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Architecture design and optimization are essential in improving the performance of lithium–sulfur batteries. Herein, an all-in-one sequential architecture of CoSe catalysts confined in MOF-derived nanoreactors (NRs) interfacially welded on carbon cloth (CC) is constructed using an in situ strategy (abbreviated: S@CoSe-NRs/CC). NRs with built-in CoSe (CoSe-NRs) have special microenvironments and domain-limited catalysis for efficient adsorption, catalysis, and deposition. The weld interface between the CoSe-NRs and the CC forms a continuous and robust all-in-one conductive network. Both experiments and theoretical calculations demonstrate that the S@CoSe-NRs/CC offers stable interconnectivity and efficient space utilization for high sulfur loads and rapid ion transport supply, providing a high initial discharge capacity of 1320 mA h g−1 at 0.1 C with a capacity decay rate as low as 0.003% per turn after 1000 cycles. This study offers unique insights and revelations for realizing the rational design of high-performance energy storage devices. [ABSTRACT FROM AUTHOR]

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R2 (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the "black box" problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production. [ABSTRACT FROM AUTHOR]

Nano-elastic microspheres are particle-absorbent polymers that can be applied in plugging water. They plug pores and throats, reducing the damage from water invasion. The plug effect and flow mechanism of nano-elastic microspheres during water invasion were investigated in this paper through laboratory experiments. The results of the plugging experiments show that the nano-elastic microspheres had a higher plugging rate and formed physical plugs in the aquiferous region, thus preventing water invasion. Online nuclear magnetic resonance experiments indicated that the nano-elastic microspheres migrated from large pores to smaller ones during the flow process, forming elastic plugs in the porous media. The nano-elastic microspheres expanded in the aquiferous layer, increasing the flow resistance through both physical and elastic plugging, thereby reducing the water cut. Nano-elastic microspheres employed physical plugging to prevent water invasion and exhibited elastic flow in the porous media during the invasion. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the clinic-pathological characteristics, prognosis and its risk factors for antineutrophil cytoplasimc antibody (ANCA)-associated vasculitis (AAV)., Methods: The basic information and clinic-pathological characteristics of AAV patients, who was diagnosed from January 2010 to January 2018 in Xiangya Hospital, Central South University, were retrospectively collected. The renal survival and patient survival were regular followed up, and their clinical pathological, prognosis data and risk factors for renal were analyzed., Results: Among 269 AAV patients, 225 patients (83.64%) were microscopic polyangiitis (MPA), 33 patients (12.27%) were granulomatosis with polyangiitis (GPA), and 11 patients (4.09%) were eosinophilic granulomatosis with polyangiitis (EGPA). Male-to-female ratio was almost 1∶1 for MPA (147/122), but GPA and EGPA was more frequent among man. The medium time from disease onset to diagnosis was 64.0 (32.5, 148.5) days, 65 patients (24.16%) were diagnosed within 30 days. A total of 94.67% patients had kidney involvement in MPA patients, which was significantly higher than that in patients with GPA (63.63%), but ear, nose, and throat manifestations were more frequent in GPA patients. The 1-year kidney survival rates of GPA and MPA patients in this study were 75% and 59.3%, respectively. The 1-year death rates were 16.7% and 16.9%, respectively. The multivariable logistic regression analysis revealed that the low platelet level, low globulin level, low immunoglobin G, and high serum creatinine level were independent risk factors for the 1-year renal survival rate in AAV patients. Multiple factor logistic regression analysis showed that the serum creatinine level was an independent risk factor for all-cause mortality., Conclusions: MPA is the priority in the AAV patients. The proportion of patients with advanced end-stage renal disease and mortality is still high, and the early diagnosis and treatment in time is crucial for the improvement of prognosis for AAV.