We  have  developed  models  capable  of describing  magnetic  brush  formation  phenomena. 
In  particular,  we  investigate  the  effects  of physical  parameters  (applied  magnetic  field, 
etc.)  on  the correlations  and  the structure.  Three models  are  presented, which  point out 
different  aspects  of the brush growth  process

Csahók, Zoltán

Honma, Toshihsa

Igarashi, Hajime

Vicsek, Tamás

English

Periodica Polytechnica (Budapest University of Technology and Economics)

PERIODICA POLYTECHNICA SER. EL. ENG. VOL. 98, NO. 4, PP. 961-972 (1994) NUMERICAL SIMULATIONS OF MAGNETIC BRUSH FORMATION PHENOMENA Zoltan CSAHOK*, Hajime IGARASHI***, Toshihsa HONMA *** and Tamas VICSEK** .. Department of Atomic Physics EOtvos University Budapest 1088 Budapest, Hungary ** Institute for Technical Physics P. O. Box 76, 1325 Budapest, Hungary *** Department of Electrical Engineering Hokkaido University Sapporo North 13. West 8., Japan Received: Nov. 1, 1994 Abstract We have developed models capable of describing magnetic brush formation phenomena. In particular, we investigate the effects of physical parameters (applied magnetic field, etc.) on the correlations and the structure. Three models are presented, which point out different aspects of the brush growth process. Keywords: magnetic brush, fractal, numerical simulation. 1. Introduction The formation of magnetic brushes is a common phenomenon in electropho-tography [1]. A magnetic brush is an aggregate of small (about lO mi-crons in diameter) paramagnetic particles forming a bristle in an applied magnetic field. In electrophotography, the toner particles form magnetic brushes on the surface of a magnetic roller which transfers them from a hopper area to the development zone. Although magnetic brush formation is a well-known phenomenon, the physical characteristics remain still un-clear. For this reason, the effects of the magnetic brush have been neglected in computer simulations of electrophotographic developments [2, 3] in spite of their importance. 2. The Models The physical system we simulate by our models consists of toner beads sed-imenting in uniform magnetic (Bezt) and gravitational (g) fields perpen-362 z. CSAH6K et cl. dicular to the baseline (Fig. 1). The gravity makes the beads to sediment and the external magnetic force causes the brushes to be formed. Based on the assumption that the third dimension has no specific role in the chain formation in our models the process is considered to be two dimensional, i.e., no movement in the y direction is allowed. These models can also have applications in studying electrorheological fluids [4J since the background processes (magnetic and electric dipole-dipole interaction) are analogous. falling bead aggregate Fig. 1. The physical process: a falling bead in uniform magnetic and gravitational field. The magnetic field at the position of the falling bead is the sum of the external field and the field due to induced magnetic momenta, which are represented by the small arrows inside the beads First we propose a very simple model (Model A) which tries to pick the key features of the sedimentation process. We consider aggregation of disks over a line, similarly to ballistic deposition models [5, 6]. Taking into account all physical parameters this makes the aggregation process very complicated and time consuming to simulate. For this reason we have chosen a simplified interaction between the depositing toner particle and the aggregate, which reflects nevertheless the main features of the magnetic dipole-dipole interaction. The particles at the top of the chains, forming the magnetic brush have uncompensated magnetic momenta, so they can attract the incoming particles from a longer distance than the ones within the chains whose momenta are compensated by the neighboring beads. Thus our model is the following: z. we choose a random position along x axis and drop a particle vertically; NUMERICAL SIMULATIONS 363 ii. if the falling particle gets closer to a top particle than a given r a distance then it changes its path and moves to this attracting particle along a straight line; iii. if the falling particle touches a particle within a chain, then it is attached to it irreversibly. Fig. 2. Aggregate generated by Model A. (32767 particles, system size 1500 particles) The aggregate considered to be static during the process and the de-position is performed by repeating steps above using periodic boundary conditions. The latter means that the right and the left sides of the system are connected, this is an approximation of an infinite system. The growth process yields a tree-like structure (Fig. 2, 32767 particles) with a charac-teristic distance A = 2ra. Fig. 3 shows the horizontal correlation function c(r) for this aggregate, which is defined as follows. Let us first introduce the function which shows if two beads are in distance r along the x axis and have the same z coordinate: .. (r) _ {1, ifr < Xij < r +!:::..r and Zij < !:::..z; 1J'J - 0, otherwise, where Xij and Zij denote the distance of the ith and the jth bead along the horizontal (x) and vertical (z) axes, respectively. !:::..r and !:::..Z are suitably chosen constants of the order of the bead size. Using the 1Jij function the 364 Z. CSAH6K el al. 4000 3500 3000 2500 ~ 2000 1 1 1 1 1500 1000 500 0 0 50 100 150 200 r Fig. 3. Correlation function for aggregate on Fig. 2 horizontal correlation