用FT—IR技术研究Mo／HZSM—5分子筛的骨架结构

在两种不同硅铝比的ＨＺＳＭ－５分子筛上担载不同量的钼物种后，对甲烷地氧脱氢芳构化反应呈现出不同的催化活性，使用ＦＴ－ＩＲ技术测定了催化剂样品的骨架结构，结果表明，在两种族中分子筛载体上，钼物处的存在状态及其与载全校是的相互作用各不同相；分子筛载的酸性环境决定了浸渍时钼物种的可能存在状态。     

1,3-二苯基-4-酰基-5-吡唑酮螯合剂离解常数和两相分配常数的测定

在水-二氧六环混合体系中,用电位滴定法测定了四种新的β－二酮螯合剂在二氧六环不同含量时的离解常数,通过外推,求得它们在纯水中的离解常数。采用两相滴定法,测定了它们在氯仿、苯和正己烷三种有机溶剂与水之间的两相分配常数。     

尼龙－1010的聚集态结构

用ＷＡＸＤ、ＤＳＣ、方差－范围函数．密度测量等方法，研究了经不同热处理的尼龙－１０１０的聚集态结构．发现退火处理更有利于尼龙－１０１０结晶的生成和稳定，且尼龙－１０１０的结晶，有一个最佳的热处理温度．在该温度附近，尼龙－１０１０的结晶度和微晶尺寸里最大值．     

三苯基锡与邻氨基苯甲酸型Schif碱配合物的合成及表征

合成了两种新的三苯基锡与邻氨基苯甲酸型Ｓｃｈｉｆ碱的配合物，经元素分析、ＵＶ、ＩＲ、１ＨＮＭＲ测定，对其结构进行了表征。结果表明，两种配合物具有不同的结构。在三苯基锡与水杨醛缩邻氨基苯甲酸Ｓｃｈｉｆ碱的配合物中，锡原子是六配位的，存在着酚羟基氧和亚氨基氮与锡的配键。在三苯基锡与２－羟基－１－萘醛缩邻氨基苯甲酸Ｓｃｈｉｆ碱的配合物中，锡原子是五配位的，酚羟基氧和亚氨基氮均未与锡配位，但存在着羰基氧与锡的配键     

统计学在理化检验中的应用第九讲某些基本的统计假设检验(续)

2　对标准偏差的一致性检验2 .1　 F检验设有两个正态总体 N( μ,σ1 ) ,N( μ,σ2 ) ,要求检验 σ21 和 σ22 是否一致。从两个总体中分别取一个样本 ,样本的容量和标准偏差分别为 n1 、S1 和 n2 、S2 。作统计量 F:F =S21 S22( 5)　　当 σ21 =σ22 成立时 ,F服从自由度为 ( n1 - 1 ,n2 -1 )的 F分布 ,对于给定的显著性水平 α,可以求得两个临界值 f1α和 f2α,使得P{F≤ f1α}=α2 ( 6)P{F≥ f2α}=α2 ( 7)　　当按式 ( 5)算得的 F值满足 :f1α 相似文献   

聚芳醚砜环状低聚物的基质辅助激光解吸电离质谱分析

采用基质辅助激光解吸电离飞行时间质谱技术分别对两种合成的聚芳醚砜环状低聚物进行了分析研究，并讨论了低聚物中不同聚合度离子组份的分布规律。     

Abinitio方法研究2，4－二甲基－1，3－戊二烯结构

用ａｂｉｎｉｔｉｏ方法研究２，４－二甲基－１，３－戊二烯各种构象异构体的结构，找到ｓ－ｔｒａｎｓ和ｓ－ｃｉｓ两种稳定构象，且ｓ－ｔｒａｎｓ更稳定．对稳定构象的几何结构、电荷分布进行了研究，讨论了不同的电子基组对优化几何的影响，并计算了稳定构象的热力学常数，与实验结果进行了比较．     

HA─A－溶液混和体系质子条件浅析

ＨＡ─Ａ~－溶液混和体系质子条件浅析周振太（新乡师范专科学校化学系河南４５３０００）ＨＡ－Ａ－混和体系的质子条件，在教科书和文献中出现了两种不同的写法。一种写法为：另一种写法为：那么，究竟哪一种写法正确呢？第一种写法见高师统编教材《分析化学》一书［１?..     

