Quantitative assessment and case study of CO2 geological storage in deep coal seams

Geological storage of CO2 as an idea carbon reduction technology is expected to become an important means of mitigating the greenhouse effect. Therefore, quantitatively assessing the potential of geological storage of CO2 in deep coal seams and investigating the interaction between supercritical CO2 and deep coal rocks have become a hot research topic. Taking Jiulishan coal sample from Jiaozuo mining area in Henan, China as the experimental research object, we analyse the mechanism of supercritical CO2 adsorption and sequestration in deep coal seams, carry out CO2 isothermal adsorption experiments of the coal samples at 35 ℃ and 45 ℃, explain and correct the error of negative adsorption isotherms under high pressure, and obtain the actual adsorption amount of CO2 of the coal sample at different temperatures. Here, we propose a new method for calculating CO2 geological storage capacity, which can not only correct the storage capacity miscalculation caused by Gibbs adsorption, but also can accurately evaluate CO2 theory and effective storage capacity in different burial depths of coal seams. results show that: ① In theory, adsorption saturation means that all adsorption sites have been occupied, the volume and density of the adsorption phase have stabilized, and the adsorption amount should no longer changes. However, all adsorption isotherms measured in the laboratory show that the adsorption amount decreases with the increase of pressure under high-pressure saturation stage, which does not conform to the Langmuir adsorption principle. Therefore, the adsorption isotherm measured in the laboratory must be corrected before it can be applied to the assessment of CO2 storage capacity in deep coal seams; ② The CO2 storage capacity in coal mainly consists of the adsorption and free CO2 amount. The adsorption CO2 amount needs to be calculated using the adsorption phase density and Gibbs adsorption amount, while the free CO2 amount needs to know the pore volume occupied by the free phase in coal. It can only be calculated based on the total pore volume in coal minus the adsorption phase volume. Therefore, the adsorption phase is the decisive factor for accurately evaluating the adsorption and free CO2 storage capacity; ③ Using a modified CO2 geological storage quantification model and taking the 800-2000 m deep coal seam in the Xiuwu research area of Jiaozuo mining area as an example, it is calculated that The theoretical storage capacity of CO2 per unit mass of coal is 1.52−2.16 mmol/g, The total effective storage capacity is 11.19×109 m3, which is equivalent to 21.97 Mt. This case not only corrected the mass balance miscalculation of Gibbs adsorption data, but also considered the impact of adsorption phase occupying pore space on free storage capacity, and thus, it has important implication for improving the accuracy of predicting CO2 geological storage capacity in deep coal seams

Hysteresis mechanism of supercritical CO2 desorption in coal and its implication for carbon geo-sequestration

Sequestration of CO2 in the unmineable coal seams is not only one of the most ideal options for reducing greenhouse gas effects, but also the only way for the coal industry to reduce CO2 emissions and achieve low carbonization sustainable development. However, the key unresolved issues regarding the CO2 geo-sequestration in coal seams is: “how long does CO2 injected into a coal seam remain in the seam?”. In this regard, on the basis of clarifying the hysteresis law of CO2 desorption in coals, this paper reveals the mechanism of supercritical CO2 desorption hysteresis, establishes a quantitative model for the geological storage of CO2, and explores the use of desorption hysteresis to achieve a long-term safe storage of CO2 in coal seams. The study results shows that the degree of desorption hysteresis of supercritical CO2 in coal is greater than that of subcritical CO2, and a stable hysteresis characteristic similar to a “parallel line” in the supercritical phase is formed between the adsorption and desorption isotherm. The fundamental reason for the desorption hysteresis is that the micro and nano sized pores in coal form curved surfaces due to their hydrophilicity, which generate strong capillary pressure following the Laplace’s equation, absorb liquid water, truncate and fix the supercritical CO2 fluid, and ultimately form CO2 residual trapping. For example, the cylindrical inorganic pores with a diameter of 40–10 nm in coal can generate a capillary pressure of 7.30–29.12 MPa, which is sufficient to block supercritical CO2. Taking the desorption isotherm of Jiulishan coal as an example, using the quantitative model for the geological storage of CO2 established in this study, it has been estimated that the total trapping capacity of the No.21 coal seam at depths of 900–1 500 m is stable at 35–37 m3/t. Among them, the adsorption trapping capacity accounts for about 80%, residual trapping capacity accounts for about 15%, and structural trapping capacity only accounts for 5%. Desorption hysteresis suggests that some measures should be taken to increase the proportion of CO2 residual trapping in coal seams as much as possible, the reason is that the residual CO2 sealed by capillary blockage is safer and has no risk of leakage compared to the free and adsorbed CO2 sealed by surrounding rock. The physical parameters such as ash content, moisture content, pore size, and morphology of coal seams are the main factors affecting the residual trapping efficiency

