This study investigates the corrosion inhibition potential of Datura stramonium seed extracts on mild steel in 1.0 mol·L^-1 HCl and 0.5 mol·L^-1 H₂SO₄, utilizing both ethanolic and aqueous extracts as eco-friendly inhibitors. Electrochemical techniques, thermodynamic studies, and quantum chemical calculations were employed to evaluate the adsorption mechanism and inhibitory action at the metal/electrolyte interface. Maximum inhibition efficiencies of 93.1% in HCl and 97.7% in H₂SO₄ were achieved with the ethanolic extract at a concentration of 0.2 g·L^-1, while the aqueous extract demonstrated 93.8% inhibition in HCl and 96.6% in H₂SO₄. Polarization curves indicated mixed-type inhibition with a slight anodic bias. The thermodynamic analysis of two extracts in both environments indicated that the K_ads increased and that the ΔG_ads were close to -40 kJ·mol^-1, suggesting that the adsorption followed the Langmuir isotherm, indicating a combination of physical and chemical adsorption. SEM/EDX analysis confirmed the formation of a protective layer, while quantum chemical studies further validated strong adsorption, evidenced by a low ΔE of 2.396 eV and an adsorption energy of -878 kcal·mol^-1 (1 kcal·mol^-1 = 4.18 kJ·mol^-1). These results demonstrate that Datura stramonium extracts are promising inhibitors, particularly in sulfuric acid, for industrial applications. Reason: Improved clarity, vocabulary, and technical accuracy while maintaining the original meaning.

Catalysis has made great contributions to the productivity of human society. Therefore, the pursuit of new catalysts and research on catalytic processes has never stopped. Continuous and in-depth catalysis research significantly increases the complexity of dynamic systems and multivariate optimization, thus posing higher challenges to research methodologies. Recently, the significant advancement of generative artificial intelligence (AI) provides new opportunities for catalysis research. Different from traditional discriminative AI, this state-of-the-art technique generates new samples based on existing data and accumulated knowledge, which endows it with attractive potential for catalysis research — a field featuring a vast exploration space, diverse data types and complex mapping relationships. Generative AI can greatly enhance both the efficiency and innovation capacity of catalysis research, subsequently fostering new scientific paradigms. This perspective covers the basic introduction, unique advantages of this powerful tool, and presents cases of generative AI implemented in various catalysis researches, including catalyst design and optimization, characterization technique enhancement and guidance for new research paradigms. These examples highlight its exceptional efficiency and general applicability. We further discuss the practical challenges in implementation and future development perspectives, ultimately aiming to promote better applications of generative AI in catalysis.

The exosomes hold significant potential in disease diagnosis and therapeutic interventions. The objective of this study was to investigate the potential of aqueous two-phase systems (ATPSs) for the separation of bovine milk exosomes. The milk exosome partition behaviors and bovine milk separation were investigated, and the ATPSs and bovine milk whey addition was optimized. The optimal separation conditions were identified as 16% (mass) polyethylene glycol 4000, 10% (mass) dipotassium phosphate, and 1% (mass) enzymatic hydrolysis bovine milk whey. During the separation process, bovine milk exosomes were predominantly enriched in the interphase, while protein impurities were primarily found in the bottom phase. The process yielded bovine milk exosomes of 2.0 × 10¹¹ particles per ml whey with high purity (staining rate>90%, 7.01 × 10¹⁰ particles per mg protein) and high uniformity (polydispersity index <0.03). The isolated exosomes were characterized and identified by transmission electron microscopy, zeta potential and size distribution. The results demonstrated aqueous two-phase extraction possesses a robust capability for the enrichment and separation of exosomes directly from bovine milk whey, presenting a novel approach for the large-scale isolation of exosomes.

