Peroxygenase-Enabled Reductive Kinetic Resolution for the Enantioenrichment of Organoperoxides.

Enantiomerically pure organoperoxides serve as valuable precursors in organic transformations. Herein, we present the first examples of unspecific peroxygenase catalyzed kinetic resolution of racemic organoperoxides through asymmetric reduction. Through meticulous investigation of the reaction conditions, it is shown that the unspecific peroxygenase from Agrocybe aegerita (AaeUPO) exhibits robust catalytic activity in the kinetic resolution reactions of the model substrate with turnover numbers up to 60000 and turnover frequency of 5.6 s-1. Various aralkyl organoperoxides were successfully resolved by AaeUPO, achieving excellent enantioselectivities (e.g., up to 99 % ee for the (S)-organoperoxide products). Additionally, we screened commercial peroxygenase variants to obtain the organoperoxides with complementary chirality, with one mutant yielding the (R)-products. While unspecific peroxygenases have been extensively demonstrated as a powerful oxidative catalysts, this study highlights their usefulness in catalyzing the reduction of organoperoxides and providing versatile chiral synthons.

Single atom solutions for carbon dioxide capture.

New solvents are considered to be one of the effective methods to facilitate the reaction rate and lower the reaction energy barrier. However, the common method to develop a new solvent has come to a dead end. Thus, a single atom in solvent to produce a single atom solution is designed to create the breakthrough. Eight kinds of single atom solutions are prepared as new absorbents. Experiments prove the single atom in the solutions and their charge-producing effects. A density functional theory model is developed to analyze the microscale characteristics. Meanwhile, it has been applied in carbon dioxide capture. The CO2 desorption rate is intensified in the single atom solution system due to the controlled reaction energy barrier. The results show that single atom solutions produce a maximum voltage of 2.12 V and, thus, contribute to near zero energy consumption by effectively harvesting the substantial waste heat below 373 K.

Ab initio study of metal carbide hydrides in the 2.25Cr1Mo0.25V steel.

2.25Cr1Mo0.25V is a state-of the-art alloy used in the fabrication of modern hydrogenation reactors. Compared to the conventional 2.25Cr1Mo steel, the 2.25Cr1Mo0.25V steel exhibits a better performance, in particular higher hydrogen damage resistance. Previous experimental studies indicate that carbides in steels may be responsible for the hydrogen-induced damage. To gain a better understanding of the mechanism of such damage, it is essential to study hydrogen uptake in metal carbides. In this study, Density Functional Theory (DFT) is used to investigate the stability of chromium, molybdenum and vanadium carbides (CrxCy, MoxCy and VxCy) in the 2.25Cr1Mo0.25V steel. The stability of their corresponding interstitial hydrides was also explored. The results showed that Cr7C3, Mo2C and V6C5 are the most stable carbides in their respective metal-carbon (Cr-C, Mo-C and V-C) binary systems. Specifically, V6C5 shows the strongest hydrogen absorption ability because of its strong V-H and C-H ionic bonds. On the other hand, V4C3, whose presence in the alloy was established in experimental studies, is predicted to be stable as well, along with V6C5. Our findings indicate that the hydrogen absorption ability of V4C3 is higher than that of V6C5. Additionally, the charge and chemical bonding analyses reveal that the stability of the metal carbide hydrides strongly depends on the electronegativity of the metal. Due to the high electronegativity of V, vanadium carbides form the strongest ionic bonds with hydrogen, compared to those of Mo and Cr. The results from this study suggest that the unique capacity of accommodating hydrogen in the vanadium carbides plays an important role in improved hydrogen damage resistance of the 2.25Cr1Mo0.25V alloy in hydrogenation reactors.

Role of native defects and the effects of metal additives on the kinetics of magnesium borohydride.

Magnesium borohydride (Mg(BH4)2) has been considered as a potential material for hydrogen storage. In this paper, density functional theory studies have been carried out on native point defects and electrically active impurities (Ni and Ti) in Mg(BH4)2. A detailed analysis of the geometrical structures, energetics and migration of the defects reveals that hydrogen related defects are charged and their formation energies are Fermi-level dependent. We propose a specific mechanism for the decomposition of Mg(BH4)2: the self-diffusion of Mgi2+ is the rate-limiting process for decomposing Mg(BH4)2. Moreover, Ni and Ti impurities can tailor the hydrogen desorption kinetics of Mg(BH4)2 by shifting the Fermi level, but the effects of impurities on shifting the Fermi levels are related to how the impurity is incorporated into Mg(BH4)2.

