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 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.

Hydrothermal carbonization (HTC) is a promising technology for the coversion of swine manure (SM) for hydrochars (HCs). Currently, information on the humification of organic matter is limited during the HTC of SM, and its potential correlation with the passivation of heavy metals (HMs) remains unclear, which is crucial referece for the land application of SM-derived HCs. This study systematically investigated the humification of organic matter and the passivation of HMs during the HTC of SM and then explored their intrinsic connection. The HTC treatment can enhance the humification of organic matter, and the HCs obtained at 240 ℃ had the best humification effect, with the highest content of humus (83.84 mg·g^-1 versus 41.97 mg·g^-1 in SM) and humification rate (28.89% versus 15.73% in SM). Dissolved organic carbons (DOC) and readily oxidized organic carbons (ROC) were more easily degraded in the HTC of SM, and part was further converted into inactive organic carbon. HMs (Cu, Zn, Pb, and Cr) were enriched in HCs, but all HMs were largely passivated. The ecological risk of multi-HMs was reduced from moderate risk in SM to low risk in HCs. The percentages of HMs in exchangeable/acid-soluble forms were positively correlated with the contents of DOC and negatively correlated with the ratio of humic acids to fulvic acids (P < 0.05). It was inferred that the humification of organic matter promoted the passivation of HMs in the HTC of SM. This study provided deeper insights into the humification of organic matter and it's intrinsic correlation with HMs-passivation during the HTC of SM.

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.

Microbubbles have been widely used in the chemical industry in recent years due to their unique physical and chemical properties. This article provides an overview of the characteristics and main generation methods of microbubbles, including physical, chemical, mechanical, and microfluidic techniques. It also explores the applications of microbubbles in the chemical industry, such as gas-liquid reaction intensification, gas separation, mineral flotation, and preparation of high-performance polyolefin materials. By analyzing the current research status of microbubble technology, the future development direction of its application in the chemical industry is discussed.

Herein, the liquid-solid mass transfer characteristics in micropacked bed reactors (μPBRs) operated with immiscible liquid-liquid two-phase flow is experimentally investigated. It is found that the overall volumetric liquid-solid mass transfer coefficient (k_sa) increases with the total flow rate and the channel-to-particle diameter ratio, while decreases with the organic-to-aqueous phase flow rate ratio. A satisfactory correlation model for calculating k_sa of the liquid-liquid μPBRs is developed. The new knowledge obtained would be useful in guiding the design and optimization of the liquid-liquid μPBRs.

Accurately acquiring catalyst size and morphology is essential for supporting catalytic reaction process design and optimal control. We report an intelligent catalyst sizing and morphological classification method based on the Mask-RCNN framework. A dataset of 9880 high-resolution images was captured by using a self-made fiber-optic endoscopic system for 13 kinds of silicoaluminophosphate-34 (SAPO-34) catalyst samples with different coke. Then there were approximately 877881 individual particles extracted from this dataset by our AI-based particle recognition algorithm. To clearly describe the morphology of irregular particles, we proposed a hybrid classification criterion that combines five different parameters, which are deformity, circularity, roundness, aspect ratio, and compactness. Therefore, catalyst morphology can be classified into two categories with four types. The first category includes regular types, such as the spherical, ellipsoidal, and rod-shaped types. And all the irregular types fall into the second category. The experimental results showed that a catalyst particle tends to be larger when its coke deposition increased. Whereas particle morphology remained primarily spherical and ellipsoidal, the ratio of each type varied slightly according to its coke. Our findings illustrate that this is a promising approach to be developing intelligent instruments for catalyst particle sizing and classification.

