8

文部科学省ナノテクノロジープラットフォーム

第18回ナノテクノロジー
総合シンポジウム
JAPAN NAN

2020

“Quantum Science, Biotechnology and AI Technology driven by Nanotechnology”

Proceedings
Date: January 31st (Fri), 2020
Venue: Tokyo Big Sight, Conference Tower(Tokyo)
Sponsored by
Nanotechnology Platform by the Ministry of Education, Culture, Sports,
Science and Technology(MEXT)
Center for Nanotechnology Platform, National Institute for Materials
Science (NIMS)

The Institutions, participating in the Nanotechnology Platform:
Hokkaido University, Chitose Institute of Science and Technology,
Tohoku University, University of Tsukuba,
National Institute for Materials Science,
National Institute of Advanced Industrial Science and Technology,
The University of Tokyo, Tokyo Institute of Technology,
Waseda University, Shinshu University,
National Institutes of Natural Sciences Institute for Molecular Science,
Nagoya University, Nagoya Institute of Technology,
Toyota Technological Institute, Kyoto University,
Japan Advanced Institute of Science and Technology,
Nara Institute of Science and Technology, Osaka University,
Japan Atomic Energy Agency,
National Institutes for Quantum and Radiological Science and Technology,
Hiroshima University, Yamaguchi University,Kagawa University,
Kyushu University,
Kitakyushu Foundation for the Advancement of Industry Science and Technology

文部科学省ナノテクノロジープラットフォーム

第18回ナノテクノロジー
総合シンポジウム
JAPAN NAN

2020

「ナノテクノロジーが切り拓く量子・バイオ・AI 技術」

講演予稿集
開催日：2020 年1月31日（金）
会

場：東京ビッグサイト
会議棟１階レセプションホール A（東京都江東区有明）

主催：文部科学省ナノテクノロジープラットフォーム
国立研究開発法人物質・材料研究機構ナノテクノロジー
プラットフォームセンター

文部科学省ナノテクノロジープラットフォーム参画機関：
北海道大学、千歳科学技術大学、東北大学、筑波大学、
物質・材料研究機構、産業技術総合研究所、東京大学、
東京工業大学、早稲田大学、信州大学、
自然科学研究機構分子科学研究所、名古屋大学、
名古屋工業大学、豊田工業大学、京都大学、
北陸先端科学技術大学院大学、
奈良先端科学技術大学院大学、大阪大学、
日本原子力研究開発機構、量子科学技術研究開発機構、
広島大学、山口大学、香川大学、九州大学、
北九州産業学術推進機構

1

January 31st (Fri.), 2020, Reception Hall A
2020年1月31日（金） 会議棟１階レセプションホール A

10:00-10:30【Opening Remarks / 開会挨拶】
10:00 -

Ministry of Education, Culture, Sports, Science and Technology/ 文部科学省

10:05 -

Kazuhito Hashimoto (President, National Institute for Materials Science, Japan)

橋本 和仁（物質・材料研究機構理事長）

10:30-11:05【Plenary Lecture / 基調講演】
Hiroshi Amano (Nagoya University, Japan)

天野 浩（名古屋大学）“En Route to Realize Zero Carbon Emission”「カーボンエミッションゼロを目指した取り組み」

11:05-11:40【Special Lecture / 特別講演】
Jinichi Igarashi (COCN(Council on Competitiveness-Nippon))

五十嵐 仁一（一般社団法人 産業競争力懇談会）“Launching the MI platform program toward energy innovation”「エネルギー革新に向けた MI 基盤の構築」

11:40-12:30【Session 1】
Quantum Science & Biotechnology / 量子と生命科学
11:40 -

Mutsuko Hatano (Tokyo Institute of Technology)

波多野 睦子（東京工業大学）“The potential of diamond solid-state quantum sensors”「ダイヤモンド量子センサの可能性」
12:05 -

Ichio Aoki (National Institutes for Quantum and Radiological Science and Technology)

青木 伊知男（量子科学技術研究開発機構）“Nano- and Quantum Technology-based MR Imaging for Life Science”「生命科学を革新するナノおよび量子技術による磁気共鳴イメージング」

12:30 - 13:30

Lunch / 昼食

13:30-13:55【Session 2】
nanotech 2019 award lecture / nano tech 大賞 2019 講演
13:30 -

Toru Ushirogochi (Ricoh)

後河内 透（株式会社リコー）“Prospects for Digital Print Manufacturing of Lithium-ion Batteries”「リチウムイオン電池のデジタル印刷を目指して」

2

13:55-14:25【Session 3】
Overseas Nanotechnology User Facility Programs / 海外ナノテク共用施設・研究成果紹介
13:55 -

Daniel J. C. Herr (Joint School of Nanoscience and Nanoengineering, USA)“The National Nanotechnology Coordinated Infrastructure (NNCI) and Selected Convergent
Technologies.”「NNCI とコンバージェントテクノロジー」

14:25 - 14:40

Coﬀee Break / 休憩

14:40-15:45【Session 4】
Topics and future perspectives of nanotechnology platforms /ナノテクノロジープラットフォームの成果と将来展望
14:40 14:45 -“Introduction”「イントロダクション」
Yasutaka Matsuo (Hokkaido University)

松尾 保孝（北海道大学）“Biotechnology research by using a nano, micro structural analysis with electron microscope”「電子顕微鏡による構造解析手法を用いたバイオ研究」
15:05 -

Kentaro Totsu (Tohoku University)

戸津 健太郎（東北大学）“R&D support by open collaboration based on shared facilities”「共用設備を基盤としたオープンコラボレーションによる研究開発支援」
15:25 -

Yoshinobu Baba (Nagoya University)

馬場 嘉信（名古屋大学）“Research Support for the Interdisciplinary Research between Nanotechnology,
Biotechnology, Quantum Technology, and AI, and Future Perspective for Nanotechnology Platform”「ナノテクノロジーとバイオ・量子・AI 融合領域の支援成果とナノテクノロジープラットフォームの将来展望」

15:45-16:35【Session 5】
Beyond Nano・AI / ナノテクノロジーのシステム化と産業応用
15:45 -

Daisuke Okanohara (Preferred Networks)

岡野原 大輔（株式会社 Preferred Networks）“Materials Discovery Using Machine Learning: Latest Trends and Future Prospects”「機械学習を使った材料探索の最新動向と今後の展望」
16:10 -

Hirohito Hirata (TOYOTA)

平田 裕人（トヨタ自動車株式会社）“Nanotechnology in automobiles : Nanomaterials in parts and units”「自動車におけるナノテクノロジー：ナノマテリアルと部品・ユニット」

16:35-16:40【Closing Remarks / 閉会挨拶】
Shigeo Tanuma(Chairperson of the Organizing Committee of JAPAN NANO 2020 / Director, Center for Nanotechnology Platform, National Institute for Materials
Science, Japan)

田沼 繁夫（JAPAN NANO 2020 組織委員長、物質・材料研究機構ナノテクノロジープラットフォームセンター長）

3

Contents / 目次

Plenary Lecture / 基調講演
“En Route to Realize Zero Carbon Emission” ………………………………………………………………………… 10「カーボンエミッションゼロを目指した取り組み」
Hiroshi Amano (Nagoya University, Japan)
天野 浩（名古屋大学）

Special Lecture / 特別講演
“Launching the MI platform program toward energy innovation” ……………………………………………………… 16「エネルギー革新に向けた MI 基盤の構築」
Jinichi Igarashi (COCN(Council on Competitiveness-Nippon))
五十嵐 仁一（一般社団法人 産業競争力懇談会）

Session 1: Quantum Science & Biotechnology / 量子と生命科学“The potential of diamond solid-state quantum sensors” ……………………………………………………………… 22「ダイヤモンド量子センサの可能性」
Mutsuko Hatano (Tokyo Institute of Technology)
波多野 睦子（東京工業大学）“Nano- and Quantum Technology-based MR Imaging for Life Science” ……………………………………………… 24「生命科学を革新するナノおよび量子技術による磁気共鳴イメージング」
Ichio Aoki (National Institutes for Quantum and Radiological Science and Technology)
青木 伊知男（量子科学技術研究開発機構）

Session 2: nanotech 2019 award lecture / nano tech大賞 2019 講演“Prospects for Digital Print Manufacturing of Lithium-ion Batteries” ………………………………………………… 30「リチウムイオン電池のデジタル印刷を目指して」
Toru Ushirogochi (Ricoh)
後河内 透（株式会社リコー）

4

Session 3: Overseas Nanotechnology User Facility Programs /海外ナノテク共用施設・研究成果紹介“The National Nanotechnology Coordinated Infrastructure (NNCI) and Selected Convergent Technologies.” ……… 36「NNCI とコンバージェントテクノロジー」
Daniel J. C. Herr (Joint School of Nanoscience and Nanoengineering, USA)

Session 4: Topics and future perspectives of nanotechnology platforms /ナノテクノロジープラットフォームの成果と将来展望“Biotechnology research by using a nano, micro structural analysis with electron microscope” ……………………… 42「電子顕微鏡による構造解析手法を用いたバイオ研究」
Yasutaka Matsuo (Hokkaido University)
松尾 保孝（北海道大学）“R&D support by open collaboration based on shared facilities” ……………………………………………………… 44「共用設備を基盤としたオープンコラボレーションによる研究開発支援」
Kentaro Totsu (Tohoku University)
戸津 健太郎（東北大学）“Research Support for the Interdisciplinary Research between Nanotechnology,
Biotechnology, Quantum Technology, and AI, and Future Perspective for Nanotechnology Platform”……………… 46「ナノテクノロジーとバイオ・量子・AI 融合領域の支援成果とナノテクノロジープラットフォームの将来展望」
Yoshinobu Baba (Nagoya University)
馬場 嘉信（名古屋大学）

