Hamaguchi Laboratory

Our recent focus in research is on plasma-material interactions in general, including their industrial applications. The aim of research is to understand fundamental mechanics of plasma-material interactions under various conditions. To achieve this, we combine plasma/beam experiments with numerical simulation/modeling. More specifically our current research topics include 1) etching, deposition, and surface modification processes for micro/nano electronics device manufacturing, 2) surface modification and functionalization of biomaterials by plasmas, 3) processing of water and biological systems by atmospheric-pressure plasmas mainly for applications in plasma medicine and plasma agriculture, and 4) dynamics and chemical reactions in plasmas under various conditions, including atmospheric-pressure plasmas.

If you are interested in our research, please feel free to contact me.

Satoshi Hamaguchi
Professor, Center for Atomic and Molecular Technologies, Osaka University.


Events & News

Farewell and Guraduation Party
We held a farewell party for Enggar and Jaber, and graduation party for Nicolas.
JVST A cover art
A figure in a paper by Dr. Nicolas Mauchamp and Prof. Satoshi Hamaguchi was adopted as a cover art of JVST A.
See here .
We had a lab BBQ party at Expo Park.
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The 2nd Workshop on Artificial Intelligence in Plasma Science (WAIPS-2) will be held in France (on-site and online).
website
Hanami and Farewell dinner for Erin/Welcome dinner for Sarah
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The Japan-RUB workshop was held (weekly seminars).
Lab Hiking: Mt. Katsuragi (Osaka & Nara)
We went to Mt. Katsuragi on the border between Osaka and Nara prefectures.

CAMT Seminars

If you are interested in attending the seminars, please contact us.

"Green Cloud Computing and Sustainability"
Ms. Neyla Benkadda
UDIMA University Madrid, Spain
Date: (Wed) 16:00-17:00 (JST)
Location: Main Conference Room (1st floor), Bldg. A12, Suita Campus, Osaka University
Webex Online Conference available

Abstract
Public sector organisations in the UK are facing increased pressure to move their operations into the cloud, both from stakeholders and the Department for Business, Energy, and Industrial Strategy. And for good reason: cloud computing is capable of improving energy efficiency by 93%, and producing 98% fewer greenhouse gas emissions than on premises IT infrastructure, according to the Microsoft-WSP collaborative study. Cloud computing is a highly scalable and cost-effective infrastructure for running high performance computing (HPC), enterprise and Web applications. However, the growing demand for Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which are not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud computing on the environment. In order to design such solutions, deep analysis of Cloud is required with respect to their power efficiency. Thus, in this talk, we will discuss the implication of these new solutions for the green economy.

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"Physics Informed Artificial Intelligence"
Prof. Sadruddin. Benkadda
CNRS-Aix Marseille University, Marseille, France
Date: (Tue) 14:00-15:00 (JST)
Location: Main Conference Room (1st floor), Bldg. A12, Suita Campus, Osaka University
Webex Online Conference available

Abstract
In simulations of multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. We will review some of the prevailing trends in embedding physics into machine learning.

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"Charged particle dynamics in capacitively coupled radiofrequency discharges driven by complex waveforms"
Dr. Zoltán Donkó
Department of Complex Fluids, Wigner Research Centre for Physics, Hungary
Date: (Tue) 14:00-15:00 (JST)
Location: Main Conference Room (1st floor), Bldg. A12, Suita Campus, Osaka University
Webex Online Conference available

Abstract
Capacitively coupled plasmas (CCP) have been used for various surface modification applications for several decades. Depending on the choice of the gases and the operating conditions the fluxes and energy distributions of the ions (and radicals) bombarding the surfaces can be adjusted over wide domains. Charged particle dynamics largely influences basic plasma characteristics in these radio frequency (RF) plasma sources. The application of multi-frequency RF excitation in CCPs is shown to allow generating a high flux of energetic electrons at times of sheath collapse, which have the potential to neutralise positive surface charges deposited within nanoscale structures in semiconducting wafers.

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"Dynamic surface surrogate model trained on atomistic data of AlN sputter depositions"
Mr. Tobias Gergs
Ruhr-Universität Bochum, Bochum & Christian-Albrechts-Universität zu Kiel, Kiel, Germany
Date: (Wed) 11:00-12:00 (JST)
Location: Main Conference Room (1st floor), Bldg. A12, Suita Campus, Osaka University
Webex Online Conference available

Abstract
Modeling plasma-surface interactions is an often encountered multi-scale and multi-physics problem. Stepwise solutions have been proposed by replacing the surface with machine learning surrogate models for non-reactive processes. However, their applicability is still limited due to missing time dependencies or computationally too demanding explorations of parameter spaces. These remedies are resolved in this work for the reactive sputter deposition of AlN by applying a novel combinatorial approach to establish an internal surface state, which may evolve in time. Surface processes are initially studied by means of hybrid reactive molecular dynamics / force-bias Monte Carlo simulations, utilizing a therefor derived charge transfer equilibration model and a revised COMB3 AlN potential. The results are used to train multiple ensembles of physics-constrained artificial neural networks, which form a dynamic surface surrogate model for a wide range of working conditions. This model can be readily coupled to plasma simulations and diagnostics to predict realistic wall interactions (with molecular dynamics fidelity) as well as the transient evolution of surfaces.

*Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 138690629 (TRR 87).

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"The role of metals in the deposition of long-lived reactive oxygen and nitrogen species into the plasma-activated liquids"
Dr. Kinga Kutasi
Institute for Solid State Physics and Optics, Wigner Research Centre for Physics, Hungary
Date: (Tue) 11:00-12:00 (JST)
Location: Main Conference Room (1st floor), Bldg. A12, Suita Campus, Osaka University
Webex Online Conference available

Abstract
The long-lived species of plasma-activated liquids (PALs) have been identified to be nitrate, nitrite, and hydrogen peroxide. Recently, it has been shown that PAL can be used to increase the stress tolerance of plants, and it is further hypothesized that the created nitrate/nitrite ions can make PAL be used as green fertilizer by providing nitrogen nutrients for plants. However, under acidic conditions, the H2O2 reaction with NO2- is very efficient, and in the case of comparable concentrations it leads to the disappearance of NO2-. Here we investigate the role of metals with high reduction potential, which have the ability to neutralize the acidification induced by the plasma treatment, on the formation and the stability of RONS in the liquids treated with a surface-wave microwave discharge.

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Past Seminars

PiAI Seminars

Seminar Series on "Physics informed Artificial Intelligence in Plasma Science"
For more information, please see here .