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This study addresses the unclear understanding of the primary factors controlling fluid mobility in the tuff reservoirs of the Huoshiling Formation from the Dehui Fault Depression, southeastern Songliao Basin. Through physical property analysis, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), thin section (TS), pressure-controlled porosimetry (PCP), rate-controlled porosimetry (RCP), and nuclear magnetic resonance experiments (NMR) on ten tuff samples, we conducted a comprehensive and in-depth exploration of the influencing factors that control the mobility of reservoir fluids. The results indicate: 1) The primary mineral types in the tuff reservoirs are quartz, feldspar, and clay minerals, with porosity predominantly characterized by dissolution pores and intergranular pores; 2) Based on the morphology of PCP intrusion curves, the tuff samples from the study area can be categorized into three types, with reservoir quality progressively deteriorating from Type I to Type III; 3) Compared to the movable fluids saturation (MFS), movable fluids porosity (MFP) is more suitable for characterizing fluid mobility. The mobility of fluids is influenced by various factors such as mineral composition, physical property, pore-throat connectivity, pore type and heterogeneity. MFS and MFP show a positive correlation with permeability, the content of quartz and feldspar, median pore-throat radius (R50), average throat radius (ATR), average pore-throat radius ratio (APT), T2 cutoff value (T2-C), average throat radius (RT), sorting coefficient (SC), and intergranular pore dominate space (Inter-DS), while a negative correlation with the content of calcite and clay minerals, average pore-throat radius ratio, and the fractal dimension from NMR (DNMR). This study elucidates the influencing factors of fluid mobility in tuff reservoirs, which has important reference significance for the scientific development of this type of gas reservoir. [ABSTRACT FROM AUTHOR]

The Upper Cretaceous Qingshankou Formation's lacustrine shales in the Songliao Basin are among China's most promising shale oil reservoirs. To elucidate their pore and fractal characteristics, a comprehensive set of analyses encompassing total organic carbon (TOC), X-ray diffraction (XRD), and low-temperature N2 adsorption (LTNA), Rock-Eval pyrolysis experiments and two-dimensional nuclear magnetic resonance (2D-NMR) were conducted. Using the Frenkel-Halsey-Hill (FHH) method, fractal dimensions (D) were calculated, and their relationship with pore metrics and shale compositions were explored. Two distinct fractal dimensions, D1 (0 < P/P0 < 0.5) and D2 (0.5

Cathodic CO2 adsorption and activation is essential for high‐temperature CO2 electrolysis in solid oxide electrolysis cells (SOECs). However, the component of oxygen ionic conductor in the cathode displays limited electrocatalytic activity. Herein, stable single Ruthenium (Ru) atoms are anchored on the surface of oxygen ionic conductor (Ce0.8Sm0.2O2‐δ, SDC) via the strong covalent metal‐support interaction, which evidently modifies the electronic structure of SDC surface for favorable oxygen vacancy formation and enhanced CO2 adsorption and activation, finally evoking the electrocatalytic activity of SDC for high‐temperature CO2 electrolysis. Experimentally, SOEC with the Ru1/SDC‐La0.6Sr0.4Co0.2Fe0.8O3‐δ cathode exhibits a current density as high as 2.39 A cm−2 at 1.6 V and 800 °C. This work expands the application of single atom catalyst to the high‐temperature electrocatalytic reaction in SOEC and provides an efficient strategy to tailor the electronic structure and electrocatalytic activity of SOEC cathode at the atomic scale. [ABSTRACT FROM AUTHOR]

Electrochemically converting CO2 to valuable chemicals holds great promise for closing the anthropogenic carbon cycle. Owing to complex reaction pathways and shared rate‐determining steps, directing the selectivity of CO2/CO electrolysis to a specific multicarbon product is very challenging. We report here a strategy for highly selective production of acetate from CO electrolysis by constructing metal‐organic interfaces. We demonstrate that the Cu‐organic interfaces constructed by in situ reconstruction of Cu complexes show very impressive acetate selectivity, with a high Faradaic efficiency of 84.2 % and a carbon selectivity of 92.1 % for acetate production, in an alkaline membrane electrode assembly electrolyzer. The maximum acetate partial current density and acetate yield reach as high as 605 mA cm−2 and 63.4 %, respectively. Thorough structural characterizations, control experiments, operando Raman spectroscopy measurements, and density functional theory calculation results indicate that the Cu‐organic interface creates a favorable reaction microenvironment that enhances *CO adsorption, lowers the energy barrier for C−C coupling, and facilitates the formation of CH3COOH over other multicarbon products, thus rationalizing the selective acetate production. [ABSTRACT FROM AUTHOR]