Session 5: Beyond Nano・AI / ナノテクノロジーのシステム化と産業応用“Materials Discovery Using Machine Learning: Latest Trends and Future Prospects” ………………………………… 52「機械学習を使った材料探索の最新動向と今後の展望」
Daisuke Okanohara (Preferred Networks)
岡野原 大輔（株式会社 Preferred Networks）“Nanotechnology in automobiles : Nanomaterials in parts and units” ………………………………………………… 54「自動車におけるナノテクノロジー：ナノマテリアルと部品・ユニット」
Hirohito Hirata (TOYOTA)
平田 裕人（トヨタ自動車株式会社）5

6

【Plenary Lecture / 基調講演】【Plenary Lecture / 基調講演】

7

8

“En Route to Realize Zero Carbon Emission”「カーボンエミッションゼロを目指した取り組み」

Hiroshi Amano (Nagoya University, Japan)
天野 浩（名古屋大学）

9

EN ROUTE TO REALIZE ZERO CARBON EMISSION
カーボンエミッションゼロを目指した取り組み
Hiroshi Amano
Institute of Materials and Systems for Sustainability (IMaSS), Nagoya University,
Room 610 CIRFE Transformative Electronics Commons (C-TECs)
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Abstract
The most important application of III nitrides is blue LEDs for full color displays and generallighting systems. Most of the commercially available blue LEDs are grown on foreign substrates suchas sapphire, Si or SiC. In spite of high dislocation density over 108 cm-2, wall plug efficiency (WPE)of blue LEDs exceeds 50%, which is extremely high compared with that of LEDs made by other III-V
compound semiconductors such as GaAs or GaP. In case of other LEDs, WPE may be less than 1% ifsuch a high density of dislocations exists in the crystal. One of the reasons of high-efficiency of blue
LEDs based on III nitrides is short diffusion length of minority carriers in GaN and InGaN. By 2020,
more than 70% of the general lighting system will be replaced from the conventional incandescentlamps or fluorescent lamps to LED lamps, by which about 7% of the total electricity consumption canbe saved. In this presentation, history and future prospects of the development of the blue LEDs will
be discussed.
AlGaN-based deep-UV (DUV) LEDs on sapphire substrates are effective for the efficient and longlife devices for sterilization and purification of water compared with conventional UV germicidal
lamps. UNICEF reported that 844 million people still lack access to safe drinking water and 2.3 billion
people do not use safe sanitation facilities. New water sterilization and purification systems have beencommercialized, in which AlGaN-based high-power DUV LEDs are installed. Other applications of
DUV LEDs include as a sterilizer for sanitation facilities, resins and the curing of inks in large printers,
detecting forged banknotes, photolithography for manufacturing semiconductor devices, and treating
skin disease, which is called dermatology.
Microwave high-electron mobility transistors (HEMT) based on AlGaN/GaN on SiC substrate
works well in spite of high dislocation density over 108 cm-2 because high-density two-dimensional
electron gas induced by polarization of these materials is used, therefore Fermi level in the channel
layer is very high and scattering by the dislocation is small. Majority of high-power amplifier in basestation of smart phone have been replaced from multichip GaAs-based HEMT to single chip GaNbased HEMT.
Breakdown field of GaN is one order of magnitude higher than that of Si. Therefore, we can reduce
not only the size of the transistors, but also power loss to one tenth. If we replace all the Si basedtransistors such as insulated gate bipolar transistors to GaN based transistors in the invertor circuits,
we can reduce 9.8% of the total electricity consumption in Japan. For power device applications,vertical structure is more feasible to operate high current. Low defect density crystal is essential for
realizing low current leakage at high voltage. Therefore, requirement specification for the substrateand epitaxial layer is much higher than that of LEDs and microwave devices. In this presentation,
substrate and epitaxy issues for the future power devices based on III nitrides will be discussed.
This research is supported by the Ministry of Education, Culture, Sports, Science and Technology(MEXT), Japan, through its “Program for research and development of next-generation semiconductor
to realize energy-saving society.

10

<CV>
Hiroshi Amano joined Prof. Isamu Akasaki's group in 1982 as an undergraduate student. In 1985, while working
on his master courses and PhD courses, Amano developed a metalorganic vapor phase epitaxy (MOVPE) process for
low-temperature deposited buffer layers for the growth of GaN single-crystal films on sapphire substrates in 1985,
which led to the realization of the first III-N semiconductor based light-emitting diodes and laser diodes. In 1989, hesucceeded in growing the first conductive p-type GaN using Mg doping followed by low energy electron beamirradiation treatment and in fabricating p-n-junction GaN-based UV and blue light-emitting diodes for the first timein the world. After graduation, he has continued to independently do world-leading research on the growth,characterization and device applications of III –N semiconductors. Amano has independently led his own world-classresearch group since 1992 and he has moved to Nagoya University where continues to lead the world in the creationof high-performance DUV LEDs, UV photodetectors and power devices.

11

12

【Special Lecture / 特別講演】【Special Lecture / 特別講演】

13

14

“Launching the MI platform program
toward energy innovation”「エネルギー革新に向けたMI基盤の構築」

Jinichi Igarashi (COCN(Council on Competitiveness-Nippon))
五十嵐 仁一（一般社団法人 産業競争力懇談会）

15

LAUNCHING THE MI PLATFORM PROGRAM TOWARD
ENERGY INNOVATION
エネルギー革新に向けたMI基盤の構築
1

J. Igarashi, 2K. Nakayama, 3K. Oyaizu, 3K. Hatakeyama-Sato, and 3H. Nishide1
Council on Competitiveness-Nippon (COCN),
2-2-1, Uchisaiwaicho, Chiyoda-ku, Tokyo 100-0011, Japan
2

Central Technical Research Laboratory, JXTG Nippon Oil & Energy Corporation,
1-2, Otemachi 1-chome, Chiyoda-ku, Tokyo 100-8162, Japan

Department of Applied Chemistry and Research Institute for Science and Engineering, Waseda University,
3-4-1 Okubo, Shinju-ku, Tokyo 165-8555, Japan

3

Abstract
Materials informatics (MI) is an emerging data-driven science, which is expected as a powerful tool
for materials innovation. We targeted to develop the MI system to design novel functional polymer
materials for energy-related devices as a benchmark of the MI-assisted materials science. With theuse of the informatics techniques and a functional materials database originally developed through thecollaboration of industry and academia, we have found intriguing highly ion-conducting polymericmaterials for lithium-ion batteries. Based on these activities, we plan to create the open platform for
the MI-assisted materials development within Waseda University. The open platform will include
high-throughput synthesis and analysis, which largely enhance materials development. Educationand development of MI human resource and difficulty in acquisition and accumulation of theexperimental data for MI, which are buried and lost in industry, are also discussed.
INTRODUCTION

Recently, materials informatics have been attracting
substantial attention to discover novel materials. Artificial
intelligence (AI) analyzes and extracts the important trends ofthe big material databases automatically. The trained AI can
predict the properties of materials with even new structures.
Such capability has been partially pursued to discover novel
ceramics, alloys, drugs, and functional molecules.
On the other hand, polymers have been still frontiers for AI,
because they have highly complicated primary- and higherorder structures. Further, the system becomes much more
complex when polymers having special functionalities such as
electric or ionic conductivities are composited with additives
to improve their performances. These features of polymericmaterials with functionalities make it extremely difficult todesign experimental polymer databases. Although databases
for polymer informatics are under development, PoLyInfo[1,2]of National Institute for Materials Science (NIMS) is a worldleading big database of polymers including functional
polymers.

a)Polymer A+

Y =f (X)

Additive B+

Additive C

+
…
b)

AI
X =f -1 (Y)Ionicconductivity
Activationenergy…

3UHGLFWLRQRILRQLF
FRQGXFWLYLW\E\$,/RJ s SUHG 6・FP

I.

/RJ s H[S 6・FP

Fig. 1 MI-assisted discovery of ion-conducting
polymers for lithium-ion batteries.

16

II.

DEVELOPMENT OF MI SYSTEM FOR FUNCTIONAL MATERIALS DESIGN

A research group in Waseda University developed AI to explore new polymeric lithium-ion conductors for solidstate secondary batteries (Fig. 1).[3,4] A new database was created from data in public literatures (with >102
scientific articles) and contained more than 104 cases of the experimental ionic conductivity data. The
relationships among the chemical structures and their conductivity were trained with the AI. The prediction
was successful not only for homopolymers but also for copolymers and even for composite materials. AI
predicted the untrained performances of the recently reported conductors accurately, which suggested the highlevel predictable capability of the present MI system. Several next-generation conductors were also proposedby the AI. Some of the conductors were actually synthesized and successfully examined. Such automatic
screening shortens the period of exploring new materials dramatically, which would facilitate future highthroughput production of functional materials. Thus, the MI tool developed by the group in Waseda University
based on a small-medium database can be a powerful tool for materials innovation.

III. CONCLUSIONS
Based on the activities described above, we are planning to create the open platform for the MI-assisted materials
development within Waseda University. The open platform will include high-throughput synthesis andanalysis which, along with the MI system, largely enhance materials development. Since education anddevelopment of MI human resource are urgently required, Waseda University launched the new curriculum for
MI in their graduate school in last fall. We will also discuss the difficulty in acquisition and accumulation of
the experimental data for MI, which are buried and lost in industry.
References[1] PoLyInfo: https://polymer.nims.go.jp/[2] R. Yoshida, et al., npj Comput. Mater., (2019) article number 66.
[3] K. Oyaizu, et al., Chem. Lett., vol. 48 (2019), pp. 130-132.
[4] K. Oyaizu, et al., Polymer Preprints, Japan, vol. 68 (2019).

Dr. Jinichi Igarashi joined Nippon Oil
Company in 1982, after he finished
Graduate School of Engineering, Tokyo
Institute of Technology. He is currently
President of JX Nippon ResearchInstitute. He also serves COCN as amember of Executive Committee.

