A Probabilistic Formulation of the Diffusion Coefficient in Porous Media as Function of Porosity

We investigate the upscaling of diffusive transport parameters using a stochastic framework. At sub-REV (representative elementary volume) scale, the complexity of the pore space geometry leads to a significant scatter of the observed diffusive transport. We study a large set of volumes reconstructed from focused ion beam-scanning electron microscopy data. Each individual volume provides us sub-REV measurements on porosity and the so-called transport-ability, being a dimensionless parameter representing the ratio of diffusive flux through the porous volume to that through an empty volume. The detected scatter of the transport-ability is mathematically characterized through a probability distribution function (PDF) with a mean and variance as function of porosity, which includes implicitly the effect of pore structure differences among sub-REV volumes. We then investigate domain size effects and predict when REV scale is reached. While the scatter in porosity observations decreases linearly with increasing sample size as expected, the observed scatter in transport-ability does not converge to zero. Our results confirm that differences in pore structure impact transport parameters at all scales. Consequently, the use of PDFs to describe the relationship of effective transport coefficients to porosity is advantageous to deterministic semiempirical functions. We discuss the consequences and advocate the use of PDFs for effective parameters in both continuum equations and data interpretation of experimental or computational work. The presented statistics-based upscaling technique of sub-REV microscopy data provides a new tool in understanding, describing and predicting macroscopic transport behavior of microporous media.

Fine pore engineering in a series of isoreticular metal-organic frameworks for efficient C2H2/CO2 separation

The separation of C2H2/CO2 is not only industrially important for acetylene purification but also scientifically challenging owing to their high similarities in physical properties and molecular sizes. Ultramicroporous metal-organic frameworks (MOFs) can exhibit a pore confinement effect to differentiate gas molecules of similar size. Herein, we report the fine-tuning of pore sizes in sub-nanometer scale on a series of isoreticular MOFs that can realize highly efficient C2H2/CO2 separation. The subtle structural differences lead to remarkable adsorption performances enhancement. Among four MOF analogs, by integrating appropriate pore size and specific binding sites, [Cu(dps)2(SiF6)] (SIFSIX-dps-Cu, SIFSIX = SiF62-, dps = 4.4’-dipyridylsulfide, also termed as NCU-100) exhibits the highest C2H2 uptake capacity and C2H2/CO2 selectivity. At room temperature, the pore space of SIFSIX-dps-Cu significantly inhibits CO2 molecules but takes up a large amount of C2H2 (4.57 mmol g−1), resulting in a high IAST selectivity of 1787 for C2H2/CO2 separation. The multiple host-guest interactions for C2H2 in both inter- and intralayer cavities are further revealed by dispersion-corrected density functional theory and grand canonical Monte Carlo simulations. Dynamic breakthrough experiments show a clean C2H2/CO2 separation with a high C2H2 working capacity of 2.48 mmol g−1.

The accumulation model of organic matters for the Niutitang Formation shale and its control on the pore structure: a case study from Northern Guizhou

Shale gas reservoir is a fine-grained sedimentary rock with component of clastic particles and organic matters, and the accumulation of the organic matters would determine the effective development of shale gas. The paleoclimate, detrital influx, redox of the water and paleoproductivity are effective geochemical indicators that could help to find the favorable shale gas reservoir stratum. In this study, the shale samples collected from Niutitang Formation (Northern Guizhou, China) were launched the measurements of the content of major elements and trace elements, and the characteristics of geochemical indicators were analyzed, which can be used to discuss the accumulation model of organic matters. Besides, the pore structure of shale sample controlled by the enrichment of organic matters is also discussed. The paleoclimate is dominant cold and dry, and it changes to warm and humid at the later Niutitang period, and the detrital influx also increased at the later Niutitang period; the water environment of Niutitang Formation shale presents as reductive, and the paleoproductivity of the Niutitang Formation shale is commonly high. The enrichment of organic matters in the Niutitang Formation is dominantly controlled by the redox of the water, while the hydrothermal activity and the paleoproductivity lead to the difference enrichment of organic matters in the Niutitang Formation shale. The accumulation model of organic matters also influences the characteristics of pore structure from the Niutitang Formation shale, and the pore structure could be divided into two types. The shale with high content of organic matters also features high content of quartz and pyrite, and these minerals contribute to the preservation of pore space in the shale, while that of the clay minerals is contrary. The high content of organic matters and preferable pore characteristics indicate the Niutitang Formation favors the development of shale gas, especially that for the lower Niutitang Formation.

