Geology topics

V. A. Drits

Publications and source records attributed to V. A. Drits.

5 recordsLinked to original sources

USGS2003

Comment on "Evaluation of X-ray diffraction methods for determining the crystal growth mechanisms of clay minerals in mudstones, shales and slates," by L. N. Warr and D. R. Peacor

A recent paper by Warr and Peacor (2002) suggested that our use of the Bertaut-Warren-Averbach technique (MudMaster computer program) for studying changes in crystallite thickness distributions (CTDs) of clay minerals during diagenesis and very low-grade metamorphism is not reliable because it is dependent on many variables which can not be fully controlled. Furthermore, the authors implied that the measured shapes of CTDs cannot be used with confidence to deduce crystal growth mechanisms and histories for clays, based on our CTD simulation approach (using the Galoper computer program). We disagree with both points, and show that the techniques are powerful, reliable and useful for studying clay mineral alteration in rocks. ?? 2003 Schweiz. Mineral. Petrogr. Ges.

Schweizerische Mineralogische und Petrographische ↗

USGS2002

On geological interpretations of crystal size distributions: Constant vs. proportionate growth

Geological interpretations of crystal size distributions (CSDs) depend on understanding the crystal growth laws that generated the distributions. Most descriptions of crystal growth, including a population-balance modeling equation that is widely used in petrology, assume that crystal growth rates at any particular time are identical for all crystals, and, therefore, independent of crystal size. This type of growth under constant conditions can be modeled by adding a constant length to the diameter of each crystal for each time step. This growth equation is unlikely to be correct for most mineral systems because it neither generates nor maintains the shapes of lognormal CSDs, which are among the most common types of CSDs observed in rocks. In an alternative approach, size-dependent (proportionate) growth is modeled approximately by multiplying the size of each crystal by a factor, an operation that maintains CSD shape and variance, and which is in accord with calcite growth experiments. The latter growth law can be obtained during supply controlled growth using a modified version of the Law of Proportionate Effect (LPE), an equation that simulates the reaction path followed by a CSD shape as mean size increases.

American Mineralogist↗

USGS2000

User's Guide to Galoper: A Program for Simulating the Shapes of Crystal Size Distributions from Growth Mechanisms - and Associated Programs

GALOPER is a computer program that simulates the shapes of crystal size distributions (CSDs) from crystal growth mechanisms. This manual describes how to use the program. The theory for the program's operation has been described previously (Eberl, Drits, and Srodon, 1998). CSDs that can be simulated using GALOPER include those that result from growth mechanisms operating in the open system, such as constant-rate nucleation and growth, nucleation with a decaying nucleation rate and growth, surface-controlled growth, supply-controlled growth, and constant-rate and random growth; and those that result from mechanisms operating in the closed system such as Ostwald ripening, random ripening, and crystal coalescence. In addition, CSDs for two types weathering reactions can be simulated. The operation of associated programs also is described, including two statistical programs used for comparing calculated with measured CSDs, a program used for calculating lognormal CSDs, and a program for arranging measured crystal sizes into size groupings (bins).

Open-File Report↗

USGS1998

Deducing growth mechanisms for minerals from the shapes of crystal size distributions

Crystal size distributions (CSDs) of natural and synthetic samples are observed to have several distinct and different shapes. We have simulated these CSDs using three simple equations: the Law of Proportionate Effect (LPE), a mass balance equation, and equations for Ostwald ripening. The following crystal growth mechanisms are simulated using these equations and their modifications: (1) continuous nucleation and growth in an open system, during which crystals nucleate at either a constant, decaying, or accelerating nucleation rate, and then grow according to the LPE; (2) surface-controlled growth in an open system, during which crystals grow with an essentially unlimited supply of nutrients according to the LPE; (3) supply-controlled growth in an open system, during which crystals grow with a specified, limited supply of nutrients according to the LPE; (4) supply- or surface-controlled Ostwald ripening in a closed system, during which the relative rate of crystal dissolution and growth is controlled by differences in specific surface area and by diffusion rate; and (5) supply-controlled random ripening in a closed system, during which the rate of crystal dissolution and growth is random with respect to specific surface area. Each of these mechanisms affects the shapes of CSDs. For example, mechanism (1) above with a constant nucleation rate yields asymptotically-shaped CSDs for which the variance of the natural logarithms of the crystal sizes (??2) increases exponentially with the mean of the natural logarithms of the sizes (??). Mechanism (2) yields lognormally-shaped CSDs, for which ??2 increases linearly with ??, whereas mechanisms (3) and (5) do not change the shapes of CSDs, with ??2 remaining constant with increasing ??. During supply-controlled Ostwald ripening (4), initial lognormally-shaped CSDs become more symmetric, with ??2 decreasing with increasing ??. Thus, crystal growth mechanisms often can be deduced by noting trends in ?? versus ??2 of CSDs for a series of related samples.

American Journal of Science↗

USGS1996

MUDMASTER: A Program for Calculating Crystalline Size Distributions and Strain from the Shapes of X-Ray Diffraction Peaks

Particle size may strongly influence the physical and chemical properties of a substance (e.g. its rheology, surface area, cation exchange capacity, solubility, etc.), and its measurement in rocks may yield geological information about ancient environments (sediment provenance, degree of metamorphism, degree of weathering, current directions, distance to shore, etc.). Therefore mineralogists, geologists, chemists, soil scientists, and others who deal with clay-size material would like to have a convenient method for measuring particle size distributions. Nano-size crystals generally are too fine to be measured by light microscopy. Laser scattering methods give only average particle sizes; therefore particle size can not be measured in a particular crystallographic direction. Also, the particles measured by laser techniques may be composed of several different minerals, and may be agglomerations of individual crystals. Measurement by electron and atomic force microscopy is tedious, expensive, and time consuming. It is difficult to measure more than a few hundred particles per sample by these methods. This many measurements, often taking several days of intensive effort, may yield an accurate mean size for a sample, but may be too few to determine an accurate distribution of sizes. Measurement of size distributions by X-ray diffraction (XRD) solves these shortcomings. An X-ray scan of a sample occurs automatically, taking a few minutes to a few hours. The resulting XRD peaks average diffraction effects from billions of individual nano-size crystals. The size that is measured by XRD may be related to the size of the individual crystals of the mineral in the sample, rather than to the size of particles formed from the agglomeration of these crystals. Therefore one can determine the size of a particular mineral in a mixture of minerals, and the sizes in a particular crystallographic direction of that mineral.

Open-File Report↗