Matsumura Tarojiro, Nagamura Naoka, Akaho Shotaro, Nagata Kenji, Ando Yasunobu
Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
Research Center for Advanced Measurement and Characterization, National Institute for Materials Science (NIMS), Tsukuba, Japan.
Sci Technol Adv Mater. 2019 Jun 27;20(1):733-745. doi: 10.1080/14686996.2019.1620123. eCollection 2019.
We introduce a spectrum-adapted expectation-maximization (EM) algorithm for high-throughput analysis of a large number of spectral datasets by considering the weight of the intensity corresponding to the measurement energy steps. Proposed method was applied to synthetic data in order to evaluate the performance of the analysis accuracy and calculation time. Moreover, the proposed method was performed to the spectral data collected from graphene and MoS field-effect transistors devices. The calculation completed in less than 13.4 s per set and successfully detected systematic peak shifts of the C 1 in graphene and S 2 in MoS peaks. This result suggests that the proposed method can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.
我们通过考虑与测量能量步长相对应的强度权重，引入了一种光谱自适应期望最大化（EM）算法，用于对大量光谱数据集进行高通量分析。将所提出的方法应用于合成数据，以评估分析精度和计算时间的性能。此外，将所提出的方法应用于从石墨烯和二硫化钼场效应晶体管器件收集的光谱数据。每组计算在不到13.4秒内完成，并成功检测到石墨烯中C 1峰和二硫化钼中S 2峰的系统峰移。该结果表明，所提出的方法可以支持峰移研究，具有两个优点：（1）可以高速处理大量数据；（2）可以轻松进行稳定且自动的计算。
