两个基于2,5-二甲氧基对苯二甲酸和咪唑衍生物的超分子钴配位聚合物的结构及光催化反应

本文以咪唑衍生物为配体,通过水热合成法与钴离子制备出两个配位聚合物:{[Co(DTA)(1,4-DIB)(H₂O)]·H₂O}_n(1)和[Co(DTA)(1,3-BMIB)]_n(2)(1,4-DIB=1,4-二(1H-咪唑-1-基)苯; 1,3-BMIB=1,3-二(4-甲基-1H-咪唑-1-基)苯;H₂DTA=2,5-二甲氧基对苯二甲酸)。利用X射线单晶衍射、粉末衍射、热失重、元素分析、红外光谱以及固体紫外-可见光谱等对两个配合物进行了表征。结构分析证实配合物1和2是通过二维结构堆积成的三维超分子化合物。粉末衍射测试则显示两个配合物在水中有很好的稳定性。固体紫外-可见光谱显示两个配合物属半导体材料,对紫外-可见光有很强的吸收作用。在光催化实验中,配合物1和2可加快亚甲基蓝的降解速度。     

A convenient synthesis of 3-aryl-5-methylidene-2-thiohydantoins

3-Aryl-5-methylidene-2-thiohydantoins were constructed in one-pot reaction of aryl isothiocyanates and 3-morpholino- alanine in alkaline medium with the subsequent treatment with boiling hydrochloric acid.     

Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

Periodicity of Two-Dimensional Bonding of Main Group Elements

In the periodic table the position of each atom follows the ‘aufbau’ principle of the individual electron shells. The resulting intrinsic periodicity of atomic properties determines the overall behavior of atoms in two-dimensional (2D) bonding and structure formation. Insight into the type and strength of bonding is the key in the discovery of innovative 2D materials. The primary features of 2D bonding and the ensuing monolayer structures of the main-group II–VI elements result from the number of valence electrons and the change of atom size, which determine the type of hybridization. The results reveal the tight connection between strength of bonding and bond length in 2D networks. The predictive power of the periodic table reveals general rules of bonding, the bonding-structure relationship, and allows an assessment of published data of 2D materials.     

High-fidelity Recognition of Organotrifluoroborate Anions (R−BF3−) as Designer Guest Molecules

The recognition of boron compounds is well developed as boronic acids but untapped as organotrifluoroborate anions (R−BF₃⁻). We are exploring the development of these and other designer anions as anion-recognition motifs by considering them as substituted versions of the parent inorganic ion. To this end, we demonstrate strong and reliable binding of organic trifluoroborates, R−BF₃⁻, by cyanostar macrocycles that are size-complementary to the inorganic BF₄⁻ progenitors. We find that recognition is modulated by the substituent's sterics and that the affinities are retained using the common K⁺ salts of R−BF₃⁻ anions.     

Rh(III)-Catalyzed One-Step Synthesis of ortho-Alkynylated Perylene Imide Dyes: Optical and Electrochemical Properties of New Derivatives

A one-step Rh-catalyzed site-selective ortho-C−H alkynylation of perylene as well as naphthalene mono- and diimides is reported. A single step regioselective access to ortho-C−H alkynylated derivatives of these ryleneimides not only increases the step economy of the ortho-functionalization on these dyes but also provides a quick access route towards highly functionalized dyes that have potential optoelectronic applications. Increased solubility of tetra(triisopropylsilyl)acetylenyl PDIs in organic solvents greatly enhances their utility for further derivatization.