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arXiv 1003.0143cond-mat.mtrl-scicond-mat.stat-mech
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随机形核和生长结晶过程中晶粒尺寸分布的时间演化

Time-evolution of grain size distributions in random nucleation and growth crystallization processes

Anthony V. Teran, Ralf B. Bergmann, Andreas Bill

摘要:

我们研究了d维固体结晶过程中晶粒尺寸分布N(r,t)的时间依赖性。一个偏微分方程,包括一个源项的核和晶粒的生长规律解析求解任何尺寸d。我们讨论的随机成核和生长(RNG)的Kolmogorov-Avrami-Mehl-Johnson模型所描述的过程中获得的解决方案。成核和生长是建立在同一基础上的,这导致了两个有效速率的时间依赖性衰减。我们详细分析了模型参数、结晶过程的维数和时间如何影响分布的形状。计算结果表明,有效成核和有效生长速率的动力学起着至关重要的作用,在确定在完全结晶得到的分布的最终形式。我们证明,对于一类成核和生长速率,分布随时间演化为Bergmann和Bill [J. Cryst. Growth 310,3135(2008)]之前讨论的对数正态形式。我们还得到了有限的最大晶粒尺寸在任何时候的解析表达式。该理论可以描述各种RNG结晶过程中的薄膜和散装材料。对实验数据分析有用的表达式的晶粒尺寸分布和矩的基本和可测量的参数的模型。

英文摘要:

We study the time dependence of the grain size distribution N(r,t) during crystallization of a d-dimensional solid. A partial differential equation including a source term for nuclei and a growth law for grains is solved analytically for any dimension d. We discuss solutions obtained for processes described by the Kolmogorov-Avrami-Mehl-Johnson model for random nucleation and growth (RNG). Nucleation and growth are set on the same footing, which leads to a time-dependent decay of both effective rates. We analyze in detail how model parameters, the dimensionality of the crystallization process, and time influence the shape of the distribution. The calculations show that the dynamics of the effective nucleation and effective growth rates play an essential role in determining the final form of the distribution obtained at full crystallization. We demonstrate that for one class of nucleation and growth rates the distribution evolves in time into the logarithmic-normal (lognormal) form discussed earlier by Bergmann and Bill [J. Cryst. Growth 310, 3135 (2008)]. We also obtain an analytical expression for the finite maximal grain size at all times. The theory allows for the description of a variety of RNG crystallization processes in thin films and bulk materials. Expressions useful for experimental data analysis are presented for the grain size distribution and the moments in terms of fundamental and measurable parameters of the model.

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