Abstract
Negative-ion sources are of considerable interest for applications such as materials processing and neutral beam injection for magnetic confinement fusion. The efficient production of negative ions in these sources often relies on surface production. Work function measurements are critical to enable a detailed understanding of the mechanisms that underpin this process for negative-ion production. In this study we used a combination of photoemission yield spectroscopy (PYS) and the Fowler method to determine the work functions of microcrystalline diamond (MCD), phosphorus doped diamond (PDD), boron doped diamond (BDD), and highly oriented pyrolytic graphite (HOPG) directly after exposure to a low-pressure inductively coupled deuterium plasma (150 W, 2 Pa). A mass spectrometer is used to measure the negative ion current from the samples. The results show that the increasing work function of the plasma exposed HOPG occurs over the same sample temperature range as the decreasing negative-ion current. In contrast, the work function of diamond does not show a clear relationship with negative-ion current, suggesting that different mechanisms influence the negative-ion production of metal-like HOPG and dielectric-like diamond.
The project that gave rise to this publication received funding from the Initiative of Excellence of Aix-Marseille University - A*Midex, Investissements d'Avenir program AMX-19-IET-013. This project has also been supported by the Région Sud PACA through the Appel à projets recherche 2021 - Volet exploratoire - project SARDINE. The financial support of the EPSRC Centre for Doctoral Training in fusion energy is gratefully acknowledged under financial code EP/S022430/1

Abstract
Numerous cases of cultural properties being damaged by microorganisms have been reported. In Takamatsuzuka and Kitora Tumuli in Nara, Japan, outbreaks of Penicillium sp. and Fusarium sp. caused damage to the wall paintings. The ability to detect and control these contaminations at an early growth stage is essential in preserving such cultural properties. Fungi release characteristic volatile organic compounds (MVOCs), the so-called moldy odor, from their early growth stages. As the number of spores increased with the fungal cultivation period, we found that the amount of ketones, aldehydes and alcohols in MVOCs also increased. 3-octanone was found to be an indicator of fungal abundance. On the other hand, sesquiterpenes showed a peak of MVOCs at a particular period right before spore reproduction and, therefore are useful to identify both fungal species and their reproduction periods1. Ion Mobility Spectrometry (IMS) is a promising method in both MVOCs and breath analysis because a portable IMS spectrometer has an ability to detect a mixture of volatile organic compounds simultaneously in tens of milliseconds with high sensitivity. We developed a software capable of on-site IMS analysis MVOCs emitted from fungi2. Due to sampling in air, sample ions often form water molecule-added cluster ions, making analysis difficult. In order to predict IMS spectra theoretically, optimized geometries and Gibbs free energies of all possible ions to be produced were calculated with DFT at the M06-2X/6-31++G(d,p) level using Gaussian 16 and GRRM 14 programs. Collision Cross Sections (CCS) of all possible ions were calculated in the classical trajectory method using MOBCAL2019 program. Calculated CCS and drift times (Td) of [propionic acid+O2]-, [acetic acid+O2]-. and [(H2O)3+O2]- were consistent with experimental values. The consecutive reactions took place in ambient air. The major ion O2-. was formed by an electron attachment reaction.
This study is partly supported by NEDO.
1. T. Takeuchi et al., Surf. Int. Anal. 44(2012)694-698.
2. Patent Application: JP2012-238876.

Abstract
Detection of NO2 from corona discharge ad plasma jet, corona discharge transformation of alkanes and their detection bay IMS, online monitoring of plant stress by IMS.

Abstract
The development of a particle-in-cell / Monte Carlo collision simulation code for a low-pressure argon capacitively coupled plasmas that includes a collisional radiative model to predict the optical emission spectrum of the discharge self-consistently. This is made possible by computing the spatial density distributions of a number of excited states of Ar, considering the stepwise excitation and ionization processes, as well as de-excitation, pooling ionization, quenching reactions, and radiative transitions. The calculations reproduce reasonably well the experimentally recorded optical emission spectra of the plasma.

Abstract
Black holes are some of the strangest, most puzzling objects in the Universe. They deform space and time to extremes, and for the longest time could only be observed indirectly via their effect on their environment. However, nowadays gravitational-wave detectors such as LIGO, Virgo and KAGRA are capable of listening to the very space-time deformation black holes produce. Those detectors have established gravitational-wave astronomy as a powerful new tool for probing some of the most extreme astrophysical events in the universe. In this presentation I will give an overview of the gravitational-wave observatories, how they work, and of the latest results from the international LIGO-Virgo-KAGRA collaboration. I will focus on how we infer the astrophysical properties of the black holes and neutron stars in binaries at the source of the gravitational-wave signals, starting from the measurement of the minute motion of the detector's mirrors. In addition, I will talk about future plans of the worldwide network of observatories and present some of the work towards high-precision gravitational-wave astronomy.

