Multicomponent Diffusion in Silicate Melts

扩散是一种无处不在的过程，起源于液体、固体或气体中粒子的随机运动。在简单系统中，这种随机运动导致从高浓度区域到低浓度区域的净传质。虽然扩散是一个微观过程，但它在许多宏观现象中发挥着关键作用，从人体内的营养物质转移、污染物的扩散，到各种火成岩过程，如火山爆发和火成岩形成。例如，爆炸性的火山喷发始于气泡的生长，气泡的生长速度由气体分子从熔体扩散到气泡中控制。顺便说一句，熔体中的气泡增长类似于啤酒、香槟和软饮料中日常遇到的气泡增长。更广义的扩散概念也被用来理解大陆的平均高度。火成岩的形成是岩浆中晶体生长的集体效应，其中扩散起着重要作用。天然硅酸盐熔体通常含有许多主要的氧化物成分，因此扩散是多组分的扩散，比简单体系中的扩散更复杂。这项研究的目标是了解多组分在硅酸盐熔体中的扩散，这将提高解释和量化此类过程的速度的能力。多组分扩散引起的并发症的一个表现是，硅酸盐熔体中的一种氧化物组分经常从低浓度扩散到高浓度，称为上行扩散。在文献中，对天然硅酸盐熔体中的扩散进行了部分处理：如果一个组分表现出正常的扩散行为，即熔剂从高浓度到低浓度，则使用有效的二元扩散处理对其进行量化。另一方面，如果一个成分表现出向上扩散，研究人员只会注意到这种行为，然后在没有量化的情况下回避它。这种上行扩散在使用硅酸盐熔体的实验中以及在自然系统中经常遇到。随着科学的快速发展，变得越来越定量和可预测，是时候面对天然硅酸盐熔体中多组分扩散的挑战了。这次调查中选择的基熔体成分将类似于大洋中脊玄武岩，这是成分范围最窄的最丰富的陆相岩石。有8种主要的氧化物成分，扩散用7×7的扩散系数矩阵来描述。具有适当组成的玻璃将被合成以形成扩散偶。将开展高温高压扩散耦合实验。淬火玻璃的成分分布将使用电子探针进行测量。利用Levenberg-MarQuardt算法对数据进行拟合，提取扩散系数矩阵。为了验证，得到的矩阵将用于计算以往矿物溶解实验中的扩散剖面，并将计算结果与测量剖面进行比较。经过验证，扩散系数矩阵将用于预测各种过程中天然岩浆中的真实扩散，包括晶体生长和溶解、岩浆混合以及熔体包裹体与宿主矿物之间的后捕获作用。研究结果将在科学会议和教学中发表和传播。

Diffusion is a ubiquitous process originating from random motion of particles in a liquid, solid or gas. In simple systems, such random motion leads to a net mass transfer from a region of high concentration to one of low concentration. Although it is a microscopic process, diffusion plays a critical role in numerous macroscopic phenomena ranging from nutrient transfer in the human body, dispersal of pollutants, to a variety of igneous processes, such as explosive volcanic eruptions and igneous rock formation. For example, explosive volcanic eruptions begin with bubble growth, whose rate is controlled by the diffusion of gas molecules from the melt into the bubbles. Incidentally, bubble growth in melts is similar to the daily encountered bubble growth in beer, champagne and soft drinks. The more generalized diffusion concept has also been applied to understand the mean height of continents. Igneous rock formation is the collective effect of crystal growth in magma, in which diffusion plays an important role. A natural silicate melt typically contains many major oxide components, and hence the diffusion is multicomponent diffusion, more complex than diffusion in simple systems. The goal of this research is to understand multicomponent diffusion in silicate melts, which would advance the ability to interpret and quantify rates of such processes. One manifestation of the complications due to multicomponent diffusion is that an oxide component in silicate melts often diffuses from low concentration to high concentration, termed uphill diffusion. In the literature, diffusion in natural silicate melts is treated partially: If a component shows 'normal' diffusion behavior, meaning the flux is from high concentration to low concentration, it is quantified using the effective binary diffusion treatment. On the other hand, if a component displays uphill diffusion, researchers simply note the behavior, and then shy away from it without quantification. Such uphill diffusion is frequently encountered in experiments using silicate melts as well as in natural systems. As science advances rapidly and becomes increasingly more quantitative and predictive, it is time to confront the challenge of multi-component diffusion in natural silicate melts. The base melt composition chosen for this investigation will be similar to a mid-ocean ridge basalt, the most abundant terrestrial rock with a narrow compositional range. There are 8 major oxide components and the diffusion is described by a 7 by 7 diffusivity matrix. Glasses with appropriate compositions will be synthesized to form diffusion couples. High-temperature and high-pressure diffusion couple experiments will be carried out. The compositional profiles in the quenched glass will be measured using an electron microprobe. The data will be fit using the Levenberg-Marquardt algorithm to extract the diffusivity matrix. For verification, the obtained matrix will be used to calculate diffusion profiles in previous mineral dissolution experiments, and the calculated results will be compared with measured profiles. After verification, the diffusivity matrix will be applied to predict real diffusion in natural magmas during a variety of processes, including crystal growth and dissolution, magma mixing, and post-entrapment interaction between a melt inclusion and the host mineral. The results will be published and disseminated in scientific meetings and in teaching.