采用激光熔覆技术在轧制态GH4169合金表面制备GH4169涂层, 重点研究涂层的显微组织、析出相、高温显微硬度和高温摩擦磨损性能。结果表明: 涂层中无气孔和裂纹等缺陷, 且主要由Laves偏析相、γ基体相和少量MC及MN相组成; 涂层的室温平均显微硬度为273.6 HV(0.3 kg试验力), 且随着测试温度的升高, 其显微硬度在不同温度下呈现下降趋势, 测试温度为600 ℃时, 显微硬度下降到197.4 HV(0.3 kg试验力), 约为室温时的72%; 高温摩擦试验结果表明, 随着测试温度的提高, 其磨损率也呈现下降趋势, 当测试温度为600 ℃时, 试样磨损率约为室温时的25%; 摩擦磨损试验结束后, 测试温度不高于500 ℃的涂层, 其常温显微硬度变化不大, 而测试温度为600 ℃的涂层, 由于强化相析出, 常温显微硬度有所提高。

熔覆层和基体之间的修复界面通常是再制造零件最为薄弱的区域，影响再制造零件的使用寿命。基于正交试验设计对激光熔覆IN718合金工艺进行优化，并研究了工艺参数对熔覆层成形尺寸的影响规律，重点分析了最佳工艺条件下IN718熔覆层的界面组织特征及力学性能。结果显示，时效态IN718合金的最佳激光熔覆工艺为900 W激光功率、6 mm/s扫描速度、10 g/min送粉速率；激光功率对熔覆层熔宽的影响最大，扫描速度对熔覆层熔高的影响最大，送粉速率对熔覆层宽高比和稀释率的影响最大；熔覆层界面底部未出现平面晶且界面周围的元素过渡均匀，无宏观偏析；基体热影响区的晶粒发生粗化，其内部残留部分MC和M₂₃C₆碳化物；IN718熔覆层的显微硬度约为280 HV，基体的显微硬度约为475 HV；激光熔覆IN718合金的平均界面剪切强度为608.87 MPa。

选择区域外延生长（SAG）技术是微纳尺度GaN基发光器件的主要制备方法之一。在选择区域外延生长中, Ⅲ族金属原子在掩模介质表面的迁移行为对微纳器件的形貌及特性有非常重要的影响。利用金属有机化学气相沉积（MOCVD）系统研究了选择区域外延生长中Ga原子在掩模介质表面上的迁移特性, 得到了不同反应腔压力和生长温度下Ga原子在掩模介质表面的迁移长度, 且在保持其他生长条件不变的情况下, 适当降低反应腔压力或提高生长温度可提高Ga原子的迁移长度。

Near infrared chemical imaging (NIR-CI) combines conventional near infrared (NIR) spectroscopy with chemical imaging, thus provides spectral and spatial information simultaneously. It could be utilized to visualize the spatial distribution of the ingredients in a sample. The data acquired using NIR-CI instrument are hyperspectral data cube (hypercube) containing thousands of spectra. Chemometric methodologies are necessary to transform spectral information into chemical information. Partial least squares (PLS) method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations. A series of samples which consisted of different CPM concentrations (w/w) were compressed and hypercube data were measured. The spectra extracted from the hypercube were used to establish the PLS model of CPM. The results of the model were R²_val0.981, RMSEC 0.384%, RMSECV 0.483%, RMSEP 0.631%, indicating that this model was reliable.

The present study aimed at investigating the relationship between tablet hardness and homogeneity of different Yinhuang dispersible tablets by near-infrared chemical imaging (NIR-CI) technology. The regularity of best hardness was founded between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets. The ingredients homogeneity of Yinhuang dispersible tablets could be spatially determined using basic analysis of correlation between analysis (BACRA) method and binary image. Then different hardnesses of Yinhuang dispersible tablets were measured. Finally, the regularity between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets was illuminated by quantifying the agglomerate of polyvinyl poly pyrrolidone (PVPP). The result demonstrated that the distribution of PVPP was unstable when the hardness was too large or too small, while the agglomerate of PVPP was smaller and more stable when the best tablet hardness was 75 N. This paper provided a novel methodology for selecting the best hardness in the tabletting process of Chinese Medicine Tablet.

Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration (R2cal) were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation (R2val) were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction(R2pre) were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

In this work, multivariate detection limits (MDL) estimator was obtained based on the microelectro- mechanical systems–near infrared (MEMS–NIR) technology coupled with two sampling accessories to assess the detection capability of four quality parameters (glycyrrhizic acid, liquiritin, liquiritigenin and isoliquiritin) in licorice from different geographical regions. 112 licorice samples were divided into two parts (calibration set and prediction set) using Kennard– Stone (KS) method. Four quality parameters were measured using high-performance liquid chromatography (HPLC) method according to Chinese pharmacopoeia and previous studies. The MEMS–NIR spectra were acquired from fiber optic probe (FOP) and integrating sphere, then the partial least squares (PLS) model was obtained using the optimum processing method. Chemometrics indicators have been utilized to assess the PLS model performance. Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels, which indicated relatively low sensitivity for low-content analytes (below 1000 parts per million (ppm)). Therefore, MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels. The result suggested that MEMS– NIR technology coupled with fiber optic probe (FOP) and integrating sphere was able to detect minor analytes. The result further demonstrated that integrating sphere mode (i.e., MDL_0.05;0.05, 0.22%) was more robust than FOP mode (i.e., MDL_0.05;0.05, 0.48%). In conclusion, this research proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS–NIR technology and successful to compare two sampling accessories.