In the News

In the News

CRUNCH group research highlighted by Soft Matter as cover article (March 2019)


 

Recent research from the CRUNCH group has been featured on the back cover of the prestigious RSC journal Soft Matter. This highlighted work was performed in the group of Prof. George Karniadakis from the Division of Applied Mathematics, Brown University, in collaboration with the group of Prof. Chao Yang from the Institute of Process Engineering, Chinese Academy of Sciences. Shown on the cover is a snapshot of multiscale simulation of a shear flow past an endothelial glycocalyx layer (EGL) in a microchannel, with the atomistic resolution locally (water molecules, heparan sulfate chains, lipid bilayer and transmembrane proteins) and the coarse-grained resolution in bulk domain. 

 View the article [hyperlink: https://doi.org/10.1039/C8SM02170H].

The study reports the multiscale modeling of soft multi-functional surfaces. The endothelial glycocalyx layer is a soft multi-functional surface, coating the endothelial cells and lining the entire vascular system. Single brute-force atomistic simulation for this problem is prohibitively expensive and limited to small scales. This study shows that an efficient parallel multiscale method can bridge the atomistic and mesoscale regimes in modeling of soft mult-functional surfaces, from nanometer to micron and beyond.

 

CRUNCH group research highlighted by LANGMUIR as cover article


Research from the CRUNCH group has been featured on the cover of the prestigious ACS journal LANGMUIR. The research was a collaborative effort by Kaixuan Zhang, Zhen Li, Martin Maxey and George Karniadakis in partnership with Shuo Chen from Tongji University (Shanghai, China). Shown on the cover are different phases of droplet coalescence dynamics on a hydrophobic surface with hierarchical roughness, in which the colors on the rough wall indicate the distribution of surface structure height, and the wavelets on the liquid droplets represent the thermal capillary waves. View the article http://dx.doi.org/10.1021/acs.langmuir.8b03664.

The study reports a novel self-cleaning mechanism of textured surfaces attributed to a spontaneous coalescence-induced wetting transition, which explains why droplets on rough surfaces are able to change from the highly adhesive Wenzel state to the low-adhesion Cassie–Baxter state and achieve self-cleaning. The study also reports that the spatial distribution of liquid components in the coalesced droplet can be controlled by properly designing the overall arrangement of multiple droplets. The findings offer new insights for designing effective biomimetic self-cleaning surfaces by enhancing spontaneous Wenzel-to-Cassie wetting transitions, and additionally, for developing new noncontact methods to manipulate liquids inside the small droplets via multiple-droplet coalescence.

 

2018 Editors' Choice Collection  

In the annual Editors' Choice collection, the JCP Editors highlight a few of the many notable articles published this year that present significant and definitive research in experimental and theoretical areas of the field. The articles below are a sneak peek of the full collection, which will be announced in early 2019.

George Em Karniadakis

George Em KarniadakisGeorge Em KarniadakisGeorge Karnidakis and Sharon Rounds have been elected fellows of the American Association for the Advancement of Science.  Professor Karniadakis was recognized for his outstanding contributions to applied mathematics, especially in developing mathematical simulations of a variety of physics and biological systems.

Quantifying Platelet Margination in Diabetic Blood Flow

In silico study of near-wall platelet dynamics in diabetic blood flow with an adhered white blood cell (WBC), showing a platelet sliding over a WBC and undergoing a flipping motion. Such WBCs  tend to reduce platelet margination while increasing the probability of platelet-WBC aggregation. See the article by Chang et al. in the October 2nd issue of Biophysical Journal.

2018 Editors' Choice article:

An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields. https://doi.org/10.1063/1.5008630

How sickled red blood cells stick to blood vessels

Analysis of blood from patients with sickle-cell disease reveals how cell clumping begins. 

MIT-Sickle Red Blood CellsMIT-Sickle Red Blood Cells

 

 

 

 

 

World's Fastest Supercomputers to Help Model How Liquids Move Through Shale.

An atomistic fingerprint algorithm for learning ab initio molecular force fields

New Machine learning system identifies shapes of red blood cells

 

 

 

 

 

 

 

 

 

 

Scientists have developed a new system to classify the shapes of red blood cells by using a computational approach known as deep learning.  This unique approach can potentially help doctors treat their patients with sickle cell disease.  The findings were published in PLOS Computational Biology.  (Read more.) Visual Credit:  Xu et al.

A new book is published by George Karniadakis and Zhongqiang Zhang.

Professor George Karniadakis has coauthored a new book with Zhongqiang Zhang entitled,  "Numerical Methods for Stochastic Partial Differential Equations with White Noise."

Computer Models provide new understanding of sickle cell disease

Simulations developmented by George Karniadakis and his research group provide new details of how sickle cell disease manifests inside red blood cells, which could provide help in developing new treatments. (Read News from Brown.)



Understanding of the interactions between sickle cell fibers

Researchers in Applied Mathematics discover ways to understand better sickle cell disease

George Karniadakis' research group including Lu Lu, He Li, Xin Bian and Xuejin Li have published research which reveals a new understanding of the intracellular polymerization of sickle hemoglobin (Hbs).  (Read more.)