamical processes lie at the heart of all physical, chemical, and biological systems. However, kinetic and thermodynamic properties which characterize these processes have largely been treated separately as they can be obtained independently for many systems at thermodynamic equilibrium. In this work we demonstrate the existence of a class of relations between kinetic and thermodynamic factors which holds even in the hydrodynamic limit, and which must be satisfied for all systems that satisfy detailed balance and Boltzmann distribution at equilibrium. We achieve this by proving that for systems with inhomogeneous equilibrium states governed by dynamics such as the Cahn-Hilliard (CH) dynamics, the chemical potential and self-diffusivity must mutually constrain each other. We discuss common issues in the literature which result in inconsistent formulations, construct the consistency requirement mathematically, develop a class of self-diffusivities that guarantee consistency, and discuss how the requirement originates from detailed balance and Boltzmann distribution, and is therefore applicable to both conserved and non-conserved dynamics.

Micron-scale colloidal particles with short-ranged attractions, e.g., colloids functionalized with single-stranded DNA oligomers, have emerged as a powerful platform for studying colloidal self-assembly phenomena with the long-term goal of identifying routes for metamaterial fabrication. Although these systems have been investigated extensively both experimentally and computationally, the role of "real world" features that may impact self-assembly in unexpected ways has been largely ignored. One such example of an important, yet underappreciated, feature is interaction heterogeneity (IH), i.e., variations in interparticle interaction strengths, which can arise from variability in the DNA strand areal density on particle surfaces during fabrication. A previous study demonstrated that IH can modulate nucleation and gelation kinetics under non-equilibrium conditions. Here, we investigate in detail the dependence of bulk fluid-crystal coexistence on IH. Using a multicomponent coexistence tracing approach, we compute phase diagrams for both Gaussian and bidisperse IH distributions, revealing that IH shifts the fluid-side coexistence boundaries outward, promoting crystallization at lower particle volume fractions while also resulting in crystals that are enhanced in the stronger binding species. Our results demonstrate that IH significantly influences crystallization behavior even under equilibrium conditions and provide a new perspective on tuning IH as a controllable parameter for optimizing colloidal self-assembly.

Thrombus growth is a complex and multiscale process involving interactions spanning length scales from individual micron-sized platelets to macroscopic clots at the millimeter scale. Here, we describe a 3D multiscale framework to simulate thrombus growth under flow comprising four individually parallelized and coupled modules: a data-driven Neural Network (NN) that accounts for platelet calcium signaling, a Lattice Kinetic Monte Carlo (LKMC) simulation for tracking platelet positions, a Finite Volume Method (FVM) simulator for solving convection-diffusion-reaction equations describing agonist release and transport, and a Lattice Boltzmann (LB) flow solver for computing the blood flow field over the growing thrombus. Parallelization was achieved by developing in-house parallel routines for NN and LKMC, while the open-source libraries OpenFOAM and Palabos were used for FVM and LB, respectively. Importantly, the parallel LKMC solver utilizes particle-based parallel decomposition allowing efficient use of cores over highly heterogeneous regions of the domain. The parallelized model was validated against a reference serial version for accuracy, demonstrating comparable results for both microfluidic and stenotic arterial clotting conditions. Moreover, the parallelized framework was shown to scale essentially linearly on up to 64 cores. Overall, the parallelized multiscale framework described here is demonstrated to be a promising approach for studying single-platelet resolved thrombosis at length scales that are sufficiently large to directly simulate coronary blood vessels.

Analysis of interdigitated transducers often relies on phenomenological models to approximate device electrical performance. While these approaches prove essential for signal processing applications, phenomenological models provide limited information on the device’s mechanical response and physical characteristics of the generated acoustic field. Finite element method modeling, in comparison, offers a robust platform to study the effects of the full device geometry on critical performance parameters of interdigitated transducer devices. In this study, we fabricate a surface acoustic wave resonator on semi-insulating GaAs (100), which consists of an interdigitated transducer and acoustic mirror assembly. The device is subsequently modeled using fem software. A vector network analyzer is used to measure the experimental device scattering response, which compares well with the simulated results. The wave characteristics of the experimental device are measured by contact-mode atomic force microscopy, which validates the simulation’s mechanical response predictions. We further show that a computational parametric analysis can be used to optimize device designs for series resonance frequency, effective coupling coefficient, quality factor, and maximum acoustic surface displacement.