具有两亲性质的异环酰基－3－二茂铁基－2－丁烯酸酯衍生物的合成

３－二茂铁基－２－丁烯酸乙酯与８种不同类型的酰氯反应，合成了８种具有两亲结构的α，β－不饱和酯二茂铁双取代新衍生物。同时，发现一种新的酰化反应现象，即３－二茂铁基－２－丁烯酸乙酯与２－丁烯酰氯或肉桂酰氯反应时，酰化反应既能在茂环上又能在α，β－不饱和酯基的烯键上发生，得到另两种新化合物。测得了上述１０种化合物的１ＨＮＭＲ谱、ＩＲ光谱及元素分析数据。     

交联网络和浓溶液中高分子链运动的NMR研究 Ⅱ质子自旋-自旋弛豫与凝胶和浓溶液体系中高分子链段运动

用自旋－自旋弛豫时间考察了线型聚苯乙烯溶液与溶胀交联聚丙烯酰胺－丙烯酸凝胶的质子弛豫行为。发现在溶胀交联体系中，不论交联度高低，聚丙烯酰胺－丙烯酸质子的弛豫时间弛豫都呈现出双指数衰减特征；而在线型溶液体系中，聚苯乙烯质子的弛豫时间弛豫符合单指数衰减特征。说明在线性聚苯乙烯溶液中，高分子的链段运动是一种均匀行为，而在溶胀交联聚丙烯酰胺－丙烯酸体系中，不论交联度高低，高分子链段运动始终存在快慢不同的两部分。     

Evaluation of Sampling and Analytical Errors in the Determination of Manganese in Soils

Sampling variances and analytical variances were estimated in a study of five chemical forms of Mn in nine soil types from a greenhouse experiment. A sampling and analytical quality control scheme and a robust analysis of variance were used for this purpose and proved appropriate. The resulting statistics were then subjected to an assessment of analytical precision and sampling precision according to certain criteria. The results showed that the sampling variances and analytical variances in this experiment were good enough to describe the natural geochemical variances.     

Regression of binary interaction parameters for thermodynamic models using an inside-variance estimation method (IVEM)

An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Most of the maximum likelihood approaches currently used to regress the parameters of thermodynamic models fix the variances, converting the problem into a traditional weighted least squares minimization. However, such approaches lead to residual variances (between measured and calculated values) that are inconsistent with the fixed variances and, thus, do not necessarily produce optimum parameters for prediction purposes. The new method (IVEM) substantially improves fluid phase equilibria predictions (as shown by the examples presented) by maintaining consistency between the residual variances and the variance used in the objective function. This results in better parameter estimation and to a direct measure of the uncertainty in the model prediction.     

On the choice of the residual norm function in the interpretation of ARXPS data by regularized fitting

Angle‐resolved X‐ray photoelectron spectroscopy (ARXPS) data taken on a polystyrene film exposed to a nitrogen plasma are interpreted by the fitting of regularized depth profiles. Three ways of measuring the goodness of fit are compared—the χ² statistic with variances drawn from the raw spectra, the χ² statistic with variances drawn from the concentration figures obtained from the data analysis, and a simple sum of the squared differences (ssd) that does not require variances to be calculated. It is shown that for these data, the depth profiles obtained using an objective method for the choice of the regularization parameter are essentially identical irrespective of whether or how the variances are introduced into the calculation. Copyright © 2010 John Wiley & Sons, Ltd.     

Measurement error models and variance estimation in the presence of rounding error effects

An approach to estimating measurement error variances for any instrument having round-off effects that might also have instrument bias is presented. Recently finite instrument resolution effects on error variances have been studied, but negligible instrument bias was assumed and the contexts were different than considered here. Our intent is to use repeated measurements on several standards to estimate the instrument’s random and systematic error variances. Recognizing that rounding impacts item bias and variance in a manner that depends on the true value, an approach is presented to estimate random error variance and instrument systematic error variance. The key finding is that item-specific bias can interfere with the estimation of overall instrument bias unless appropriate error modeling and associated inference steps are taken.     

Testing shelled corn for aflatoxin, Part I: estimation of variance components

The variability associated with testing lots of shelled corn for aflatoxin was investigated. Eighteen lots of shelled corn were tested for aflatoxin contamination. The total variance associated with testing shelled corn was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased as aflatoxin concentration increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. Test results on a lot with 20 parts per billion aflatoxin using a 1.13 kg sample, a Romer mill, 50 g subsamples, and liquid chromatographic analysis showed that the total, sampling, sample preparation, and analytical variances were 274.9 (CV = 82.9%), 214.0 (CV = 73.1 %), 56.3 (CV = 37.5%), and 4.6 (CV = 10.7%), respectively. The percentage of the total variance for sampling, sample preparation, and analytical was 77.8, 20.5, and 1.7, respectively.     

