Clay minerals are the most reactive and important inorganic components in soils, but soil mineralogy classifies as a minor topic in soil sciences. Revisiting soil mineralogy has been gradually required. Clay minerals in soils are more complex and less well crystallized than those in sedimentary rocks, and thus, they display more complicated X-ray diffraction (XRD) patterns. Traditional characterization methods such as XRD are usually expensive and time-consuming, and they are therefore inappropriate for large datasets, whereas visible and near-infrared reflectance spectroscopy (VNIR) is a quick, cost-efficient, and nondestructive technique for analyzing soil mineralogic properties of large datasets. The main objectives of this review are to bring readers up to date with information and understanding of VNIR as it relates to soil mineralogy and attracts more attention from a wide variety of readers to revisit soil mineralogy. We begin our review with a description of fundamentals of VNIR. We then review common methods to process soil VNIR spectra and summary spectral features of soil minerals with particular attention to those <2 μm fractions. We further critically review applications of chemometric methods and related model building in spectroscopic soil mineral studies. We then compare spectral measurement with multivariate calibration methods, and we suggest that they both produce excellent results depending on the situation. Finally, we suggest a few avenues of future research, including the development of theoretical calibrations of VNIR more suitable for various soil samples worldwide, better elucidation of clay mineral-soil organic carbon (SOC) interactions, and building the concept of integrated soil mapping through combined information (e.g., mineral composition, soil organic matter-SOM, SOC, pH, and moisture).

To validate the transport (fluid and electrical) and elastic properties computed on CT scan pore-scale volumes of natural rock, we first contrast these values to physical laboratory measurements. We find that computational and physical data obtained on the same rock material source often differ from each other. This mismatch, however, does not preclude the validity of either of the data type — it only implies that expecting a direct match between the effective properties of two volumes of very different sizes taken from the same heterogeneous material is generally incorrect. To address this situation, instead of directly comparing data points generated by different methods of measurement, we compare trends formed by such data points. These trends include permeability versus porosity; electrical formation factor versus porosity; and elastic moduli (elastic-wave velocity) versus porosity. In the physical laboratory, these trends are generated by measuring a significant number of samples. In contrast, in the computational laboratory, these trends are often hidden inside a very small digital sample and can be derived by subsampling it. Hence, we base our validation paradigm on the assumption that if these computational trends match relevant physical trends and/or theoretical rock physics transforms, the computational results are correct. We present examples of such validation for clastic and carbonate samples, including drill cuttings.

A facile generic environmental strategy is employed to prepare hierarchical yolk-shell hybrid NiO@C materials viz. metal-organic frameworks. The intrinsic yolk-shell nature as well as the multi-element characteristics of active components of the unique nanostructures contributes greatly to the outstanding electrochemical performance.