Study on green closed-loop regeneration of waste lithium iron phosphate based on oxalic acid system.

The recovery of valuable metals from used lithium batteries is essential from an environmental and resource management standpoint. However, the most widely used acid leaching method causes significant ecological harm. Here, we proposed a method of recovering Li and Fe selectively from used lithium iron phosphate batteries by using low-concentration organic acid and completing the closed-loop regeneration. Low-concentration oxalic acid is used to carry out PO43-, which is significantly less soluble in aqueous solution than Li, two-stage selective leaching Li, where the leaching rate of Li reaches 99 %, and the leaching rate of Fe is only 2.4 %. The leach solution is then decontaminated. The solubility of Li3PO4 in aqueous solution is much smaller than that of Li2C2O4, which was required to recover Li to change the pH and Li can be recovered as Li3PO4; Fe can be retrieved as FeC2O4·2H2O, and re-prepared into lithium iron phosphate.

In-depth analysis of ultrasonic-induced geological pore re-structures.

Understanding and manipulating geological pore structures is of paramount importance for geo-energy productions and underground energy storages in porous media. Nevertheless, research emphases for long time have been focused on understanding the pore configurations, while few work conducted to modify and restructure the porous media. This study deploys ultrasonic treatments on typical geological in-situ core samples, with follow-up processes of high-pressure mercury injections and nitrogen adsorptions and interpretations from nuclear magnetic resonance and x-ray diffraction. The core permeability and porosity are found to increase by 8.3 mD, from 4.1 to 12.4 mD, and by 0.95%, from 14.03% to 14.98%, respectively. Meanwhile, the number and size of the micro- and mesopore are increased with progressing of ultrasonic treatment, while those of the macropore decrease, which finally increase the permeability and porosity. The increase of micro- and mesopore number, from x-ray diffraction results, is attributed to the migration and precipitation of clay minerals caused through ultrasonic wave. The relocation of clay minerals also helps to improve the pore-throat connectivity and modify the micro-scale heterogeneity. Basically, this study reveals the characterizations of geological pore reconfigurations post-ultrasonic treatments and interprets the associated mechanisms, which provides guidance to manipulate the geological pores and be of benefit for further porous media use in science and engineering.

Potential health risks of the interaction of microplastics and lung surfactant.

Microplastics (MPs), as pollutants of environmental concern, are correlated with increased risk of various respiratory diseases. Nevertheless, whether or not MPs have adverse influences on the interfacial properties of lung surfactant (LS), and its effect on the generation of reactive oxygen species are poorly understood. In the present study, natural LS extracted from porcine lungs was used to investigate the interaction with polystyrene as a representative MPs. The results showed that the phase behavior, surface tension, and membrane structure of the LS were altered in the presence of polystyrene. Adsorption experiments demonstrated that in the mixed system of polystyrene and LS (the main active ingredients are phospholipids and proteins), adsorption of phospholipid components by polystyrene was notably higher than that of proteins. Moreover, polystyrene can accelerate the conversion between ascorbic acid and deoxyascorbic acid, thereby producing hydrogen peroxide (HOOH) in simulated lung fluid (containing LS) and further giving rise to an increase in the content of hydroxyl radicals (•OH). This work provides new insight into the potential hazard of MPs in human respiratory system, which is helpful for deeply understanding the unfavorable physicochemical effects of MPs exposure and the role of inhaled MPs on lung health.

Integrated Hierarchy-Correlation Model for Evaluating Water-Driven Oil Reservoirs.

