Monte Carlo simulation of induction time and metastable zone width; stochastic or deterministic?

被引:6
|
作者
Kubota, Noriaki [1 ]
机构
[1] Iwate Univ, Dept Chem & Biol Sci, 4-3-5 Ueda, Morioka, Iwate 0208551, Japan
关键词
Nucleation; Monte Carlo simulation; Induction time; Metastable zone width; Industrial crystallization; PRIMARY NUCLEATION; CRYSTAL NUCLEATION; CRYSTALLIZATION; KINETICS; VOLUME; MODEL;
D O I
10.1016/j.jcrysgro.2017.12.031
中图分类号
O7 [晶体学];
学科分类号
0702 ; 070205 ; 0703 ; 080501 ;
摘要
The induction time and metastable zone width (MSZW) measured for small samples (say 1 mL or less) both scatter widely. Thus, these two are observed as stochastic quantities. Whereas, for large samples (say 1000 mL or more), the induction time and MSZW are observed as deterministic quantities. The reason for such experimental differences is investigated with Monte Carlo simulation. In the simulation, the time (under isothermal condition) and supercooling (under polythermal condition) at which a first single crystal is detected are defined as the induction time t and the MSZW Delta T for small samples, respectively. The number of crystals just at the moment of t and Delta T is unity. A first crystal emerges at random due to the intrinsic nature of nucleation, accordingly t and Delta T become stochastic. For large samples, the time and supercooling at which the number density of crystals N/V reaches a detector sensitivity (N/V)(det) are defined as t and Delta T for isothermal and polythermal conditions, respectively. The points of t and Delta T are those of which a large number of crystals have accumulated. Consequently, t and Delta T become deterministic according to the law of large numbers. Whether t and Delta T may stochastic or deterministic in actual experiments should not be attributed to change in nucleation mechanisms in molecular level. It could be just a problem caused by differences in the experimental definition of t and Delta T. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 7
页数:7
相关论文
共 50 条
  • [21] Comparison of Deterministic Approach and Monte Carlo Methods for Stochastic PERT Chart
    Doubravsky, Karel
    Doskocil, Radek
    VISION 2020: SUSTAINABLE GROWTH, ECONOMIC DEVELOPMENT, AND GLOBAL COMPETITIVENESS, VOLS 1-5, 2014, : 1923 - 1929
  • [22] Using Monte Carlo Simulation to Solve Machine Time Scheduling Problems With Stochastic Processing Time
    Hassan, S. A.
    Tharwat, A. A.
    El-Khodary, I. A.
    El-Sawy, A. A.
    PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE MATHEMATICAL METHODS IN ECONOMICS 2003, 2003, : 103 - 111
  • [23] Effects of Ultrasound and Its Amplitude on the Metastable Zone Width, Induction Time, and Nucleation Kinetics of Pyrazinamide in Acetone
    Maharana, Abhishek
    Sarkar, Debasis
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2022, 61 (30) : 11262 - 11275
  • [24] Comparison of the Nucleation Kinetics Obtained from the Cumulative Distributions of the Metastable Zone Width and Induction Time Data
    Shiau, Lie-Ding
    MOLECULES, 2022, 27 (09):
  • [25] Stochastic Representation and Monte Carlo Simulation for Multiterm Time-Fractional Diffusion Equation
    Lv, Longjin
    Wang, Luna
    ADVANCES IN MATHEMATICAL PHYSICS, 2020, 2020
  • [26] Monte-Carlo simulation of a stochastic differential equation
    Arif ULLAH
    Majid KHAN
    M KAMRAN
    R KHAN
    盛正卯
    Plasma Science and Technology, 2017, 19 (12) : 10 - 18
  • [27] Stochastic theory of direct simulation Monte Carlo method
    Hasan Karabulut
    Huriye Arıman Karabulut
    Theoretical Chemistry Accounts, 2009, 122 : 227 - 243
  • [28] Equivalent linearization and Monte Carlo simulation in stochastic dynamics
    Proppe, C
    Pradlwarter, HJ
    Schuëller, GI
    PROBABILISTIC ENGINEERING MECHANICS, 2003, 18 (01) : 1 - 15
  • [29] Monte-Carlo simulation of a stochastic differential equation
    Ullah, Arif
    Khan, Majid
    Kamran, M.
    Khan, R.
    Sheng, Zhengmao
    PLASMA SCIENCE & TECHNOLOGY, 2017, 19 (12)
  • [30] Stochastic Analysis of Structures in Fire by Monte Carlo Simulation
    Shi, Kaihang
    Guo, Qianru
    Jeffers, Ann E.
    JOURNAL OF STRUCTURAL FIRE ENGINEERING, 2013, 4 (01) : 37 - 46