Nimrod Segall

PhD Student 
Computational Soft and Biological Active matter
Tel Aviv University
nimrods6@mail.tau.ac.il
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Contact Me


If you have any questions, any ideas regarding my work, or if you just want to chat or say hi, send me an e-mail on  nimrods6@mail.tau.ac.il .

If you're ever in Tel Aviv and wish to give a talk on subjects relating to soft matter physics or biophysics at the Biosoft Center Seminar , send me an email to  biosoftc@tauex.tau.ac.il .


My Office is located in Tel Aviv University in Wolfson Mechanical Engineering 224, phone +972-3-640-5299.

Jamming vs. Caging in 3D

In 2D Jamming = Caging. In 3D that does not have to be the case!




Glassy behavior is observed not only in 'chemical' glasses, but also in granular materials and colloidal suspensions. These systems are characterized by having a glass transition temperature. That is, a temperature in which a mixed order out of equilibrium phase transition occurs and the relaxation times of the system diverge.


This critical slowing down in dynamics is one among many reasons as to why studying glassy behavior is challenging both experimentally and theoretically. Kinetically constrained models have been successful in this as they are simple enough to easily simulate large systems and are sometimes analytically solvable.

Formation of cages in the 3D extension of the Spiral Model

We have studied a three dimensional  [1] extension of the 2D Spiral Model   [2]. The 2D model undergoes jamming at a critical density in which a fraction of the particles in the system cannot move anymore and form 1D clusters of frozen sites.  The particles that can still move become rattlers - they are confined to some local area of the system and cannot diffuse far away.

In 3D however, that is not the case; a fraction of the particles in the system become frozen and creates a 1D frozen cluster, however mobile particles can diffuse over long distances. The reason is geometric - in 2D one-dimensional frozen clusters of particles act as effective walls, and particles are unable to cross these walls and travel to the other side. In 3D particles use the third dimension to travel around these clusters. We investigated the existence of a second critical density, one in which frozen clusters become 2D and particles become confined in local cages in the system.

The probability an infinite cage exist as a function of density of free volume sites. Two phase transitions appear, one for jamming and the other for caging. The jamming transition relates to directed percolation while the caging transition is a random percolation transition.

Using a fast and efficient culling algorithm, we bypass running the dynamics of the system and find the underlying structural property of the system determining the diffusivity of particles.


We indeed find a second critical density. Its critical exponents value agree with those of random percolation.

My Research Interests

My research focus is on glassy dynamics, jamming, active frustrated materials and statistical physics of active living matter.

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Jamming vs. Caging in 3D


In 2D a system undergoing jamming will cause particles to be confined in local cages. In 3D however that does not have to be the case - frozen clusters of particles appear at a density in which particles may still diffuse across the system. Only at a higher density particles become caged. The reason is geometrical. We use a fast algorithm bypassing the need to run the dynamics of the system to uncover the underlying structure of the system ...




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Formation of cages in the 3D extension of the Spiral Model
My MSc Thesis
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CV
Work Experience
  • Tel Aviv University, Teaching Assistant; General Chemistry 1 (Chemistry Department), Kinetics (Chemistry Department). Homework grading.
  • Private Tutor; Physics, Mathematics, Chemistry.

Academic Record

  • BSc. - Tel Aviv University, Raymond and Beverly Sackler faculty of exact sciences, school of chemistry.
  • MSc. - Tel Aviv University, Raymond and Beverly Sackler faculty of exact sciences, school of chemistry.

List of Publication

  • N.Segall, R. Chatterjee, Y. Shokef, Diffusion and Aggregation in a Driven Lattice Glass Model, In preparation...
  • R. Chatterjee, N.Segall, Y. Shokef, Active Tracer Dynamics in a 2D Disordered Medium, In preparation...
  • N. Segall, E. Teomy, and Y. Shokef, Jamming vs. Caging in Three-Dimensional Jamming Percolation. Journal of Statistical Mechanics - Theory and Experiment, 054051 (2016). 
  • N. Segall, E. Teomy, and Y. Shokef, The critical density in 3D jamming percolation, In preparation...