Solid oxide electrolysis cells provide a practical solution for the direct conversion of CO2 to other chemicals (i.e. CO), however, an in-depth mechanistic understanding of the dynamic reconstruction of active sites for perovskite cathodes during CO2 electrolysis remains a great challenge. Herein, we identify that iridium-doped Sr2Fe1.45Ir0.05Mo0.5O6-δ (SFIrM) perovskite displays a dynamic electrochemical reconstruction feature during CO2 electrolysis with abundant exsolution of highly dispersed IrFe alloy nanoparticles on the SFIrM surface. The in situ reconstructed IrFe@SFIrM interfaces deliver a current density of 1.46 A cm−2 while maintaining over 99% CO Faradaic efficiency, representing a 25.8% improvement compared with the Sr2Fe1.5Mo0.5O6-δ counterpart. In situ electrochemical spectroscopy measurements and density functional theory calculations suggest that the improved CO2 electrolysis activity originates from the facilitated formation of carbonate intermediates at the IrFe@SFIrM interfaces. Our work may open the possibility of using an in situ electrochemical poling method for CO2 electrolysis in practice. [ABSTRACT FROM AUTHOR]

Perovskites exhibit excellent high‐temperature oxygen evolution reaction (OER) activities as the anodes of solid oxide electrolysis cells (SOECs). However, the relationship between ion ordering and OER performances is rarely investigated. Herein, a series of PrBaCo2−xFexO5+δ perovskites with tailored ion orderings are constructed. Physicochemical characterizations and density functional theory calculations confirm that the oxygen bulk migration and surface transport capacities as well as the OER activities are promoted by the A‐site cation ordering, but weakened by the oxygen vacancy ordering. Hence, SOEC with the A‐site‐ordered and oxygen‐vacancy‐disordered PrBaCo2O5+δ anode exhibits the highest performance of 3.40 A cm−2 at 800 °C and 2.0 V. This work sheds light on the critical role of ion orderings in the high‐temperature OER performance and paves a new way for screening novel anode materials of SOECs. [ABSTRACT FROM AUTHOR]

Electrochemical conversion of nitrate (NO3−) into ammonia (NH3) represents a potential way for achieving carbon‐free NH3 production while balancing the nitrogen cycle. Herein we report a high‐performance Cu nanosheets catalyst which delivers a NH3 partial current density of 665 mA cm−2 and NH3 yield rate of 1.41 mmol h−1 cm−2 in a flow cell at −0.59 V vs. reversible hydrogen electrode. The catalyst showed a high stability for 700 h with NH3 Faradaic efficiency of ≈88 % at 365 mA cm−2. In situ spectroscopy results verify that Cu nanosheets are in situ derived from the as‐prepared CuO nanosheets under electrochemical NO3− reduction reaction conditions. Electrochemical measurements and density functional theory calculations indicate that the high performance is attributed to the tandem interaction of Cu(100) and Cu(111) facets. The NO2− generated on the Cu(100) facets is subsequently hydrogenated on the Cu(111) facets, thus the tandem catalysis promotes the crucial hydrogenation of *NO to *NOH for NH3 production. [ABSTRACT FROM AUTHOR]

Graph: (b) Graph: (c) Graph: (d) Graph: Figure 5 Sample image: (a) low brightness; (b) high brightness; (c) low contrast; (d) high contrast. Graph: (b) Graph: (c) Graph: (d) Graph: (e) Graph: (f) Graph: (g) Graph: (h) DIAGRAM: Figure 8 Low contrast image simulation diagram: (a) max; (b) average; (c) sqrt; (d) SD; (e) ECM; (f) CDF; (g) IMF; (h) new. Graph: (b) Graph: (c) Graph: (d) Graph: (e) Graph: (f) Graph: (g) Graph: (h) Table 3 Objective evaluation results. [Extracted from the article]