Heterogeneous Catalysts in N-Heterocycles and Aromatics as Liquid Organic Hydrogen Carriers (LOHCs): History, Present Status and Future.

The continuous decline of traditional fossil energy has cast the shadow of an energy crisis on human society. Hydrogen generated from renewable energy sources is considered as a promising energy carrier, which can effectively promote the energy transformation of traditional high-carbon fossil energy to low-carbon clean energy. Hydrogen storage technology plays a key role in realizing the application of hydrogen energy and liquid organic hydrogen carrier technology, with many advantages such as storing hydrogen efficiently and reversibly. High-performance and low-cost catalysts are the key to the large-scale application of liquid organic hydrogen carrier technology. In the past few decades, the catalyst field of organic liquid hydrogen carriers has continued to develop and has achieved some breakthroughs. In this review, we summarized recent significant progress in this field and discussed the optimization strategies of catalyst performance, including the properties of support and active metals, metal-support interaction and the combination and proportion of multi-metals. Moreover, the catalytic mechanism and future development direction were also discussed.

Characterization of Fatigue Crack Growth Based on Acoustic Emission Multi-Parameter Analysis.

In engineering structures that are subject to cyclic loading, monitoring and assessing fatigue crack growth (FCG) plays a crucial role in ensuring reliability. In this study, the acoustic emission (AE) technique was used to monitor the FCG behavior of 2.25Cr1Mo0.25V steel in real-time. Specifically, an AE multi-parameter analysis was conducted to qualitatively assess the crack growth condition and quantitatively correlate the crack growth rate with AE. Various AE parameters were extracted from AE signals, and the performances of different AE parameters were analyzed and discussed. The results demonstrated that four stages of FCG, which correspond to macrocrack initiation, stable crack growth with low crack growth rate, stable crack growth with high crack growth rate, and unstable crack growth, are distinctly identified by several AE time domain parameters. The sudden and continuous occurrence of many AE signals with high count (>100) and high energy (>40 mV·ms) can provide early and effective warning signs for accelerated crack growth before final failure occurs. Moreover, linear correlations between crack growth rate and different AE parameters are established for quantifying crack growth. Based on the AE multi-parameter analysis, it was found that the count, energy, and kurtosis are superior AE parameters for both qualitatively and quantitatively characterizing the FCG in 2.25Cr1Mo0.25V steel. Results from this research provide an AE strategy based on multi-parameter analysis for effective monitoring and assessment of FCG in engineering materials.

Research on Carbide Characteristics and Their Influence on the Properties of Welding Joints for 2.25Cr1Mo0.25V Steel.

The carbide characteristics of 2.25Cr1Mo0.25V steel have an extremely important influence on the mechanical properties of welding joints. In addition, hydrogen resistance behavior is crucial for steel applied in hydrogenation reactors. The carbide morphology was observed by scanning electron microscopy (SEM) and the carbide microstructure was characterized by transmission electron microscopy (TEM). Tensile and impact tests were carried out and the influence of carbides on properties was studied. A hydrogen diffusion test was carried out, and the hydrogen brittleness resistance of welding metal and base metal was studied by tensile testing of hydrogenated samples to evaluate the influence of hydrogen on the mechanical properties. The research results show that the strength of the welding metal was slightly higher and the Charpy impact value was significantly lower compared to the base metal. The hydrogen embrittlement resistance of the welding metal was stronger than that of the base metal. The presence of more carbides and inclusions was the main cause of the decreased impact property and hydrogen brittleness resistance of the welding metal. These conclusions have certain reference value for designing and manufacturing hydrogenation reactors.

Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm.

In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear Tikhonov-PSO regularization method. The regularization parameters were selected by L-curve method. The estimation results with different regularization matrixes showed that the confidence interval with high-order regularization matrix is narrower than that with zero-order regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear Tikhonov-PSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear Tikhonov-PSO method with transformed linear inverse model has higher computation efficiency than nonlinear Tikhonov-PSO method. The confidence intervals from linear Tikhonov-PSO are more reasonable than that from nonlinear method. The estimation results from linear Tikhonov-PSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by Tikhonov-PSO method. Therefore, the presented linear Tikhonov-PSO regularization method is a good potential method for hazardous emission source parameters identification.

Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere.

Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.