The design of Co-Mn composite oxides catalysts derived from MOF is significant for catalytic combustion of toluene. Here, a series of M-Co_aMn_bO_x, with enhanced catalytic properties compared with that of M-Co₃O₄, were successfully prepared through pyrolysis of Mn-doped Co-MOF. The as-synthesized M-Co₁Mn₁O_x (Co:Mn = 1:1) exhibits an optimal catalytic activity with 90% toluene conversion reached at 227 ℃, which benefits from the increase of Co³⁺, O_ads and the synergistic effect between Mn and Co. According to the analysis of the in situ diffuse reflectance infrared Fourier transform spectroscopy, toluene could be degraded easier on M-Co₁Mn₁O_x with lower activation energy than M-Co₃O₄. The main intermediate products are benzaldehyde, benzoic acid, anhydride, and maleate species. Those findings reveal the value of Mn doping for improved activity of toluene oxidation on MOF derived Co₃O₄, which provide a feasible method for the construction of toluene-oxidation catalysts.

The crystallization has significant influence on fluidity of slag and slag discharge of entrained-flow-bed (EFB) gasifier. The crystallization characteristics and fluidity of five synthetic slags with different MgO/CaO ratios prepared on the basis of the range of oxide contents of Zhundong coal ash were investigated in this study. The results show that with the MgO/CaO ratio increase, the initial crystallization temperature increases, and the main temperature range of crystallization ratio growth moves to higher temperature range gradually which causes T_p25 (T_p25 is the temperature corresponding to the viscosity of 25 Pa·s) to increase. Mg-rich crystals are formed preferentially than Ca-rich crystals when adding the same amount of MgO and CaO during cooling. The effective slagging operating temperature range decrease from 217 ℃ for the slag with a 0:4 MgO/CaO ratio to 44 ℃ for the slag with a 4:0 MgO/CaO ratio with the MgO/CaO ratio increase. The slags with 2:2 and 1:3 MgO/CaO ratios show similar effective slagging operating temperature range, T_p25 and the temperature corresponding to the viscosity of 2 Pa·s. However, compared with the slag with a 1:3 MgO/CaO ratio, the crystallization ratio and rate of slag with a 2:2 MgO/CaO ratio are lower within lower temperature range (1300–1200 ℃), causing its lower critical viscosity temperature and wider actual operating temperature range. Of the five slags, the widest effective slagging operating temperature range and the lowest T_p25 of the slag with a 0:4 MgO/CaO ratio due to its low crystallization ratio, and wider actual operating temperature range of the slag with a 2:2 MgO/CaO ratio make the two slags suitable for slag discharge of EFB gasifier.

The kinetic behavior of esterification between methacrylic acid and methanol catalyzed by NKC-9 resin was studied in a fixed bed reactor. The reaction was conducted in the temperature range of 323.15 to 368.15 K with the molar ratio of reactants from 0.8 to 1.4 under certain pressure. The measurement data were regression with the pseudo-homogeneous (P-H), Eley-Rideal (E-R), and Langmuir-Hinshelwood (L-H) heterogeneous kinetic models. Independent adsorption experiments were implemented to gain the adsorption equilibrium constants of four components. Among the above three models, the L-H model exhibited the best fitting results. The stability of NKC-9 was evaluated by long-term running with the yield of methyl methacrylate no decrease during 3000 h operation. The structure and physicochemical properties of the new and used catalyst were performed by several characterizations including thermogravimetric analysis (TG), scanning electron microscope (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) and so on.

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deep-learning algorithm based on a fully convolutional neural network (FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory (LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

Catalytic pyrolysis of digestate to produce aromatic hydrocarbons can be combined with anaerobic fermentation to effectively transform and utilize all biomass components, which can achieve the meaningful purpose of transforming waste into high-value products. This study explored whether catalytic pyrolysis of digestate is feasible to prepare aromatic hydrocarbons by analyzing the thermogravimetric characteristics, pyrolysis characteristics, and catalytic pyrolysis characteristics of digestate. For digestate pyrolysis, an increase in temperature was found to elevate the CO, CH₄, and monocyclic aromatic hydrocarbon (benzene, toluene, and xylene; BTX) content, whereas it decreased the contents of phenols, acids, aldehydes, and other oxygenates. Furthermore, the catalytic pyrolysis process effectively inhibited the acids, phenols, and furans in the liquid, whereas the yield of BTX increased from 25.45% to 45.99%, and the selectivity of xylene was also increased from 10.32% to 28.72% after adding ZSM-5. ZSM-5 also inhibited the production of nitrogenous compounds.