Dr. Keisuke Nakayama joined Nippon
Mitsubishi Oil Corporation in 1999,
after he finished Graduate School ofApplied Chemistry, the University ofTokyo. He is currently manager of
new business Promotion group.

Kenichi Oyaizu is Professor at Waseda
University. Ph.D. in 1995. Board
Member of the Society of Polymer
Science, Japan and the Chemical Societyof Japan.

Kan Hatakeyama-Sato is Assistantprofessor at Waseda University.
Ph.D. in 2018. Visiting Research
Fellow at Texas A&M University,
2017.

Hiroyuki Nishide is Appointed
Research Professor at Waseda
University. Ph.D. in 1975. He is PastPresident of the Federation of Asian
Polymer Societies and Project Leader of
COCN “MI toward Energy Innovation”.

17

18

Session 1【Quantum Science & Biotechnology / 量子と生命科学】

Session 1【Quantum Science & Biotechnology /量子と生命科学】

19

20

“The potential of diamond solid-state quantum sensors”「ダイヤモンド量子センサの可能性」

Mutsuko Hatano (Tokyo Institute of Technology)
波多野 睦子（東京工業大学）“Nano- and Quantum Technology-based
MR Imaging for Life Science”「生命科学を革新するナノおよび量子技術による磁気共鳴イメージング」

Ichio Aoki (National Institutes for Quantum and Radiological Science and Technology)
青木 伊知男（量子科学技術研究開発機構）

21

The potential of diamond solid-state quantum sensors
The potential of diamond solid-state quantum sensors
ダイヤモンド量子センサの可能性
ダイヤモンド量子センサの可能性
Mutsuko Hatano
Mutsuko Hatano
Department of Electrical and Electronic Engineering, Tokyo Institute of Technology,
Department of2-12-1,
Electricaland Electronic
Engineering,
Institute
Ookayama,
Meguro-ku,
Tokyo Tokyo
152-8552,
Japanof Technology,
2-12-1, Ookayama, Meguro-ku, Tokyo 152-8552, JapanThe electron systems localized at point defects in solid are stable sources of controllable spin states.
The electron systems localized at point defects in solid are stable sources of controllable spin states.
Nitrogen-vacancy (NV) centers in diamond have superior physical properties at room temperature for
Nitrogen-vacancy (NV) centers in diamond have superior physical properties at room temperature forquantum sensing of magnetic fields, electric field, strain, and temperature enabling scalable
quantum sensing of magnetic fields, electric field, strain, and temperature enabling scalable
applications from atomic to macroscopic range. In this talk, we review the sensor materials, quantum
applications from atomic to macroscopic range. In this talk, we review the sensor materials, quantum
control technology, and applications. The perfectly-aligned NV ensemble is well suited for sensing ofcontrol technology, and applications. The perfectly-aligned NV ensemble is well suited for sensing of
biological/ medical systems and battery/ power energy electronics.
biological/ medical systems and battery/ power energy electronics.
The NV center consisting of a nitrogen atom and a lattice vacancy in diamond (Fig. 1 (a)) preserves the
The NV center consisting of a nitrogen atom and a lattice vacancy in diamond (Fig. 1 (a)) preserves thequantum coherence even at room temperature under ambient conditions. The spin state of electrons localized atquantum coherence even at room temperature under ambient conditions. The spin state of electrons localized at
the NV center exhibits spin-1 triplet ground state (Fig. 1 (b)). The energy levels in the ground state are modulated
the NV center exhibits spin-1 triplet ground state (Fig. 1 (b)). The energy levels in the ground state are modulated
by external magnetic field [1,2], electric field, strain, and temperature. In conjunction with spin state
by external magnetic field [1,2], electric field, strain, and temperature. In conjunction with spin state
manipulation using microwave radiation, optically detected magnetic resonance (ODMR) can be utilized. The
manipulation using microwave radiation, optically detected magnetic resonance (ODMR) can be utilized. The
quantum state of the NV center can be detected by the red fluorescence difference between the quantum states
quantum state of the NV center can be detected by the red fluorescence difference between the quantum states
ms=0 and ms =± 1 when excited by a green light source. The energy levels of the state ms=± 1 split depending
m =0 and ms =± 1 when excited by a green light source. The energy levels of the state ms=± 1 split depending
ons the magnetic
field due to the Zeeman effect, so the system is used as a highly sensitive magnetometer.
on the magnetic field due to the Zeeman effect, so the system is used as a highly sensitive magnetometer.
Table1 shows a comparison of the quantum magnetic sensors. The diamond sensors can operate at a wide
Table1 shows a comparison of the quantum magnetic sensors. The diamond sensors can operate at a wide
temperature range (several mK - 700 K) and are superior in the dynamic range and the linearity to the magnetic
temperature range (several mK - 700 K) and are superior in the dynamic range and the linearity to the magnetic
field. The spatial resolution is scalable from nm to mm by using single to ensemble NV centers. The diamondfield. The spatial resolution is scalable from nm to mm by using single to ensemble NV centers. The diamond
sensors have the unique functions of the vector magnetic field imaging, as well as multi-modality such as
sensors have the unique functions of the vector magnetic field imaging, as well as multi-modality such astemperature and magnetic fields.
Due to the wide range of spatial resolution, the diamond quantum sensor
temperature and magnetic fields.
Due to the wide range of spatial resolution, the diamond quantum sensor
has many potential applications, particularly well suited for sensing the magnetic field in the biological/ medical
has many potential applications, particularly well suited for sensing the magnetic field in the biological/ medical
systems and battery/ power electronics (Fig. 2). Measurement set-up illustrations for magnetic field imaging ofsystems and battery/ power electronics (Fig. 2). Measurement set-up illustrations for magnetic field imaging ofliving cells are shown in Fig.3 as an example.
living cells are shown in Fig.3 as an example.
We will introduce the core technologies on material, devices, and sensor systems;
We will introduce the core technologies on material, devices, and sensor systems;➢ Materials and devices [3-7]➢ Materials and devices [3-7]- Selectively-aligned NV ensemble formed by distinctive CVD-growth for scalable applications.
- Selectively-aligned NV ensemble formed by distinctive CVD-growth for scalable applications.
- The heteroepitaxial growth of NV-contained diamond on Si substrate for large area and on chip
- The heteroepitaxial growth of NV-contained diamond on Si substrate for large area and on chip
integration.
integration.
- Sensing devices by band engineering using pn junctions.
- Sensing devices by band engineering using pn junctions.
➢ Multi-scale and multi-modal sensor systems for biological and energy electronics applications. [8, 9]➢ Multi-scale and multi-modal sensor systems for biological and energy electronics applications. [8, 9]This study was supported in part by MEXT Q-LEAP JPMXS0118067395,
This study was supported in part by MEXT Q-LEAP JPMXS0118067395,
and KAKENHI (17H01262 and 18H01472).
and KAKENHI (17H01262 and 18H01472).
References
References[1] F. Jelezko, J. Wrachtrup et al., Phys. Rev. Lett. 92, 076401 (2004).
[1]
Jelezko,
et al., Phys.
Rev. Lett.et92,(2004).
[2] F.
D Le
Sage,J.K.Wrachtrup
Arai, A.Yacoby,
R.Walsworthal.,076401
Nature 496,
486 (2013).
[2]
D
Le
Sage,
K.
Arai,
A.Yacoby,
R.Walsworthetal.,Nature496,
486MH(2013).
[3] H. Ozawa, MH et al., APEX 10, 045501 (2017). [4] H. Ishiwata,
et al., APL 111, 043103 (2017).
[3]
H.
Ozawa,
MH
et
al.,
APEX
10,
045501(2017).
[4]
H.
Ishiwata,
MH
APL
111,
043103(2017).
[5] T. Suto, MH et al., APL 110, 062102 (2017).
[6] T. Murai, MH et etal.,al.,APL
112,
111903(2018).
[5]
T.
Suto,
MH
et
al.,
APL
110,
062102(2017).
[6]
T.
Murai,
MH
et
al.,
APL
112,
111903(2018).
[7] M. Shimizu, MH et al., APEX 11, 033004 (2018).
[7]
Shimizu,MHMHetetal.,al.,Physica
APEX Status
11, 033004[8] M.
Y. Hatano,
Solidi(2018).
(A), 215(2018).
[8]
Y.
Hatano,MHetal.,Physica
StatusSolidi215(2018).
[9] T. Iwasaki, MH et al., ACS Nano, 11, 1238 (A),(2017).
[9] T. Iwasaki, MH et al., ACS Nano, 11, 1238 (2017).

22

(a)

(b)

Table 1 Comparison of quantum magnetic sensors.

Fig. 1. Overviews of the NV center quantum system in daimond:(a) Schematic of the NV center in diamond.
(b) Energy diagram of the NV center.
The energy levels in the grand state are modulated by external
magnetic field, electric field, strain, and temperature.

Fig.2. Potential on the scalable application of diamond quantum sensors
and quantum materials by CVD technologies.

Fig.3. Potential on the scalable
application of diamond quantum
sensors and quantum materials by
CVD technologies.

<CV>
Mutsuko Hatano received the Ph.D. degree from Keio University. She joined Central
Research Laboratory, Hitachi Ltd., Tokyo, Japan, and was engaged in research anddevelopment on the superconducting devices, mobile displays, and power electronics. She
was a chief researcher at the CRL and the project manager of the environment electronics.
She was a visiting researcher at the University of California, Berkeley from 1998 to 2000.
In 2010, she joined Tokyo Institute of Technology as a professor of Electrical andElectronic Engineering. Her main research field is wide-gap semiconductor devices for
power electronics and for quantum sensing. She is a member of the Science Council ofJapan, a fellow and a vice president of the Japan Society of Applied Physics, and a councilof Tokyo Institute of Technology.