The limitations of swirl separation in removing microfine oil droplets in water have driven the development of hydrocyclone technology coupled with multiphase or multifield techniques. To enhance microfine oil droplets separation, a novel hydrocyclone separation coupled with fiber coalescence (HCCFC) was designed. The interaction between fiber balls and oil droplets inside the hydrocyclone, including droplet coalescence and breakage, was investigated. The influence of different operating parameters on separation efficiency was discussed. The results showed that fiber balls promoted oil droplet coalescence when the inlet droplet size (D₄₃) was below 22.37 μm but caused droplet breakage above this threshold. The coalescence performance of HCCFC improved with increasing inlet oil content but declined beyond 450 mg·L^-1. Separation experiments confirmed that HCCFC outperformed conventional hydrocyclone, with separation efficiency increasing by 2.9% to 20.0%. As the fiber ball content and inlet flow rate increased, the separation efficiency showed a trend of first increasing and then decreasing. Additionally, HCCFC's separation efficiency varied with inlet oil droplet size distribution, showing the most significant enhancement when D₄₃ was 22.37 μm, where separation efficiency increased by 14.4%. These findings offer insights into the development and application of multiphase coupled with hydrocyclone technology.

Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions; however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.

The production of medical waste (MW) is a growing concern, particularly in light of the increasing annual generation and the exacerbating effects of the COVID-19 pandemic. Traditional techniques such as incineration and landfilling present significant limitations. In this study, a self-designed 50 kW arc plasma reactor was employed to conduct gasification experiments on nitrile-butadiene rubber (NBR) which served as a model of MW and a mixture of NBR/SiO₂ which served as a model of glass-containing MW, using CO₂ as the working gas. The CO₂ thermal plasma gasification process not only ensures the safe and efficient disposal of MW, but also facilitates its effective conversion into H₂ and CO, achieving a carbon conversion efficiency of 94.52%. The yields of H₂ and CO reached 98.52% and 81.83%, respectively, and the specific energy consumption was as low as 3.55 kW·h·k·g^-1. Furthermore, the addition of SiO₂ was found to inhibit the gasification of NBR and cause damage to the reactor. Therefore, it is recommended that glass waste should be removed prior to the treatment of MW. The CO₂ thermal plasma gasification technology can not only eliminate environmental and health risks posed by MW, but also convert it into syngas for further utilization. This provides a promising approach to the harmless and resource disposal of MW, while also contributing to the comprehensive utilization of greenhouse gases.

During the production of natural gas hydrates, micron-sized sand particles coexist with hydrate within the transportation pipeline, posing a significant threat to the safety of pipeline flow. However, the influence of sand particles on hydrate formation mechanisms and rheological properties remains poorly understood. Consequently, using a high-pressure reactor system, the phase equilibrium conditions, hydrate formation characteristics, hydrate concentration, and the slurry viscosity in micron-sized sand system are investigated in this work. Furthermore, the effects of sand particle size, sand concentration, and initial pressure on these properties are analyzed. The results indicate that a high concentration of micron-sized sand particles enhances the formation of methane hydrates. When the volume fraction of sand particles exceeds or equals 3%, the phase equilibrium conditions of the methane hydrate shift to the left relative to that of the pure water system(lower temperature, higher pressure). This shift becomes more pronounced with smaller particle sizes. Besides, under these sand concentration conditions, methane hydrates exhibit secondary or even multiple formation events, though the formation rate decreases. Additionally, the torque increases significantly and fluctuates considerably. The RoscoeBrinkman model yields the most accurate slurry viscosity calculations, and as sand concentration increases, both hydrate concentration and slurry viscosity also increase.

This study utilizes a visualization nozzle and spray experimental platform to experimentally investigate the flow focusing/blurring nozzle. It is found that the working mode of the nozzle transitions from flow focusing to flow transition and eventually to flow blurring as the gas flow rate increases or the tube hole distance decreases. Conversely, an increase in liquid flow rate only facilitates the transition from flow focusing to flow transition. Changes in the gas/liquid flow rate or tube hole distance influence the gas shear effect and the gas inertial impact effect inside the nozzle, which in turn alters the working mode. An increase in gas flow rate results in a shift of the droplet size distribution towards smaller particle sizes in the flow blurring mode, whereas an increase in liquid flow rate produces the opposite effect. Notably, the impact of the gas flow rate on these changes is more pronounced than that of the liquid flow rate.