渐进取样法及其应用

提出了渐进取样法,通过样本数目的累积,使总方差估计值达到所需的精度。应用Ｍｏｎｔｅ Ｃａｒｌｏ模拟技术考察了满足一定取样精度的样本数目及其偏差。将该方法应用于散装生铁块中Ｓｉ、Ｍｎ、Ｃ、Ｓ、Ｐ含量分析的取样,结果令人满意。该项研究对于大宗货物的实际随机取样具有重要参考价值。     

Testing green coffee for ochratoxin A, part I: estimation of variance components

The variability associated with testing lots of green coffee beans for ochratoxin A (OTA) was investigated. Twenty-five lots of green coffee were tested for OTA contamination. The total variance associated with testing green coffee was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased with an increase in OTA concentration. Using regression analysis, mathematical expressions were developed to model the relationship between OTA concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific OTA concentration. Testing a lot with 5 microg/kg OTA using a 1 kg sample, Romer RAS mill, 25 g subsamples, and liquid chromatography analysis, the total, sampling, sample preparation, and analytical variances were 10.75 (coefficient of variation [CV] = 65.6%), 7.80 (CV = 55.8%), 2.84 (CV = 33.7%), and 0.11 (CV = 6.6%), respectively. The total variance for sampling, sample preparation, and analytical were 73, 26, and 1%, respectively.     

Accurate measurements of peak variances: importance of this accuracy in the determination of the true corrected plate heights of chromatographic columns

The true efficiency of a column is derived from the differences between the variances of the peak profiles of the same compound recorded in the presence and the absence of the chromatographic column. These variances are usually derived using one of three methods: (1) the retention time of the peak apex and its half-height width; (2) the moments of the best fit between the experimental data and a hybrid response function, e.g., an exponentially convoluted Gaussian; or (3) the exact moments of the experimental band profiles. Comparisons of the results of these methods show that the first method is always inaccurate because all the band profiles recorded are strongly tailing. The peak fit method is accurate only for 4.6mm I.D. columns operated with instruments having low extra-column volume but fails for short narrow-bore columns due to the severe tailing of peaks passing through the complex channels of the extra-column volumes and to the inaccuracies in the fit of experimental data to the selected function. Although far better, the moment method may be inaccurate when the zero dead volume union used to measure the extra-column peak variances has a higher permeability than the column, causing the upstream part of the instrument to operate under comparatively low pressures.     

Variation of analytical results for peanuts in energy bars and milk chocolate

Peanuts contain proteins that can cause severe allergic reactions in some sensitized individuals. Studies were conducted to determine the percentage of recovery by an enzyme-linked immunosorbent assay (ELISA) method in the analysis for peanuts in energy bars and milk chocolate and to determine the sampling, subsampling, and analytical variances associated with testing energy bars and milk chocolate for peanuts. Food products containing chocolate were selected because their composition makes sample preparation for subsampling difficult. Peanut-contaminated energy bars, noncontaminated energy bars, incurred milk chocolate containing known levels of peanuts, and peanut-free milk chocolate were used. A commercially available ELISA kit was used for analysis. The sampling, sample preparation, and analytical variances associated with each step of the test procedure to measure peanut protein were determined for energy bars. The sample preparation and analytical variances were determined for milk chocolate. Variances were found to be functions of peanut concentration. Sampling and subsampling variability associated with energy bars accounted for 96.6% of the total testing variability. Subsampling variability associated with powdered milk chocolate accounted for >60% of the total testing variability. The variability among peanut test results can be reduced by increasing sample size, subsample size, and number of analyses. For energy bars the effect of increasing sample size from 1 to 4 bars, subsample size from 5 to 20 g, and number of aliquots quantified from 1 to 2 on reducing the sampling, sample preparation, and analytical variance was demonstrated. For powdered milk chocolate, the effects of increasing subsample size from 5 to 20 g and number of aliquots quantified from 1 to 2 on reducing sample preparation and analytical variances were demonstrated. This study serves as a template for application to other foods, and for extrapolation to different sizes of samples and subsamples as well as numbers of analyses.