23

生命科学を革新するナノおよび量子技術による磁気共鳴イメージング
Nano- and Quantum Technology-based MR Imaging for Life Science
1,2


青木伊知男 Ichio AOKI

国立研究開発法人量子科学技術研究開発機構量子生命科学領域

放射線医学総合研究所
〒千葉市稲毛区穴川丁目番号

Abstract
ナノ粒子を使った造影剤の進歩は、05, 診断技術に大きな発展をもたらしている。ナノ粒子をプラットフォームとした薬剤送達システム（''6）は、治療だけでなく、生体イメージング等の画像診断のツールとして大きな貢献が期待される。05, で可視化できるナノ粒子は、病巣内に送達される薬剤の濃度推定や、患者間の分布の差異を治療開始前に可視化できるため、臨床での治療効果の最適化のみならず、薬剤の開発段階においても重要な技術である（コンパニオン造影剤）。加えて、従来の造影剤の感度を理論値で  万倍まで向上させる「超偏極」という量子技術が注目されている。本講演
では小動物を対象とした前臨床 05, において、高解像度の 05, とナノ粒子造影剤の応用例を、最近の進歩と我々の取り組みを中心に紹介し、加えて、超高感度を実現する超偏極 05, とナノ技術の組合せがもたらす将来を展望したい。

I. INTRODUCTION
05, は磁場中にある水プロトンが電磁波と共鳴現象を生じる核磁気共鳴（105）を利用した生体断層イメージング法であり、基本特性として、高空間分解能（マウスで µm 程度）、軟部組織の高い
コントラスト、放射線を使用しない無侵襲性などに加えて、生体の形状だけでなく機能（循環、血管、
脳機能、線維走行など）や代謝など多様なイメージング法が可能であること等を特徴とする。臨床においても全国で６千台以上も普及し大半の病院で稼働するなど大きな市場性があり、また小動物を対象とした前臨床装置も研究用途に使われ、小動物から臨床への接続が比較的容易なモダリティであると言える。


II. MAIN TEXT
幾つかの技術革新が、最近の 05, に大きな進歩をもたらしており、その一つがナノ粒子を使った造影剤の進歩である。ナノ粒子をプラットフォームとした薬剤送達システム（''6）は、治療だけでなく、生体イメージング等の画像診断あるいは体外診断のツールとして大きな貢献が期待され、多様な研究成果が報告されている。ナノ粒子の 05, への応用は、①ナノ粒子を 05, 造影剤で標識し動態を観察する「コンパニオン診断」（）、②ナノ粒子中に造影剤と薬剤の両方を包含する「セラノスティクス」（）、③05, 造影剤に標的性を持たせる「標的性造影剤」、④生体や病巣の環境に応答して信号を変化させる「反応性造影剤」（）、⑤感度あるいは安全性を向上させる「高感度・高安全性型」などで進歩している。とりわけ、ナノ粒子による ''6 を患者に応用する際、患者毎に病巣内の分布が大きく異なるなど、特有の現象が生じることが指摘されており、事前に可視化可能なナノ粒子を投与して、病巣内に送達される濃度を推定することは、臨床のみならず、開発段階においても重要な技術である。
 さらに最近、量子技術の進展に注目が集まっている。量子とは「粒子と波の性質をあわせ持った物
質やエネルギーの単位」のことで、原子・電子・中性子・陽子などを指す。第１次の量子革命は、
年代後半から始まったトランジスタや半導体に代表される技術で現在の 05, 技術もその範疇に入る。
最近の進展は、第２次量子革命に繋がる技術革新として語られ、量子特有の性質である量子もつれや重ね合わせ等を利用して、これまでにない飛躍的な性能を持つコンピュータ、通信、暗号などを生み

24

出そうとする動きである。05, に関連しては、従来の造影剤の感度を理論値で  万倍まで向上させる「超偏極（ちょうへんきょく）」という技術が注目されている。105 や 05, では、信号に寄与する
核スピンの数が磁場強度に依存しており、それゆえに高磁場の装置ほど信号雑音比が高くなる。一方、超偏極技術では対象となる化合物（例えば &）の電子スピンを励起し、それを原子核スピンに遷すことで核スピンの大多数を励起できるため、磁場強度に非依存的で、劇的な信号上昇を可能とする。
 本講演では、小動物を対象とした前臨床 05, において、高解像度の 05, とナノ粒子造影剤の応用
例を、最近の進歩と我々の取り組みを中心に紹介する（）。併せて、次世代量子技術の一つに期待される超偏極を紹介し、ナノ技術と高感度 05, がもたらす将来を展望したい。
References[1] Nanomedicine 2018, 14(4):1315-1324.
[2] Cancer Res 2010, 70, 7031-7041.
[3] Nat Nanotechnol 2016, 11, 724-730.
[4] Nanomedicine. 2015 Jan;11(1):229-38.
[5] Proc Natl Acad Sci USA 2003, 100, 10158-10163.
[6] Adv Drug Deliv Rev. 2014 Jul;74:75-94.

青木伊知男（,FKLR$RNL3K'）
国立研究開発法人量子科学技術研究開発機構
量子生命科学領域・統括グループリーダー

量子生命科学領域・量子制御05,グループ・グループリーダー
放射線医学総合研究所・分子イメージング診断治療研究部・機能分子計測チーム・グループリーダー
05,アライアンス&KDLU
,6050-3&&KDLUDRNLLFKLR#TVWJRMS
略歴：
福岡県北九州市出身。機能性05,造影剤の研究で3K'を取得後、年から米国1,+1,1'6における機能分子イメージング研究室（.RUHWVN\$3ディレクター）にて超高磁場05,と機能性造影剤の生体適用を研究。
帰国後年、放射線医学総合研究所・分子イメージング研究センター上席研究員、年よりチームリーダーを務め、高磁場05,を用いた機能性およびナノ造影剤の研究開発および病態応用研究を進める。
年月より組織統合により、国立研究開発法人量子科学技術研究開発機構・放射線医学総合研究所・分子イメージング診断治療研究部・機能分子計測チームのグループリーダー、年から量子科学技術研究開発機構・量子生命科学領域・統括グループリーダーおよび量子制御05,研究グループ・グループリーダーを務める。主な研究テーマは、05,の生体・病態応用で、特にナノ粒子による''6イメージングと腫瘍への標的化治療（セラノスティクス）、マンガン造影05,による神経賦活画像法などへの応用、機能性造影剤の開発と生体適用など。
一般社団法人日本磁気共鳴医学会・理事、日本分子イメージング学会・理事・事務局長、日本''6学会・評議員、,6050-3&・&KDLU、05,アライアンス・&KDLU、3/2621(誌学術エディター他。

25

26

Session 2【nanotech 2019 award lecture / nano tech 大賞 2019 講演】

Session 2【nanotech 2019 award lecture /nano tech 大賞 2019 講演】

27

28

“Prospects for Digital Print
Manufacturing of Lithium-ion Batteries”「リチウムイオン電池のデジタル印刷を目指して」

Toru Ushirogochi (Ricoh)
後河内 透（株式会社リコー）

29

Prospects for Digital Print Manufacturing of Lithium-ion Batteries
リチウムイオン電池のデジタル印刷を目指して
1,2

T.Ushirogochi, 2H. Kuriyama, 2E.Suzuki, 2H.Yanagita and 1T.Furushima
RICOH COMPANNY, LTD.
Innovation / R&D Division, 2 Printed Battery Material & System Development Center

1

2-7-1 Izumi, Ebina-city, Kanagawa, 243-0460 Japan

Abstract
A new approach has been developed to produce lithium-ion batteries (LIB) using inkjet printing. Newly
developed nanodispersants have been found to successfully disperse relatively large and heavy
particles such as ceramics and graphite used in lithium-ion battery electrodes. Additional materials for“inkjet printable nanopore paper” have also been developed and all three major LIB components(cathode, anode, separator) can be applied to roll-to-roll LIB manufacturing using digital inkjet printing.
These technologies are suitable for the production of IoT devices and wearables that require batteriesof various shapes. This technology is expected to allow device designers to choose battery placementin the final design phase. Meanwhile, hybrid inkjet printing methods that integrate inkjet printing withexisting electrode coating lines are also being developed. This method can be expected to improve both
LIB safety and yield in lithium-ion battery manufacturing. By taking advantage of these printing
technologies, the prospects for future manufacturing technologies that use digital printing methods togenerate LIBs have been explained.
I. Introduction

This year is a memorable year when Dr. Yoshino et.al., who invented modern
LIBs won the Nobel Prize1)2). We would like to express our gratitude andcongratulations as one of the researchers in this field.
This may be the result of pursuing mass production for the spread of lithiumion batteries, but in recent years, the flexibility of lithium-ion battery
manufacturing has declined. In response, several attempts3) have been made toenable digital printing of lithium-ion batteries, but there have been few reports
of practical application. This time, the authors have developed a technology for
inkjet printing of lithium-ion batteries into more free shapes using
nanotechnology.

II. Developing Inkjet Printable Battery Materials

Major approaches of Inkjet Printable Battery Materials are how does it convert
the high-viscosity slurries typically used in the battery die-coating to low
viscosity inks. Conventional LIB cathode and anode slurries both containing
several mixtures of material and polymer dispersant, so the viscosity of them areusually very high (c.f. thousand mPa.s.), and total solids amount in them is forthe range of 50–60%. Therefore, new nanodispersant technologies aredeveloped for inkjet printable battery inks. This technology was applicable tomost types of electrode materials used in existing LIBs, not only acheving
relatively higher concentration up to 40wt%, but also typically low viscosity as
several tens of mPa.s. We have developed high printing speeds using these inks
and in-house printer that are acceptable for production performance. In addition
to the electrode material, we have succeeded in develop a printable separator
called “ink-jet printable nanopore paper” with high lithium ion mobility. These
LIB component inks can be printed using RICOH MH / GH series industrial

30

Fig. 1 Typical application of IJ
printed battery.
Figure above: Postcard blinking
with printed battery.
Figure below: Running an IoT
wireless device with a battery
printed in an R shape.

printheads. Using these inks, heads, and prototype printer systems, a freely shaped unique LIB sample is shownin Figure 1. Inkjet printable LIB has also fined to have high output performance of 30C higher. these type LIBs
are thought to be suitable for a power source in IoT devices, that usually implemented process to supply higher
power for transmission, such as Bluetooth. Our goal is to supply power to a myriad of IoT edge devices equippedwith batteries by flexible printer system (Fig.2).