function is defined as its sum over all possible disk pairs 1 N c(r) = '2 L 1]ij(r), i=l=j where N is the number of beads in the aggregate. The summation yields an average over the z direction, for this reason this function is called horizontal correlation function. The arrows in the figure show the expected correlation maxima for ,\ = 40, rather good coincidence can be seen, the first maximum of c( r) is at r = 40, the next maxima are not that pronounced but still close to their theoretical values (r = 80,120,160). We showed that the c( r) function is a good mean for characterizing periodicity of the magnetic brush. As a next step we simulated the deposition by taking into account the magnetic field of the entire aggregate, which is a more appropriate model for the real physical process. Model B is as follows: we take a toner bead high above the aggregate, and let it fall. There will be gravitational and magnetic forces acting on it. Gravity is a constant field, and magnetic forces in the dipole approxima-tion are calculated from -! \i' (mB), the negative gradient of the magnetic potential energy of the bead. ill is the induced magnetic momentum of the bead, and B is the field at the position of the bead which has contributions NUMERICAL SIMULATJONS 365 both from the constant background field Bezt and the field due to induced magnetic momenta of the beads already in the aggregate: N B = Bezt + L Bb k=l (1) where Bk is the magnetic field induced by the kth bead's momentum. Here we suppose that characteristic length for changes in B is much longer than the bead size (a). As far as m is proportional to B [7]: m = 41T fL - 1 a3 B, fLO fL+ 2 8 (2) where fL is the relative permeability of the beads and considered to be constant, that we have to calculate is actually _Y'(B2). The analytical expression can be found for the gradient and the program uses it rather than calculating numerical gradient, which could lead to instabilities. During the falling of the particle the magnetic momenta and the po-sition of the rest of the aggregate are fixed, the sedimentation is considered to be adiabatic. In fact it does not hold for the magnetic field but for sim-plicity we neglect the effect of the falling bead on the magnetic momenta of the beads already in the aggregate. This consideration becomes more exact during the aggregation process, since there will be more beads in the aggregate so one single bead will have smaller influence on their momenta. We also consider overdamped dynamics, the force is proportional to the speed, not to the acceleration. The path of the falling bead is calculated using the Euler method r(t + 1) = r(t) + (F magn + mg).6.t, where r(t) is the position of the bead at time t and .6.t is a small time step. When the falling bead touches another bead in the aggregate, it gets stuck to it, and its position will be fixed during the consequent iterations. Magnetic momenta are recalculated using an iterative algorithm, and the process is repeated again. In this model the boundary conditions are not periodic, this algorithm models the aggregation of magnetic particles com-ing from a gap above the surface. For control parameter we have chosen the relative strength of the magnetic force compared to the gravity, this ratio is denoted by a dimen-sionless number K,. The magnitude of the magnetic force is proportional to B~xt and the gravitational force is the weight of the bead (mg), so that B~xt K, I'V --. mg 366 Z. CSAH6K el al. Fig. 4. Aggregate generated by Model B for K = 5 :J 25 30 Fig. 5. Path of sedimenting particles in the field of a fixed bead at (15,15). (K = 2000) I"\, is also proportional to Z~~ (see Eq. (2)), so all references to low magnetic field case can be thought as the limit f.L -T l. The results for the high magnetic field case (I"\, ~ 1, Fig. 4) are just the same as the ones obtained using the simple Model A. Like in Model A, there is also a well defined upper limit for the initial distance of two NUMERICAL SIMULATIONS 367 stuck beads, that's a falling bead cannot reach another bead if it starts far enough along the x axis. The explanation for this feature can be given by looking at the paths of beads falling in the field of one fixed bead (Fig. 5). They cannot reach the fixed bead if they start further than a certain critical distance (re). This distance is a function of the parameter 1\,: for I\, ~ 1 the fixed bead can attract the moving one from any distance (re - 00), and for I\, ~ 1 the gravity dominates the movement and this critical distance vanishes (re - 0). In Model A the role of re is played by the external parameter ra. Fig. 6. Aggregate generated by Model B for K = 0 The low magnetic field case (I\, ~ 1) gives aggregates resembling off-lattice ballistic aggregates [8, 9] (Fig. 6). This is not surprising because in this limit the two processes are equal since only the gravity gives relevant contribution to the force acting on a bead. They fall along vertical lines and stop when they touch the aggregate. The surface of the aggregate, which is formed by the topmost beads is a fractal surface [5, 6] with roughness exponent et = 1/2. The whole aggregate is not fractal since it has finite density of beads and an object of finite density cannot be fractal. Even for very strong external field no straight brushes are formed in Model B as it could be expected naturally. This behavior is caused by the lack of relaxation mechanisms in the model. Our third model (Model C) is an enhanced version of Model B, taking into account relaxational 368 Z. CSAH6K 01 al. Fig. 7. Aggregate generated by Model C for K. = 0 Fig. 8. Aggregate generated by Model C for K. = 1 effects which can lead to mechanical instability of the chains [10, 11]: the chains are broken if the direction of the external field is changed or they NUMERICAL SIMULATIONS 369 Fig. 9. Aggregate generated by Model C for K = 1.25 Fig. 10. Aggregate generated by Model C for K = 1.5 become too long. Relaxation can also cause change in the structure, it allows the beads to find an energetically more optimal position and form 370 Z. CSAH6K el al. Fig. 11. Aggregate generated by Model C for K = 2 Si ,"', !