Bioelectrochemical regulation has been proved to enhance the traditional anaerobic digestion (AD) of organic wastes. However, few investigations have explored whether it is possible to enhance the production of biomethane from raw corn stover (CS). A single-chamber microbial electrolysis cell (MEC) was incorporated with an AD to form a new system (MEC-AD) with aiming at more efficient bioconversion of CS to biomethane. The performance and microbiological characteristics of MEC-AD was investigated, and compared with conventional AD, which were inoculated with original inoculum (UAD) and electrically domesticated inoculum (EAD), respectively. The results showed that MEC-AD achieved the highest CH₄ yield of 239.13 ml·g^-1 volatile solids (VS), which was 29.28% and 12.44% higher than those of UAD and EAD, respectively. MEC-AD also achieved higher substance conversion rates of 73.24% VS, 91.16% cellulose, and 77.24% hemicellulose, respectively. The community characteristics of microorganisms revealed that the relative abundance and interactions of functional microorganisms in MEC-AD were obviously different from UAD and EAD. In MEC-AD, Electroactive bacteria (Sedimentibacter) with electrotrophic methanogens (Methanosarcina and Methanosaeta) in anodic biofilms established electrotrophic methanogenesis through direct interspecies electron transfer (DIET). The process of methanotrophic methanogenesis was facilitated by the interactions between fermentative acid-producing bacteria (FABs), syntrophic organic acid oxidation bacteria (SOBs), and methylotrophic methanogens (Methyl-HMs) in MEC-AD suspensions. Efficient synergistic interactions between these functional microorganisms improved the performance of MEC-AD in converting CS to produce biomethane. The study could provide an effective means for achieving higher AD biomethane production from raw CS.

The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.

This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering: predicting and optimizing light olefin yields in industrial methanol-to-olefins (MTO) processes. Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary, highly volatile production data in large-scale chemical manufacturing. The framework employs the maximal information coefficient (MIC) algorithm to analyze and select the significant variables from MTO process parameters, forming a robust dataset for model development. We implement a transformer-based time series forecasting model, enhanced through positional encoding and hyperparameter optimization, significantly improving predictive accuracy for ethylene and propylene yields. The model's interpretability is augmented by applying SHapley additive exPlanations (SHAP) to quantify and visualize the impact of reaction control variables on olefin yields, providing valuable insights for process optimization. Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability, effectively handling nonlinear, non-stationary, highvolatility, and long-sequence data challenges in olefin yield prediction. This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry, offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.

Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential. It provides theoretical support for salt lake resource development and wastewater treatment technologies. This study proposes an innovative solubility prediction approach. It addresses the limitations of traditional thermodynamic models. This is particularly important when experimental data from various sources contain inconsistencies. Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture. This methodology effectively removes local outliers while preserving data distribution integrity, and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases. Experimental validation demonstrates exceptional prediction performance across temperatures from -20 °C to 150 °C, achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization. This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models, offering theoretical guidance for industrial applications in salt lake resource utilization, separation process optimization, and environmental salt management systems.

A pilot plant integrating pervaporation membrane bioreactor and mechanical vapor compression for bioethanol production was designed and constructed in the study, with a bioethanol production of 300 t·a^-1. Key equipment in the process were designed based on bench test data. A pilot-scale fermenter with 20 m³ in volume, 4 m in height and 2.5 m in diameter was designed based on geometric similarity criterion and power equality criterion. An integrated plate-frame membrane module with 105 plates was newly developed. Compared with conventional batch fermentation, the improvement of equipment utilization efficiency and the cell utilization efficiency can be expected as 1.5-2.0 times and 2-10 times, respectively, with waste water reduced by 70% to 85%. The high-exergy energy requirement for pilot plant was 57.5 kW, of which the broth preheater occupied 85.7%, following by the compressor 1.1%, pump 1.9% and fermenter agitator 0.3%. The total energy requirement including distillation for producing 1 kg ethanol (95%(mass)) achieved an energy surplus of 15.6 MJ.