Fig. 2 Prototype Printer System (Left) and image of Flexible Printer System (Right) of LIBs.

III. Hybrid Printing System Removing the Risk of Battery

Prior to the realization of fully printed LIBs, we found that using a hybrid printing concept that combines
inkjet printing with existing electrode coating systems effectively improves LIB performance. The main
advantages of inkjet printing are non-contact and thin printing properties, the ability to form layers on anelectrode, or arbitrarily positioned printing capabilities. Promising applications having advantage of these are
the realization of ceramic and / or separator layers those are either directly coated or partially printed at thehazardous location of the electrode. This has been shown to reduce the risk of ignition in abuse tests such as thenail penetration test4) and the heat-resistant oven test. Similar approaches using gravure coating or small gap die
coating found to have productivity disadvantages such as electrode layer damage and final electrode breakage.
On the other hand, the inspection system installed in the coating line also offers the possibility of detecting
defects and fixing during manufacturing. At this time, an inkjet print can be used to wipe out these defects inthe desired location. This system, which prevents defects with real-time feedback, will become a futureapplication of inkjet battery printing that is attracting attention.
References[1] K. Mizushima et.al., Materials Research Bulletin,Vol.15, Issue 6 (1980), pp783-789.
[2] A. Yoshino, K. Jitsuchika, T. Nakashima, Japanese Patent 1989293 (issued 1985/5/10).
[3] J. Huang, J. Yang, W. Li, W. Cai, Z. Jiang, Thin Solid Films, 516 (2008) 3314–3319.
[4] 第５９回電池討論会（2018）「電池におけるDPM(1) 電極層上への新しいセラミック印刷法」座間優、広渡杏奈、升澤正弘、
柳田英雄、後河内透

T.Ushirogochi
After joined Toshiba Corp. in 1985, he worked in nanotechnology applications and served as the leader of the
Functional Materials. After joining Ricoh Institute for Future Technology in 2016, He is currently working as an
executive engineer in the Ricoh Innovation / R & D division. He is also engaged in the development leader of battery
manufacturing technology using inkjet printing.
・

31

32

Session 3【Overseas Nanotechnology User Facility Programs / 海外ナノテク共用施設・研究成果紹介】

Session 3【Overseas Nanotechnology User Facility
Programs / 海外ナノテク共用施設・研究成果紹介】

33

34

“The National Nanotechnology CoordinatedInfrastructure (NNCI) andSelected Convergent Technologies.”「NNCIとコンバージェントテクノロジー」

Daniel J. C. Herr (Joint School of Nanoscience and Nanoengineering, USA)35

THE NATIONAL NANOTECHNOLOGY COORDINATEDINFRASTRUCTURE (NNCI) AND SELECTED CONVERGENTTECHNOLOGIESNNCIとコンバージェントテクノロジー
1
1

D. J. C. Herr

Department of Nanoscience, University of North Carolina at Greensboro,
Joint School of Nanoscience and Nanoengineering,
2907 East Gate City Boulevard., Greensboro, NC 27401 USA

Abstract
In 2015, The U.S. National Science Foundation (NSF) launched the National Nanotechnology
Coordinated Infrastructure (NNCI). This network of user facilities was created to provide academic,
industrial and government researchers with open access to state-of-the art nanofabrication andcharacterization facilities, tools, instrumentation, and expertise within all disciplines of nanoscalescience, engineering and technology across the U.S. Its overarching goal is to “advance research innanoscale science, engineering and technology”.1 The NNCI framework builds on the National
Nanotechnology Infrastructure Network (NNIN), which enabled major discoveries, innovations, andcontributions to education and commerce for more than 10 years. Specifically, the NNCI broadens thescope of potential users, as it supports the infrastructure needs of traditional, convergent and emerging
nanotechnologies.
This five year program builds and maintains a distributed network of sixteen academic clusters acrossthe U.S. that leverages and engages twenty-four universities, as well as an additional thirteen nonuniversity partners. The NNCI encourages each cluster to compose, integrate and coordinate its corefacilities, so-as-to offer unique sets of resources that benefit and align with the local, regional andnational needs of its user base. The distribution of the lead institutions for each of the sixteen academic
clusters is shown in Figure 1 below.

Fig. 1. A site map of the lead academic institutions for each of the sixteen NNCI clusters.

36

The NNCI also strategically supports and shares the development and integration of best practices andresources in education and outreach programs across the network. Additionally, it assesses the impactof these programs at the local, regional, and national levels. Finally, the NNCI’s social and ethicalimplications programs enable social scientists to interact and engage with nanoscale scientists,
engineers, students and the community. Such programs foster critical thinking and a common language,
so that all stakeholders can proactively consider the potential impacts and unintended consequencesthat new discoveries might have on society. This integrated network approach effectively makes thewhole more than the sum of its parts.
This presentation provides the background and justification for the creation of the National
Nanotechnology Coordinated Infrastructure. It also offers an overview of the NNCIs structure, key
attributes and performance metrics. Additionally, it will briefly examine selected NNCI related
convergent nanotechnologies, such as the SemiSynBio Roadmap initiative4, as well as selected recent
research highlights.
References[1] https://www.nsf.gov/news/news_summ.jsp?cntn_id=136211[2] https://www.nsf.gov/awardsearch/showAward?AWD_ID=1626153[3] NNCI Overview, 4th Annual NNCI Conference, October 24-25, 2019.
[4] Victor V. Zhirnov and Daniel Rasic (Primary Authors/Editors), 2018 Semiconductor Synthetic Biology Roadmap,
Semiconductor Research Corporation: RTP, NC, (October 2018); DOI: 10.13140/RG.2.2.34352.40960.

Daniel J.C. Herr
Department of Nanoscience, Joint School of Nanoscience and Nanoengineering,
University of North Carolina at Greensboro, Greensboro, NC, USAe-mail: djherr@uncg.edu
Education
Ph.D.
1984 University of California at Santa Cruz, Physical Organic Photochemistry
B.A.
1976 Wesleyan University, Middletown, CT, Chemistry (Honors)
Appointments
2019
Professor, Nanoscience, UNC Greensboro
2019
Adjunct Associate Professor, Biomedical Engineering, NC State University
2018 Member, Board of Directors, Berkshire Corporation
2015 North American Regional Editor, The Journal of Nanoparticle Research2013 Director, Nanomanufacturing Innovation Consortium, NC
2011 Member, Advisory Board Centre for Quantum Computation and Communication2011 - 2019
Professor and Chair, Nanoscience, UNC Greensboro
2007 Senior Editor, IEEE Transactions in Nanotechnology
2006 - 2016
Founding Co-Chair, ITRS ERM Working Group
2005 Fellow, International Society for Optical Engineering [SPIE]
2001 – 2018
Adjunct Professor, Dept. of Materials Science and Eng., NC State University
1999 – 2005
Adjunct Professor, Dept. of Electrical and Computer Eng., NC State University
1994
Chair, ITRS MBP Working Group NTRS
1992 - 2011
Science Area Director, Semiconductor Research Corporation
1989 - 1992
Group Leader, Shipley Company/Shipley Far East, Ltd./Rohm and Haas
1984 - 1988
Principal Engineer, Honeywell, Inc.
1984
Instructor, Organic Chemistry, UC Santa Cruz

37

38

39

Session 4【Topics and future perspectives of nanotechnology platforms /ナノテクノロジープラットフォームの成果と将来展望】

Session 4【Topics and future perspectives ofnanotechnology platforms /ナノテクノロジープラットフォームの成果と将来展望】

40

“Biotechnology research by using a nano,micro structural analysis with electron microscope”「電子顕微鏡による構造解析手法を用いたバイオ研究」

Yasutaka Matsuo (Hokkaido University)
松尾 保孝（北海道大学）“R&D support by open collaboration
based on shared facilities”「共用設備を基盤としたオープンコラボレーションによる研究開発支援」

Kentaro Totsu (Tohoku University)
戸津 健太郎（東北大学）“Research Support for theInterdisciplinary Research between
Nanotechnology, Biotechnology, Quantum Technology, and AI,
and Future Perspective for Nanotechnology Platform”「ナノテクノロジーとバイオ・量子・AI融合領域の支援成果とナノテクノロジープラットフォームの将来展望」

Yoshinobu Baba (Nagoya University)
馬場 嘉信（名古屋大学）

41

Biotechnology research by using a nano, micro structural analysis
with electron microscope
電子顕微鏡による構造解析手法を用いたバイオ研究1
1

2

Yasutaka Matsuo, 2Tamaki Shibayama

Reserch Institute for Electronic Science, Hokkaido University
N21W10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan

Center for Advanced Research of Energy and Materials, Faculty of Engineering, Hokkaido University
N13W8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan

Abstract
Hokkaido University provides support for the analysis of (1) surface structure, (2) internal/3D
structure and (3) electronic state. The surface analysis equipment includes the latest X-ray
photoelectron spectrometer, electron probe microanalyzer and UHV-SPM. The internal structure canbe studied by several electron microscopes including the world’s only ultrahigh-voltage electron
microscope with two high-energy ion accelerators and laser, FIB-SEM and so on. The electronic statesof nanomaterials can be measured using a time-resolved photoelectron microscope. We provide
comprehensive care and total supports for advanced characterization and analysis of material surface
and 3D structure. In this presentation, we will show you some supporting experimental of
biotechnology fields.
I. INTRODUCTION

Various electron microscopes play a very important role in the biotechnology research field. In particular, it
has a wide range of applications, from structural analysis of proteins for drug development to structural analysisof whole tissues in nanometer order. In addition, various kinds of nanoparticles are developed for cancer
treatment and exploring in vivo structures, and it is also necessary to investigate the structure and dynamics invivo of them. Here, applications of TEM and FIB-SEM will be reported, we talk about how our advanced
characterization nanotechnology platform program should interact with your research in the future.

