The enthalpies of vaporization and intermolecular interaction energies of 1-chloroalkanes

The enthalpies of vaporization of 1-chloroalkanes containing n = 3–20 carbon atoms were calculated from saturated vapor pressure data. It was shown that the enthalpies of vaporization of 1-chloroalkanes could be calculated on the basis of model concepts of the structure of liquids from experimental liquid density and sound velocity data.     

On Lorentz invariant distributions

n-point Lorentz invariant tempered distributions with the supports for one-point only in are described.     

Butterfly and Nest-Shaped Tp*-W-Cu-S Cluster Monomers and Dimers with Hexamethylenetetramine as Ligand: Anion-Dependent Structures and Nonlinear Optical Properties

The assembly of[Et4N][Tp*WS3](1) with CuX (Tp* =hydridotris(3,5-dimethylpyrazol-1-yl)borate;X =Cl,Br,and I) or[Cu(MeCN)4][PF6]in the presence of tetratopic liga...     

金属组学在急性心肌梗死早期诊断的应用

为探讨急性心肌梗死(AMI)患者血清中K⁺、Na⁺、Ca²⁺、Fe²⁺、Mg²⁺含量变化,并研究其与心肌梗死患者之间的关系。选取2022年5月至2023年2月收治的AMI患者37例,同时选取健康体检者35例作为对照组。依据入院时或体检时收集的抽血样本进行临床生化分析,比较两组间血清K⁺、Na⁺、Ca²⁺、Fe²⁺、Mg²⁺含量,采用判别方程、主成分分析法(PCA),判断分析哪种金属离子对于心肌梗死的诊断价值大。结果表明,AMI患者的血清中Ca²⁺和Fe²⁺含量低于健康对照组,差异具有统计学意义。基于血钙、铁水平两组具有显著性差异,以它们为基础进行判别分析,获得判别函数式。将血清中K⁺、Na⁺、Ca²⁺、Fe²⁺、Mg²⁺     

锗元素测定方法综述

锗在国防工业、航空航天和通信等领域中的战略性,锗含量的测定对于保证材料质量和满足国际标准至关重要。本文综述了锗含量测定方法的多种技术,包括分光光度法、原子荧光光谱法、原子吸收光谱法、电感耦合等离子体原子发射光谱法、电感耦合等离子体质谱法以及滴定法。在每个检测方法的介绍中,详细探讨了方法的原理、前处理步骤以及应用范围,并分别总结了各个方法的优势和不足。最后,强调了锗含量测定方法的意义,特别是在满足出口监管和促进科学研究方面的作用。同时对锗元素的测定方法进行了展望,为未来的发展提供了参考方向。     

A general electron donor–acceptor complex for photoactivation of arenes via thianthrenation

General photoactivation of electron donor–acceptor (EDA) complexes between arylsulfonium salts and 1,4-diazabicyclo[2.2.2]octane with visible light or natural sunlight was discovered. This practical and efficient mode enables the production of aryl radicals under mild conditions, providing an unrealized opportunity for two-step para-selective C–H functionalization of complex arenes. The novel mode for generating aryl radicals via an EDA complex was well supported by UV-vis absorbance measurements, nuclear magnetic resonance titration experiments, and density functional theory (DFT) calculations. The method was applied to the regio- and stereo-selective arylation of various N-heterocycles under mild conditions, yielding an assembly of challengingly linked heteroaryl–(hetero)aryl products. Remarkably, the meaningful couplings of bioactive molecules with structurally complex drugs or agricultural pharmaceuticals were achieved to display favorable in vitro antitumor activities, which will be of great value in academia or industry.

General photoactivation of EDA complexes between arylsulfonium salts and 1,4-diazabicyclo[2.2.2]octane was discovered. This practical mode enables the generation of aryl radicals for C–H functionalization of arenes.     

Investigation of InxGa1−xAs films grown from thin solution layer

In_xGa_1−xAs films with x⩽0.12 were grown from a thin solution layer between substrates. The calculation of final film thickness as a function of liquid phase composition, based on supposition of film deposition only on the substrate, is in a good agreement with experimental results. The dependences between compositions of liquid and solid phases at 800°, 750° and 700°C were determined. The morphology of the film surface was investigated as a function of liquid phase composition and (100), (111) A, (111) B substrate orientations. Dislocation density increases from 10⁴ cm⁻² to 10⁷ cm⁻² with change of x from 0 to 0.12.     

Why heterogeneous single-atom catalysts preferentially produce CO in the electrochemical CO2 reduction reaction

Formate and CO are competing products in the two-electron CO₂ reduction reaction (2e CO₂RR), and they are produced via *OCHO and *COOH intermediates, respectively. However, the factors governing CO/formate selectivity remain elusive, especially for metal–carbon–nitrogen (M–N–C) single-atom catalysts (SACs), most of which produce CO as their main product. Herein, we show computationally that the selectivity of M–N–C SACs is intrinsically associated with the CO₂ adsorption mode by using bismuth (Bi) nanosheets and the Bi–N–C SAC as model catalysts. According to our results, the Bi–N–C SAC exhibits a strong thermodynamic preference toward *OCHO, but under working potentials, CO₂ is preferentially chemisorbed first due to a charge accumulation effect, and subsequent protonation of chemisorbed CO₂ to *COOH is kinetically much more favorable than formation of *OCHO. Consequently, the Bi–N–C SAC preferentially produces CO rather than formate. In contrast, the physisorption preference of CO₂ on Bi nanosheets contributes to high formate selectivity. Remarkably, this CO₂ adsorption-based mechanism also applies to other typical M–N–C SACs. This work not only resolves a long-standing puzzle in M–N–C SACs, but also presents simple, solid criteria (i.e., CO₂ adsorption modes) for indicating CO/formate selectivity, which help strategic development of high-performance CO₂RR catalysts.

This report discloses a nontrivial role of the CO₂ adsorption mode in governing the CO/formate selectivity of single-atom catalysts towards two-electron CO₂ reduction.     

Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals

In recent decades, emotion recognition has received considerable attention. As more enthusiasm has shifted to the physiological pattern, a wide range of elaborate physiological emotion data features come up and are combined with various classifying models to detect one’s emotional states. To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA) architecture with unsupervised pre-training, followed by supervised fine-tuning. In this paper, we compare the performances of different features and models through three binary classification tasks based on the Valence-Arousal-Dominance (VAD) affection model. Decision fusion and feature fusion of electroencephalogram (EEG) and peripheral signals are performed on hand-engineered features; data-level fusion is performed on deep-learning methods. It turns out that the fusion data perform better than the two modalities. To take advantage of deep-learning algorithms, we augment the original data and feed it directly into our training model. We use two deep architectures and another generative stacked semi-supervised architecture as references for comparison to test the method’s practical effects. The results reveal that our scheme slightly outperforms the other three deep feature extractors and surpasses the state-of-the-art of hand-engineered features.