A new type of rubidium ion-imprinted polymer has been synthesized by the surface-imprinting technique using methacrylic acid as the functional monomer, the rubidium ion as the template, methanol as the solvent, and silica as a carrier. Ethylene glycol dimethacrylate and 2,2-azobisisobutyronitrile were used as acrosslinker and an initiator, respectively. In addition, based on the macrocyclic effect of crown ethers, the 18-crown-6 ligand was introduced as a ligand to fix the template ions better. Scanning electron microscopy, zeta-potential analysis, Fourier transform infrared spectroscopy, thermogravimetric analysis, and X-ray photoelectron spectroscopy were performed to characterize the ion-imprinted polymer. The effects of the preparation and adsorption conditions on the adsorption performance of the rubidium ion-imprinted polymer were investigated. The results indicated that the rubidium ion-imprinted polymer has high selectivity and faster kinetics than other adsorbents, with an equilibrium adsorption capacity of 200.19 mg·g^-1 at 298 K within 25 min. The sorption isotherm was well described by the Freundlich isotherm model, while the adsorption kinetics fitted the pseudo-second-order kinetic model. Consecutive adsorption–desorption experiments showed that the ion-imprinted polymer had good chemical stability and reusability.

Highly efficient and robust electrocatalysts have been in urgent demand for oxygen evolution reaction (OER). For this purpose, high-cost carbon materials, such as graphene and carbon nanotubes, have been used as supports to metal oxides to enhance their catalytic activity. We report here a new Co₃O₄-based catalyst with nitrogen-doped porous carbon material as the support, prepared by pyrolysis of porous polyurea (PU) with Co(NO₃)₂ immobilized on its surface. To this end, PU was first synthesized, without any additive, through a very simple one-step precipitation polymerization of toluene diisocyanate in a binary mixture of H₂O-acetone at room temperature. By immersing PU in an aqueous solution of Co(NO₃)₂ at room temperature, a cobalt coordinated polymer composite, Co(NO₃)₂/PU, was obtained, which was heated at 500 ℃ in air for 2 h to get a hybrid, Co₃O₄/NC, consisting of Co₃O₄ nanocrystals and sp²-hybridized N-doped carbon. Using this Co₃O₄/NC as a catalyst in OER, a current density of 10 mA·cm^-2 was readily achieved with a low overpotential of 293 mV with a Tafel slope of 87 mV·dec^-1, a high catalytic activity. This high performance was well retained after 1000 recycled uses, demonstrating its good durability. This work provides therefore a facile yet simple pathway to fabrication of a new transition metal oxides-based N-doped carbon catalyst for OER with high performance.

Bioethanol, as a clean and renewable fuel, has gained increasing attention due to its major environmental benefits. Pervaporation (PV) is a promising and competitive technique for the recovery of ethanol from bioethanol fermentation systems due to the advantages of environmental friendliness, low energy consumption and easy coupling with fermentation process. The main challenge for the industrial application of ethanol perm-selective membranes is to break the trade-off effect between permeability and selectivity. As membrane is the heart of the pervaporation separation process, this article attempts to provide a comprehensive survey on the breakthroughs of ethanol perm-selective PV membranes from the perspectives of tailoring membrane materials to enhance PV separation performance. The research and development of polymeric and organic/inorganic hybrid membranes are reviewed to explore the fundamental structure–property-performance relationships. It is found that mixed matrix membranes with well-designed membrane structures offer the hope of better control overphysi-/chemical micro-environment and cavity/pore size as well as size distribution, which may provide both high permeability and membrane selectivity to break the trade-off effect. The tentative perspective on the possible future directions of ethanol perm-selective membranes is also briefly discussed, which may provide some insights in developing a new generation of high-performance PV membranes for ethanol recovery.

