Revealing the Effect of Surface Composition on Multiwalled Carbon Nanotubes Supported Pt-Fe Alloy Electrocatalysts for Methanol Oxidation Performance

The designs of efficient and inexpensive Pt-based catalysts for methanol oxidation reaction (MOR) are essential to boost the commercialization of direct methanol fuel cells. Here, the highly catalytic performance PtFe alloys supported on multiwalled carbon nanotubes (MWCNTs) decorating nitrogen-doped carbon (NC) have been successfully prepared via co-engineering of the surface composition and electronic structure. The Pt₁Fe₃@NC/MWCNTs catalyst with moderate Fe³⁺ feeding content (0.86 mA/mg_Pt) exhibits 2.26-fold enhancement in MOR mass activity compared to pristine Pt/C catalyst (0.38 mA/mg_Pt). Furthermore, the CO oxidation initial potential of Pt₁Fe₃@NC/MWCNTs catalyst is lower relative to Pt/C catalyst (0.71 V and 0.80 V). Benefited from the optimal surface compositions, the anti-corrosion ability of MWCNT, strong electron interaction between PtFe alloys and MWCNTs and the N-doped carbon (NC) layer, the Pt₁Fe₃@NC/MWCNTs catalyst presents an improved MOR performance and anti-CO poisoning ability. This study would open up new perspective for designing efficient electrocatalysts for the DMFCs field.     

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.     

Visible-light-induced,autopromoted nickel-catalyzed three-component arylsulfonation of 1,3-enynes and mechanistic insights

Science China Chemistry - A light-induced, nickel-catalyzed three-component arylsulfonation of 1,3-enynes in the absence of photocatalyst is reported. This methodology exhibited mild conditions,...     

Exact solutions of an asymmetric double well potential

Journal of Mathematical Chemistry - The analytical solutions of an asymmetric double well potential $$V(x)=a\, x^2-b\, x^3+c\, x^4$$ are found to be a triconfluent Heun function $$H_{T}(\alpha ,...     

Simultaneous determination of cinnamaldehyde and its metabolite in rat tissues by gas chromatography–mass spectrometry

Cinnamaldehyde (CA), an active ingredient isolated from the traditional Chinese medicine Cortex Cinnamomi, has a wide range of bioactivities. To clarify the distribution characteristics of CA, a selective and sensitive method utilizing gas chromatography–mass spetrometry was initially developed for simultaneously determining the concentration of CA and its metabolite cinnamyl alcohol in rat tissues. Selected ion masses of m/z 131, 105 and 92 were chosen, and separation of the analytes was performed on a DB‐5 ms (30 m × 0.25 mm, 0.25 µm, thickness) capillary column by gas chromatography–mass spectrometry. The calibration curves demonstrated good linearity and reproducibility over the range of 20–2000 and 20–4000 ng/mL for various tissue samples. Recoveries ranged from 86.8 to 107.5%, while intra‐ and interday relative standard deviations were all <11.3%. The analysis method was successfully applied in tissue distribution studies for CA and cinnamyl alcohol. As CA and cinnamyl alcohol may inter‐convert to one another, simultaneous determination of both analytes provides a comparative and accurate data for tissue study. The concentrations of CA and cinnamyl alcohol remaining in spleen were the highest among the main organs, including heart, liver, spleen, lung, kidney and brain. In addition, there was no long‐term accumulation of CA in rat tissues. Copyright © 2014 John Wiley & Sons, Ltd.     

DECOMPOSITION OF （1＋1）-DIMENTIONAL,（2W1）-DIMENTIONAL SOLITON EQUATIONS AND THEIR QUASI-PERIODIC SOLUTIONS

A new spectral problem is proposed, and nonlinear differential equations of the corresponding hierarchy are obtained. With the help of the nonlinearization appr...     

Electrochemiluminescence Enhanced by the Synergetic Effect of Porphyrin and Multi-walled Carbon Nanotubes for Uric Acid Detection

The design and exploration of efficient, stable and environmentally compatible organic emitters for an electrochemiluminescence (ECL) sensor is a promising topic. Herein, a novel environmentally-friendly luminophore, ZnBCBTP@MWCNTs, were fabricated via self-assembly of porphyrin molecules (ZnBCBTP) onto multi-walled carbon nanotubes (MWCNTs). The resulting luminophore ZnBCBTP@MWCNTs displayed not only the highly ECL property and but also the good accelerated electron mobility. Then, a label-free ECL biosensor based ZnBCBTP@MWCNTs was constructed for the ultrasensitive detection of uric acid. Excitingly, this proposed ECL biosensor performed a good linear relationship in the range of 0–300 μM with a low detection limit of 1.4 μM, thus offering another reliable and feasible sensing platform for clinical bioanalysis with good selectivity, stability, and repeatability.     

Investigations in the recrystallization of evolved gases from pyrolysis process of melamine

Journal of Thermal Analysis and Calorimetry - A complete analysis of the thermal process about melamine was presented, in which different methods were applied to determine the characteristic of the...     

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。