Work Presented In

Talks:

  • N. Segall, E. Teomy, and Y. Shokef, Jamming vs. Caging in Three Dimensional Jamming Percolation, Israel Physical Society, Bar-Ilan University 2015.
  • N. Segall, E. Teomy, and Y. Shokef, Jamming vs. Caging in Three Dimensional JammingPercolation, The Raymond and Beverly Sackler Center for Molecular and Materials Science Retreat, Neve-Ilan 2015.
Posters: 
  • N.Segall, Y. Shokef, Diffusion and Aggregation in a Driven Lattice Glass Model, TAU - LMU Workshop on Biophysics, Tel-Aviv University 2018.
  • N.Segall, Y. Shokef, Driven Dynamics in an Active Frustrated Material, Israeli Physics Society, The Technion 2017.
  • N. Segall, E. Teomy, Y. Shokef, Jamming vs. Caging in Three Dimensional Jamming Percolation, Mechanical Instabilities in Solids, The Hebrew University 2017.
  • N. Segall, E. Teomy, Y. Shokef, Jamming vs. Caging in Three Dimensional Jamming Percolation, Stochasticity of Cells and Genes, Tel-Aviv University 2016.
  • N. Segall, E. Teomy, Y. Shokef, Jamming vs. Caging in Three Dimensional Jamming Percolation, Viscous Liquids IV, University of Montpellier, France 2015.

Other

  • I attended the 2015 Boulder School: Soft Matter In and Out of equilibrium.
  • 2017 - Present day: I coordinate the seminars of the Center of Physics and Chemistry of Biological Systems in Tel Aviv University:  Biosoft Center
Nimrod Segall, MSc

I am an engineering grad student in Tel Aviv University, currently working on my PhD under the supervision of Yair Shokef.

My research focus is on glassy dynamics, jamming, active frustrated materials and active living matter, mostly using computational tools to tackle these subjects.

I have a  multidisciplinary background; undergrad in Chemistry, Master thesis on jamming and caging in glassy systems, and my current work is on the subject of active frustrated materials.

News

Formation of cages in the Spiral Model's Jammed State [1]
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Diffusion and Aggregation in a Driven Lattice Glass Model


Cells have been shown to share similarities with glasses. Amorphous structure and dynamical heterogeneities are among several such examples.  The N3 model is known to have some glassy properties such as critical slowing down. Inspired by these biological systems and we propose an active N3 model which shows a rich variety of dynamics, most notably aggregation of  particles due to activity ...




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Clustering of Particles in the Active N3 Model

Diffusion and Aggregation in a Driven Lattice Glass Model

Cells can be glassy. We investigate this phenomenon using an active lattice model




Cells, in some instances [1] , have been known to behave in a glass like manner. Some cell tissues do not crystallize and interesting dynamical properties such as dynamical heterogeneities have been shown on experiments. The N3 lattice model [2] is defined on a square lattice such that particles exclude one an other up to 3rd order nearest neighbors, meaning that the closest particles can get to one an other is 4th order neighbors. These rules virtually results in 'plus' shaped particles which cannot co-penetrate one an other. The system is geometrically frustrated and exhibit fascinating glassy properties such as a non-thermal glass transition at which dynamics critically slow down.



We propose an active variant of the N3 model, in which particles have a preferred direction to which they are more likely to move. Particles can also attempt to rotate, thus, changing their preferred direction. However, a particle may not rotate if it has even one closest neighbor (4th order, see figure to the right).

Black arrows indicate particle's preferred direction.
Red arrows indicate possible thermal translation moves.
Blue arrows indicate possible rotation moves.
Green arrows indicate possible active translation moves.

We simulate the model and find that adding activity results in aggregates; particles band together to form compact structures. The more active the system is, the less free particles are found in the system. This behavior seems consistent through a wide range of densities and amounts of activity.  As more particles band together, more moves become forbidden, thus resulting in slower dynamics. To avoid wasting computational resources we run a rejection free simulation in which only allowed moves are considered. We calculate the actual MC time out of the number of rejected moves. 

