Nd：YAG晶体1．064μm受激发射截面随温度变化特性研究

测量了Nd：YAG晶体从-70℃到 80℃不同温度下的荧光发射光谱和荧光寿命，计算了该晶体在不同温度下1．064μm受激发射截面，首次获得在此温度变化范围内受激发射截面随温度的线性变化关系．     

Non-destructive determination of niobium in steel by neutron activation followed by X-ray spectrometry

Neutron activation followed by X-ray spectrometry and magnetic deflection of interfering β particles is applied to the non-destructive determination of niobium in steels. AMS 5643 (17-4PH) and B.C.S. No. 320–330 steel samples containing Nb in the range of 0.02–0.3% were analyzed. By using Nb₂O₅ in cellulose as standard, an overall error of ±20% was obtained.     

Homogeneously alloyed CdSxSe1-x nanocrystals: synthesis, characterization, and composition/size-dependent band gap

Alloy nanocrystals provide an additional degree of freedom in selecting desirable properties for nanoscale engineering because their physical and optical properties depend on both size and composition. We report the pyrolytic synthesis of homogeneously alloyed CdS(x)Se(1-x) nanocrystals in all proportions. The nanocrystals are characterized using UV-visible absorption spectroscopy, transmission electron microscopy, X-ray diffractrometry, and Rutherford backscattering spectrometry to determine precisely structure, size, and composition. The dependence of band gap on nanocrystal size and composition is elucidated, yielding a bowing constant of 0.29, in agreement with bulk values. In addition, the morphology of the resultant nanocrystals can be altered by changing the reaction conditions, generating structures ranging from homogeneous, spherical nanocrystals to one-dimensional gradient nanorods.     

Elasticity of crystalline beta-sheet monolayers

Designed amphiphilic beta-sheet peptides with the sequence Pro-Glu-(Phe-Glu)(n)-Pro (n = 2-7) were previously shown by grazing incidence X-ray diffraction (GIXD), to form ordered two-dimensional (2-D) monolayer structures at interfaces induced by the proline residues at peptide termini. The GIXD diffraction pattern was modeled with two coexisting lattice arrangements, suggesting structural flexibility exhibited in the multiple ways by which beta-strands and their amino acid side chains pack into ordered 2-D structures. Here, we find by in-situ GIXD measurements that the ordered beta-sheet assemblies may undergo a quasi-reversible compression and expansion cycle at the air-water interface. The diffraction measurements indicate that on compression the repeat distance that corresponds to the long axes of the peptide strands may decrease by up to 37% in length. Upon expansion the compressed beta-sheet assemblies revert elastically to their original conformation. The interstrand repeat distance along the peptide hydrogen bonds apparently does not change along the film compression and expansion. Based on the GIXD data, at surface pressures higher than approximately 3 mN/m, beyond the peptide limiting area per molecule, the compressibility is 7.4 +/- 0.6 m/N. The out-of-plane Bragg rod diffraction patterns imply that in the compressed state the beta-strands buckle up in reaction to the increase in surface pressure. At low surface pressure, the 2-D compressibility of the crystalline beta-sheet was estimated at approximately 32 m/N attributed to interdomain rearrangements.     

A dimension gap for continued fractions with independent digits—the non stationary case

We show there exists a constant 0 < c₀ < 1 such that the dimension of every measure on [0, 1], which makes the digits in the continued fraction expansion independent, is at most 1 ? c₀. This extends a result of Kifer, Peres and Weiss from 2001, which established this under the additional assumption of stationarity. For k ≥ 1 we prove an analogous statement for measures under which the digits form a *-mixing k-step Markov chain. This is also generalized to the case of f-expansions. In addition, we construct for each k a measure, which makes the continued fraction digits a stationary and *-mixing k-step Markov chain, with dimension at least 1 ? 2^3?k.     

Is it possible to predict the average surface hydrophobicity of a protein using only its amino acid composition?

Hydrophobicity is one of the most important physicochemical properties of proteins. Moreover, it plays a fundamental role in hydrophobic interaction chromatography, a separation technique that, at present time, is used in most industrial processes for protein purification as well as in laboratory scale applications. Although there are many ways of assessing the hydrophobicity value of a protein, recently, it has been shown that the average surface hydrophobicity (ASH) is an important tool in the area of protein separation and purification particularly in protein chromatography. The ASH is calculated based on the hydrophobic characteristics of each class of amino acid present on the protein surface. The hydrophobic characteristics of the amino acids are determined by a scale of aminoacidic hydrophobicity. In this work, the scales of Cowan-Whittaker and Berggren were studied. However, to calculate the ASH, it is necessary to have the three-dimensional protein structure. Frequently this data does not exist, and the only information available is the amino acid sequence. In these cases it would be desirable to estimate the ASH based only on properties extracted from the protein sequence. It was found that it is possible to predict the ASH from a protein to an acceptable level for many practical applications (correlation coefficient > 0.8) using only the aminoacidic composition. Two predictive tools were built: one based on a simple linear model and the other on a neural network. Both tools were constructed starting from the analysis of a set of 1982 non-redundant proteins. The linear model was able to predict the ASH for an independent subset with a correlation coefficient of 0.769 for the case of Cowan-Whittaker and 0.803 for the case of Berggren. On the other hand, the neural model improved the results shown by the linear model obtaining correlation coefficients of 0.831 and 0.836, respectively. The neural model was somewhat more robust than the linear model particularly as it gave similar correlation coefficients for both hydrophobicity scales tested, moreover, the observed variabilities did not overcome 6.1% of the mean square error. Finally, we tested our models in a set of nine proteins with known retention time in hydrophobic interaction chromatography. We found that both models can predict this retention time with correlation coefficients only slightly inferior (11.5% and 5.5% for the linear and the neural network models, respectively) than models that use the information about the three-dimensional structure of proteins.