The separation of racemic molecules is of substantial significance not only for basic science but also for technical applications, such as fine chemicals and drug development. Here we report two ...isostructural chiral metal-organic frameworks decorated with chiral dihydroxy or -methoxy auxiliares from enantiopure tetracarboxylate-bridging ligands of 1,1'-biphenol and a manganese carboxylate chain. The framework bearing dihydroxy groups functions as a solid-state host capable of adsorbing and separating mixtures of a range of chiral aromatic and aliphatic amines, with high enantioselectivity. The host material can be readily recycled and reused without any apparent loss of performance. The utility of the present adsorption separation is demonstrated in the large-scale resolution of racemic 1-phenylethylamine. Control experiments and molecular simulations suggest that the chiral recognition and separation are attributed to the different orientations and specific binding energies of the enantiomers in the microenvironment of the framework.
Two chiral carboxylic acid functionalized micro‐ and mesoporous metal–organic frameworks (MOFs) are constructed by the stepwise assembly of triple‐stranded heptametallic helicates with six carboxylic ...acid groups. The mesoporous MOF with permanent porosity functions as a host for encapsulation of an enantiopure organic amine catalyst by combining carboxylic acids and chiral amines in situ through acid–base interactions. The organocatalyst‐loaded framework is shown to be an efficient and recyclable heterogeneous catalyst for the asymmetric direct aldol reactions with significantly enhanced stereoselectivity in relative to the homogeneous organocatalyst.
Two chiral carboxylic acid functionalized micro‐ and mesoporous metal–organic frameworks (MOFs) are constructed. The mesoporous MOF functions as a host for encapsulation of an enantiopure organic amine by acid–base interactions. The organocatalyst‐loaded MOF is an efficient and recyclable heterogeneous catalyst for asymmetric direct aldol reactions, with significantly enhanced stereoselectivity relative to the homogeneous organocatalyst.
A chiral porous metal-organic framework of an axially C2-symmetric 1,1'-biphenol ligand is constructed and can be used as a solid-state host to enanioselectively adsorb mandelates with up to 93.1% ee ...and to entrap achiral tropolone ethers and induce their asymmetric photocyclization with up to 98.5% ee.
Apixaban is a highly potent, selective, and efficacious inhibitor of blood coagulation factor Xa. A practical and efficient process has been developed for the preparation of the key intermediate of ...apixaban. Starting from inexpensive 4-chloronitrobenzene and piperidine, an eight-step procedure for the intermediate has been developed. In this case, sodium chlorite is used twice to oxidize the piperidine cycle to the corresponding lactam under a CO
2
atmosphere, resulting in the construction of two lactams. Furthermore, most of these reactions are highly efficient and practical as they occur under mild conditions. Most of the intermediates can be obtained through simple slurry or recrystallization, and column chromatography purification is not necessary.
Graphical abstract
The utilization efficiency of land resources in the coastal area of the Yellow River Delta has been deeply affected by salinization hazards. Key to improvement of the utilization efficiency of ...resources in this area is to grasp the spatio–temporal variability law of soil salinity and identify the driving factors of salinization. Wudi County in the coastal area of the Yellow River Delta is taken as the study area. Based on the data obtained from field measurements and laboratory analysis, the characteristics of soil salinity in spring and summer were analyzed by classical statistical methods; the spatial differentiation characteristics of salinization were analyzed from two–dimensional and three–dimensional perspectives using the geographic information system (GIS) and groundwater modeling system (GMS); the time variation characteristics of salinization were quantitatively analyzed by introducing the salinization severity index (Si) and the dominant index of salinization degree change (Ci). The results show that: (1) In the study area, the soil salinity of the surface layer (0–15 cm) in summer is lower than that in spring, but the sub–surface layer (15–30 cm), the middle layer (30–45 cm) and the bottom layer (45–60 cm) are all larger than the corresponding layers in spring, and the correlation between the soil salinity of each layer in summer is generally lower than that in spring. (2) In two–dimensional space, the areas with a surface soil salinity greater than 0.4% in both seasons are mainly located in the northern part of the study area; in three–dimensional space, the soil is mainly moderately salinized in both seasons, and the complexity of the distribution of the salt profile is higher in summer than in spring; (3) Mashanzi Town was the area most seriously affected by salinization in both seasons (Si values were greater than three); In the process of seasonal alternation, the dominant change type of salinized soil is from mild aggravation to moderate, with Ci value of 38.43%, followed by severe alleviation to moderate, with Ci value of 35.49%; (4) The driving factors of soil salinization in spring are mainly the soil salinity of the subsurface and middle layer, and soil water content; and in summer, mainly the soil salinity of subsurface layer, vegetation coverage and vegetation cover type. The interaction between any two factors has greater influence on the spatial variation of salinization than the corresponding single factor.
Cross-domain scene classification refers to the scene classification task in which the training set (termed source domain) and the test set (termed target domain) come from different distributions. ...Various domain adaptation methods have been developed to reduce the distribution discrepancy between different domains. However, current domain adaptation methods assume that the source domain and target domain share the same categories. In reality, it is hard to find a source domain that can completely cover all the categories of target domain. In this article, we propose to use multiple complementary source domains to form the categories of target domain. A multisource compensation network (MSCN) is proposed to tackle these challenges: distribution discrepancy and category incompleteness. First, a pretrained convolutional neural network (CNN) is exploited to learn the feature representation for each domain. Second, a cross-domain alignment module is developed to reduce the domain shift between source and target domains. Domain shift is reduced by mapping the two domain features into a common feature space. Finally, a classifier complement module is proposed to align categories in multiple sources and learn a target classifier. Two cross-domain classification data sets are constructed using four heterogeneous remote sensing scene classification data sets. Extensive experiments are conducted on these datasets to validate the effectiveness of the proposed method. The proposed method can achieve 81.23% and 81.97% average accuracies on two-source-complementary data set and three-source-complementary data set, respectively.