State of the system after 10,000 successful moves.
Left: Low ActivityRight : High Activity

WORK IN PROGRESS

The systems starts from an initial random configuration. Every time step a move is selected at random and if that move is possible then it is performed. The entire dynamics is governed by the density and the two ratios between the rates of motion: the ratio between the active rate and the thermal rate, and the ratio between the rotational rate and thermal rate.

We plot the mean squared displacement (MSD) of particles in the system as a function of time for different rates of rotation. At low densities the system exhibits short time ballistic behavior and long time diffusive behavior, with the crossover time scaling as the inverse of the rate of rotation. This is the expected result for a collection of active particles.


When density is increased the motion of particles is governed more by the formation of vacancies and less by the active nature of particles. As a result the observed MSD is more similar to diffusive then ballistic.


Current work:

Generate a phase diagram of the system as a function of activity, rotation, and density.


Future work:

Give a more quantitative analysis to the phenomena observed in our system.

(a)
(b)
(a) - Low density MSD versus time for different rates of rotation. Particles become diffusive at long times.
(b) - Higher density MSD versus time for different rates of rotation. Particle motion is affected more by the formation of vacancies than activity.
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Active Tracer Dynamics in a 2D Disordered Medium


The motion of a charged tracer particle moving through a system of diffusing particles had been studied widely both theoretically and experimentally. Here we study the motion of an active particle tracer through a thermal geometrically frustrated glassy system. This simple model allows us to better understand the motion of the particle analytically and gain deeper insight on the diffusivity of active particles ...



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Active N3 tracer Moving Through a Disordered Thermal System

Active Tracer Dynamics in a 2D Disordered Medium

We simulate a single active particle moving through a glassy system and compare results with theory



WORK IN PROGRESS

The N3 lattice model [1] is defined on a square lattice such that particles exclude one an other up to 3rd order nearest neighbors, meaning that the closest particles can get to one an other is 4th order neighbors. These rules virtually results in 'plus' shaped particles which cannot co-penetrate one an other. The system is geometrically frustrated and exhibit fascinating glassy properties such as a non-thermal glass transition at which dynamics critically slow down. Cells, in some instances [2] , have been known to behave in a glass like manner. To further understand the motion of active agents within a glassy regime we simulate a single active particle within a bath of thermal N3 particles and develope analytical expression for the diffusivity of the particle. The active tracer may move thermally, but moreover has a preferred direction to which motion is biased. It may attempt to move towards that preferred direction (active move) or may attempt to change its preferred direction (rotation move). 

Equation for the velocity of an active non-rotating N3 particle within a thermal bath.
RA - Active Motion Rate
RT - Thermal Motion Rate

We start from a simple case of a non-rotating active tracer, set with an initial condition where its preferred direction is set towards the x+ direction.


The active particle attempts to move at a thermal rate RT, and an active rate RA. For the move to be successful, the five sites in front of the particle must be unoccupied (see illustration). From these simple considerations we can write a simple probabilistic equation for the position of the particle as a function of time. By averaging over it, taking the third neighbors exclusion principle and deriving it with respect to time we obtain the expression for the drift velocity of the particle.

Tracer particle (blue) in a thermal system (red)
Tracer started in a point denoted by A and ended in a point denoted by B while following the blue line. Simulated with periodic boundary conditions.

The analytical prediction is in excellent agreement with the numerical calculation.


Currently work:

Developing an analytical expression that depends only on the equilibrium properties of the system.


Future work:

Expand this result for the rotating case (Levy walk).

Drift velocity as a function of activity
Red - Velocity measured from simulation
Blue - Velocity calculated analytically 
Nimrod Segall, Computational Soft and Biological Active Matter
 Tel Aviv University, Wolfson Engineering 224
nimrods6@mail.tau.ac.il +972-3-640-5299