大尺寸电阻加热式碳化硅晶体生长热场设计与优化

大尺寸低缺陷碳化硅(SiC)单晶体是功率器件和射频(RF)器件的重要基础材料,物理气相传输(physical vapor transport, PVT)法是目前生长大尺寸SiC单晶体的主要方法。获得大尺寸高品质晶体的核心是通过调节组分、温度、压力实现气相组分在晶体生长界面均匀定向结晶,同时尽可能减小晶体的热应力。本文对电阻加热式8英寸(1英寸=2.54 cm)碳化硅大尺寸晶体生长系统展开热场设计研究。首先建立描述碳化硅原料受热分解热质输运及其多孔结构演变、系统热输运的物理和数学模型,进而使用数值模拟方法研究加热器位置、加热器功率和辐射孔径对温度分布的影响及其规律,并优化热场结构。数值模拟结果显示,通过优化散热孔形状、保温棉的结构等设计参数,电阻加热式大尺寸晶体生长系统在晶锭厚度变化、多孔介质原料消耗的情况下均能达到较低的晶体横向温度梯度和较高的纵向温度梯度。     

Mononuclear and Dinuclear Copper Complexes of Tridentate Redox-active Ligands with Tunable H-bonding Donors: Structure,Spectroscopy and H+/e− Reactivity

In this research article, we describe the synthesis and characterization of mononuclear and dinuclear Cu complexes bound by a family of tridentate redox-active ligands with tunable H-bonding donors. The mononuclear Cu-anion complexes were oxidized to the corresponding “high-valent” intermediates by oxidation of the redox-active ligand. These species were capable of oxidizing phenols with weak O−H bonds via H-atom abstraction. Thermodynamic analysis of the H-atom abstractions, which included reduction potential measurements, pK_a determination and kinetic studies, revealed that modification of the anion coordinated to the Cu and changes in the H-bonding donor did not lead to major differences in the reactivity of the “high-valent” CuY complexes (Y: hydroxide, phenolate and acetate), which indicated that the tridentate ligand scaffold acts as the H⁺ and e⁻ acceptor.     

离子对反相高效液相色谱法同时测定尿液中复方α-酮酸片的5种有效成分

建立了同时测定尿液中复方α-酮酸片5种有效成分(消旋羟蛋氨酸钙(HMACa)、酮缬氨酸钙(KVCa)、消旋酮异亮氨酸钙(KILCa)、酮亮氨酸钙(KLCa)和酮苯丙氨酸钙(KPACa))的离子对反相高效液相色谱法。分别考察了离子对试剂浓度、缓冲盐浓度和流动相pH值等参数对分离情况的影响。最终采用Waters Symmetry C18色谱柱(250 mm×4.6 mm,5μm);以乙腈和含15 mmol/L四丁基氢氧化铵的20 mmol/L磷酸盐缓冲液(pH 7.0)为流动相进行梯度洗脱,流速为1.0 mL/min;柱温为35℃;检测波长为210 nm。在上述条件下,5种有效成分得到很好的分离,并且不受尿液中内源性基质的干扰,在20~200 mg/L范围内线性关系良好(r≥0.999 0);HMACa、KVCa、KILCa、KLCa和KPACa的检出限(S/N=3)分别为3.0、5.0、3.6、5.7和2.5 mg/L;定量限(S/N=10)分别为9.6、16.7、12.0、19.0和8.3 mg/L;日间、日内精密度均小于7%;平均回收率在86.79%~112.00%之间,RSD小于9%(n=5)。该方法准确,灵敏度高,重现性好,适用于尿液中这5种有效成分的检测。     

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。     

The Calibrating Theory in the Network Parameters Measurement of Microwave and MMW Devices

The paper introduces a special system calibration technology in s-parameters measurement of microwave and millimeter wave devices. The 8-term errors module is built by analyzing the signals flowing in the measurement system. Then the calibration technology using non-standard kits is designed on the base. Finally, the experiment using the calibration technology is introduced.     

工作气压对电子束沉积ZrO2薄膜折射率和聚集密度的影响

采用电子束蒸发的方法，用石英晶体振荡法监控薄膜的蒸发速率，在不同工作气压下制备了ZrO2薄膜样品.在相调制型椭圆偏振光谱仪和分光光度计上对样品的光谱特性进行了测试.根据波长漂移的理论，计算出薄膜的聚集密度.结果表明，随着工作气压的降低，薄膜的聚集密度和折射率都随之增大.     

Crystallographic report: catena‐Aqua(2,2′‐bipyrimidine)lithium(I) perchlorate

A lithium(I) coordination polymer has been formed from LiClO₄ and the 2,2′‐bipyrimidine (bpym) ligand in which each square pyramidal lithium(I) atom is coordinated in the basal plane by four nitrogen donor atoms derived from two bpym ligands and one water molecule at the apical position. These are connected into a layer structure via hydrogen‐bonding interactions involving the perchlorate anions. Copyright © 2004 John Wiley & Sons, Ltd.