Postcombustion CO₂ Capture in Functionalized Porous Coordination Networks

Motivated by recent experimental reports of zirconium porous coordination networks (PCNs) [<i>J. Am. Chem. Soc.</i> <b>2012</b>, <i>134</i>, 14690–14693], which have demonstrated a good stability and CO₂ adsorption capacity, we investigate the influence of flue gas impurities and functional groups on the performance of PCN frameworks in selective CO₂ capture. Using a combination of grand canonical Monte Carlo (GCMC) simulations and first-principles calculations, we find that O₂ and SO₂ impurities in flue gas have a negligible influence on CO₂ selectivity in all PCN frameworks. However, because of strong electrostatic interaction between H₂O molecules and the framework, CO₂ selectivity decreases in all PCN structures in the presence of water impurities in the flue gas. Our studies suggest that the PCN-59 framework can be a good candidate for selective CO₂ separation from a predehydrated flue gas mixture

Additional file 4 of Rice (Oryza sativa L.) cytochrome P450 protein 716A subfamily CYP716A16 regulates disease resistance

Additional file 4: Supplementary Table S3. Differentially expressed metabolites in the leaves of the WT and CYP716A16-OE lines

Additional file 3 of Rice (Oryza sativa L.) cytochrome P450 protein 716A subfamily CYP716A16 regulates disease resistance

Additional file 3: Supplementary Table S2. Differentially expressed genes between WT and CYP716A16-OE lines

Additional file 2 of Rice (Oryza sativa L.) cytochrome P450 protein 716A subfamily CYP716A16 regulates disease resistance

Additional file 2: Supplementary Table S1. The sequence data for the transcriptome

A multi-period restoration approach for resilience increase of active distribution networks by considering fault rapid recovery and component repair

As the frequency of extreme weather events continues to rise, there is an urgent need to strengthen the safe and stable operation of active distribution networks (ADNs), and it is of great value to establish highly resilient ADNs to withstand multi-faults caused by extreme weather events. This paper proposes a multi-period restoration approach for the resilience increase of ADNs by considering fault rapid recovery and component repair under typhoon disasters. Firstly, based on the structural reliability theory, the failure rate model of the main components is established, and in light of the system information entropy, the typical fault scenario selection strategy is designed to determine the branches with high fault probability. Then, according to the fault islanding division and network reconfiguration, a fault rapid recovery method is suggested for the ADNs, where the impact of typhoon disasters on the output features of distributed generators (DGs) are taken into account, and meanwhile, the network structure and the output power of the DGs are jointly optimized to minimize the operating cost of the ADNs. Further, a fault component repair model is formulated by adopting the adaptive ant colony algorithm, and a multi-period restoration approach is proposed for the ADNs to fulfill a rolling optimization of the network reconfiguration and fault component repair. The improved IEEE 33-node and IEEE 118-node systems are used for the approach verification, and the results show that the proposed approach can effectively improve the overall load restoration level and increase the component repair efficiency. Following a multi-criteria resilience evaluation system, the proposed approach enables the ADNs to more effectively withstand typhoon disasters, offering a resilience increase of 6.93 % and 32.24 % regarding the 33-node and 118-node systems

Additional file 4 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 4. The distribution of lymphoid follicles in the nasal respiratory region of pigs at different ages. Representative images of HE-stained nasal respiratory regions from pigs at different ages, including 7 days old (A), 60 days old (B) and 180 days old (C). The red frame in each figure indicates the different parts of the inferior nasal concha (a and b), and magnified images of the corresponding region are shown on the right of the figure. Black asterisks mark the lymphoid follicle. CSII: the anterior part of the respiratory region; CSIII: the rear part of the respiratory region; Scale bars: (A to C) 2 mm; (a, b) 200 μm

Additional file 5 of Rice (Oryza sativa L.) cytochrome P450 protein 716A subfamily CYP716A16 regulates disease resistance

Additional file 5: Supplementary Table S4. The primers sequences used in this study

Additional file 1 of Rice (Oryza sativa L.) cytochrome P450 protein 716A subfamily CYP716A16 regulates disease resistance

Additional file 1: Supplementary Figure S1. The data of important agronomic traits of WT, CYP716A16-OE, CYP716A16-RNAi lines. Supplementary Figure S2. The expression pattern of OsAOC, OsACS2, and OsNPR1 in WT and CYP716A16-OE plants after inoculation with R. solani AG1-IA. Supplementary Figure S3. The levels of (JA, JA-Ile, SA, and ET) in WT and CYP716A16-OE plants after inoculation 24 h with R. solani AG1-IA. Supplementary Figure S4. The contents of phytoalexin (MA and MB) in WT and CYP716A16-OE plants after inoculation 24 h with R. solani AG1-IA

A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks

Introducing energy storage systems (ESSs) into active distribution networks (ADNs) has attracted increasing attention due to the ability to smooth power fluctuations and improve resilience against fault disturbances. This paper proposes a methodology for simultaneously optimizing the configuration of battery ESSs and the operation of ADNs, and the goal is to increase the resilience of the ADNs withstanding multi-faults. Firstly, based on random sampling and K-means clustering, a generation strategy of typical N-1 and N-2 fault scenarios is designed for the ADNs. Then, a two-layer optimization model is established, where the inner model is to optimize the fault recovery performance from the operational perspective, and the outer model is to obtain the optimal site and size of ESSs from the economic perspective. Further, the second-order cone relaxing (SOCR) method and the hybrid gray wolf optimal and particle swarm optimal (GWO-PSO) algorithm are applied to solve the optimization model. Using MATLAB, the modified IEEE 33-node and 118-node systems are built to check the proposed approach's performance. Different periods are considered to show the multi-faults' development, and by introducing a resilience assessment system with node voltage deviation, fault recovery rate, and network loss rate, the resilience of the ADNs is analyzed. From the comparative results, the proposed approach can optimally configure the battery ESSs, and adjust the network structure as well as the distributed generation outputs. Following the ESS configuration cost reduction of 53.19% and 9.8%, the resilience of the ADNs against the multi-faults will increase by 13.36% and 8.25% for the 33-node and 118-node systems

Additional file 5 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 5. HE staining of the olfactory region of the nasal cavity in different growth stages. (A) Diagrams of pig nasal cavity cross-section IV (corresponding to the olfactory region). (B-F) Representative images of HE-stained nasal olfactory regions from pigs at different ages, including 0 days old (B), 7 days old (C), 30 days old (D), 60 days old (E), and 180 days old (F). The red frame in each figure indicates the middle nasal concha (a) and nasal septum (b); magnified images of the corresponding region are shown on the right of the figure. Scale bars: (B-F) 2 mm; (a, b) 50 μm