PSO-LSSVM对LIBS定量分析精度的提高

针对土壤定量分析受基体效应影响大，LIBS定量分析精度不佳等问题，采用粒子群算法对LSSVM进行优化，提高模型的精确度。选取Pb Ⅰ 405.78 nm和Cr Ⅰ 425.44 nm作为分析谱线进行分析。采集十二个不同浓度样品的特征光谱，每个浓度样品在不同点采集20组数据，将其中17组数据设为训练集，3组数据设为预测集，用LSSVM和PSO-LSSVM两种方法建立定标模型。对比两种模型的拟合相关系数（R2）、训练集均方根误差（RMSEC）和预测集均方根误差(RMSEP)。由于自吸收效应的影响，随着浓度的增加，预测值逐渐低于实际值，LSSVM定标模型的拟合程度较低，无法达到实验要求，模型性能有待提高。利用粒子群算法对LSSVM的模型参数惩罚系数和核函数参数进行优化，得到最佳的参数组合，Pb元素为(8 096.8, 138.865 7)，Cr元素为(4 908.6, 393.563 5)，用最佳的参数组合构建LSSVM的定标模型。相比于LSSVM，PSO-LSSVM定标模型的精确度更高，Pb和Cr元素的R2提高到了0.982 8和0.985 0，拟合效果明显提升。Pb和Cr元素的训练集均方根误差由0.026 0 Wt%和0.027 2 Wt%下降到0.022 4 Wt%和0.019 1 Wt%，预测集均方根误差由0.101 8 Wt%和0.078 8 Wt% 下降到0.045 8 Wt%和0.042 0 Wt%，模型的稳定性进一步提高。说明PSO-LSSVM算法能够更好地降低土壤基体效应和自吸收效应带来的影响，提高分析结果的精确度与稳定性。     

In Situ Generated Gold Nanoparticles on Active Carbon as Reusable Highly Efficient Catalysts for a C −C Stille Coupling

Gold nanoparticle catalysts are important in many industrial production processes. Nevertheless, for traditional C ?C cross‐coupling reactions they have been rarely used and Pd catalysts usually give a superior performance. Herein we report that in situ formed gold metal nanoparticles are highly active catalysts for the cross coupling of allylstannanes and activated alkylbromides to form C ?C bonds. Turnover numbers up to 29 000 could be achieved in the presence of active carbon as solid support, which allowed for convenient catalyst recovery and reuse. The present study is a rare case where a gold metal catalyst is superior to Pd catalysts in a cross‐coupling reaction of an organic halide and an organometallic reagent.     

Asymptotic growth bounds for the 3‐D Vlasov‐Poisson system with point charges

In this article, we consider the asymptotic behavior of the classical solution to the 3‐dimensional Vlasov‐Poisson plasma interacting repulsively with N point charges. The large time behavior in terms of diameters of its velocity‐spatial supports is improved to O(t^2/3+ϵ) for any ϵ>0.     

靶丸支撑膜对ICF内爆性能影响的模拟研究

使用二维多群辐射扩散流体力学程序LARED-S，模拟研究靶丸支撑膜在惯性约束聚变氘氘（DD）气体靶内爆过程中的扰动演化过程及其对内爆性能的影响.二维模拟表明：靶丸支撑膜显著降低DD气体靶内爆的中子产额，二维模拟产额为一维结果的55.2%.内爆性能下降的主要物理机制是支撑膜使靶壳生成大幅度的尖钉深入DD气体区，使烧蚀层与DD气体之间物质界面处的电子热传导漏失功率显著增大，导致DD核反应速率显著降低，中子反应速率峰值时刻（bang-time时刻）提前.相比一维理想内爆的模拟结果，支撑膜引入的扰动显著降低bang-time时刻DD气体压强与内爆动能转化为DD气体内能的效率，壳层剩余动能相应大幅增加.     

基于SVM的固化土无侧限抗压强度模型

为实现较少试验次数下固化土无侧限抗压强度(qu)的准确预测, 提出了基于支持向量机(SVM)的固化土qu的预测模型. 以固化剂各组分掺入比、龄期、初始含水量、固化剂掺量等因素为输入量, 固化土的qu作为输出量, 以径向基为核函数, 采用网格搜索法和交叉验证法进行参数优化, 建立了基于SVM的固化土qu的预测模型. 算例分析表明: 该模型适用于任意条件下固化土qu的精确预测, 且在较小试验成本下实现与响应面法相当的预测精度.     

Hydrogenation of acetylene into ethane–ethene mixtures over modified Pd–alumina catalysts

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载体材料对单Pd三效催化剂性能及催化活性的影响

采用共沉淀法制备了耐高温高比表面的La₂O₃-Al₂O₃(LA)以及铈含量分别为15%、33%和47%的储氧材料CeO₂-ZrO₂-La₂O₃-Al₂O₃(CZLA)、CeO₂-ZrO₂-La₂O₃+La₂O₃-Al₂O₃(CZL+LA)和CeO₂-ZrO₂-La₂O₃(CZL)₄类载体材料,并用浸渍法制备了整体式Pd/LA、Pd/CZLA、Pd/CZL+LA和Pd/CZL汽油车尾气净化三效催化剂,考察了载体材料对单Pd三效催化剂的影响。采用低温N₂吸附-脱附、H₂-程序升温还原(H₂-TPR)以及X射线光电子能谱(XPS)对载体材料及催化剂进行了表征,并考察了催化剂的空燃比性能和三效催化性能。结果表明,CZLA有效地结合了铈基和铝基载体材料的优点,表现出了优异的织构性能、热稳定性及还原性能。老化前后,其负载的单Pd三效催化剂在低温还原率、表面元素含量及Pd的电子结合能等性能方面表现出了最小的差异。催化剂活性测试结果表明,Pd/CZLA的三效窗口明显较宽,且拥有最低的起燃温度,尤其经1000℃老化处理后,其催化活性最高,C₃H₈、NO_x和CO的起燃温度分别为370、257和223℃。可见,相较于其他3种载体材料,CZLA更适合于负载单Pd三效催化剂,从而满足更高标准的三效催化剂的性能要求。     

