Novel alternating copolyoxamides with high crystallinity and heat resistance

Two series of novel alternating copolyoxamides (PAnT-alt-n2 and PAn2-alt-62) are synthesized via solution/solid-state polycondensation (SSP). The alternating structures are analyzed carefully with ¹H NMR and ¹³C NMR spectra. The melting behaviors, thermal stabilities, crystal structures and crystallinities are systematically evaluated by DSC, TGA and WAXD. The results reveal that these alternating copolyoxamides possess almost perfect alternating chain structures and have high melting temperature (T_m > 270 °C), high crystallinity (X_c > 32%) and high decomposition temperature (T₅ > 405 °C) as well as low saturated water absorption (<3.5 wt%), which suggests that they have high potential as engineering plastic of high heat resistant.     

Electro-assisted Molecular Assembly Giving Atomic-Scale Catalytic Active-Site Detection

The artificially accurate design of nonmetal electrocatalysts’ active site has been a huge challenge because no pure active species with the specific structure could be strictly controlled by traditional synthetic methods. Species with a multiconfiguration in the catalyst hinder identification of the active site and the subsequent comprehension of the reaction mechanism. We have developed a novel electro-assisted molecular assembly strategy to obtain a pure pentagon ring on perfect graphene avoiding other reconstructed structures. More importantly, the active atom was confirmed by the subtle passivation process as the topmost carbon atom. Recognition of the carbon-defect electrocatalysis reaction mechanism was first downsized to the single-atom scale from the experimental perspective. It is expected that this innovative electro-assisted molecular assembly strategy could be extensively applied in the active structure-controlled synthesis of nonmetal electrocatalysts and verification of the exact active atom.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.     

Flow-induced shear stress and deformation of a core–shell-structured microcapsule in a microchannel

A numerical model was developed and validated to investigate the fluid–structure interactions between fully developed pipe flow and core–shell-structured microcapsule in a microchannel. Different flow rates and microcapsule shell thicknesses were considered. A sixth-order rotational symmetric distribution of von Mises stress over the microcapsule shell can be observed on the microcapsule with a thinner shell configuration, especially at higher flow rate conditions. It is also observed that when being carried along in a fully developed pipe flow, the microcapsule with a thinner shell tends to accumulate stress at a higher rate compared to that with a thicker shell. In general, for the same microcapsule configuration, higher flow velocity would induce a higher stress level over the microcapsule shell. The deformation gradient was used to capture the microcapsule's deformation in the present study. The effect of Young's modulus on the microcapsule shell on the microcapsule deformation was investigated as well. Our findings will shed light on the understanding of the stability of core–shell-structured microcapsule when subjected to flow-induced shear stress in a microfluidic system, enabling a more exquisite control over the breakup dynamics of drug-loaded microcapsule for biomedical applications.     

Majorana Representation for a Composite System

International Journal of Theoretical Physics - The Majorana representation, which provides an intuitive way to represent the quantum state by stars on the Bloch sphere, has drawn considerable...     

Formation of Azulene-Embedded Nanographene: Naphthalene to Azulene Rearrangement During the Scholl Reaction

Incorporation of a non-hexagonal ring into a nanographene framework can lead to new electronic properties. During the attempted synthesis of naphthalene-bridged double [6]helicene and heptagon-containing nanographene by the Scholl reaction, an unexpected azulene-embedded nanographene and its triflyloxylated product were obtained, as confirmed by X-ray crystallographic analysis and 2D NMR spectroscopy. A 5/7/7/5 ring-fused substructure containing two formal azulene units is formed, but only one of them shows an azulene-like electronic structure. The formation of this unique structure is explained by arenium ion mediated 1,2-phenyl migration and a naphthalene to azulene rearrangement reaction according to an in-silico study. This report represents the first experimental example of the thermodynamically unfavorable naphthalene to azulene rearrangement and may lead to new azulene-based molecular materials.     

Polypyrrole Shell@3D‐Ni Metal Core Structured Electrodes for High‐Performance Supercapacitors

Three‐dimensional (3D) nanometal films serving as current collectors have attracted much interest recently owing to their promising application in high‐performance supercapacitors. In the process of the electrochemical reaction, the 3D structure can provide a short diffusion path for fast ion transport, and the highly conductive nanometal may serve as a backbone for facile electron transfer. In this work, a novel polypyrrole (PPy) shell@3D‐Ni‐core composite is developed to enhance the electrochemical performance of conventional PPy. With the introduction of a Ni metal core, the as‐prepared material exhibits a high specific capacitance (726 F g^?1 at a charge/discharge rate of 1 A g^?1), good rate capability (a decay of 33 % in C_sp with charge/discharge rates increasing from 1 to 20 A g^?1), and high cycle stability (only a small decrease of 4.2 % in C_sp after 1000 cycles at a scan rate of 100 mV s^?1). Furthermore, an aqueous symmetric supercapacitor device is fabricated by using the as‐prepared composite as electrodes; the device demonstrates a high energy density (≈21.2 Wh kg^?1) and superior long‐term cycle ability (only 4.4 % and 18.6 % loss in C_sp after 2000 and 5000 cycles, respectively).     

紫色球杆菌视紫红质光谱特性的机器学习研究

近年来，机器学习等人工智能技术被应用于蛋白质工程，其在蛋白质结构、功能预测、催化活性等研究中具有独特优势。在未知蛋白质结构的情况下，将蛋白质序列和功能特性与机器学习相结合，基于序列-活性关系（innovative sequence-activity relationship，ISAR）算法，将蛋白质氨基酸序列数字化，用快速傅里叶变换（fast four transform，FFT）进行预处理，再进行偏最小二乘回归建模，可在数据集较少情况下拟合得到最佳模型。通过机器学习对紫色球杆菌视紫红质（gloeobacter violaceus rhodopsin，GR）的突变体蛋白质氨基酸序列与光谱最大吸收波长进行建模，获得了最佳模型。用最佳索引LEVM760106建模得到的确定系数R² 为0.944，均方误差E为11.64。用小波变换进行的预处理，其R² 虽也约为0.944，但E大于11.64，不及FFT进行的预处理。方法较好地解决了蛋白质序列与功能特性之间的数学建模问题，在蛋白质工程中可为预测更优的突变体提供支持。