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•CumNin/CoOx was used for oleic acid hydrodeoxygenation with isopropanol.•100% conversion and 91.3% heptadecane yield were obtained at 240 °C for 8 h.•Strong synergy between Cu, Ni ...and CoOx favored H2 production ability.•The rate determining step relied on octadecanol dehydrogenation.•Oleic acid hydrodeoxygenation occurred via decarbonylation of stearaldehyde.
Bifunctional CumNin/CoOx catalysts were fabricated by co-precipitation method and applied in in-situ hydrodeoxygenation (HDO) of biomass derived oleic acid to n-heptadecane with isopropanol as hydrogen source. Upon incorporating Cu or Ni into CoOx, the catalytic performance could be easily boosted. By varying Cu/Ni ratio, reduction temperature as well as catalyst loading, the optimized Cu1Ni1/CoOx exhibited >99% conversion and 91.3% n-heptadecane yield at 240 °C in 8 h. Characterizations showed that synergistic effect between Cu, Ni and CoOx was existed, thus strengthening H2 production ability towards isopropanol and favoring the generation of n-heptadecane. Kinetic studies revealed HDO of oleic acid to alkane mainly proceeded via decarbonylation of stearaldehyde rather than direct decarboxylation of stearic acid, and the rate determining step relied on octadecanol dehydrogenation. This catalyst could be magnetically separated for 5 times recycling and was versatile for in situ HDO of various fatty acids, thus achieving a low-cost, energy-saving biomass-to-biofuel conversion.
•A crystal graph convolutional neural network for classifying metal-organic frameworks.•The model exhibits a high prediction accuracy for Identifying hydrogen storage materials.•It can be used to ...classify unseen MOFs for hydrogen storage with high transferability.•The adsorption mechanism of top-performing MOFs for hydrogen storage is elucidated.
Metal-organic frameworks (MOFs) have been considered as promising physical adsorbents for hydrogen storage due to their high porosity and structural tunability. We selected 7643 real MOFs from the computation-ready MOF 2019 database to screen high-performance materials for hydrogen storage based on the grand canonical Monte Carlo (GCMC) simulations. Based on the obtained data set, we proposed a deep learning classification model powered by the crystal graph convolutional neural networks (CGCNN) for the discovery of the optimal hydrogen storage MOFs. It is demonstrated that our classification model based on CGCNN algorithms exhibits a high prediction accuracy with the area under the ROC-plot curve (AUC) of 0.9208 for hydrogen storage performance of volumetric deliverable capacity (VDC). Compared with the classification models based on other machine learning algorithms such as random forest, our CGCNN model demonstrates the advantages of fast prediction with no feature extraction and little accuracy loss. When the trained CGCNN model was used to predict the classification of the unfamiliar samples (the randomly selected 1000 MOFs from the 137,953 hypothetical MOF database), we also obtain high accuracy with AUC > 0.81, indicating that this model exhibits reliable transferability for other types of MOFs. Meanwhile, we also elucidated the relationship between structure and performance of MOFs for hydrogen storage using the decision tree algorithms and quantitative structure-property analysis. Furthermore, the hydrogen adsorption performance and mechanism of top-performance MOFs were analyzed by adsorption isotherms, radial distribution functions, and mass center density distribution of equilibrium configurations. All those insights from atomic simulations and machine learnings can accelerate the discovery of new nanoporous materials not only for gas adsorption in MOFs but also for gas separation in other types of porous materials.
A systematic study on microwave-assisted oxidative degradation of lignin model compounds, such as 2-phenoxy-1-phenylethanol, vanillyl alcohol, and 4-hydroxybenzyl alcohol, was performed by evaluating ...the catalytic activity of 14 types of metal salts. The acidity of each metal salt solution for the oxidative degradation of 2-phenoxy-1-phenylethanol, vanillyl alcohol, and 4-hydroxybenzyl alcohol under the microwave irradiation and conventional heating conditions was measured and compared. The results showed that CrCl3 and MnCl2 were the most effective for the degradation of the lignin model compounds. The acidity of metal salt is in favor of the catalytic activity for the degradation of 2-phenoxy-1-phenylethanol, vanillyl alcohol, and 4-hydroxybenzyl alcohol, and microwave irradiation is able to accelerate the degradation rate in a large scale. The possible mechanisms for the degradation of 2-phenoxy-1-phenylethanol, vanillyl alcohol, and 4-hydroxybenzyl alcohol are proposed on the basis of the product distributions.