Prediction of Thermal Properties of Zeolites through Machine Learning

The use of machine learning for the prediction of physical and chemical properties of crystals based on their structure alone is currently an area of intense research in computational materials science. In this work, we studied the possibility of using machine learning-trained algorithms in order to calculate the thermal properties of siliceous zeolite frameworks. We used as training data the thermal properties of 120 zeolites, calculated at the DFT level, in the quasi-harmonic approximation. We compared the statistical accuracy of trained models (based on the gradient boosting regression technique) using different types of descriptors, including ad hoc geometrical features, topology, pore space, and general geometric descriptors. While geometric descriptors were found to perform best, we also identified limitations on the accuracy of the predictions, especially for a small group of materials with very highly negative thermal expansion coefficients. We then studied the generalizability of the technique, demonstrating that the predictions were not sensitive to the refinement of framework structures at a high level of theory. Therefore, the models are suitable for the exploration and screening of large-scale databases of hypothetical frameworks, which we illustrate on the PCOD2 database of zeolites containing around 600,000 hypothetical structures.

Evaluation of the DNDC Model to Estimate Soil Parameters, Crop Yield and Nitrous Oxide Emissions for Alternative Long-Term Multi-Cropping Systems in the North China Plain

Optimizing crop rotations is one of the proposed sustainable management strategies for increasing carbon sequestration. The main aim of this study was to evaluate the DeNitrification-DeComposition (DNDC) model for estimating soil parameters (temperature, moisture and exchangeable NO3− and NH4+), crop yield and nitrous oxide (N2O) emissions for long-term multi-cropping systems in Hebei, China. The model was validated using five years of data of soil parameters, crop yields and N2O emissions. The DNDC model effectively simulated daily soil temperature, cumulative soil nitrogen and crop yields of all crops. It predicted the trends of observed daily N2O emissions and their cumulative values well but overestimated the magnitude of some peaks. However, the model underestimated daily water filled pore space, especially in dry seasons, and had difficulties in correctly estimating daily exchangeable NO3− and NH4+. Both observed and simulated cumulative N2O results showed that optimized and alternative cropping systems used less nitrogen fertiliser, increased grain yield and decreased N2O emissions compared to the conventional cropping system. Our study shows that although the DNDC model (v. 9.5) is not perfect in estimating daily N2O emissions for these long-term multi-cropping systems, it could still be an effective tool for predicting cumulative emissions.

Differential Analysis of Volumetric Strain Method Characterization in the Context of Phase Change of Water in Carbonate Rocks

Modernized technological processes or increasing demands on building materials force the scientific community to analyze in more detail the suitability of individual raw materials and deposits. New or modernized research methodologies make it possible to better understand not only the geometrical structure of the pore space of materials but also the processes taking place in them and the interaction of many factors at the same time. Despite the extensive literature in the field of research on capillary-porous materials, scientists still face many challenges because not everything is known. Carbonate rocks are the most common (one-tenth of Earth’s crust) sedimentary rocks. Analysis of the test results obtained with the use of the modernized differentia analysis of volumetric strain (DAVS) methodology allows for a better adjustment of rock deposits to the products that can be produced from them. In this manner, it is possible that it will contribute to a more rational use of exhaustible rock deposits and not only carbonate ones. This research subject is of great importance for modern science, which was also noted in many of science publications.

The percolation model of relative conductivities and phase permeabilities

The universal mathematical model of relative conductivities of percolation clusters and phase permeabilities of oil-water-saturated rocks is presented. It is obtained on the basis of percolation theory, porous body physics and statistics. The model takes into account the influence of change in pore space surface properties and the nature of fluid flow on the studied characteristics and may be applied for comprehensive analysis and modeling of technological processes of oil production.