Interdigitated transducer devices may provide an advantageous platform to study stress-enhanced interfacial phenomena at elevated temperatures, and an appropriate device design requires a thorough understanding of temperature-dependent material properties. In this study, the scattering parameter response for a surface acoustic wave resonator is simulated using a frequency-domain finite element method from 20 to 177 °C. Experimental device measurements are taken for the interdigitated transducer device fabricated on semi-insulating GaAs 100 oriented in the 110 direction, and the results are in good agreement with the simulation. Surface acoustic wave analysis provides the magnitude of bulk stress values and surface displacement over the experimental temperature range produced by a standing surface acoustic wave. The computational analysis combined with experimental verification suggests that such devices, when optimized for the maximum magnitude, can produce strain levels that could influence chemical potential associated with crystalline growth, atomic diffusion, and catalytic reactions. The modeling results demonstrate an interdigitated transducer's potential as an experimental platform to study the impact of strain on temperature-sensitive surface and bulk phenomena in piezoelectric materials.

It has long been suggested that the familiar intrinsic point defects (vacancies and self-interstitials) encountered in crystals at low temperatures (T ) transform into extended domains characterized by a missing or excess atom compared with the same-sized region in the perfect crystal so that such extended defects may be viewed as dropletlike regions of enhanced or diminished density. However, the implications of such a transformation, or whether it even occurs in crystalline Si, remain uncertain. To address this fundamental problem, we consider a comprehensive thermodynamic analysis of the thermodynamics of vacancy and self-interstitial formation over a broad T range based on thermodynamic integration with a focus on entropic contributions. In cooled liquids, it is well known that the form of the intermolecular potential can greatly influence the configurational entropy S-c, and correspondingly, we analyze several empirical Si potentials to determine how the potential influences both the T dependence of S-c and the enthalpy and entropy of defect formation. We indeed find that the S-c associated with point defects increases significantly upon heating, consistent with the existence of extended defects. Moreover, each type of defect species gives a significantly different contribution to S-c at elevated T and to a qualitive difference in the T dependence of the entropy of defect formation in the extended defect regime. We discuss some potential consequences of these thermodynamic changes of defect formation on the T dependence of diffusion in heated crystals.

SignificanceThe free energy functional is a central component of continuum dynamical models used to describe phase transitions, microstructural evolution, and pattern formation. However, despite the success of these models in many areas of physics, chemistry, and biology, the standard free energy frameworks are frequently characterized by physically opaque parameters and incorporate assumptions that are difficult to assess. Here, we introduce a mathematical formalism that provides a unifying umbrella for constructing free energy functionals. We show that Ginzburg-Landau framework is a special case of this umbrella and derive a generalization of the widely employed Cahn-Hilliard equation. More broadly, we expect the framework will also be useful for generalizing higher-order theories, establishing formal connections to microscopic physics, and coarse graining.

