Per- and Polyfluoroalkyl Substances (PFAS) bioaccumulate in the human body, presenting potential health risks and cellular toxicity. Their transport mechanisms and interactions with tissues and the circulatory system require further investigation. This study investigates the interaction mechanisms of six PFAS with Human Serum Albumin (HSA) using multi-spectroscopy, DFT and a molecular dynamics approach. Multi-spectral analysis shows that perfluorononanoic acid (PFNA) has the best binding capabilities with HSA. The order of binding constants (298 K) is as follows: “Perfluorononanoic Acid (PFNA, 7.81 × 10⁶ L·mol⁻¹) > Perfluoro-2,5-dimethyl-3,6-dioxanonanoic Acid (HFPO-TA, 3.70 × 10⁶ L·mol⁻¹) > Perfluorooctanoic Acid (PFOA, 2.27 × 10⁵ L·mol⁻¹) > Perfluoro-3,6,9-trioxadecanoic Acid (PFO3DA, 1.59 × 10⁵ L·mol⁻¹) > Perfluoroheptanoic Acid (PFHpA, 4.53 × 10³ L·mol⁻¹) > Dodecafluorosuberic Acid (DFSA, 1.52 × 10³ L·mol⁻¹)”. Thermodynamic analysis suggests that PFNA and PFO3DA’s interactions with HSA are exothermic, driven primarily by hydrogen bonds or van der Waals interactions. PFHpA, DFSA, PFOA, and HFPO-TA’s interactions with HSA, on the other hand, are endothermic processes primarily driven by hydrophobic interactions. Competitive probe results show that the main HSA–PFAS binding site is in the HSA structure’s subdomain IIA. These findings are also consistent with the findings of molecular docking. Molecular dynamics simulation (MD) analysis further shows that the lowest binding energy (−38.83 kcal/mol) is fund in the HSA–PFNA complex, indicating that PFNA binds more readily with HSA. Energy decomposition analysis also indicates that van der Waals and electrostatic interactions are the main forces for the HSA–PFAS complexes. Correlation analysis reveals that DFT quantum chemical descriptors related to electrostatic distribution and characteristics like ESP and ALIE are more representative in characterizing HSA–PFAS binding. This study sheds light on the interactions between HSA and PFAS. It guides health risk assessments and control strategies against PFAS, serving as a critical starting point for further public health research.

The textile industry is an important contributor to the growth of the global economy. However, a huge quantity of wastewater is generated as a by-product during textile manufacturing, which hinders the ongoing development of textile industry in terms of environmental sustainability. Membrane distillation (MD), which is driven by thermal-induced vapor pressure difference, is being considered as an emerging economically viable technology to treat the textile wastewater for water reuse. So far, massive efforts have been put into new membrane material developments and modifications of the membrane surface. However, membrane wetting, direct feed solution transport through membrane pores leading to the failure of separation, remains as one of the main challenges for the success and potential commercialization of this separation process as textile wastewater contains membrane wetting inducing surfactants. Herein, this review presents current progress on the MD process for textile wastewater treatment with particular focuses on the fundamentals of membrane wetting, types of membranes applied as well as the fabrication or modification of membranes for anti-wetting properties. This article aims at providing insights in membrane design to enhance the MD separation performance towards commercial application of textile wastewater treatment.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert–Huang transform (HHT) was applied to each source signal to obtain a joint time–frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Although gas flooding has been successfully applied for enhanced oil recovery throughout the world, most of its applications are conducted in low, ultralow, and tight oil reservoirs with light oil. Studies focused on a low-permeability reservoir with medium viscous oil are rarely reported, and there is little experience in the development of this type of reservoir for reference. Based on the special characteristics of low-permeability formation coupled with medium viscous oil in Niuquanhu Block, China, the gas-oil interactions of different gases are first evaluated through the pressure-volume-temperature (PVT) analysis. Then, the minimum miscibility pressure (MMP) of different gases is compared using slim tube experiments and a numerical simulation method. The oil recovery of different gas floodings is studied using core flooding experiments, and then the output oil compositions of different gas floodings are analyzed using a total hydrocarbon chromatograph. The PVT analysis results show that the interactions between hydrocarbons and medium viscous oil are the strongest compared with other gases. Under formation conditions, about 70 mol % hydrocarbons is dissolved in the medium viscous oil, leading to a 69% reduction of oil viscosity and a 48% increment of oil swelling, which is 1.19 and 1.35 times those of CO₂. The reduction of oil viscosity and increment of oil swelling of hydrocarbons are 1.64 and 2.02 times those of CH₄. And the reduction of oil viscosity and increment of oil swelling of hydrocarbons are 2.23 and 2.67 times those of N₂. Slim tube experiments show that the MMPs of hydrocarbon flooding, CO₂ flooding, CH₄ flooding, and N₂ flooding are 28.5, 36.8, 46.4, and 61.6 MPa, respectively. Therefore, compared with the other three types of gases, hydrocarbon flooding has the lowest MMP for low-permeability reservoirs with medium viscous oil. The errors between the MMPs of hydrocarbon flooding, CO₂ flooding, CH₄ flooding, and N₂ flooding obtained by the numerical simulation method and the results obtained by the slim tube experiment method are 0.71, 0.82, 0.87, and 0.98%, respectively. The numerical simulation results of the slim tube experiment show that the flooding pressure and oil recovery curves of different gas floodings all have inflection points. Under the existing condition, the inflection point pressure value is unchanged by changing the PV values of different gas injections. According to the results of a series of core flooding experiments and the analysis of the output oil compositions of different gas floodings, the oil recoveries of hydrocarbon flooding, CO₂ flooding, CH₄ flooding, and N₂ flooding are 57.02, 51.11, 36.88, and 21.35%, respectively, and the extraction ability of different gases to light hydrocarbon components is ranked as hydrocarbons > CO₂ > CH₄ > N₂. The findings revealed in this paper can provide technical support for gas flooding development in similar low-permeability reservoirs with medium viscous oil.

The treatment of livestock manure caused by the expansion of the breeding industry in China has attracted wide attention. Heavy metals in pig manure can pollute soil and water and even transfer to crops, posing harm to humans through the food chain. In this study, corn straw was selected as the additive and introduced into the anaerobic digestion. Sepiolite (SE), ferric oxide (Fe₂O₃), attapulgite (AT) and ferric sulfate (FeSO₄) were used as passivators to compare the effects of these inorganic passivators on gas production and passivation of heavy metals during the process of the anaerobic digestion. When the dry mass ratio of pig manure to straw is 8:2, the gas production efficiency is optimal. SE, AT and ferric sulfate have a much stronger ability to improve gas production performance than Fe₂O₃. The total gas production increased by 10.34%, 6.62% and 4.56%, and the average methane production concentration increased by 0.7%, 0.3% and 0.4%, respectively. The influence of SE, AT and ferric sulfate on the passivation of heavy metals is much better than Fe₂O₃, and the fractions in biological effective forms of Cu and Zn reduced by 41.87 and 19.32%, respectively. The anaerobic digestion of mixed materials is conducive to the gas production and the passivation of heavy metals. Therefore, SE, AT and ferric sulfate are selected as composite passivators, and the optimal ratio of inorganic composite passivators i: AT 7.5 g/L, ferric sulfate 5 g/L and SE 7.5 g/L, according to the results of orthogonal experiments. This study can provide a theoretical basis for the safe application of biogas fertilizers.