Shared and distinct patterns of dynamical degree centrality in bipolar disorder across different mood states

BackgroundPrevious studies have probed the brain static activity pattern in bipolar disorder across different states. However, human intrinsic brain activity is time-varying and dynamic. There is a lack of knowledge about the brain dynamical pattern in bipolar disorder across different mood states.MethodsThis study used the dynamical degree centrality (dDC) to investigate the resting-state whole-brain dynamical pattern voxel-wise in a total of 62 bipolar disorder [28 bipolar depression (BD), 13 bipolar mania (BM), 21 bipolar euthymia (BE)], and 30 healthy controls (HCs). One-way analysis of variance (ANOVA) was applied to explore the omnibus differences of the dDC pattern across all groups, and Pearson’s correlation analysis was used to evaluate the relationship between the dDC variability in detected regions with clinical symptom severity.ResultsOne-way ANOVA analysis showed the omnibus differences in the left inferior parietal lobule/middle occipital gyrus (IPL/MOG) and right precuneus/posterior cingulate cortex (PCUN/PCC) across all groups. The post hoc analysis revealed that BD showed decreased dDC in the IPL/MOG compared with all other groups, and both BD and BM exhibited decreased dDC in the PCUN/PCC compared with BE and HCs. Furthermore, correlation analysis showed that the dDC variability of the IPL/MOG and PCUN/PCC negatively correlated with the depression symptom levels in all patients with bipolar disorder.ConclusionThis study demonstrated the distinct and shared brain dynamical pattern of the depressive, manic, and euthymia states. Our findings provide new insights into the pathophysiology of bipolar disorder across different mood states from the dynamical brain network pattern perspective

Causal relationship between depression and metabolic dysfunction-associated steatotic liver disease: a bidirectional Mendelian randomized study

BackgroundWith the global rise in obesity, metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the most common chronic liver disease. Concurrently, depression is a highly prevalent mental disorder. As the incidence of MASLD and depression continues to increase, a growing body of research indicates a potential association between the two conditions. However, the direction of causality between depression and MASLD remains uncertain. To address this gap, our study utilizes a two-sample Mendelian randomization (MR) approach to explore the bidirectional causal relationship between depression and MASLD.MethodsWe extracted single nucleotide polymorphisms (SNPs) associated with depression and MASLD from pooled data of genome-wide association studies (GWAS). A comprehensive assessment of possible causality was also performed. Possible mediating effects of liver enzymes on MASLD were also assessed.ResultsA total of three GWAS pooled data on depression as well as GWAS data related to MASLD and GWAS data on four liver enzymes were used in this study. Our findings indicated a strong causal relationship between depression and MASLD (OR, 1.557; 95% CI, 1.097–2.211; P = 0.016). And we found a mediating effect of gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). ALT 10% (95% CI: 7% - 13%, P< 0.0002). AST, 4.14% (95% CI: 2.34% - 5.94%, P < 0.05). GGT 0.19% (95% CI: 0.15% - 0.22%, P< 0.000000002). However, we did not find a mediating effect of alkaline phosphatase (ALP). Our inverse MR analysis did not reveal any causal relationship between MASLD and depression.ConclusionsThe MR analysis revealed a positive causal relationship between depression and MASLD, while no reverse causal relationship was identified. Liver enzymes may mediate the role between depression and MASLD

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.National Natural Science Foundation of China [32022078]; National Key R&D Program of China [2022YFF1000900]; Local Innovative and Research Teams Project of Guangdong Province [2019BT02N630]; China Agriculture Research System [CARS-35]. Funding for open access charge: National Natural Science Foundation of China [32022078].info:eu-repo/semantics/publishedVersio

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of 232 complex traits in pigs

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds. We identified 6878 quantitative trait loci (QTL) for 139 complex traits. Leveraging the Pig Genotype-Tissue Expression resource, we systematically investigated the biological context and regulatory mechanisms behind these trait-QTLs, ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits. For instance, rs344053754 regulates UGT2B31 expression in the liver and intestines, potentially by modulating enhancer activity, ultimately influencing litter weight at weaning in pigs. Furthermore, we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs. Overall, our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.</p