In the current era of renewable energy prominence, the wide operational capacity of coal-fired boilers has emerged as crucial for ensuring the sustainability of power plants. However, attaining ultra-low nitrogen oxides (NO_x) emissions during periods of low-load operations presents a significant and persistent challenge for coal power enterprises. While techniques such as biomass re-burning and advanced re-burning have shown promise in enhancing NO reduction efficiency above 800 ℃, their elevated levels of chlorine (Cl) and alkali metals pose potential risks to boiler equipment integrity. Therefore, this study proposes the utilization of biomass char derived from pyrolysis as a dual-purpose solution to enhance NO reduction efficiency while safeguarding boiler integrity during low-load operations. Findings indicate that pyrolysis treatment effectively reduces the Cl and alkali metal content of biomass. Specifically, it was determined that biomass char produced through deeply pyrolysis at 300 ℃ achieves the highest NO reduction efficiency while minimizing the presence of harmful components. At a reduction temperature of 700 ℃, both re-burning and advanced re-burning techniques exhibit NO reduction efficiencies of 55.90% and 62.22%, which is already an ideal deficiency at low temperatures. The addition of water vapor at 700-800 ℃ obviously avoids the oxidation of ammonia to NO in advanced re-burning. Upon further analysis, denitrification efficiency in biomass char re-burning and advanced re-burning is influenced not only by volatile content but also by physicochemical properties such as porosity and surface functional group distribution under certain reaction conditions. This study provides a theoretical framework for the industrial implementation of biomass char for NO control in coal-fired power plants, offering insights into optimizing NO reduction efficiency while mitigating potential risks to boiler equipment.

Molecular dynamics simulations were performed to study the microscopic working mechanism of fast hydrate formation from active ice. We successfully simulated the cyclic process of ice melt-hydrate formation-ice melt. The simulation results showed that active ice could significantly accelerate the formation of hydrates and exhibit high gas storage capacity. The oxygen atoms of the sulfate group in SDS formed hydrogen bonds with the hydrogen atoms of water molecules in the ice, destroying the orderly arranged structures of the ice surface. SDS also acted as a promoter to accelerate the mass transfer of guests in the liquid phase, thereby promoting the nucleation and growth of hydrates. The ordered structures of liquid phase formed by ice melting and the formation of cage-like structures facilitated by ice surface defects were beneficial to the nucleation and growth of hydrates. The formation of the hydrate shell accelerated the migration of the guests from the gas phase to the liquid phase. As the ice continued to melt, sufficient guests and water molecules ensured the stable growth of hydrates.

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively.

The direct oxidation of methane to methanol (DOMM) has been recognized as a significant technology for efficiently utilizing low-concentration coalbed methane (LCMM) and supplying liquid fuel. Herein, the noble metals (Pt, Pd and Ru) modified Cu/alkalized sepiolite (CuX/SEPA) catalysts were prepared and used for the DOMM in a gas-phase system at low temperatures. The CuRu/SEPA exhibited the highest methanol production of 53 μmol·g^-1·h^-1 and methanol selectivity of 90% under the optimal reaction conditions. Various characterizations demonstrated that the addition of Ru promoted the formation of Cu²⁺ and the contraction of Cu—Si/Al bonds to reduce the distance between framework Al atoms of SEPA to further generate more Al pairs, which facilitated the formation of reactive dicopper species ([Cu₂O]²⁺ or [Cu₂O₂]²⁺). Investigation of the reaction mechanism revealed that [Cu₂O]²⁺ or [Cu₂O₂]²⁺ species could adsorb and activate methane to form CH₃O* species and ultimately generated methanol with the assistance of water.

The distillation process is an important chemical process, and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling, thus improving the efficiency of process optimization or monitoring studies. However, the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals, which brings challenges to accurate data-driven modelling of distillation processes. This paper proposes a systematic data-driven modelling framework to solve these problems. Firstly, data segment variance was introduced into the K-means algorithm to form K-means data interval (KMDI) clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction. Secondly, maximal information coefficient (MIC) was employed to calculate the nonlinear correlation between variables for removing redundant features. Finally, extreme gradient boosting (XGBoost) was integrated as the basic learner into adaptive boosting (AdaBoost) with the error threshold (ET) set to improve weights update strategy to construct the new integrated learning algorithm, XGBoost-AdaBoost-ET. The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.

SAPO-5 zeolite supported RuMn was a highly efficient catalyst for the aqueous-phase selective hydrodeoxygenation of guaiacol to cyclohexanol. The optimal catalyst achieved a high cyclohexanol yield of 93.7% at full guaiacol conversion under mild conditions, with a high TOF of 920 h^-1. Moreover, the catalyst displayed remarkable performance for the hydrogenation of phenol to cyclohexanol, where a 100% yield of cyclohexanol was obtained at a phenol-to-Ru molar ratio of about 17900. In particular, the catalyst exhibited excellent recyclability and could be recycled for 20 times without obvious activity loss. The as-prepared RuMn/SAPO-5 catalyst exhibited higher performance than most of the reported Ru-based catalysts.