A high performance liquid chromatographic method was developed for the separation and determination of centellasaponin A and asiaticoside isomers in Centella asiatica products. The method comprises ...separation by reversed phase chromatography on a Synergi 4μ Hydro-RP 80A reversed-phase column using acetonitrile-water (21:79, v/v) as mobile phase. Centellasaponin A and asiaticoside were separated with high resolution by the developed method. The method's linearity, precision, and recovery were validated. The effects of mobile phase composition, temperature, and flow rate on the separation of the two isomers were also investigated. Experimental results indicated that acetonitrile-water as the mobile phase offered enhancing resolution to the polar isomers. Furthermore, it was observed that the sequence of centellasaponin A and asiaticoside was reversed in peak consequence by using methanol-water as the mobile phase compared to acetonitrile-water, which could be used for the separation of other isomeric triterpenoid saponins.
Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Liquid Chromatography & Related Technologies to view the free supplemental file.
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•Ni/MgxAlyO(x+1.5y) possessed larger surface areas than those of Ni/MgO and Ni/Al2O3.•Ni/MgxAlyO(x+1.5y) were more active for LA hydrogenation than Ni/MgO and Ni/Al2O3.•Yield of GVL ...at 160°C, 1h and 3MPa H2 reached 99.7% over Ni/MgAlO2.5.•Ni/MgAlO2.5 could be recycled without obvious loss of its initial activity.
Mixed MgO–Al2O3 (with different Mg/Al ratio) supported nickel catalysts were prepared via co-precipitation method and used for hydrogenation of levulinic acid to γ-valerolactone under mild condition. Characterization results indicated that mixed MgO–Al2O3 supported Ni catalysts possessed bigger surface area than that of Ni/MgO and Ni/Al2O3, and Ni dispersed highly on the surface of mixed MgO–Al2O3 support. It was found that mixed MgO–Al2O3 supported Ni catalysts were more active and selective for the hydrogenation of levulinic acid to γ-valerolactone than that of Ni/MgO and Ni/Al2O3, and the best yield of γ-valerolactone at 160°C, 1h and 3MPa H2 reached 99.7% over Ni/MgAlO2.5, and Ni/Mg2Al2O5 could be recycled without obvious loss of its initial activity.
•Methane adsorption isotherms of 17,644 MOFs were calculated by cDFT.•A framework based on neural network (NN) synergistic with cDFT was proposed.•The framework can rapidly predict methane adsorption ...isotherms.•Several strategies were discussed to improve the prediction accuracy.
A rapid method for predicting the unary gas adsorption isotherms of metal–organic frameworks (MOFs) is of great significance for the engineering design of both gas storage and gas separation. Here, we proposed a framework based on neural network (NN) synergistic with classical density functional theory (cDFT) that can be used to accurately predict methane adsorption isotherms of MOFs on a large scale. First, 17,644 candidate MOFs with orthorhombic unit cells were chosen from 324,426 hypothetical structures, and their methane adsorption isotherms were calculated by cDFT. Furthermore, we obtained the isotherm parameters for each MOF by fitting the isotherms with Langmuir equation. Second, the geometrical and chemical properties of all candidates were then calculated to construct the fingerprint descriptors of MOFs. These descriptors and isotherm parameters were used as inputs and targets, respectively, to train a deep neural network model. The trained neural network model can rapidly predict the isotherm parameters and further calculate the methane adsorption isotherms of the MOFs. During the model training, we proposed a physical constraint method embedded in the loss function to make the model better approximate the target isotherm parameters. Finally, we discussed several strategies to improve the prediction accuracy of the neural network.
We report herein the first kinetics studies of hydrothermal decarboxylation of a fully halogenated benzoic acid and a heterocyclic aromatic diacid. Decarboxylation was the only reaction path ...observed, and there was no evidence of dehalogenation. Experiments at different initial reactant concentrations and different batch holding times revealed that both compounds exhibited first-order kinetics. Experiments at different temperatures showed that the first-order rate constants displayed Arrhenius behavior with activation energies of 157 kJ mol−1 for pentafluorobenzoic acid decarboxylation and 141 kJ mol−1 for quinolinic acid decarboxylation.