II. SUPPORT EXAMPLES FOR BIOTECHNOLOGY RESEARCH

Recent efforts to make effective and safe vaccinesor drug delivery system (DDS) have focused on the(b)development of various kinds of nanoparticles. In
order to avoid antibody production, gold
nanoparticles (AuNPs) are strong candidates. In fact,
gold nanoparticles (AuNPs) have already been used
as antigen carriers for subunit vaccines without the
production of anti-AuNP antibodies. In this supportcase, binary mixtures of small and large GNPs
150 nmmodified with a glucose-terminated fluorinated OEG
ligand (GFL) spontaneously self-assemble into a
Fig. 1size-segregated yolk/shell structure in solution, in(a) SE-STEM and (b) HAADF-STEM images of small
which clusters of large GNPs are covered with aand large Au NPs mixtures with a glucose-terminatedmonolayer of small GNPs due to entropy-driven size
fluorinated OEG ligand (GFL)
segregation was reported ([1]-[3]). The surface andinternal structure of binary mixtures of GNPs were clarified by using two observation modes of a STEM. (Fig.
1)

42In addition, three-dimensional observation of the tissue internal structure by FIB-SEM and analysis of thetissue structure by TEM tomography were performed in our supports.

III. NEXT STEP OF ADVANCED NANO-CHARACTERIZATION SUPPORTIn biotechnology research, electron microscopes are used in various objective. Recently, it has become possible
to observe chemical reaction dynamics in a solution through the development of a high-speed camera, a liquid
MEMS cell holder, and it is also possible to observe the dynamics of living tissue with an electron microscope.
On the other hand, in life science, optical microscopes, including super-resolution microscopes, have advanced
one step further, and it has become possible to observe living individuals and brain tissue in vivo. It is importantthat there is a complementary relationship with each other, and it is thought to advance new research field
through seamless the sample transfer and data interpolation and the development of new technology.
Another important issues in the microscope observation, especially in the field of biological research, areproblems of how to prepare the sample, how to find the best condition of observation and how to analyze thecaptured image. Numerous data (sample preparation, measurement conditions) have been accumulated in the
nanotechnology platform program. By processing these many support contents with AI technology, it is thoughtthat we will be able to extract the optimal experimental conditions and the optimal measurement method. We
would like to accelerate user’s research in this way.
References[1] K. Niikura, K. Nagakawa, N. Ohtake. T. Suzuki, Y. Matsuo, H. Sawa, K. Ijiro, Bioconjugate Chem. 2009, 20, 10,
1848-1852[2] J. Wei, K. Niikura, T. Higuchi, T. Kimura, H. Mitomo, H. Jinnai, Y. Joti, Y. Bessho, Y. Nishino, Y. Matsuo, K. Ijiro,
J. Am. Chem. Soc. 2016, 138, 10, 3274-3277[3] N. Ohtake, K. Niikura, T. Suzuki, K. Nagakawa, H. Sawa, K. Ijiro, Bioconjugate Chem. 2008, 19, 2, 507-515

Yasutaka Matsuo
Professor of Co-creative Research Support Department, Promotion Office for
Nanotechnology Collaboration, RIES, Hokkaido University

He received B.S. and M.S of Department of Applied Physics in Osaka University. And he received Ph. D(Engineering) from Hokkaido University in 2001. After receiving his Ph. D, he started research carrier as a
research fellow in RIES, Hokkaido University. In 2004, he was Postdoctoral Fellow (PD) of Japan Society for the
Promotion of Science (JSPS) and assistant professor at RIES Hokkaido University. He moved to Nanotechnology
Research Center of RIES as an associate professor in 2010. He was promoted to a professor at the same institute in2018. He is now managing the Promotion Office for Nanotechnology Collaboration.
His main research is the creation of functional surface by Nanofabrication method and analysis of it.

43

共用設備を基盤としたオープンコラボレーションによる研究開発支援
R&D Support by Open Collaboration Based on Shared Facilities
戸津 健太郎、森山 雅昭、江刺 正喜
東北大学 マイクロシステム融合研究開発センター〒980-0845 仙台市青葉区荒巻字青葉519-1176

概要
微細加工によるデバイス開発の特徴の一つは、様々な装置、技術を用いて加工を行っていくことである。適切な材料、加工方法、装置、評価方法を選択し、進めていく必要があり、研究開発者には一連の経験も要求される。このようなリソースを自前で構築して維持することは容易ではないが、共用設備を基盤とするオープンコラボレーションにより、これらの困難を乗り越える場が提供される。オープンコラボレーションとは、共用設備、技術情報に研究開発者がアクセスし、加工結果などの情報を他と共有しながら、デバイス開発を加速させるものである。成功に近づくように、経験豊富な技術支援スタッフが全面的にサポートする。このような拠点には設備、技術が蓄積し、それらが他の開発にも活用されるポジティブフィードバックが働く。実際の経験を有し、技術の本質を知った、その先の研究開発を切り拓く人材も育成される。ここでは、東北大学
での実践例を紹介した上で、今後の展開を考える。

I. 東北大学試作コインランドリでの実践

2010年から東北大学西澤潤一記念研究センター内の1,800 m2の大型クリーンルームを中心として、「試作コインランドリ」と名付けた共用設備を運営している。4/6イ
ンチウェハ用を主とする試作ラインで、30年以上前の半導体製造装置と新しい装置の計120台以上が混在している。受託開発（技術代行）は行わず、利用者が直接、装置を操作する（機器利用）。各装置を初めて利用する際には、担当の技術支援スタッフ（計8名、図1）が操作説
明を行うが、2回目以降は必要とされるときにオンデマン
ドで技術支援を行う。各装置の原理、特徴をできるだけ理解していただくことにより、さらなる応用が検討できるなど、その先の研究開発に活かされる。技術の本質を捉え、自ら考えて進められる研究開発人材の育成につながるほか、運営側としても、限られたリソースを有効活用するために適した方法と考える。設備や技術を共有することで、研究開発を加速させるオープンコラボレーシ
ョンの考えで、加工結果の情報は蓄積され、他の利用者とも共有する。蓄積した経験や参考文献の情報をもとにアドバイスを行い、すべての利用者がよい成果を得られるように、利用者に寄り添った支援を心がけている1。経験の浅い若手人材を積極的に派遣し、育成とデバイス開発を同時に、効果的に進めている例も多い。
 この8年間で320以上の機関（企業：267、大学・高専：
35、公的機関：21）が利用し、2018年度は年間1万件超の装置利用（約80 %が企業の利用）、約1.8億円の利用料収入（事業費全体の約70 %をカバー）があった（図2, 3）。
増加傾向が続いており、2019年度も前年度比10 %増を見込んでいる。利用企業による製品化も続いている2,3。

44

図 1. 8 名の技術支援スタッフ

図 2. 毎月の装置利用件数の推移
合計 255,203千円

収入

支出
繰越額

繰越額

管理運営費

6,199
9,415

3,159

間接経費等

12,624

単位：千円

施設使用料
29,862

人件費

文科省ナノテクプラットフォーム
委託費(直接経費)

54,190

利用料
176,562
共同研究による
利用を含む

83,894

消耗品費・設備維持費
66,944

光熱水費
58,720

図 3. 2018 年度の収支(事業費：約 2.6 億円)

II. 今後の展開
これまで、Si系材料を主に用いるデバイス開発支援で実績を残してきたが、最近は例えば化合物半導体やセラ
ミックス、金属と多岐に渡る材料について微細加工の要望が増加している。今後は、微細加工を必要とする分野
全般、とくに新規材料を含めた多種材料加工の支援に力をいれる。具体的には、設備の多重化により、クロスコ
ンタミ（汚染）を気にせずに加工できるようにする。さらに、社会実装を加速させるため、デバイスコスト低減、
高機能CMOSとの集積化に必須となる8インチウェハ試
図 4. 3 日間の職場体験の中でフォ作にも対応できるようにするとともに、IoT用途に代表さトリソグラフィに取り組む仙台市内
れるモジュール化のための実装技術も提供できるようにの中学生（2019 年度は 4 校から計 11
したい。利用増に伴い混雑が目立ってきている装置があ
名が参加）
るが、設備増強と夜間休日の有効活用によるスループッ
ト向上を図りたい。人材育成も重要であり、従来と同様に機器利用と技術支援による研究開発者の育成にも取り組み、微細加工が社会のより多くの場面で活用されるようにする。今年度から市内中学生の職場体験を受け入れているが、将来活躍する科学技術人材の育成にもつながるようにしたい（図4）。委託加工の要望については、これまでどおり、地域の企業が受託する形を基本とする。
人材の交流を一層活発にするなど、地域の微細加工リソースとの連携を強化し、幅広いニーズに応えられるよう
図 5. 地域の微細加工リソース（設備、に開発支援体制を発展させる（図5）。東北地域の公設試
人材、技術）が連携したナノテク研究開の技術者が、その地元の企業の開発を支援するために、付き添って利用するケースも出てきている。このように、 発支援体制のイメージ
各県の公設試とも連携して、東北地域全体の微細加工のニーズにも応えたい。
 微細加工は、材料科学、生命科学などのサイエンスをSociety5.0やSDGsの社会で使える形に実装するための重要なツールでもある。研究基盤、産業基盤として重要な「微細加工」を必要とする
ときに、いつでも、だれでも、実施できる開発支援環境を構築し、社会に貢献したい。
参考文献[1] K. Totsu, M. Moriyama and M. Esashi, Nat. Electron., 2 (2019) pp.134-136.
[2] 小林 広樹 他，音響光学フィルタの開発，NanotechJapan Bulletin, vol. 9, No. 3 (2016).
[3] 杉山 厚志 他，広帯域波長掃引パルス量子カスケードレーザの開発，NanotechJapan Bulletin, vol. 12, No. 2 (2019).