Recovering waste heat is essential for primary energy savings and carbon emission reduction. To provide direct and reliable suggestions for factories to recover waste heat, energetic, economic and exergoeconomic comparison between direct heat exchange (DHE) and open-cycle mechanical heat pump (MHP) under various operating conditions is carried out in this work. The price ratios R_ES (electricity to steam) and R_HS (hot water to steam) are introduced to quantify regional impacts and conduct quantitative analysis. A semi-empirical formula is obtained to explore the exergoeconomic performance of the two systems. For waste heat within 373.15-423.15 K, the exergy efficiency of the DHE with a temperature difference of 10-90 K is always lower than that of the MHP with a temperature lift of 10-50 K. The economic performance of the two systems has a break-even point, depending on the operating parameters and relative prices of electricity, steam, and hot water. Under the average R_ES (3.8) in China, if R_HS is higher than 0.748, the annual revenue of the DHE is always higher, whereas the MHP is more economical when R_HS is lower than 0.110. In regions where R_ES is higher than 4.353, the annual revenue of the MHP will be negative in some cases.

Microemulsions are usually used to prepare nanomaterials. The formation behavior of microemulsions is crucial to the preparation of nanomaterials. Water in the internal phase is usually replaced by electrolyte solutions to prepare nanomaterials. Knowing the effects of electrolyte solution on the phase behavior of microemulsion is significant to the nanomaterial preparation. Microemulsion systems were studied by a conductivity method with cyclohexane as oil, Triton X-100 as surfactant, hexanol as cosurfactant, and deionized water or the electrolyte solutions of Cu(Ac)₂ and Zn(Ac)₂ as aqueous phases. The results showed that the replacement of water with electrolyte solution had a strong effect on the phase behavior of microemulsion system. The O/W microemulsion zone in water system was not observed in the studied electrolyte system. The shape and area of the corresponding phase zone in electrolyte system were different from that in water system. The microemulsion regions of electrolyte solution systems were always larger than that of water system. Zn(Ac)₂ showed a larger microemulsion region than Cu(Ac)₂ at 0.1 mol·L^-1. The microemulsion phase region formed by 0.1 mol·L^-1 Zn(Ac)₂ + 0.1 mol·L^-1 Cu(Ac)₂ was smaller than that formed by 0.1 mol·L^-1 Zn(Ac)₂ or 0.1 mol·L^-1 Cu(Ac)₂ lonely. With the increase of electrolyte concentration in the electrolyte solution and the rise of temperature, the microemulsion region shrank gradually. The changes of interactions between different components in the system should be responsible to the variation of phase behavior. The results provide important information for the microemulsion system with electrolyte solution as aqueous phase.

Photocycloaddition affords opportunities to engage in advanced fuels with high-strained cyclobutyl-containing structures. Herein, the one-step route for the synthesis of high-energy-density caged fuel, tetracyclo [4.2.1.0^2,5.0^3,7]nonane (TCN) with high-strained four-membered structure, has been developed via photosensitized [2 + 2] cycloaddition of 5-vinyl-2-norbornene (VNB). The reaction conditions are optimized to obtain a high conversion of VNB of 91.9%. The triplet quenching and Stern-Volmer quenching studies indicate that [2 + 2] photocycloaddition follows the triplet-triplet energy transfer process, and a kinetic model is expressed as a reaction rate equation correlated with the incident light flux. Importantly, the obtained TCN shows a high density of 1.003 g·cm^-3 and volumetric net heat of combustion of 42.31 MJ·L^-1, which can serve as an excellent high-energy additive for blending with liquid fuels.

Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases, naphtha, diesel, and other products through cracking reactions. Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield, or to increase product yield while reducing energy consumption. This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm, which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm. The reactor model used in this article is simulated based on a twenty-five lumped kinetic model. Through model and test function verification, the proposed optimization scheme exhibits significant advantages in the multi-objective optimization process of hydrocracking.