A new series of imidazol-2-one derivatives were prepared and investigated as anticancer agents.
A new series of aryl substituted imidazol-2-one derivatives structurally related to combretastatin A-4 ...(CA-4) were synthesized and evaluated for their cytotoxic activities in vitro against various human cancer cell lines including MDR cell line. The cytotoxic effects of compounds
7b and
7i proved to be similar to or greater than that of docetaxel. The highly active compound
7b also exhibited excellent inhibitory activity on tumor growth in vivo.
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As efficient drug carriers, stimuli-responsive mesoporous silica nanoparticles are at the forefront of research on drug delivery systems. An acid-responsive system based on silyl ...ether has been applied to deliver a hybrid prodrug. Thiol-ene click chemistry has been successfully utilized for tethering this prodrug to mesoporous silica nanoparticles. Here, by altering the steric bulk of the substituent on the silicon atom, the release rate of a model drug, camptothecin, was controlled. The synthesized drug delivery system was investigated by analytical methods to confirm the functionalization and conjugation of the mesoporous silica nanoparticles. Herein, trimethyl silyl ether and triethyl silyl ether were selected to regulate the release rate. Under normal plasma conditions (pH 7.4), both types of camptothecin-loaded mesoporous silica nanoparticles (i.e., MSN-Me-CPT and MSN-Et-CPT) did not release the model drug. However, under in vitro acidic conditions (pH 4.0), based on a comparison of the release rates, camptothecin was released from MSN-Me-CPT more rapidly than from MSN-Et-CPT. To determine the biocompatibility of the modified mesoporous silica nanoparticles and the in vivo camptothecin uptake behavior, MTT assays with cancer cells and confocal microscopy observations were conducted, with positive results. These functionalized nanoparticles could be useful in clinical treatments requiring controlled drug release.
As the release rate of drug from drug-carrier plays important role in therapy effects, trimethyl silyl ether (TMS) and triethyl silyl ether (TES) were selected as acid-sensitive silanes to control the release rates of model drugs conjugated from MSNs by thiol-ene click chemistry. The kinetic profiles of TMS and TES materials have been studied. At pH 4.0, the release of camptothecin from MSN-Et-CPT occurred after 2h, whereas MSN-Me-CPT showed immediate drug release. The results showed that silyl ether could be used to control release rates of drugs from MSNs under acid environment, which could be useful in clinical treatments requiring controlled drug release.
The distillation of azeotropic mixtures is commonly and widely performed in the pharmaceutical, petroleum, and chemical industries. Deep eutectic solvents (DESs) are environmentally-friendly ...entrainers that have many properties similar to ionic liquids (ILs) but are also simple in preparation and cheap in price. Also, the ethanol/water system is a typical industrial azeotropic mixture. In this work, the relative volatility of ethanol and water at the azeotropic point was increased from 1.00 to 4.70 with 0–51.0 mass % ChCl/urea (1:2, mol/mol), with ChCl/urea showing a remarkable entrainer performance in this separation. Isobaric vapor–liquid equilibrium (VLE) data of four systems, water + ethanol, water + ChCl/urea, ethanol + ChCl/urea, and water + ethanol + ChCl/urea (at 10, 20, and 30 mass%), were determined using a modified Othmer equilibrium still at 101.32 kPa. After addition of ChCl/urea, the ethanol + water mixture’s azeotropic point was eliminated. The parameters of the nonrandom two-liquid (NRTL) model for these systems were calculated and the predicted values for these systems were found to fit the experimental VLE data quite well.
Effect of choline chloride (ChCl)/urea (1:2) on the vapor–liquid equilibria of ethanol(1) + water(2) at 101.32 kPa. Display omitted
•The relative volatility of ethanol/water at azeotropic point was increased from 1.00 to 4.70.•Isobaric VLE data containing ChCl/urea were measured.•The parameters of NRTL for pseudobinary and pseudoternary systems were obtained.
