About

Talid R. Sinno is a Professor of Chemical and Biomolecular Engineering at the University of Pennsylvania, where he has been a member of the standing faculty since 1999. He holds a B.S. in Chemical Engineering and a B.A. in Chemistry from the University of Pennsylvania, and earned his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology in 1998. Following his doctoral studies, he spent a year as a postdoctoral researcher and lecturer at MIT. Professor Sinno also holds a secondary appointment in the Department of Mechanical Engineering and Applied Mechanics at Penn and is a member of the Graduate Groups in Materials Science and Engineering as well as Physics and Astronomy. He currently serves as the Founding Director of the master’s degree program in Scientific Computing at Penn, which integrates coursework and research training at the intersection of modern scientific computing, artificial intelligence, and data science. His research focuses on computational materials science, specifically multiscale modeling and simulation of nucleation, aggregation, and crystallization processes across various material systems. His work includes studies on semiconductor microstructure evolution during crystal growth, self-assembly of colloidal crystals, and platelet cell aggregation in blood flow, utilizing computational techniques such as molecular dynamics, Monte Carlo methods, and continuum scale modeling.

Research topics

• Computer Science
• Medicine
• Physics
• Mechanics
• Biology
• Internal medicine
• Biomedical engineering
• Materials science
• Mathematics
• Nanotechnology
• Biophysics
• Emergency medicine
• Bioinformatics
• Thermodynamics
• Medical emergency
• Chemical physics
• Cardiology
• Crystallography
• Statistical physics
• Optics
• Chemistry
• Geometry
• Physical chemistry

Selected publications

• Volume electron microscopy reveals heterogeneity of the hemostatic response in veins and arteries

  Blood Advances · 2026-02-12 · 1 citations

  articleOpen access

  ABSTRACT: Intravital imaging studies have provided insights into the spatial and temporal variations of platelet activation and thrombin generation that occur during hemostasis; however, these studies are generally limited to small vessels due to the practical limitations of imaging in thicker tissues. Recent advances in cleared tissue fluorescence imaging as well as volume electron microscopy (vEM) coupled with machine learning-based image segmentation provide an opportunity for analysis of the 3-dimensional structure of complex tissues. We utilized these technologies to examine hemostatic plugs from murine jugular veins and carotid arteries to investigate the spatial distribution of platelet activation and biochemical responses in these disparate physiologic contexts. Both venous and arterial hemostatic plugs had a heterogeneous structure with regions of sparsely and densely packed platelets. Despite similar injury sizes, arterial hemostatic plugs were at least an order of magnitude larger than venous plugs. The difference in plug size was primarily due to a 19-fold increase in the population of densely packed platelets in the extravascular compartment. Venous plugs displayed significant platelet aggregation extending into the vessel lumen and developed distinctive fibrin and red blood cell-filled cavities. Complementary fluorescence microscopy revealed that platelet activation was spatially heterogeneous in both contexts, with α-granule secretion and phosphatidylserine exposure confined to specific microenvironments, highlighting tightly regulated thrombin activity. Overall, our findings reveal both conserved and distinct mechanisms of hemostatic thrombus formation in different physiologic contexts. They also demonstrate the power of vEM coupled with machine learning-based image segmentation for the quantitative analysis of large imaging data sets from complex tissues.

  PublisherOA PDFDOI

• Tracing degradation mechanisms of epitaxially grown GaAsSb on InP by InSb formation at elevated temperatures

  Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2025-03-01

  article

  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.

  PublisherDOI

• From imaging to computational domains for physics-driven molecular biology simulations: Hindered diffusion in platelet masses

  PLoS Computational Biology · 2025-07-07

  articleOpen accessSenior author

  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.

  PublisherDOI

• Multiscale simulations that incorporate patient-specific neural network models of platelet calcium signaling predict diverse thrombotic outcomes under flow

  PLoS Computational Biology · 2025-05-06 · 1 citations

  articleOpen access

  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 TXA2, 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.

  PublisherDOI

• From imaging to computational domains for physics-driven molecular biology simulations: Hindered diffusion in platelet masses

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-08

  preprintOpen accessSenior authorCorresponding

  Abstract 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 large stacks of 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 a computational domain 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 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. Author Summary Hemostasis and coagulation are two exquisitely complex, intertwined, and tightly regulated biological processes. Dysregulation of either process may lead to severe health consequences or death. Coagulation reactions have been extensively studied under static laboratory conditions, which are different from in vivo conditions. It is therefore imperative to understand if and how the chemical reactions underlying coagulation are regulated by the environment where they occur. In vivo experimentation enables us to image hemostasis, but not chemical reactions. Physics-driven molecular simulations of chemical reactions can bridge the gap, provided the physical environment is correctly represented computationally. The present work serves as a much-needed foundation for image-to-computation for physics based molecular simulations in biological domains.

  PublisherDOI

• Thermodynamic Relations between Free Energy and Mobility

  arXiv (Cornell University) · 2024-06-11

  preprintOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Multiparticle collision dynamics simulations of hydrodynamic interactions in colloidal suspensions: How well does the discrete particle approach do at short range?

  The Journal of Chemical Physics · 2024-05-07 · 3 citations

  articleSenior author

  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.

  PublisherDOI

• Computational analysis of the effect of interaction heterogeneity on fluid–crystal coexistence in micron-scale colloidal systems

  The Journal of Chemical Physics · 2024-12-23

  articleSenior author

  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.

  PublisherDOI

• Development of a parallel multiscale 3D model for thrombus growth under flow

  Frontiers in Physics · 2023-09-05 · 4 citations

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• PB0875 A Parallel Kinetic Monte Carlo Simulation of Human Stenotic Thrombus Growth with Single Platelet Resolution from Plaque Rupture to Occlusion

  Research and Practice in Thrombosis and Haemostasis · 2023-10-01

  articleOpen access
  PublisherDOI

Recent grants

• CDS&E: Collaborative Research: Data-Driven Predictive Modeling of Flows Containing Aggregating Particles

  NSF · $575k · 2014–2018

• Collaborative Research: Atomic Displacement Engineering of Post-epitaxial Thin-films (ADEPT)

  NSF · $266k · 2018–2022

• NIRT: Directed Assembly of Nanostructures: Theory, Simulations, and Experiments in Hard and Soft Materials

  NSF · $1.3M · 2004–2009

• Collaborative Research: Large-Scale Patterning of Germanium Quantum Dots by Stress Transfer

  NSF · $310k · 2011–2015

• Collaborative Proposal: Low-Cost Substrates for III-V Photovoltaics by Self-Templated Selective Epitaxial Growth of Germanium on Silicon

  NSF · $299k · 2009–2013

Frequent coauthors

• John C. Crocker

  47 shared

• Scott L. Diamond

  24 shared

• Ian Jenkins

  21 shared

• Sang M. Han

  University of New Mexico

  19 shared

• Daniel Kaiser

  University of Pennsylvania

  13 shared

• Sumeet S. Kapur

  11 shared

• Wilfried von Ammon

  Roth and Rau (Germany)

  10 shared

• Robert A. Brown

  10 shared

Awards & honors

• 2001 NSF CAREER Award