scholarly journals Particle simulation of Coulomb collisions: Comparing the methods of Takizuka & Abe and Nanbu

2008 ◽  
Vol 227 (9) ◽  
pp. 4308-4329 ◽  
Author(s):  
Chiaming Wang ◽  
Tungyou Lin ◽  
Russel Caflisch ◽  
Bruce I. Cohen ◽  
Andris M. Dimits
2010 ◽  
Vol 38 (9) ◽  
pp. 2394-2406 ◽  
Author(s):  
Bruce I. Cohen ◽  
Andris M. Dimits ◽  
Alex Friedman ◽  
Russel E. Caflisch

Author(s):  
David C. Joy ◽  
Suichu Luo ◽  
John R. Dunlap ◽  
Dick Williams ◽  
Siqi Cao

In Physics, Chemistry, Materials Science, Biology and Medicine, it is very important to have accurate information about the stopping power of various media for electrons, that is the average energy loss per unit pathlength due to inelastic Coulomb collisions with atomic electrons of the specimen along their trajectories. Techniques such as photoemission spectroscopy, Auger electron spectroscopy, and electron energy loss spectroscopy have been used in the measurements of electron-solid interaction. In this paper we present a comprehensive technique which combines experimental and theoretical work to determine the electron stopping power for various materials by electron energy loss spectroscopy (EELS ). As an example, we measured stopping power for Si, C, and their compound SiC. The method, results and discussion are described briefly as below.The stopping power calculation is based on the modified Bethe formula at low energy:where Neff and Ieff are the effective values of the mean ionization potential, and the number of electrons participating in the process respectively. Neff and Ieff can be obtained from the sum rule relations as we discussed before3 using the energy loss function Im(−1/ε).


1999 ◽  
Vol 75 (10) ◽  
pp. 1188-1194 ◽  
Author(s):  
Taro MATSUMOTO ◽  
Shinji TOKUDA ◽  
Yasuaki KISHIMOTO ◽  
Tomonori TAKIZUKA ◽  
Hiroshi NAITOU

1999 ◽  
Vol 75 (2) ◽  
pp. 131-142 ◽  
Author(s):  
Yasuhiro IDOMURA ◽  
Shinji TOKUDA ◽  
Masahiro WAKATANI

Nanophotonics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 3303-3313 ◽  
Author(s):  
Wen Jun Ding ◽  
Jeremy Zhen Jie Lim ◽  
Hue Thi Bich Do ◽  
Xiao Xiong ◽  
Zackaria Mahfoud ◽  
...  

AbstractParticle simulation has been widely used in studying plasmas. The technique follows the motion of a large assembly of charged particles in their self-consistent electric and magnetic fields. Plasmons, collective oscillations of the free electrons in conducting media such as metals, are connected to plasmas by very similar physics, in particular, the notion of collective charge oscillations. In many cases of interest, plasmons are theoretically characterized by solving the classical Maxwell’s equations, where the electromagnetic responses can be described by bulk permittivity. That approach pays more attention to fields rather than motion of electrons. In this work, however, we apply the particle simulation method to model the kinetics of plasmons, by updating both particle position and momentum (Newton–Lorentz equation) and electromagnetic fields (Ampere and Faraday laws) that are connected by current. Particle simulation of plasmons can offer insights and information that supplement those gained by traditional experimental and theoretical approaches. Specifically, we present two case studies to show its capabilities of modeling single-electron excitation of plasmons, tracing instantaneous movements of electrons to elucidate the physical dynamics of plasmons, and revealing electron spill-out effects of ultrasmall nanoparticles approaching the quantum limit. These preliminary demonstrations open the door to realistic particle simulations of plasmons.


2021 ◽  
Vol 129 (18) ◽  
pp. 183306
Author(s):  
Xin-chun Zhang ◽  
Feng Wang ◽  
Nan-nan Liu ◽  
An-qi Li ◽  
Wei-li Fan

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3611
Author(s):  
Yang Gong ◽  
Chen Cui

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.


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