A method based on ultra high performance liquid chromatography-electrostatic field orbitrap high resolution mass spectrometry (UHPLC-Orbitrap HRMS) was established for the determination of genotoxic ...impurities 2, 6, and 12 in nifedipine. After extraction with methanol, the sample was injected into the UHPLC-Orbitrap HRMS system for analysis. An ACE EXCEL
3 C18-AR column (150 mm×4.6 mm, 3 μm) was used for chromatographic separation. The mobile phase was methanol-0.1% formic acid aqueous solution (65∶35, v/v). The flow rate was 0.6 mL/min, while the column temperature and autosampler temperature were set as 35 ℃ and 8 ℃, respectively. The divert valve switching technique was used to protect the mass spectrometer. The six-way valve was set to divert the eluent of 7.5-11.6 min to waste and the rest of the eluent into the mass spectrometer. The Orbitrap mass spectrometer was coupled with the UHPLC system by an electrospray ion (ESI) source. The sheath gas and auxiliary gas flow rates were 60 and 20 arb (arbitrary units), respectively. The spray voltage was 3.5 kV, while the capillary temperature and auxiliary gas heater temperature were set as 350 ℃ and 400 ℃, respectively. The positive ion parallel reaction monitoring (PRM) scanning mode was adopted, and the mass spectral resolution was set to 35000 FWHM. The accurate masses of the M+H
precursor ions of impurities 2, 6, and 12 were m/z 347.1230, 361.1026, and 347.1230, respectively. The accurate masses of the extracted M+H
fragment ions of impurities 2, 6, and 12 were m/z 315.0968, 298.1069, and 315.0968, respectively. The normalized collision energies (NCEs) were optimized to 10%, 42%, and 10% for impurities 2, 6, and 12, respectively. The external standard method was utilized for quantitative analysis. The established method was validated in detail by investigating the specificity, linear range, limit of detection (LOD), limit of quantification (LOQ), recovery, precision, and stability. This method had good specificity, and the solvent did not interfere with the determination of impurities. The peak areas of impurities 2, 6, and 12 as well as their concentrations showed good linear relationships in the ranges of 0.2-100 ng/mL, with all correlation coefficients (r)≥0.9998. The recoveries of impurities 2, 6, and 12 at three levels (low, medium, and high) were in the range of 96.9%-105.0%, while the RSDs were between 1.21% and 5.12%. The LODs were 0.05 ng/mL and the LOQs were 0.2 ng/mL for all three impurities. This analytical method was used to determine impurities 2, 6, and 12 in three batches of nifedipine samples. Impurity 6 was not detected in the three batches, but impurities 2 and 12 were detected in all the three samples, and the detection amount was within the limit. The developed method is sensitive, fast, accurate, and easy to operate. It can provide a reference for the quality control of nifedipine by pharmaceutical companies and extend strong technical support for the supervision by drug regulatory authorities.
Remote sensing images contain a wealth of spatial information. Efficient scene classification is a necessary precedent step for further application. Despite the great practical value, the mainstream ...methods using deep convolutional neural networks (CNNs) are generally pretrained on other large datasets (such as ImageNet) and thus fail to capture the specific visual characteristics of remote sensing images. For another, it lacks the generalization ability to new tasks when training a new CNN from scratch with an existing remote sensing dataset. This article addresses the dilemma and uses multiple small-scale datasets to learn a generalized model for efficient scene classification. Since the existing datasets are heterogeneous and cannot be directly combined to train a network, a multitask learning network (MTLN) is developed. The MTLN treats each small-scale dataset as an individual task and uses complementary information contained in multiple tasks to improve generalization. Concretely, the MTLN consists of a shared branch for all tasks and multiple task-specific branches with each for one task. The shared branch extracts shared features for all tasks to achieve information sharing among tasks. The task-specific branch distills the shared features into task-specific features toward the optimal estimation of each specific task. By jointly learning shared features and task-specific features, the MTLN maintains both generalization and discrimination abilities. Two types of MTL scenarios are explored to validate the effectiveness of the proposed method: one is to complete multiple scene classification tasks and the other is to jointly perform scene classification and semantic segmentation.
Cross-domain scene classification identifies scene categories by learning knowledge from a labeled data set (source domain) to an unlabeled data set (target domain), where the source data and the ...target data are sampled from different distributions. A lot of domain adaptation methods are used to reduce the distribution shift across domains, and most existing methods assume that the source domain shares the same categories with the target domain. It is usually hard to find a source domain that covers all categories in the target domain. Some works exploit multiple incomplete source domains to cover the target domain. However, in such setting, the categories of each source domain are a subset of the target-domain categories, and the target domain contains "unknown" categories for each source domain. The existence of unknown categories results in the conventional domain adaptation unsuitable. Known and unknown categories should be treated separately. Therefore, a separation mechanism is proposed to separate the known and unknown categories in this article. First, multiple-source classifiers trained on the multiple source domains are used to coarsely separate the known/unknown categories in the target domain. The target images with high similarities to source images are selected as known categories, and the target images with low similarities are selected as unknown categories. Then, a binary classifier trained using the selected images is used to finely separate all target-domain images. Finally, only the known categories are implemented in the cross-domain alignment and classification. The target images get labels by integrating the hypotheses of multiple-source classifiers on the known categories. Experiments are conducted on three cross-domain data sets to demonstrate the effectiveness of the proposed method.
PDF