With the increasing demands on energy and environmental domains, not only high oil production but also its accurate quantification has become one of the most important topics in academia and industry. This paper initially proposes a comprehensive workflow in which an integrated hierarchy-correlation model is used to thoroughly evaluate the influences of all relevant reservoir parameters on the ultimate oil recovery for water-flooding oil reservoirs. More specifically, the analytic hierarchy process, grey relation, and entropy weight are combined through the multiplicative weighting method to quantitatively describe the production parameters. Accordingly, novel multivariable linear and nonlinear correlations are developed to predict the production performance and validated through comparisons with numerical reservoir simulations. Seven factors, including five reservoir parameters, namely, permeability and its contrast, porosity, thickness, and saturation, and two production parameters, namely, the injection-production ratio and the operating pressure, have been identified as the most influential factors on recovery performances and thus are employed in the proposed correlations to predict the ultimate oil recovery factor. The results obtained by the proposed method are quite close to the real-time simulation data, while the accuracy is retained. The numerical results show that the recovery factors of water-flooding oil reservoirs are about 33.5-59.5%, and the corresponding linear and nonlinear correlation coefficients are 0.903 and 0.789, respectively. In comparison with the numerical simulation, the approximation error by the linear correlation is about 0.5%, which is lower than that of nonlinear correlation, for example, 12.3%. This study will be beneficial to analyze the reservoir-related parameters and provide a useful tool for rapid production performance evaluation of the water-flooding production scenario.

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction.

Tight oil fields are affected by factors such as geology, technology, and development, so it is difficult to directly obtain an accurate recovery rate. The accurate prediction of the recovery rate is very important for measuring reservoir development effects and dynamic analysis. Traditional tight oil recovery predictions are obtained by conventional formula calculations and curve fitting, which are less applicable and very different from actual conditions. Machine learning can make accurate predictions based on a large amount of data, so it is used to predict the recovery rate of tight oil reservoirs. The recovery rate of 200 wells in M tight oil reservoirs ranges widely between 8.8 and 27.6%, with more than 14 factors affecting the recovery rate, and the overall declining rule is not clear. Therefore, this research combines the production data of horizontal wells with random forest, support vector regression (SVR), and other methods, establishing recovery prediction models to gain more accurate recovery predictions. First, the Pearson correlation coefficient and the random forest (RF) machine learning method are used to measure and calculate the degree of nonlinear influence of factors on oil well recovery. Second, SVR and optimization of support vector regression by particle swarm (PSO-SVR) recovery prediction models are developed and tested, with 75% of the data being used to train SVR and PSO-SVR recovery prediction models and 25% to verify the model. Third, the accuracy of the results of these two SVR oil recovery prediction models is compared, suggesting that when the data are scarce, the optimized model is more accurate than the unoptimized one by 10.85%. Thus, this model can assure a relatively more accurate prediction of oil recovery. Machine learning recovery prediction, being more accurate and applicable, enables the data of factors such as construction and production systems to be optimized in the future, enhancing the oil recovery rate.

Interaction of industrial smelting soot particles with pulmonary surfactant: Pulmonary toxicity of heavy metal-rich particles.

Inhalable particles can influence the interfacial behavior of pulmonary surfactant (PS) resulting in various pulmonary diseases. However, the effects of actually airborne particles on the interfacial behavior of PS and its role in the alteration for soluble metal fraction in particles are entirely unexplored. Herein, we investigated the interaction of PS extracted from porcine lungs with smelting soot fine particles as a model of inhaled heavy metal-rich particles. Our results showed that the phase behavior and foamability of PS were obviously altered in the presence of smelting soot fine particles. In addition, the soluble heavy metals in smelting soot fine particles notably increased in the presence of PS as compared to that of saline solution. Further experiments conducted by adding PS's major components (dipalmitoylphosphatidylcholine, DPPC; bovine serum albumin, BSA) demonstrated that comparison of DPPC, adsorbed BSA is beneficial for the dissolution of heavy metals in smelting soot fine particles. Dynamic light scattering experiments verified that the well dispersion of smelting soot fine particles in the presence of BSA may be responsible for the higher solubility of heavy metals. These findings indicate that PS's interfacial behavior change and PS-enhanced solubilization release of metal components may increase the potentially pulmonary risk in the exposure of airborne fine particles enriched with heavy metals.

