利用金属有机化合物化学气相沉积（MOCVD）技术，在不同晶面的蓝宝石（Al₂O₃）衬底上实现了极性（0002）面、半极性（11-22）面和非极性（11-20）面InN薄膜的外延生长，并通过多种表征手段对三个不同极性面InN薄膜的结构和光学特性进行了系统研究。X射线衍射（XRD）曲线展示了（0002）、（11-22）和（11-20）面InN较强的衍射峰，表明InN薄膜具有较高的成膜质量。通过扫描电子显微镜（SEM）表面图发现，极性（0002）面InN的表面形貌较光滑，而半极性和非极性InN表面均存在未完全合并的孔洞。光致发光（PL）光谱展示，不同极性面InN的峰值能量在0.63 eV附近，并从极性、半极性到非极性逐渐红移。此外，可见-红外分光光度计测得的透射谱显示，极性（0002）面InN的吸收边约为0.85 eV，而半极性（11-22）面和非极性（11-20）面InN的吸收边约为0.78 eV，表明极性InN具有更大的斯托克斯位移。

The evolution of ablative Rayleigh–Taylor instability (ARTI) induced by single-mode stationary and time-varying perturbations in heat flux is studied numerically in two dimensions. Compared with the stationary case, time-varying heat-flux perturbation mitigates ARTI growth because of the enhanced thermal smoothing induced by the wave-like traveling heat flux. A resonance is found to form when the phase velocity of the heat-flux perturbation matches the average sound speed in the ablation region. In the resonant regime, the coherent density and temperature fluctuations enhance the electron thermal conduction in the ablation region and lead to larger ablation pressure and effective acceleration, which consequently yield higher linear growth rate and saturated bubble velocity. The enhanced effective acceleration offers increased implosion velocity but can also compromise the integrity of inertial confinement fusion shells by causing faster ARTI growth.

以苯甲酸乙酯为骨架，在不同位置引入不同数量的三苯乙烯和四苯乙烯，制备了含苯甲酸乙酯结构的系列共轭化合物EB1~EB4。紫外‐可见吸收光谱和荧光发射光谱测试结果表明，所合成的四种共轭材料均具有明显的聚集诱导发光特性。将四种共轭材料作为荧光探针，成功用于硝基爆炸物的水相识别。荧光滴定结果表明，该系列共轭化合物对苦味酸（PA）具有潜在的传感性能，化合物EB1~EB4对溶液中PA的检测限分别为1.35 × 10^-6，1.69 × 10^-6，1.12 × 10^-6，8.88 × 10^-7 mol/L；做成试纸检测器的裸眼检测限为1.14 ng/cm²。这类含苯甲酸乙酯结构的共轭材料及其衍生物为新型荧光材料的制造提供了重要思路。

在间接驱动激光聚变研究中，平响应X射线二极管是X射线辐射能流测量的主要探测器。为了获得理想的平响应效果，采用传统方法需要花费大量时间优化二极管的复合滤片参数，为此引入了粒子群优化算法，将之用于平响应X射线二极管复合滤片参数的优化，该方法可更快捷、更准确地得到复合滤片的优化参数。提出了新的滤片组合方式，并优化其平响应特性，得到了比传统滤片组合更优的参数配比。该项工作为平响应X射线二极管复合滤片参数的寻优提供了一种更高效的方法。

Pulse shaping is a powerful tool for mitigating implosion instabilities in direct-drive inertial confinement fusion (ICF). However, the high-dimensional and nonlinear nature of implosions makes the pulse optimization quite challenging. In this research, we develop a machine-learning pulse shape designer to achieve high compression density and stable implosion. The facility-specific laser imprint pattern is considered in the optimization, which makes the pulse design more relevant. The designer is applied to the novel double-cone ignition scheme, and simulation shows that the optimized pulse increases the areal density expectation by 16% in one dimension, and the clean-fuel thickness by a factor of four in two dimensions. This pulse shape designer could be a useful tool for direct-drive ICF instability control.