Modeling thrombus growth in pathological flows allows evaluation of risk under patient-specific pharmacological, hematological, and hemodynamical conditions. We have developed a 3D multiscale framework for the prediction of thrombus growth under flow on a spatially resolved surface presenting collagen and tissue factor (TF). The multiscale framework is composed of four coupled modules: a Neural Network (NN) that accounts for platelet signaling, a Lattice Kinetic Monte Carlo (LKMC) simulation for tracking platelet positions, a Finite Volume Method (FVM) simulator for solving convection-diffusion-reaction equations describing agonist release and transport, and a Lattice Boltzmann (LB) flow solver for computing the blood flow field over the growing thrombus. A reduced model of the coagulation cascade was embedded into the framework to account for TF-driven thrombin production. The 3D model was first tested against in vitro microfluidics experiments of whole blood perfusion with various antiplatelet agents targeting COX-1, P 2 Y 1 , or the IP receptor. The model was able to accurately capture the evolution and morphology of the growing thrombus. Certain problems of 2D models for thrombus growth (artifactual dendritic growth) were naturally avoided with realistic trajectories of platelets in 3D flow. The generalizability of the 3D multiscale solver enabled simulations of important clinical situations, such as cylindrical blood vessels and acute flow narrowing (stenosis). Enhanced platelet-platelet bonding at pathologically high shear rates (e.g., von Willebrand factor unfolding) was required for accurately describing thrombus growth in stenotic flows. Overall, the approach allows consideration of patient-specific platelet signaling and vascular geometry for the prediction of thrombotic episodes.

The collagen receptor glycoprotein VI (GPVI) drives strong platelet activation, however its role at later stages of clotting remains less clear. Controlled timing of addition of anti-human GPVI Fab (clone E12) with microfluidic venous whole blood flow over collagen (± lipidated tissue factor, TF) produced distinct effects on platelets, fibrin, P-selectin exposure, and phosphatidylserine (PS) exposure. On collagen alone, Fab present initially potently reduced platelet deposition on collagen, while Fab added 90 s after initial platelet deposition, stopped subsequent platelet accumulation (despite the absence of fibrin). With thrombin generation via TF, Fab added at either t = 0 or 90 s had no effect on platelet deposition. However, Fab added initially, but not at 90-s, blocked fibrin formation. Gly-Pro-Arg-Pro ablated fibrin formation without effect on platelet accumulation (regardless of Fab added at t = 0 or 90 s), indicating thrombin signaling can suffice over GPVI signaling. Still, Fab moderately reduced P-selectin exposure with thrombin present and fibrin absent. On collagen/TF, Fab present initially ablated PS exposure, but had no effect when added 30 to 90-s later. The thrombin generated via PS exposure had an important role in driving platelet deposition in the presence of Fab, since inhibition of PS via annexin V binding in the presence of Fab significantly inhibited platelet deposition. We conclude GPVI signaling in the first platelet layer on collagen dictates thrombin and fibrin production, but the role of GPVI at subsequent times after formation of the first monolayer is obscured by thrombin-induced signaling.

Risk quantification algorithms in the ICU can provide (1) an early alert to the clinician that a patient is at extreme risk and (2) help manage limited resources efficiently or remotely. With electronic health records, large data sets allow the training of predictive models to quantify patient risk. A gradient boosting classifier was trained to predict high-risk and low-risk trauma patients, where patients were labeled high-risk if they expired within the next 10 hours or within the last 10% of their ICU stay duration. The MIMIC-III database was filtered to extract 5,400 trauma patient records (526 non-survivors) each of which contained 5 static variables (age, gender, etc.) and 28 dynamic variables (e.g., vital signs and metabolic panel). Training data was also extracted from the dynamic variables using a 3-hour moving time window whereby each window was treated as a unique patient-time fragment. We extracted the mean, standard deviation, and skew from each of these 3-hour fragments and included them as inputs for training. Additionally, a survival metric upon admission was calculated for each patient using a previously developed National Trauma Data Bank (NTDB)-trained gradient booster model. The final model was able to distinguish between high-risk and low-risk patients to an AUROC of 92.9%, defined as the area under the receiver operator characteristic curve. Importantly, the dynamic survival probability plots for patients who die appear considerably different from those who survive, an example of reducing the high dimensionality of the patient record to a single trauma trajectory.

We present the growth of In 0.53 Ga 0.47 Sb on InP using the interfacial misfit dislocation growth mode. The growth is performed by using an intermediate InGaAs layer and achieving an As for Sb anion exchange. Characterization results show the formation of an interfacial misfit dislocation array, however the epilayer shows significant phase segregation.