Interaction of pulmonary surfactant with silica and polycyclic aromatic hydrocarbons: Implications for respiratory health.

Understanding the interaction between pulmonary surfactant (PS) and inhalable pollutants is vital for risk assessment of respiratory health. Here, PS extracted from porcine lung (EPS) was used to investigate the interaction of PS with nano-silica particles and polycyclic aromatic hydrocarbons (PAHs). Our results demonstrated that silica significantly affected the phase behavior and foaming ability of EPS; EPS and its major components (dipalmitoyl phosphatidylcholine, DPPC; bovine serum albumin, BSA) exhibited great enhancing effect on PAHs solubility, which follows the order: EPS > DPPC > BSA, and it was positively correlated with the hydrophobicity of PAHs. Further experiments demonstrated that mixed phospholipids of EPS were largely responsible for the solubilization of EPS on PAHs. In the presence of EPS, DPPC or BSA, adsorption of PAHs by silica was notably inhibited, indicating competitive adsorption between PAHs and PS components on silica. These findings provide evidence for the surface chemistry by which PS facilitates the solubilization of PAHs and reducing the adsorption of PAHs on silica, which may be helpful for deeply understanding the effects of particulate matter and PAHs on lung health.

Interaction of inhalable volatile organic compounds and pulmonary surfactant: Potential hazards of VOCs exposure to lung.

Exposure of volatile organic compounds (VOCs) towards lung leads to pulmonary dysfunctions and various lung diseases. However, the interaction of VOCs with pulmonary surfactant (PS) that directly comes into contact with inhaled VOCs is unknown. Here, simulated PS extracted from porcine lungs (EPS) was used to study the interaction with BTEX (i.e., benzene, toluene, ethylbenzene, and p-xylene) as representatives of VOCs. Surface pressure-area (π-A) isotherms showed that in the presence of individual BTEX, EPS monolayer's phase conversion from gas to liquid expanded phase was dramatically influenced and its collapse pressure decreased greatly compared to those of EPS alone, which was attributed to the alteration of EPS monolayer's microstructure characterized by atomic force microscopy and Brewster angle microscopy. Solubilization experiments manifested that EPS and its major components (dipalmitoyl phosphatidylcholine, DPPC; bovine serum albumin, BSA) exhibited obvious solubilization effects on individual BTEX. The solubilization capacity followed an order: EPS > DPPC > BSA, which was positively correlated with hydrophobicity of individual BTEX. Synergistic solubilization test unveiled that the mixed phospholipid components were largely responsible for the solubilization capacity of EPS. These findings indicate that VOCs exposure may induce potential pulmonary health risk due to the alteration of gas-liquid interfacial properties of PS.

Interaction of nano carbon particles and anthracene with pulmonary surfactant: The potential hazards of inhaled nanoparticles.

Understanding the alteration of the air-liquid interfacial properties of pulmonary surfactant (PS) in the presence of nanoparticles (NPs) and polycyclic aromatic hydrocarbons (PAHs) is particularly important for pulmonary risk assessment. Here, we investigated the interaction of natural PS (extracted from pig's lungs) with nano carbon particles (NCPs) and anthracene as a representative PAH. Our results showed that PS exhibited a significant solubilization effect on anthracene. Solubilization experiment for the substructures of PS demonstrated that the mixed phospholipid components of PS played the primary role in the solubilization of PS for anthracene. Adsorption experiment indicated that in the mixed system of PS, NCPs, and anthracene, PS can inhibit the adsorption of anthracene on NCPs due to the solubilization, agglomeration, and competitive adsorption. In addition, the surface tension, phase behavior, and foaming ability of PS were obviously altered in the presence of NCPs. These findings indicate that the solubilization effect of PS on anthracene, the inhibitive effect of PS for the adsorption of anthracene on NCPs, and the alternation of air-liquid interfacial properties of PS containing NCPs may increase the pulmonary risk in the exposure of atmospheric environment containing both PAHs and NCPs.