In this paper, we investigated the effect of rapid thermal annealing （RTA） on solar cell performance. An opto-electric conversion efficiency of 11.75% （Voc = 0.64 V, Jsc = 25.88 mA/cm2, FF=72.08%） was obtained und...In this paper, we investigated the effect of rapid thermal annealing （RTA） on solar cell performance. An opto-electric conversion efficiency of 11.75% （Voc = 0.64 V, Jsc = 25.88 mA/cm2, FF=72.08%） was obtained under AM 1.5G when the cell was annealed at 300℃ for 30 s. The annealed solar cell showed an average absolute efficiency 1.5% higher than that of the as-deposited one. For the microstructure analysis and the physical phase confirmation, X-ray diffraction （XRD）, Raman spectra, front surface reflection （FSR）, internal quantum efficiency （IQE）, and X-ray photoelectron spectroscopy （XPS） were respectively applied to distinguish the causes inducing the efficiency variation. All experimental results implied that the RTA eliminated recombination centers at the p-n junction, reduced the surface optical losses, enhanced the blue response of the CdS buffer layer, and improved the ohmic contact between Mo and Cu（In, Ga）Se2 （CIGS） layers. This leaded to the improved performance of CIGS solar cell.展开更多
坝肩边坡变形在外部因素影响下呈现出不确定性和随机性,从而不易预测。基于聚类模态分解(EEMD)、样本熵(SE)和改进型粒子群算法优化的最小二乘支持向量机(IPSO LSSVM)方法,提出一种名为EEMD SE IPSO LSSVM的坝肩边坡变形预测模型。首先...坝肩边坡变形在外部因素影响下呈现出不确定性和随机性,从而不易预测。基于聚类模态分解(EEMD)、样本熵(SE)和改进型粒子群算法优化的最小二乘支持向量机(IPSO LSSVM)方法,提出一种名为EEMD SE IPSO LSSVM的坝肩边坡变形预测模型。首先,利用EEMD将原始坝肩边坡变形时间序列分解为若干个不同复杂度的子序列,并基于SE判定各子序列的复杂度,将相近的子序列进行合并重组以减少计算规模;然后,分别对各重组子序列建立IPSO LSSVM预测模型;最后,将各预测分量进行叠加重构,得到最终的大坝变形预测值。以澜沧江苗尾水电站左岸坝肩边坡为例,将BPNN、RBFNN、LSSVM、EEMD SE LSSVM与EEMD SE PSO LSSVM进行对比研究。结果表明,该模型的计算精度优于其他神经网络模型,具有较好的适宜性和稳定性,是一种可靠的坝肩边坡变形预测方法,能够为大坝安全监测提供有价值的参考。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 60876045)the Shanghai Leading Basic Research Project, China (Grant No. 09JC1405900)+1 种基金the Shanghai Leading Academic Discipline Project, China (Grant No. S30105)the R & D Foundation of SHU-SOENs PV Joint Laboratory, China (Grant No. SS-E0700601)
文摘In this paper, we investigated the effect of rapid thermal annealing （RTA） on solar cell performance. An opto-electric conversion efficiency of 11.75% （Voc = 0.64 V, Jsc = 25.88 mA/cm2, FF=72.08%） was obtained under AM 1.5G when the cell was annealed at 300℃ for 30 s. The annealed solar cell showed an average absolute efficiency 1.5% higher than that of the as-deposited one. For the microstructure analysis and the physical phase confirmation, X-ray diffraction （XRD）, Raman spectra, front surface reflection （FSR）, internal quantum efficiency （IQE）, and X-ray photoelectron spectroscopy （XPS） were respectively applied to distinguish the causes inducing the efficiency variation. All experimental results implied that the RTA eliminated recombination centers at the p-n junction, reduced the surface optical losses, enhanced the blue response of the CdS buffer layer, and improved the ohmic contact between Mo and Cu（In, Ga）Se2 （CIGS） layers. This leaded to the improved performance of CIGS solar cell.
文摘坝肩边坡变形在外部因素影响下呈现出不确定性和随机性,从而不易预测。基于聚类模态分解(EEMD)、样本熵(SE)和改进型粒子群算法优化的最小二乘支持向量机(IPSO LSSVM)方法,提出一种名为EEMD SE IPSO LSSVM的坝肩边坡变形预测模型。首先,利用EEMD将原始坝肩边坡变形时间序列分解为若干个不同复杂度的子序列,并基于SE判定各子序列的复杂度,将相近的子序列进行合并重组以减少计算规模;然后,分别对各重组子序列建立IPSO LSSVM预测模型;最后,将各预测分量进行叠加重构,得到最终的大坝变形预测值。以澜沧江苗尾水电站左岸坝肩边坡为例,将BPNN、RBFNN、LSSVM、EEMD SE LSSVM与EEMD SE PSO LSSVM进行对比研究。结果表明,该模型的计算精度优于其他神经网络模型,具有较好的适宜性和稳定性,是一种可靠的坝肩边坡变形预测方法,能够为大坝安全监测提供有价值的参考。