In this study, four types of spiral fins with varying parameters were mounted on an upstream cylinder, and the effects of spiral fins on the vibration response of heat exchange tubes and the vortex structure in cross flow were studied through experiments and numerical simulations. The results indicate a strong dependency of the cylinder's vibration response on the fin parameters. The results indicate that the vibration response and wake structure of the cylinder are significantly influenced by the parameters of the fins. The introduction of a finned cylinder affects both its own vibration amplitude and frequency, as well as the downstream cylinder. The amplitudes of finned cylinders I and III are reduced by 57.8% and 59.9%, respectively, compared to the bare cylinder. This reduction helps to restrain vibration and diminishes the amplitudes of the downstream cylinder. Although finned cylinder II slightly decreases its own vibration, it increases the amplitude of the downstream cylinder by 13.7%. The mean drag coefficient and the root mean square of the lift coefficient of the finned cylinder are higher than those of the bare cylinder when the finned cylinder is positioned upstream. Smaller pitch and larger equivalent diameter will lead to increased drag, resulting in enhanced vortex shedding in the wake, which amplifies the vibrations of the cylinder in that wake. The downstream of finned cylinder II has the widest wake and higher vortex strength, and the dynamic load and vibration of the downstream cylinder are increased. The vortex intensity decays faster in the wake of finned cylinder III, and the vibration of the downstream cylinder is weaker.

G protein coupled receptor kinase 2 (GRK2) is a kinase that regulates cardiac signaling activity. Inhibiting GRK2 is a promising mechanism for the treatment of heart failure (HF). Further development and optimization of inhibitors targeting GRK2 are highly meaningful. Therefore, in order to design GRK2 inhibitors with better performance, the most active molecule was selected as a reference compound from a data set containing 4-pyridylhydrazone derivatives and triazole derivatives, and its scaffold was extracted as the initial scaffold. Then, a powerful optimization-based framework for de novo drug design, guided by binding affinity, was used to generate a virtual molecular library targeting GRK2. The binding affinity of each virtual compound in this dataset was predicted by our developed deep learning model, and the designed potential compound with high binding affinity was selected for molecular docking and molecular dynamics simulation. It was found that the designed potential molecule binds to the ATP site of GRK2, which consists of key amino acids including Arg199, Gly200, Phe202, Val205, Lys220, Met274 and Asp335. The scaffold of the molecule is stabilized mainly by H-bonding and hydrophobic contacts. Concurrently, the reference compound in the dataset was also simulated by docking. It was found that this molecule also binds to the ATP site of GRK2. In addition, its scaffold is stabilized mainly by H-bonding and π-cation stacking interactions with Lys220, as well as hydrophobic contacts. The above results show that the designed potential molecule has similar binding modes to the reference compound, supporting the effectiveness of our framework for activity-focused molecular design. Finally, we summarized the interaction characteristics of general GRK2 inhibitors and gained insight into their molecule-target binding mechanisms, thereby facilitating the expansion of lead to hit compound.

CO₂ hydrate-based sequestration in submarine sediments shows great potential for carbon emission reduction. Considering the proportional relationship of CO₂ and water for hydrates formation, their existing ratio largely determines the CO₂ sequestration density and phase state. Here, this work focuses on determining the optimal ratio of CO₂ to seawater in sediments simulated with 20-40 mesh (0.42-0.85 mm) quartz sand, in order to maximize CO₂ hydrate conversion in sediments. The results show that the conversion rate of CO₂ hydrate increases with the initial water saturation, reaching 15.3% at 80% initial water saturation. The optimal CO₂ hydrate formation occurs at 30% initial water saturation, with the corresponding CO₂ storage density in hydrate form of 33.09 kg·m^-3 and the hydrate saturation of 22.3%. However, CO₂ hydrate conversion rate is <10%, which implies that most CO₂ still exists in liquid state, despite the presence of free water. The total CO₂ sequestration density is negatively correlated with the initial water saturation, and at 10% initial water saturation, 398.73 kg·m^-3 of CO₂ is sequestered, of which only 18.02 kg·m^-3 is hydrated. Additionally, the lower initial water saturation corresponds to the shorter time to achieve t₉₀ of CO₂ consumption, and the water conversion rate to hydrate reaches 90% at 10% initial water saturation. In summary, adjusting the volume ratio of liquid CO₂ to seawater can effectively increase the sequestration amount of CO₂ hydrates, but methods to increase CO₂ conversion to hydrate still need to be established.