Due to outstanding performance in cheminformatics, machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data. The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration. However, many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters, which can easily lead to achieving a suboptimal hyper-parameter configuration. In this study, Hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms. Six drug discovery datasets, including solubility, probe-likeness, hERG, Chagas disease, tuberculosis, and malaria, are used to compare different machine learning algorithms with ECFP6 fingerprints. This contribution aims to evaluate whether the Bernoulli Naïve Bayes, logistic linear regression, AdaBoost decision tree, random forest, support vector machine, and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC, F1-score, Cohen’s kappa, Matthews correlation coefficient, recall, precision, and accuracy. Based on the rank normalized score approach, the Hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets, showing significant improvement achieved by employing the Hyperopt library. The open-source code of all the 6 machine learning frameworks employed in the Hyperopt python package is provided to make this approach accessible to more scientists, who are not familiar with writing code.

β-lactam antibiotics in aquatic environment have severely damaged ecological stability and caused a series of environmental pollution problems to be solved urgently. Herein, a novel composite photocatalyst prepared by loading carbon dots (CDs) onto rod-like CoFe₂O₄ (CFO), which can effectively degrade amoxicillin (AMX) by photocatalytic/peroxymonosulfate (PMS) activation under visible light irradiation. The degradation results exhibits that the optimal degradation efficiency with 97.5% within 80 min is achievd by the CDs-CFO-5 composite. Such enhanced activity is ascribed to the introduction of CDs that effectively improves the separation efficiency of photogenerated electron pairs and creates new active sites as electron bridges that improve the photocatalytic performance. More importantly, a strong synergistic between CDs and photo-induced electrons generated from CFO can further activiate PMS to provide more SO₄^-· and ·OH radicals for boosting the degradation ability towards AMX. The present study aims to elucidate positive role of CDs in photocatalytic/peroxymonosulfate activation during the degradation reaction.

Surface modification of natural cellulose fibers with nanomaterials is an effective strategy for producing functional textiles for multiple applications. A4-sized printing paper is a commonly used, cheap, and easily acquirable office supply which is mainly made of cellulose fibers. Here, we report green and simple nanofabrication of A4 paper to endow it with high capability for fragrance encapsulation and sustained release, and strong adsorption to indoor air pollutants. The method utilizes the sugar molecule of cellulose for in-situ growth of γ-cyclodextrin (γ-CD) metal–organic frameworks (MOFs) on A4 paper. The obtained γ-CD-MOF/A4 nanocomposites have superior specific surface area and high porous structure. The γ-CD-MOF/A4 nanocomposites can effectively encapsulate fragrant molecules through host–guest interaction. The γ-CD-MOF/A4 nanocomposites also show strong absorption capability to formaldehyde and carbon dioxide through the formation of hydrogen bonding and chemical bonds. These γ-CD-MOF/A4 nanocomposites combine the advantages of both A4 paper and γ-CD-MOF, which can be used in indoor air freshening and cleaning.

Nowadays, efficient removal of antibiotic (e.g. tetracycline, TC) from water bodies has been of great global concern. Hereby, a novel Zn₂SnO₄/SnO₂@ZIF-8 photo-catalyst was first synthesized by a facile self-assembly in-situ growth method. Zn₂SnO₄/SnO₂@ZIF-8 exhibits a remarkable photocatalytic activity towards TC under visible-light driven with a rapid rate constant of 1.5×10^-2 min^-1, and a removal efficiency of 81.2%, which is superior to some catalysts in the literature. Importantly, the photocatalytic activity of Zn₂SnO₄/SnO₂@ZIF-8 could still remain ~77% after the fifth cycle, indicating its good stability and reusability. The remarkable performances of adsorption and photocatalytic were attributed to associative effects of catalytic activity of Zn₂SnO₄/SnO₂ and the unique porous nanostructure and stability of ZIF-8. This work could aid the future design and preparation of novel MOFs composite catalysts for efficient elimination of antibiotics from water.

