Influence of CO₂ Exposure on High-Pressure Methane and CO₂ Adsorption on Various Rank Coals: Implications for CO₂ Sequestration in Coal Seams

There exist complex interactions between coal and CO₂ during the process of CO₂ sequestration in coal seams with enhanced coalbed methane recovery (CO₂-ECBM). This work concentrated on the influence of CO₂ exposure on high-pressure methane and CO₂ (up to 10 MPa) adsorption behavior of three types of bituminous coal and one type of anthracite. The possible mechanism of the dependence of CO₂ exposure on adsorption performance of coal was also provided. The results indicate that the maximum methane adsorption capacities of various rank coals after CO₂ exposure increase by 3.45%–10.37%. However, the maximum CO₂ adsorption capacities of various rank coals decrease by 9.99%–23.93%. TG and pore structure analyses do not observe the obvious changes on the inorganic component and pore morphology of the coals after CO₂ exposure. In contrast, CO₂ exposure makes changes in surface chemistry of the coals, according to the results from FTIR analysis, which is the main reason for increases in the maximum adsorption capacity of methane and decreases in the maximum adsorption capacity of CO₂ for the coals after CO₂ exposure. The different role of CO₂ exposure on methane and CO₂ adsorption is detrimental to CO₂-ECBM. Thus, the implementation of CO₂-ECBM must take into account the influence of CO₂ exposure on the adsorption performance of the target coal seams

Influences of SO₂, NO, and CO₂ Exposure on Pore Morphology of Various Rank Coals: Implications for Coal-Fired Flue Gas Sequestration in Deep Coal Seams

Carbon dioxide (CO₂) sequestration in deep coal seams with enhanced coal-bed methane recovery is a promising way to store the main anthropogenic greenhouse gas, CO₂, in geologic time. Recently, injection of CO₂ mixed with coal-fired flue gas components, i.e., SO₂ and NO_<i>x</i>, into coal seams has gained attention because it offers great advantages in reducing the cost of CO₂ capture, flue gas desulfuration, and denitration. As a preliminary investigation on the feasibility of coal-fired flue gas sequestration in deep coal seams, the influences of SO₂, NO, and CO₂ exposures on the pore morphology of various rank coals are addressed in this work. Considering the optimum coal reservoir conditions for flue gas sequestration, the interaction of CO₂ with coals was studied at a temperature of 45 °C and a pressure of 12 MPa. The results show that both CO₂ exposure and SO₂ exposure lead to decreases in both the specific surface area and pore volume of micropores of various rank coals. The micropore morphology of both Hulunbuir coal and Shenmu coal after NO exposure exhibits degradation, while the opposite trend is found for Erdos coal and Yangquan coal. The average micropore size of all the coals after contact with CO₂, NO, and SO₂ decreases. The CO₂, NO, and SO₂ dependences of the meso- and macropore surface area and volume of coals are complex and strongly related to the coal rank. Fractal analyses show that the pore surfaces of coals after CO₂, NO, and SO₂ exposures become smooth, as indicated by the surface fractal dimension determined from the Neimark model, which is consistent with the increasing trend of the average meso- and macropore size. Generally, the influences of SO₂, NO, and CO₂ exposures on pore morphology of various rank coals may play an important role in the diffusion and adsorption performance of fluid within the target coal reservoir. Thus, comprehensive evaluation of the dependence of coal pore morphology on fluid exposure is needed for the practical coal-fired flue gas sequestration in deep coal seams

Partitioning variations in LA, LL, LW, and LOV across three populations.

<p>Partitioning variations in LA, LL, LW, and LOV across three populations.</p

Trait correlations between LA, LL, LW, and LOV variations based on BLUP values across three populations.

<p>Right-top represents the correlation coefficients among four traits. Diagonal represents the frequency distribution for each of four traits. Left-bottom represents the scatter distribution among four traits.</p

The mean, range, and difference within three populations and broad-sense heritability estimates (H²) across three populations for four traits.

<p>The mean, range, and difference within three populations and broad-sense heritability estimates (H²) across three populations for four traits.</p

Genomic predictions of the leaf angle (LA), leaf length (LL), leaf width (LW), and leaf orientation value (LOV) in all RILs by GBLUP.

<p>A random sample of 20%, 40%, 60%, and 80% of the RILs in all populations for the calibrated GBLUP model to predict the BLUP line means variation in (A) LA, (C) LL, (E) LW, and (G) LOV in the remaining lines. All RILs in all populations were used to calibrate the GBLUP model to explain variations in (B) LA, (D) LL, (F) LW, and (H) LOV.</p

Exploring Identity-By-Descent Segments and Putative Functions Using Different Foundation Parents in Maize

<div><p>Maize foundation parents (FPs) play no-alternative roles in hybrid breeding because they were widely used in the development of new lines and hybrids. The combination of different identity-by-descent (IBD) segments and genes could account for the formation patterns of different FPs, and knowledge of these IBD regions would provide an extensive foundation for the development of new candidate FP lines in future maize breeding. In this paper, a panel of 304 elite lines derived from FPs, i.e., B73, 207, Mo17, and Huangzaosi (HZS), was collected and analyzed using 43,252 single nucleotide polymorphism (SNP) markers. Most IBD segments specific to particular FP groups were identified, including 116 IBD segments in B73, 105 in Mo17, 111 in 207, and 190 in HZS. In these regions, 423 quantitative trait nucleotides (QTNs) associated with 15 agronomic traits and 804 candidate genes were identified. Some known adaptation-related genes, e.g., <i>dwarf8</i> and <i>vgt1</i> in HZS, <i>zcn8</i> and <i>epc</i> in Mo17, and <i>ZmCCT</i> in 207, were validated as being tightly linked to particular IBD segments. In addition, numerous new candidate genes were also identified. For example, GRMZM2G154278 in HZS, which belongs to the cell cycle control family, was closely linked to a QTN of the ear height/plant height (EH/PH) trait; GRMZM2G051943 in 207, which encodes an endochitinase precursor (EP) chitinase, was closely linked to a QTN for kernel density; and GRMZM2G170586 in Mo17 was closely linked to a QTN for ear diameter. Complex correlations among these genes were also found. Many IBD segments and genes were included in the formation of FP lines, and complex regulatory networks exist among them. These results provide new insights on the genetic basis of complex traits and provide new candidate IBD regions or genes for the improvement of special traits in maize production.</p></div

Expression of genes in kernels.

<p>The arrows indicate genes commonly expressed in kernels 15 days after pollination. The histogram shows the change in the expression level between different FP groups. The number in each histogram is the log₂(fold change), and a minus sign before a number indicates the direction of change.</p

Genetic frame diagram of the identity-by-descent (IBD) regions for different foundation parent (FP) groups.

<p>The numbers “1”, “2”, “3”, and “4” below each histogram represent 207, B73, HZS, and Mo17, respectively. Transverse lines (“—”) of different colors represent IBD regions for different FP groups. Asterisks (*) and vertical lines (“|”) of different colors represent QTNs and QTLs significantly associated with different agronomic traits, respectively.</p

Genetic structure described by clustering and principal component (PC) analysis.

<p>“a” presents the cladogram constructed using the un-weighted pair group method with arithmetic mean algorithm (UPGMA) based on the modified Euclidean genetic distance. “b” shows the genetic structure described by PC1 and PC3 obtained from PCA on the 304-line panel and the 180-line panel, with 304- and 180- in the picture legend. The numbers inside the brackets show the proportion of the total variance for each PC.</p