低温燃料电池的纳米电催化材料:综述(英文)

Low temperature fuel cells are an attractive technology for transportation and residential applica‐tions due to their quick start up and shut down capabilities. This review analyzed the current status of nanocatalysts for proton exchange membrane fuel cells and alkaline membrane fuel cells. The preparation process influences the performance of the nanocatalyst. Several synthesis methods are covered for noble and non‐noble metal catalysts on various catalyst supports including carbon nanotubes, carbon nanofibers, nanowires, and graphenes. Ex situ and in situ characterization methods like scanning electron microscopy, transmission electron microscopy, X‐ray photoelectron spectroscopy and fuel cell testing of the nanocatalysts on various supports for both proton exchange and alkaline membrane fuel cells are discussed. The accelerated durability estimate of the nanocat‐alysts, predicted by measuring changes in the electrochemically active surface area using a voltage cycling method, is considered one of the most reliable and valuable method for establishing durabil‐ity.     

载体表面酸碱性质对无碱水溶液中Au催化的甘油氧化反应产物选择性的调控作用

甘油(GL)是一种重要的生物平台分子,通过催化选择氧化反应将其转化为具有高附加值化学品是可持续发展化学化工的重要课题之一.以Au为催化剂的GL水相选择氧化反应可以生成甘油酸(GLA)、二羟基丙酮(DHA)、羟基丙二酸(TTA)、羟基乙酸(GCA)和乳酸(LA)等多种产物.通常,该反应需要碱(NaOH)存在时才能进行,产物往往以GLA为主(选择性40%-70%),副产物主要有GCA, TTA和草酸(OA).一般认为,可溶性碱(OH-)是通过夺取GL分子中羟基上的质子而诱发反应的.尽管在Au催化的反应体系中从未检测到有甘油醛(GLD)生成, GLD和/或DHA被认为是该反应的中间物种.本课题组前期工作表明,氧化物(TiO2, Al2O3, ZrO2, CuO等)负载的纳米Au催化剂能够在无碱(无外加OH-)水溶液中选择性催化GL氧化生成DHA(而不是GLA).因此, OH-的存在与否很可能会改变水溶液中Au催化剂上GL氧化反应的途径.本文试图回答当GL的水溶液中不存在NaOH时, Au催化剂载体的表面酸碱性质是否也会对GL氧化反应的选择性产生调控作用.我们选用Mg/Al比(x)不同的MgO-Al2O3样品为Au催化剂的载体,以尿素为沉淀剂,采用沉积沉淀法制备了相应的Au/MgO-Al2O3(x)催化剂样品.采用X射线衍射、电感耦合等离子体-原子发射光谱仪、透射电镜以及N2吸附-脱附等温线等对MgO-Al2O3(x)和/或Au/MgO-Al2O3样品的物相、元素组成、Au颗粒大小以及比表面积等进行了表征分析;采用NH3和CO2程序升温脱附(TPD)分别对MgO-Al2O3(x)载体表面的酸、碱性进行了测定. NH3-TPD和CO2-TPD结果表明,随着Mg/Al比x从0增加至4.8, MgO-Al2O3(x)的表面酸量从0.94降到0.20μmol/m2,而其表面碱量却从0.05剧增至0.80μmol/m2.因此,载体中MgO含量越多或Mg/Al比越大,其酸性越弱而碱性越强.在无碱水溶液中的催化反应结果表明, Au/MgO-Al2O3(x)上GL氧化反应的主要产物为DHA, GLA以及GCA等.随着x值(催化剂表面碱性)不断增大,产物DHA的选择性从约80%下降到10%左右,而GLA的选择性却从约4%增加至约50%.当载体为酸性最强的Al2O3(x =0)时,产物DHA的选择性为最高(80%).由此可见,载体表面的酸碱性质决定了无碱水溶液中Au催化剂上的GL氧化产物的分布. 此外,当保持Au粒子的尺寸基本不变(如3.1或6.6 nm左右),而改变载体的酸碱性质时, Au/MgO-Al2O3催化GL氧化反应的活性(TOF)可相差8-9倍.本文还通过改变Au/MgO-Al2O3样品焙烧温度,制备了表面酸碱性质相同而颗粒大小不同的三个Au/MgO-Al2O3(0.2)催化剂,考察了Au粒径对GL氧化反应选择性的影响.在这三个催化剂上, Au颗粒的平均尺寸分别为2.8,3.2和6.6 nm, GL氧化反应的产物选择性近乎相同(DHA和GLA的选择性分别为65%和15%左右),但平均尺寸为6.6 nm Au粒子的催化活性(TOF)是3.2 nm Au粒子的1.6倍,2.8 nm Au粒子的2.7倍.因此,本文建立了载体表面酸碱性质与无碱水溶液中GL氧化产物选择性之间的关系,通过改变载体表面酸碱性质实现了对无碱水溶液中Au催化剂上GL氧化反应选择性的调控.尽管载体酸碱性质和Au粒子尺寸都对Au/MgO-Al2O3催化剂的本征活性有重要影响,但载体酸碱性质的影响更显著.     

Development of models for prediction of the antioxidant activity of derivatives of natural compounds

Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure–activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.