As a renewable energy source, the thermal conversion of poultry manure, is a promising waste treatment solution that can generate circular economic outputs such as energy and reduce greenhouse gas emissions. Currently, pressurized gasification of poultry manure is still a novel research field, especially when combined with a novel technological route of oxy-fuel gasification. Oxy-fuel gasification is a newly proposed and promising gasification technology for power generation that facilitates future carbon capture and storage. In this work, based on a commercially operated industrial-scale chicken manure gasification power plant in Singapore, we presented an interesting first exploration of the coupled pressurization technology for oxy-fuel gasification of poultry manure using CFD numerical simulation, analyzed the effects of pressure and oxygen enrichment concentration as well as the coupling mechanism between them, and discussed the conversion and emission of nitrogen- and sulfur-containing pollutants. The results indicate that under oxy-fuel gasification condition (Oxy-30, i.e., 30%O₂/70%CO₂), as the pressure increases from 0.1 to 0.5 MPa, the CO concentration in the syngas increases slightly, the H₂ concentration increases to approximately 25%, and the CH₄ concentration (less than 1%) decreases, resulting in an increase in the calorific value of syngas from 5.2 to 5.6 MJ·m^-3. Compared to atmospheric pressure conditions, a relatively higher oxygen-enriched concentration interval (Oxy-40 to Oxy-50) under pressurized conditions is advantageous for autothermal gasification. Pressurization increases NO precursors production and also promotes homogeneous and heterogeneous reduction of NO, and provides favorable conditions for self-desulfurization. This work offers reference for the realization of a highly efficient and low-energy-consumption thermochemical treatment of livestock manure coupled with negative carbon emission technology.

One of the main challenges in oil-water separation of traditional Chinese medicines (TCM) is to obtain essential oils from the aromatic water of TCM. In this study, silicon dioxide/polyvinylidene fluoride (SiO₂/PVDF) membranes were prepared using nonsolvent induce phase separation. Then polydimethylsiloxane (PDMS) was coated to obtain PDMS/SiO₂/PVDF membranes. Separated essential oils and water from aromatic water in the gaseous state by vapor permeation membrane separation technology. The relationship between membrane structure and membrane separation effect was investigated. Response surface methodology was used to develop a quadratic model for the separation factor, membrane permeation separation index and membrane preparation process. The optimal process parameters for the membrane separation were 12.31% (mass) concentration of PVDF solution, 9.6% (mass) of N,N-dimethylacetamide in the solidification bath, and 0.2 g hydrophobic nano-SiO₂ incorporation, with a separation factor of 14.45, and a membrane flux of 1203.04 g·m^-2·h^-1. Compared with the PDMS/PVDF membranes, the separation factor and membrane flux were increased by 68.59% and 3.46%, respectively. Compared with the SiO₂/PVDF membranes, the separation factor and membrane flux were increased by 478% and 79.33%, respectively. Effectively mitigated the limitations of traditional polymer membrane material performance affected by the “trade-off” effect. Attenuated total internal reflection-Fourier transform infrared spectroscopy, contact angle, scanning electron microscopy and energy dispersive spectroscopy were used to characterize the PDMS/SiO₂/PVDF membranes, and gas chromatography was used to characterize the permeate. In addition, the contents of L-menthol, L-menthone, menthyl acetate and limonene in the permeate, conformed to the European Pharmacopoeia standards. This study provided an effective preparation strategy of a feasible hydrophobic powder polymer membrane for the separation of essential oils from gaseous peppermint aromatic water.

In the petroleum industry, the properties of catalysts play a crucial role in the performance of hydroprocessing reactions. Carbon modification can effectively regulate the physicochemical properties of catalysts, but further in-depth research is necessary. In this study, ethylene glycol was used as the carbon source to investigate the impact of varying carbon amounts on the performance of the Mo-Ni/Al₂O₃ hydrogenation catalyst. The results showed that both the pore structure and surface hydroxyl groups of catalysts can be adjusted after carbon modification. As the carbon content increased, the surface acidity of catalysts gradually decreased, and the interaction between carrier and active metal gradually weakened, leading to more octahedral coordination in form of polynuclear polymolybdic acid. The dispersion and sulfidation degree of Mo species improved, ultimately resulting in more hydrogenation active phases. Consequently, the catalyst exhibited enhanced hydrodesulfurization (HDS) and hydrodenitrification (HDN) activities.