戸津 健太郎 Kentaro Totsu
1999年東北大学工学部機械電子工学科卒業。2004年同大学院博士課程修了。同年東北大学大学院工学研究科ナノメカニクス専攻助手。2007年東北大学産学官連携推進本部助教。2010年東北大学マイクロシステム融合研究開発センター准教授、2017年より同副センター長。これまでMEMS、微細加工に関する研究や産学連携に従事しているほか、2010年からは東北大学試作コインランドリの長として微細加工共用設備の運営を行う。試作コインランドリの取組に対して、2013年産学官連携功労者表彰経済産業大臣賞受賞。2007年から2019年まで仙台
市MEMS開発ディレクター（仙台市経済局非常勤嘱託職員）として、地域のMEMS関連産業振興にも取り組んでいる。

45

RESEARCH SUPPORT FOR THE INTERDISCIPLINARY RESEARCH
BETWEEN NANOTECHNOLOGY, BIOTECHNOLOGY,QUANTUM TECHNOLOGY, AND AI, AND
FUTURE PERSPECTIVE FOR NANOTECHNOLOGY PLATFORM
ナノテクノロジーとバイオ・量子・AI 融合領域の支援成果とナノテクノロジープラットフォームの将来展望
1,2
1

Yoshinobu Baba

Institute of Nano-Life-Systems, Institutes of Innovation for Future Society
Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

2
Institute of Quantum Life Science,
National Institutes for Quantum and Radiological Science and Technology
Anagawa 4-9-1, Inage-ku, Chiba, 263-8555, Japan

Nagoya University’s nanotechnology platform for molecule and material synthesis has been
supporting the interdisciplinary research between nanotechnology and biotechnology and more
recently we are expanding to support the AI system for nanobiodevices and quantum technology forbiomolecular imaging.
We have supported to develop nanofluidic devices, in which billions of nanowire structures arefabricated. This nanofluidic devices efficiently isolate billions of extracellular vesicles in a 1-mL ofbody fluid sample, such as blood and urine. The performance of the isolation for extracellular
vesicles is much superior to those for the conventional techniques, including ultracentrifugation andisolation kits. The nanofluidic devices also enable us to detect over two thousand miRNAs, which
can extract from the billions of extracellular vesicles. This efficiency is also much higher than theconventional techniques. We can isolate extracellular vesicles and detect miRNAs from over five
hundred patients and normal person. Machine learning system is applicable to this big date set todiagnose brain tumor, liver cancer, lung cancer, pancreas cancer, bladder cancer, and prostate cancer.
We have supported to prepare quantum sensor based on diamond nanoparticles, which has nitrogenvacancy center and develop the method for transfection of nanodiamond quantum sensors into thestem cells and cancer cells. These nanodiamond quantum sensors are successfully applicable tohighly sensitive measurements of temperature inside stem cells and cancer cells with higher spatial
resolution less than 100 nm. The information obtained by nanodiamond quantum sensors are so
useful to know the functions of stem cells and cancer cells.
Nagoya University’s nanotechnology platform will expanding the research support from theinterdisciplinary research between nanotechnology and biotechnology to material technology,
quantum technology, AI, and bio-economy in order to support not only each research topics but also
the prototyping, the process science/engineering, and clinical research to accelerate interdisciplinary
research and industrial-university collaborations as the open innovation platform.

46

References[1] T. Yasui, T. Yanagida, S. Ito, Y. Konakade, D. Takeshita, T. Naganawa, K. Nagashima, T. Shimada, N. Kaji, Y.
Nakamura, I. A. Thiodorus, Y. He, S. Rahong, M. Kanai, H. Yukawa, T. Ochiya, T. Kawai and Y. Baba, Science
Advances, 3, e1701133 (2017).
[2] M. Fujiwara, R. Tsukahara, Y. Sera, H. Yukawa, Y. Baba, S. Shikata, H. Hashimoto, RSC Advances, 9, 12606-12614(2019).
[3] T. Yasui, T. Yanagida, T. Shimada, K. Otsuka, M. Takeuchi, K. Nagashima, S. Rahong, T. Naito, D. Takeshita, A.
Yonese, R. Magofuku, Z. Zhu, N. Kaji, M. Kanai, T. Kawai, Y. Baba, ACS Nano, 13, 2262–2273 (2019).

Yoshinobu Baba, PhD, Fellow of Royal Society of Chemistry
Professor, Department of Chemistry and Biotechnology, School of Engineering, Nagoya University
Director, Institute of Nano-Life-Systems, Institutes of Innovation for Future Society, Nagoya University
Professor, Department of Advanced Medical Science, Graduate School of Medicine, Nagoya University
Steering Committee, Institute for Advanced Research, Nagoya University
Director General, Institute of Quantum Life Science, National Institutes for Quantum and Radiological Science andTechnology
Research Supervisor, JST (Japan Science Technology Agency) CREST Extracellular Fine Particles Project
Dr. Yoshinobu Baba received PhD degree in 1986 from Kyushu University. After Assistant Professor at Oita
University and Associate Professor at Kobe Pharmaceutical University, he was promoted to the full professor at The
University of Tokushima in 1997. He was moved to Nagoya University in 2004. He is now a Professor of
Department of Chemistry and Biotechnology, School of Engineering, a Professor of Department of Advanced Medical
Science, School of Medicine, and a Director of Institute of Nano-Life-Systems, Nagoya University. He is also a
Director General of Institute of Quantum Life Science, National Institutes for Quantum and Radiological Science andTechnology, and a Research Supervisor, JST (Japan Science and Technology Agency) CREST “Extracellular fineparticles” Project. He is a co-initiator for microTAS meeting and the world largest Nanotech International Meeting.
He is a general chair of numerous international meetings (microTAS, MSB, nanotech, ISMM). He is an Associate
Editor of Anal. Chem. of American Chemical Society and serving to over 15 scientific journals as an editorial/advisoryboard member. He has been admitted as a Fellow of the Royal Society of Chemistry and received over 85 awards
for his contributions in nanobiotechnology: MERCK Award in 2004, The CSJ (Chemical Society of Japan) award for
creative work in 2008, The Japan Society for Analytical Chemistry Award in 2015, and The Commendation for
Science and Technology by the Minister of Education, Culture, Sports, Science and Technology in 2016. Dr. Baba’s
research studies are directed at the development of nanobiodevices for omics, systems biology, medical diagnosis,
tissue engineering, and molecular imaging. He is the author or co-author of 1,022 publications, including research
papers, proceedings, reviews, and books and is also an inventor of over 100 patents. He has delivered more than 1,000
plenary and invited lectures at conferences. His work has been cited on 472 occasions by newspapers and television.

47

48

Session 5【Beyond Nano・AI / ナノテクノロジーのシステム化と産業応用】

Session 5【Beyond Nano・AI /ナノテクノロジーのシステム化と産業応用】

49

50

“Materials Discovery Using Machine Learning :Latest Trends and Future Prospects”「機械学習を使った材料探索の最新動向と今後の展望」

Daisuke Okanohara (Preferred Networks)
岡野原 大輔（株式会社Preferred Networks）“Nanotechnology in automobiles :Nanomaterials in parts and units”「自動車におけるナノテクノロジー：ナノマテリアルと部品・ユニット」

Hirohito Hirata (TOYOTA)
平田 裕人（トヨタ自動車株式会社）

51

Materials Discovery Using Machine Learning:
Latest Trends and Future Prospects
機械学習を使った材料探索の最新動向と今後の展望
1

Daisuke Okanohara

1Preferred Networks,
Otemachi Building 1-6-1 Otemachi Chiyoda-ku Tokyo,
100-0004 Japan

Abstract
Machine learning technologies, such as logistic regression, support vector machines, and random
forests. have been widely used for materials discovery. In particular, deep learning has recently been
applied to the challenging problems of generative design of molecules, estimation of properties ofmolecules, and retro-synthesis planning. In this talk, I present a basis of deep learning and the latest
works of materials discovery with deep learning and also discuss the remaining problems and prospects.
Ⅰ. Deep Learning
Machine learning technologies, such as logistic regression regressions, support vector machines, and random
forests, have been widely used for materials discovery. In recent years, a new machine learning paradigm, deeplearning has emerged and been applied to many challenging problems in materials discovery.
Deep learning [1] is a machine learning framework that uses large neural networks. A neural network represents
a complicated function by combining many simple computation units. These units are called neurons, andparameterized connections between these neurons are called synapses. The backpropagation algorithm computes
the gradient of the function and updates these synapses very efficiently. Although a neural network is one of theoldest models in machine learning research, only recently, wide and deep neural networks were found to be very
effective, and they are called deep learning. Deep learning can solve many challenging problems such as image
recognition, speech recognition, natural language processing. Unlike a traditional neural network with three tofive layers with hundreds of neurons in each layer, deep learning uses a much larger neural network with up to
hundreds of layers with 0.1~1 million neurons per layer. The success of deep learning was first examined
experimentally. Recent theoretical findings support these successes and show that deep learning has much more
representation power, and unlike the traditional machine learning common sense, the bigger network is easierto train and generalize better. Also deep learning can now handle structured data, like graphs, and set and utilize
more data found in materials discovery. Deep learning frameworks and libraries support the rapid developmentof deep learning models.
The success of deep learning is based on more computational power, larger data, and new techniques, such as
piecewise linear activation function (for example, ReLU), batch normalization, skip connections, attention
modules, and accelerated optimization techniques.
The training of deep learning models requires huge computational resources, but inference is fast. In particular,when a deep learning model is used for approximating existing simulation results, it can approximate the result
100~1 million times faster than conventional simulators. This is because deep learning is very good at theinterpolation problem and can learn a smooth function that can approximate simulation results well in aninteresting region. Besides, deep learning fits well for parallel computation and can benefit from recent advancesof accelerator chips such as GPUs and new AI chips.
Let me give three application examples where deep learning is effective for materials discovery.