~ :" g [~ )~, !~ Fig. 12. Aggregate generated by Model C for K = 4 vertical chains. The key difference between the two models is that in Model B particles are not allowed to roll on each other, but in Model C they are NUMERICAL SIMULATIONS 371 400r---~-----r-----r----~----r-----r----,~ 350 300 : 100 50 : o '~ o 6 10 12 14 Fig. IS. Correlation function for aggregates generated by Model C for", = 0 ( solid line) and '" = 4 (dashed line) allowed to do so. The final position of a bead is when its local energetical minimum is reached. This is still not a total relaxation procedure (i.e., a bead does not reach its global energy minimum position) but enough to demonstrate the difference in the aggregates. This approximation is still on the basis of adiabatical aggregation, therefore no chain breaking can occur. One should notice that when the beads are in contact we are out of the range of validity of the dipole approximation, the interaction is stronger which allows us to consider rolling of the beads on each other only. Aggregates can be seen in Figs. 7-12 generated by Model C for various K, values. Both aggregates consist of 1000 beads and the system size is 60 beads. Onset of periodicity, i.e., brush formation can be clearly seen as K, is increased. This feature was not present either in Model A or in Model B. Fig. 13 shows the same type of correlation function as Fig. 3 for Model C aggregates, the data in this figure are rather noisy because of the relatively small number of beads in the aggregate. Nevertheless, there is a well defined maximum at T ::::: 3.2, which is a non-trivial value and expected to grow when increasing parameter "'. Using appropriate data structure implementing Model A gives very fast code, since the algorithm is based on integer arithmetics only. The program for the Model B runs much slower than the program for the Model A. It does 1000 iterations in about 5 hours on a fast IBM RlSC6000/375 372 Z. CSAHOK el al. workstation. This is not surprising and is related to the fact that· Model B uses heavy floating point arithmetics and its algorithm is O(N3). The program is vectorizable so using vector supercomputers great speed-up can be reached, and it can also allow calculation of correlation functions to higher precision and measurement of surface roughness exponents. 3. Conclusions We have investigated three numerical models for magnetic brush formation phenomena. We can conclude that realistic chain-like patterns appear only in Model C, that is both the magnetic and the gravitational forces and relaxation have great importance on the physical process. Further work needs to be done on analyzing the stability of the aggregates against change of direction of the external magnetic field and temperature (Le., possibility for full relaxation), applying these results in modelling the development process and for modelling magnetic fluids. Larger scale computations need to be carried out for finding fractal characterization of the surface of the brushes, which are expected to be self-affine. We would like to thank A. Jak6 for coding Model A. The present research was supported by JSAEM Grant-In-Aid for foreign researchers. References 1. BU!l.LAND, D. M. - SCHEIN, L. B.: Physics of Electrophotography, Physics Today, May 1986 pp. 46. 2. LEAN, M. H.: Simulation and Visualization of 3D Particle Cloud Electrodynamics, IEEE Trans. Mag. Vo!. 28, p. 1271 (1992). 3. INOUE, H. - AIZAWA M. - HAYASHI, K.: Analysis of Toner Image in Developed Process for Electrophotographic Systems, IEEE Trans. Mag. Vo!. 29, p. 1981 (1993). 4. HALSEY, T. C.: Electrorheological Fluids, Science, Vo!. 258, p. 761 (1992). 5. FAMILY, F. - VICSEK, T. eds., Dynamics of Fractal Surfaces (World Scientific, Singa-pore, 1991). 6. VICSEK, T.: Fractal Growth Phenomena (World Scientific, Singapore, 1992). 7. JACKSON, J. D.: Classical Electrodynamics, (Wiley, New York). 8. MEAKIN, P. - JULLIEN, R.: Restructuring Effects in the Rain Model for Random Deposition, J. Physique, Vo!. 48, p. 1651 (1987). 9. CSAHOK, Z. - VICSEK, T.: Kinetic Roughening in a Model of Sedimentation of Gran-ular Materials, Phys. Rev.,A, Vo!. 46, p. 4577 (1992). 10. PARANJPE, R. S. - ELROD, H. G.: Stability of Chains of Permeable Spherical Beads in an Applied Magnetic Field, J. Appl. Phys. Vo!. 60, p. 418 (1986). 11. PARANJPE, R. S. - ELROD, H. G.: A Magnetomechanical Model of the Magnetic Brush, J. Appl. Phys. Vo!. 63, p. 2136 (1988). 

NUMERICAL SIMULATIONS OF MAGNETIC BRUSH FORMATION PHENOMENA

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NUMERICAL SIMULATIONS OF MAGNETIC BRUSH FORMATION PHENOMENA

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