The equilibrium segregation of impurities at the melt–solid interface during silicon crystallization is a key factor in determining the impurity concentration and distribution in the crystal. Unfortunately, this property is difficult to measure experimentally due to the presence of complex transport physics in the melt. Here, using the Tersoff family of empirical potential models, we describe a thermodynamic integration framework for computing the interstitial oxygen and substitutional carbon segregation coefficients in silicon. Thermodynamic integration using an ideal gas reference state for the impurity atoms is shown to be an efficient and convenient pathway for evaluating impurity chemical potentials in both solid and liquid phases. We find that the segregation coefficient is captured well for substitutional carbon impurity while it is significantly underestimated for interstitial oxygen. The latter discrepancy is partially attributed to the qualitatively incorrect silicon solid-to-liquid density ratio predicted by the empirical interatomic potential.

Many biologically important cell binding processes, such as the rolling of leukocytes in the vasculature, are multivalent, being mediated by large numbers of weak binding ligands. Quantitative agreement between experiments and models of rolling has been elusive and often limited by the poor understanding of the binding and unbinding kinetics of the ligands involved. Here, we present a cell-free experimental model for such rolling, consisting of polymer microspheres whose adhesion to a glass surface is mediated by ligands with well-understood force-dependent binding free energy-short complementary DNA strands. We observe robust rolling activity for certain values of the shear rate and the grafted DNA strands' binding free energy and force sensitivity. The simulation framework developed to model leukocyte rolling, adhesive dynamics, quantitatively captures the mean rolling velocity and lateral diffusivity of the experimental particles using known values of the experimental parameters. Moreover, our model captures the velocity variations seen within the trajectories of single particles. Particle-to-particle variations can be attributed to small, plausible differences in particle characteristics. Overall, our findings confirm that state-of-the-art adhesive dynamics simulations are able to capture the complex physics of particle rolling, boding well for their extension to modeling more complex systems of rolling cells.

PURPOSE:Coronary flow is affected by evolving events such as atherosclerotic plaque formation, rupture, and thrombosis, resulting in myocardial ischemia and infarction. Highly resolved 3D hemodynamic data at the stenosis is essential to model shear-sensitive thrombotic events in coronary artery disease.METHODS:We developed a hybrid 1D-3D simulation framework to compute patient-specific coronary hemodynamics efficiently. A 1D model of the coronary flow is coupled to an image-based 3D model of the region of interest. This framework affords the advantages of reduced-order modeling, decreasing the global computational cost, without sacrificing the accuracy of the quantities of interest.RESULTS:We validated our 1D-3D model against full 3D coronary simulations in healthy and diseased conditions. Our results showed good agreement between the 3D and the 1D-3D models while reducing the computational cost by 40-fold compared to the 3D simulation. The 1D-3D model predicted left/right coronary flow distribution within 3% and provided an accurate estimation of fractional flow reserve and wall shear stress distribution at the stenosis comparable to the 3D simulation.CONCLUSION:Savings in computational cost may be significant in situations with changing geometry, such as growing thrombosis. Also, this approach would allow quantifying the time-dependent effect of thrombotic growth and occlusion on the global coronary circulation.

Distinct from dilute, isotropic, and homogeneous reaction systems typically used in laboratory kinetic assays, blood is concentrated, two-phase, flowing, and highly anisotropic when clotting on a surface. This review focuses on spatial gradients that are generated and can dictate thrombus structure and function. Novel experimental and computational tools have recently emerged to explore reaction-transport coupling during clotting. Multiscale simulations help bridge tissue length scales (the coronary arteries) to millimeter scales of a growing clot to the microscopic scale of single-cell signaling and adhesion. Microfluidic devices help create and control pathological velocity profiles, albeit at a low Reynolds number. Since rate processes and force loading are often coupled, this review highlights prevailing convective-diffusive transport physics that modulate cellular and molecular processes during thrombus formation.