- DA.Bo@nims.go.jp
- Address
- 305-0047 1-2-1 Sengen Tsukuba Ibaraki JAPAN [Access]
Research
- Keywords
Nanomaterial, Surface analysis, Background signal, Secondary electron
Dr Bo Da has been engaged in developing novel measurement-analysis methods to extract more information from measured spectra by surface analysis techniques. For instance, he developed the reverse Monte Carlo method to extract optical constant of bulk material from measured surface electron spectra, and the extended Mermin method to determine low energy electron mean free path of bulk material. Most recently, Da’s research focus has been largely related to development of new measurement-analysis method for nanomaterial samples. The virtual substrate method developed by him represents a benchmark for surface analysis to provide “free-standing” information about supported nanomaterials, and brought him President’s Prize awarded by NIMS. He has had ten first-author papers related to these new methods published in various journals, including Nature Communications, Physical Review Letters and Journal of Applied Physics, among others.
Characterization techniques available for bulk or thin-film solid-state materials have been extended to substrate-supported nanomaterials, but generally non-quantitatively. This is because the nanomaterial signals are inevitably buried in the signals from the underlying substrate in common reflection-configuration techniques. Here, we propose a virtual substrate method, inspired by the four-point probe technique for resistance measurement as well as the chop-nod method in infrared astronomy, to characterize nanomaterials without the influence of underlying substrate signals from four interrelated measurements. This method in secondary electron (SE) microscopy, a SE spectrum (white electrons) associated with the reflectivity difference between two different substrates can be tracked and controlled. The SE spectrum is used to quantitatively investigate the covering nanomaterial based on subtle changes in the transmission of the nanomaterial with high efficiency rivaling that of conventional core-level electrons. The virtual substrate method represents a benchmark for surface analysis to provide “free-standing” information about supported nanomaterials.
PublicationsNIMS affiliated publications since 2004.
Research papers
- Bo Da, Jiangwei Liu, Mahito Yamamoto, Yoshihiro Ueda, Kazuyuki Watanabe, Nguyen Thanh Cuong, Songlin Li, Kazuhito Tsukagoshi, Hideki Yoshikawa, Hideo Iwai, Shigeo Tanuma, Hongxuan Guo, Zhaoshun Gao, Xia Sun, Zejun Ding. Virtual substrate method for nanomaterials characterization. Nature Communications. 8 (2017) 15629 10.1038/ncomms15629 Open Access
- H. Xu, B. Da, J. Tóth, K. Tőkési, Z. J. Ding. Absolute determination of optical constants by reflection electron energy loss spectroscopy. Physical Review B. 95 [19] (2017) 195417 10.1103/physrevb.95.195417
- B. Da, H. Shinotsuka, H. Yoshikawa, Z. J. Ding, S. Tanuma. Extended Mermin Method for Calculating the Electron Inelastic Mean Free Path. Physical Review Letters. 113 [6] (2014) 063201 10.1103/physrevlett.113.063201 Open Access
Proceedings
- Jiangwei Liu, Hirotaka Ohsato, Bo Da, Yasuo Koide. Diamond Metal-Oxide-Semiconductor Field-Effect Transistors on a Large-Area Wafer. 2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT). (2023) 10.1109/iceict57916.2023.10245613
Presentations
- 達 博, 吉川 英樹, 田沼 繁夫. Electron transport properties of monolayer graphene measured from secondary electron microscopy according to the substrate variational method. EMAS Workshop / IUMAS Meeting. 2017 Invited
- 達 博, 吉川 英樹, 田沼 繁夫. Extracting pure nanomaterial information by surface analysis using the chop-nod method. the 20th International Vacuum Congress. 2016
- 達 博, 吉川 英樹, 田沼 繁夫, 岩井 秀夫. Low Energy Electron-Electron Interaction Information of Graphene Measured from Secondary Electron Microscopy. 7th International Symposium on Practical Surface Analysis. 2016 Invited
Published patent applications
- 顕微分光データ測定装置および方法 (2017)
- 3次元ラマン分光方法 (2018)
- 被測定細胞の弾性特性分布を解析する方法及び装置、並びに原子間力顕微鏡の探針の形状パラメータを定める方法及び装置 (2021)
Society memberships
日本表面真空学会
Awards
- 倉田奨励金 (2023)
- 花王科学奨励賞 (2020)
- Best Poster Award, by the Executive Committee of Joint Symposium on Materials Integration and Advanced Materials Characterization, Japan (Tsukuba). (2018)
- President’s Prize for Advances in Science and Technology for Young Scientists, by National Institute for Materials Science, Japan (Tsukuba). (2017)
- Best Poster Award, by the Executive Committee of Joint Symposium on Intercommunity and Measurement, Japan (Tsukuba). (2016)
- Best Poster Award, by the Executive Committee of Joint Symposium on Intercommunity and Measurement, Japan (Tsukuba). (2014)
- Student Award, by The 141st Committee on Microbeam Analysis of Japan Society for the Promotion of Science, Korea (Seoul). (2011)
- Best Poster Award, by The Surface Science Society of Japan, Japan (Tokyo). (2011)
Center for Basic Research on Materials
Heuristic data-driven spectral analysis for nanomaterial information embed in the background
Nanomaterial, Surface analysis, Background signal, Secondary electron
Overview
Low energy electron–electron interactions in nanomaterial, low energy electron transport properties of nanomaterial, electronic properties of nanomaterial at low energies. Extracting meaningful information from the background and characterizing material using energy-filtered scanning electron microscopy.
Novelty and originality
1. finding proper AD descriptors that meet the actual requirement according to the experience learned from many measurements under different experimental conditions
2. an extension of the traditional data-driven analysis approach
3. holds immense potential to become a new benchmark method in data processing
Details
Firstly, the proposed heuristic data-driven analysis method represents a benchmark to extract meaningful information from background data, which, in principle, can be easily implemented in many more reflection-configuration techniques than surface analysis techniques and does not demand extra investment in equipment. Therefore, such technique will generate broad interest from numerous fields that use reflection-onfiguration techniques for ultrathin material characterization.
Secondly, implemented in SE spectra, this method expands the energy scale of analysis down to several electron volts and thus allows one to quantitatively probe the e–e interactions of a nanomaterial and hold to potential to observe ‘hidden’ electronic energy transfer to and from a nanomaterial on a substrate.
Finally, with the exploit of SE signals, the heuristic data-driven analysis method can be extended to energy-filtered SEM for material characterization, and rival conventional techniques based on core-level signals in signal-to-noise ratio by orders of magnitude, holding great potential for manufacture monitoring and quality control. This new technique is of particularly importance for steel industry because the nanometer-thick passive films on stainless steel could be automatically characterized without any additional procedures other than processing scanning energy-filtered SEM images.
Summary
The virtual substrate method represents a benchmark for surface analysis to provide “free-standing” information about supported nanomaterials.