The bare amorphous Al₂O₃-AlPO₄ and Cs/Al₂O₃-AlPO₄ catalysts were developed for the aldol condensation of methyl acetate with formaldehyde to methyl acrylate. The structure and property of catalyst were characterized by XRD, XPS, BET, Pyridine-IR, FT-IR, ²⁷Al-MASNMR, NH₃-/CO₂-TPD and SEM. The correlation between structural features and acid-base properties was established, and the loading effect of the cesium species was investigated. Due to cooperative catalytic effects between the penta-coordinated Al and Al₂O₃, the weak-II acid and medium acid site densities and the product selectivity were improved. While the basic site densities of these catalysts were almost in proportion to the conversion of methyl acetate. The loaded Cs could form new basic sites and change the distribution of acid sites which further enhance the catalytic performance. As a result, the 10Cs/8AlP was proved to be an optimal catalyst with the yield and selectivity of 21.2% and 85% for methyl acrylate respectively. During the reaction, a deactivation behavior was observed on 10Cs/8AlP catalyst due to the carbon deposition, however, it could be regenerated by thermal treatment in the air atmosphere at 400 ℃.

To better understand the benzene alkylation with chloroaluminate ionic liquids (ILs) as catalyst, the interfacial properties between the benzene/butene binary reactants and chloroaluminate ILs with varying cation alkyl chain length and different anions were investigated using molecular dynamics (MD) simulations. The results indicate that ILs can obviously improve the interfacial width, solubility and diffusion of reactants compared to H₂SO₄. The longer alkyl chains of cations present a density enrichment at the interface and protrude into the binary reactants phase. Furthermore, the ILs consisting of 1-octyl-3-methylimidazolium cations ([Omim]⁺) and the stronger acidity heptachlorodialuminate anions ([Al₂Cl₇]^-) are more beneficial to promote the interfacial width and facilitate the dissolution and diffusion of benzene in both the IL bulk and the interfacial region in comparison to the ones with shorter alkyl chains cations and weaker acidity anions. The information gives us a better guideline for the design of ILs for benzene alkylation.

Novel organo-inorganic hybrid materials (MTW-x-SO₃H) have been fabricated by immobilizing 3-mercaptopropyltriethoxysilane onto mesopore MTW zeolites, which is treated via a simple oxidation process with hydrogen peroxide as the oxidant to transform sulfhydryl group into sulfonic acid group. The organic sulfhydryl groups are covalently bonded to the external surface of MTW zeolites through the condensation between siloxane arising from organic fragments with silanol groups on the surface of MTW zeolites, the hybrids contain sulfonic acid group within the external surface of MTW zeolites and an opened mesoporous system in the matrix of MTW zeolites, which provide enough accessible Brønsted acid sites for the alkylation between phenol with tert-butyl alcohol. Through this methodology it’s possible to prepare multifunctional materials where the plenty of mesopores are benefit for the introduction of larger numbers of sulfonic acid groups that contributes to activity during reactions, resulting in high activity (>55%) of MTW-4-SO₃H and desired selectivity (>56%) of 2-TBP (2-tert-butyl phenol) in the alkylation between phenol with tert-butyl alcohol.

The oxygen distribution and evolution within the oxygen carrier exert significant influence on chemical looping processes. This paper describes the influence of oxygen bulk diffusion within FeVO₄ oxygen carrier pellets on the chemical looping oxidative propane dehydrogenation (CL-ODH). During CL-ODH, the oxygen concentration at the pellet surface initially decreased and then maintained stable before the final decrease. At the stage with the stable surface oxygen concentration, the reaction showed a stable C₃H₆ formation rate and high C₃H₆ selectivity. Therefore, based on Fick’s second law, the oxygen distribution and evolution in the oxygen carrier at this stage were further analyzed. It was found that main reactions of selective oxidation and over-oxidation were controlled by the oxygen bulk diffusion. C₃H₈ conversion rate kept decreasing during this stage due to the decrease of the oxygen flux caused by the decline of oxygen gradient within the oxygen carrier, while C₃H₆ selectivity increased due to the decrease of over-oxidation. In addition, reaction rates could increase with the propane partial pressure due to the increase of the oxygen gradient within the oxygen carrier until the bulk transfer reached its limit at higher propane partial pressure. This study provides fundamental insights for the diffusion-controlled chemical looping reactions.