A cryogenic visible calibration and image evaluation facility (VCCIEF) was constructed to assess the effectiveness of electrical capacitance tomography systems in cryogenic conditions, known as Cryo-ECT. This facility was utilized to conduct dynamic, real-time imaging trials with liquid nitrogen (LN2). The actual flow patterns were captured using a camera and contrasted with the imaging outcomes. The capacitance data collected from these experiments were subsequently processed using three distinct methods: linear back projection, Landweber iteration, a fully connected deep neural network, and a convolutional neural network. This allowed for a comparative analysis of the performance of these algorithms in practical scenarios. The findings from the LN2 experiments demonstrated that the Cryo-ECT system, when integrated with the VCCIEF, was capable of successfully executing calibration, generating flow patterns, and performing imaging tasks. The system provided observable, clear, and precise phase distributions of the liquid nitrogen-vaporous nitrogenflow within the pipeline.

The mass transport and ohmic losses in proton exchange membrane fuel cells (PEMFCs) is significantly influenced by the channel to rib width ratio (CRWR), particularly when accounting for the interfacial contact resistance between bipolar plates (BPs) and gas diffusion layers (GDLs) (ICR_BP-GDL). Both the determination of the optimal CRWR value and the development of an efficient flow field structure are significantly influenced by ICR_BP-GDLs. To investigate this, three-dimensional numerical models were developed, revealing that selecting an optimal CRWR tailored to specific ICR_BP-GDL values can effectively balance mass transport and ohmic losses. Building on this insight, a novel island two-dimensional flow field design is proposed, demonstrating the ability to enhance oxygen transport to the catalyst layer (CL) and achieve a more uniform oxygen distribution without increasing ohmic losses. Compared to conventional straight and serpentine flow fields, the island flow field improves output power density by 4.5% and 3.5%, respectively, while reducing the liquid water coverage ratio by 30%. Additionally, the study identifies optimal CRWR values for conventional flow fields corresponding to ICR_BP-GDLs of 2.5, 5, 10, 20, and 40 mΩ·cm² as 1.5, 1.5, 1.0, 0.67, and 0.43, respectively. For the island flow field, the optimal CRWRs are consistently smaller—1.5, 1.0, 0.67, 0.43, and 0.43—due to its superior mass transfer capability. This work provides a valuable framework for optimizing flow field designs to achieve improved PEMFC performance.

In this paper, the pyrolysis characteristics of waste tire rubber with catalyst addition were experimentally studied. Pyrolysis experimentations of waste tire rubber with either base, acid or Zeolite catalysts were performed in a Thermal Gravimetric Analyzer, a one-stage test rig and a two-stage test rig respectively. This is followed by analysis into the rates of pyrolysis reactions and the yields and distribution of the three-phase products using thermogravimetric infrared spectroscopy (TG-IR) and gas chromatography-mass spectrometry (GC-MS). Results indicated that the transition metal chloride catalysts improved the reaction rate and were overall effective than the solid acid-base catalysts. Benzene and toluene yields were improved by all three catalysts in the primary pyrolysis, and the best performance was achieved at 550 °C and 600 °C with 30% NaOH. With ZSM-5 in the secondary pyrolysis, proportion of high calorific gases components as H₂ and CH₄ were increased, and the arylation and isomerization reactions were also promoted. The optimum aromatics yield was achieved at 600 °C and 50% ZSM condition. This study would provide a reference for resourceful utilization of waste tires.

The ammonium salt precipitation method is frequently utilized for extracting vanadium from the leaching solution obtained through sodium roasting of vanadium slag. However, Na⁺ and NH₄⁺ ions in the vanadium precipitation solution can not be effectively separated, leading to a large amount of ammonia-nitrogen wastewater which is difficult to treat. In this study, the manganese salt pretreatment process is used to extract vanadium from a sodium roasting leaching solution, enabling the separation of vanadium and sodium. The vanadium extraction product of manganese salt is dissolved in acid to obtain vanadium-containing leaching solution, then vanadium is extracted by hydrolysis and vanadium precipitation, and V₂O₅ is obtained after impurity removal and calcination. The results show that the rate of vanadium extraction by manganese salt is 98.23%. The vanadium extraction product by manganese salt is Mn₂V₂O₇, and its sodium content is only 0.167%. Additionally, the acid solubility of vanadium extraction products by manganese salt is 99.52%, and the vanadium precipitation rate of manganese vanadate solution is 92.34%. After the removal of manganese and calcination process, the purity of V₂O₅ product reached 97.73%, with a mere 0.64% loss of vanadium. The Mn²⁺ and NH₄⁺ ions in the solution after vanadium precipitation are separated by precipitation method, which reduces the generation of ammonia-nitrogen wastewater. This is conducive to the green and sustainable development of the vanadium industry.