Ⅱ. Generative Design of Molecules
A generative model is a model that can generate a set of data in the target domain. It was very difficult todesign generative models for high-dimensional data such as images and speeches. Now, deep learning can
generate high-dimensional data very well, and generated ones are indistinguishable from real data even by52humans. Such a generative model that uses deep learning is called a deep generative model. The representative
examples of deep generative models are variational autoencoder (VAE), generative adversarial network(GAN), normalizing flow, and auto-regressive models. They have different strengths and weaknesses, andusers should use appropriate generative models based on their goal.
Such real-world high-dimensional data tend to lie on low-dimensional manifolds embedded within highdimensional space, so-called manifold hypothesis. In other words, such data can be described by a small
number of parameters, and users can generate data interactively by changing these parameters. Deep
generative models are good at discovering such manifolds, and by changing the latent parameters, they cansmoothly change the complex data.
Such deep generative models are now used for generating molecules [2]. For generating molecules, not only
structures, but also their positions, need to be generated, and new techniques are introduced to generate valid
structures and positions.

Ⅲ. Estimation of Energy potential and properties
Deep learning is good at classification and regression, and it can learn to estimate the property of moleculesfrom training data in a supervised way. Recently deep learning is used for modeling the energy potential ofmolecules [3][4]. Although ab initio calculation of energy potential requires very high computational cost,neural network models can approximate energy potential with a fixed cost and 1 million times faster in somecases.
Using such a very fast energy potential evaluation, we can examine the properties and reactivity more quickly
and exhaustively.

IV. Retrosynthetic planning
Retrosynthetic planning is a task for searching a path backward from a target molecule to simpler starting
materials through a series of reactions. Since the search space is probably too large to enumerate, researchers’
heuristics are critical. Recently deep reinforcement learning (reinforcement learning with deep learningmodels) achieved remarkable results in planning tasks such as Game of Go (e.g., AlphaGo), and thesetechnologies are also useful for retrosynthetic planning. Such successes are achieved by better learningalgorithms, exploration strategies, and a deep learning model that can learn and approximate better.
References[1] Y. LeCun, Y. Bengio and G. Hinton, “Deep Learning”, Nature 521, 436-444, 2015.
[2] D. C. Elton, Z. Boukouvalas, M. D. Fuge and P. W. Chung, “Deep learning for molecular design – a review of thestate of the art”, Mol. Syst. Des. Eng., 2019, 4, 828—849[3] JS Smith, O Isayev, AE Roitberg, “ANI-1: an extensible neural network potential with DFT accuracy at force fieldcomputational cost”, Chem Sci 2017, Apr 1;8(4):3192-3203[4] K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “SchNet – A deep learning
architecture for molecules and materials", J. Chem. Phys. 148, 241722 (2018)<CV>
Daisuke Okanohara is the co-founder and Representative Director of Preferred
Networks Inc. He received his Ph.D. in Computer Science from the University of Tokyoin 2010, the super-creator award from Mito Software Project in 2005, University ofTokyo’s president award in 2007, best presentation awards from NLP conferences in2009, 2010. He founded Preferred Infrastructure in 2006 and Preferred Networks in
2014.

53

自動車におけるナノテクノロジー：ナノマテリアルと部品・ユニット
Nanotechnology in automobiles : Nanomaterials in parts and units
平田 裕人
トヨタ自動車株式会社 先端材料技術部
〒静岡県裾野市御宿

 年のベンツによる世界初の実用的なガソリンエンジン自動車の実用化、 年のフォードによる世界初の大量生産方式の自動車「モデル 7」の販売開始から、 年余り経った現在、世界中で年間  万台以上の自動車が生産され、重要な移動、物流手段の一つとなっている。
近年、自動車業界は、 年に一度の大変革の時代に入っており、「電動化」、「自動化」、「コネクティッド」、「シェアリング」などの技術革新が急速に進んでいる。
これら技術革新のうち、ナノテクノロジーが貢献する部分が多い「電動化」に関して、当社は、
年にエンジンとモーター動く世界初の量産型ハイブリッド車（+9）、プリウスを発表して以来、ハイブリッド技術の改良と普及を推し進めて来た。そして、この +9 技術を様々な電動車両にも共通して利用可能なコア技術と考えている。すなわち、+9 の電池容量を増やして外部充電機能を追加すれば、プラグインハイブリッド車（3+9）に、3+9 から、エンジンと燃料タンクを取り除くと電気自動車（(9）に、+9 のエンジンと燃料タンクを、燃料電池と水素タンクに置き換えれば、燃料電池車（)&9）になる。このように、+9 の要素技術は、各種の次世代車両にも共通しており、+9 で培った技術、ノウハ
ウが迅速に応用できると考えている。このイメージを図  に示す。

図  ハイブリッド技術の様々な電動車両への応用イメージ図  に、電動化に必要な部品と部品を構成する材料の例を示す。トヨタでは、電動車両の性能向上に向け、ニッケル水素電池用の水素吸蔵合金、正極活物質、セパレーター、リチウムイオン電池では、
活物質、電解液などの研究開発、さらにモーター用の磁石、電磁鋼板、ワイヤー、インバーターでは半導体、接着剤などの検討を行っている。)& ではそれらに加え、)& スタックに必要な触媒技術、電解質膜、カーボン、高圧タンクに使用される &)53 など研究開発に取り組んでいる。

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図  電動車両に使われているユニットと材料

ここでは、自動車におけるナノテクノロジーとして、電動化車両専用および従来型のエンジン車との共通のユニット・部品で使われる”ナノ材料そのもの”と”ナノ材料の特性を活かすための材料
が部品・ユニット内部で形成する階層構造、三次
元ネットワーク構造”に関する概略を紹介する。


図  )& スタック内の階層構造平田 裕人（ひらた ひろひと）Hirohito Hirata年 名古屋工業大学大学院 博士後期課程修了
年 トヨタ自動車（株）入社
年 第材料技術部 グループ長
年 先端材料技術部主査
年 先端材料技術部チーフプロフェッショナルエンジニア年 先端材料技術部部長

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組織委員会/Organizing Committee
田沼 繁夫（委員長） Shigeo Tanuma (Chair)
小出 康夫

Yasuo Koide

小寺 秀俊

Hidetoshi Kotera

田中 信夫

Nobuo Tanaka

馬場 嘉信

Yoshinobu Baba

藤田 大介

Daisuke Fujita

横山 利彦

Toshihiko Yokoyama

プログラム委員会/Program Committee
三留 正則（委員長） Masanori Mitome (Chair)
秋永 広幸

Hiroyuki Akinaga

河合 壮

Tsuyoshi Kawai

島本 直伸

Naonobu Shimamoto

田沼 繁夫

Shigeo Tanuma

野田 哲二

Tetsuji Noda

花方 信孝

Nobutaka Hanagata

馬場 嘉信

Yoshinobu Baba

日原 岳彦

Takehiko Hihara

平山 司

Tsukasa Hirayama

藤田 大介

Daisuke Fujita

協56

物質・材料研究機構/National Institute for Materials Science
物質・材料研究機構/National Institute for Materials Science
理化学研究所/RIKEN

名古屋大学/Nagoya University
名古屋大学/Nagoya University

物質・材料研究機構/National Institute for Materials Science

自然科学研究機構分子科学研究所/Institute for Molecular Science
物質・材料研究機構/National Institute for Materials Science
京都大学/Kyoto University

奈良先端科学技術大学院大学/Nara Institute of Science and Technology
東京大学/The University of Tokyo

物質・材料研究機構/National Institute for Materials Science
物質・材料研究機構/National Institute for Materials Science
物質・材料研究機構/National Institute for Materials Science
名古屋大学/Nagoya University

名古屋工業大学/Nagoya Institute of Technology

ファインセラミックスセンター/Japan Fine Ceramics Center

物質・材料研究機構/National Institute for Materials Science

賛/Cooperating Organizations

IEEE 東京支部

IEEE Tokyo Section

応用物理学会

The Japan Society of Applied Physics

高分子学会

The Society of Polymer Science, Japan

電気学会

The Institute of Electrical Engineers of Japan

電子情報通信学会

The Institute of Electronics, Information and Communication Engineers

ナノ学会

The Society of Nano Science and Technology

ナノテクノロジービジネス推進協議会

Nanotechnology Business Creation Initiative

日本ＭＲＳ

The Materials Research Society of Japan

日本化学会

The Chemical Society of Japan

日本金属学会

The Japan Institute of Metals and Materials

日本顕微鏡学会

The Japanese Society of Microscopy

日本再生医療学会

The Japanese Society for Regenerative Medicine

日本材料学会

The Society of Materials Science, Japan

日本人工臓器学会

Japanese Society for Artificial Organs

日本生体医工学会

Japanese Society for Medical and Biological Engineering

日本セラミックス協会

The Ceramic Society of Japan

日本ＤＤＳ学会

The Japan Society of Drug Delivery System

日本バイオマテリアル学会

Japanese Society for Biomaterials

日本表面真空学会

The Japan Society of Vacuum and Surface Science

日本物理学会

The Physical Society of Japan

マイクロマシンセンター

Micromachine Center

ファインセラミックスセンターJapan Fine Ceramics Center−禁無断掲載−
Copyright (c)文部科学省ナノテクノロジープラットフォーム
第 18 回ナノテクノロジー総合シンポジウム
JAPAN NANO 2020
発
行 2020年（令和2年）1月
編集・発行 国立研究開発法人 物質・材料研究機構
ナノテクノロジープラットフォームセンター〒305-0047 茨城県つくば市千現1-2-1
電話：029-859-2777

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JAPAN NAN

2020