The concept of “carbon neutrality” poses a huge challenge for chemical engineering and brings great opportunities for boosting the development of novel technologies to realize carbon offsetting and reduce carbon emissions. Developing high-efficient, low-cost, energy-efficient and eco-friendly microfluidic-based microchemical engineering is of great significance. Such kind of “green microfluidics” can reduce carbon emissions from the source of raw materials and facilitate controllable and intensified microchemical engineering processes, which represents the new power for the transformation and upgrading of chemical engineering industry. Here, a brief review of green microfluidics for achieving carbon neutral microchemical engineering is presented, with specific discussions about the characteristics and feasibility of applying green microfluidics in realizing carbon neutrality. Development of green microfluidic systems are categorized and reviewed, including the construction of microfluidic devices by bio-based substrate materials and by low carbon fabrication methods, and the use of more biocompatible and non-destructive fluidic systems such as aqueous two-phase systems (ATPSs). Moreover, low carbon applications benefit from green microfluidics are summarized, ranging from separation and purification of biomolecules, high-throughput screening of chemicals and drugs, rapid and cost-effective detections, to synthesis of fine chemicals and novel materials. Finally, challenges and perspectives for further advancing green microfluidics in microchemical engineering for carbon neutrality are proposed and discussed.

The membrane fouling phenomenon, reflected with various fouling characterization in the membrane bioreactor (MBR) process, is so complicated to distinguish. This paper proposes a multivariable identification model (MIM) based on a compacted cascade neural network to identify membrane fouling accurately. Firstly, a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network, which could avoid the interference of the overlap inputs. Secondly, an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM. Thirdly, a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online. Finally, the proposed model was tested in real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

Excellent mechanical properties are the prerequisite for the application of superhydrophobic polymer coatings. However, significantly improving the mechanical properties without affecting other properties such as hydrophobicity is a huge challenge. In this study, a superhydrophobic coating with excellent mechanical properties was prepared by spraying a mixture of polysiloxane resins based on three siloxane monomers, hexadecyltrimethoxysilane (HDTMS) modified nano-SiO₂ particles (SiO₂-HDTMS) and polystyrene-grafted halloysite nanotubes (HNTs-PS). SiO₂-HDTMS dispersed homogeneously in polysiloxane coatings and the water contact angle of corresponding coating exceeding 150°, achieving superhydrophobicity. The SiO₂-HDTMS/HNTs-PS/polysiloxane composite coatings showed excellent abrasion resistance with the water coating contact angle remaining above 150° after 90 abrasion cycles, indicating that HNTs-PS can significantly improve the mechanical properties of the coating without affecting the hydrophobic properties of the coating. The achieved coating also exhibited excellent antifouling and acid and alkali corrosion resistance. This work provides a convenient and ecologically friendly method to prepare superhydrophobic polysiloxane composite coating with excellent mechanical properties, which is promising in the application of anti-fouling, anti-corrosion, and oil–water separation etc.

Anthraquinone dyes are a class of typical carcinogenic and hard-biodegradable organic pollutants. This study aimed to enhance the decolorization of anthraquinone dye by rationally designing an expected immobilized system. Reactive blue 4 (RB4) was used as a substrate model and a previous isolated dye-degrading strain Aspergillus flavus A5p1 was purposefully immobilized. Considering the effects of cell attachment and mass transfer, the polyurethane foam (PUF) with open pore structure was selected as the immobilization carrier. Results showed that the RB4 decolorization efficiency was significant enhanced after immobilization. Compared to the free mycelium system, the decolorization time of 200 mg·L^-1 RB4 was shortened from 48 h to 28 h by the PUF-immobilized cell system. Moreover, the PUF-immobilized system could tolerate RB4 up to 2000 mg·L^-1. In the packed bed bioreactor (PBBR), an average decolorization efficiency of 93.3% could be maintained by the PUF-immobilized system for 26 days. The decolorization process of RB4 was well described by the logistic equation and the degradation pathway was discussed. It was found that the higher specific growth rate of the PUF-immobilized cells was one of reasons for the enhanced decolorization. The good performance of the PUF-immobilized cell system would make it have potential application value for RB4 bioremediation.