Abstract
In this talk, I will review my work on using classical molecular dynamics simulations to study Si-Cl2-Ar+ atomic layer etching (ALE) processes. During recent years, ALE has obtained much attention from both industry and academia for its promise of atomic-level control of surface etching. While these processes are used in practice there is still a lot of fundamental understanding lacking. Classical molecular dynamics (MD) simulations are naturally well posed to help address this knowledge gap. We focus on the relatively simple case of silicon etching by chlorine gas and argon plasma. Our MD model is first compared to ion beam experiments to validate the accuracy of our force fields. We next simulate ALE and closely examine the near-surface region and etching behavior in order to gain insights into the process. We find a much more complex picture of ALE than the "ideal" schematic suggests. Finally, I will present a recent comparison of the MD to optical emission spectroscopy experiments by our collaborators at the University of Houston. This comparison allows us to identify areas of improvement of our simulation procedure.

Abstract
Amino-group-containing plasma polymers (PPs) have been of particular interest for various biomaterials applications because they allow amide covalent coupling and, under certain pH conditions, offer a positively-charged surface for electrostatic binding. Therefore, they were successfully applied to immobilize DNA and various proteins. However, assessing what plasma parameters and the film structural properties are essential for successful and stable immobilization is difficult. PPs do not have a well-defined structure of conventional polymers that can be described as a repetition of a particular unit and the molecular weight of the polymer chain. Since they are deposited from a complex environment of many gaseous reactants created by dissociating original gas feed, their chemical structure cannot be derived only from the starting reactants. We studied the plasma polymerization of cyclopropylamine (CPA) mixed with Ar in three CCP reactor setups and different plasma conditions. The response of immunosensors constructed with the plasma polymerized CPA (PP-CPA) films revealed the surprisingly long reactivity of the film surfaces. It opened the question of whether such phenomena could be explained by the free radicals (species with unpaired spins) trapped in the PP films. Therefore, we investigated the presence and dynamics of the free radicals in the PP-CPA films deposited under mild varied conditions using electron paramagnetic resonance (EPR). Another open question in the performance of amine PPs is the role of primary amine groups (-NH2). In the example of two immunosensors having the same nitrogen and NH2 amount, we demonstrated that nitrile groups belong to other key factors when discussing the surface reactivity in aqueous media.

Abstract
Low-temperature plasma sources at atmospheric- and low-pressure are used for a wide variety of applications. The plasma chemistry occurring in these systems, which leads to the production of reactive components, is key to the outcomes of applications. In order to control application outcomes, a quantitative understanding of this chemistry is required. Experimental and computational approaches are highly complementary to achieve such understanding. In this talk, I will highlight several areas of research in our group where optical diagnostics and computational simulations are combined to probe, and develop control strategies for, application-relevant plasma parameters such as reactive species concentrations and VUV photon formation in low and atmospheric pressure plasma systems.

Abstract
The interaction of plasma and hot gases, for example argon (Ar) with walls (Beryllium) and the divertor (Tungsten) is one of the main problems in fusion devices. One way to describe some aspects of these interactions and systems are molecular dynamics simulations with atomistic potential energy functions. High-dimensional Neural Network Potentials (NNPs), based on quantum chemical calculations are increasingly used in materials science. They are able to describe efficiently and accurately the complicated interactions in these systems that involve the breaking and formation of bonds and can steer the MD simulations.
An overview of these relatively new potential energy functions is given. The scenarios we concentrate on are sputtering, adsorption, reflection and diffusion on surfaces. The various macroscopic parameters (energies, angles, temperature …) present in the input or the output of the simulations, or in both of them, are discussed on basis of some examples of our work.

Abstract
Wavelet analysis and compression tools are reviewed and different applications to study fluid and plasma turbulence are presented. We introduce the continuous and the discrete wavelet transform and detail several statistical diagnostics based on the wavelet coefficients. We then show how to extract coherent structures out of fully developed turbulent flows using wavelet-based denoising. Finally, some extensions to multiresolution analysis on graphs, for instance, relevant for analyzing the dynamics of point clouds, are described. Several examples for analyzing, compressing, and computing turbulent flows are presented.

This work is joint work with Marie Farge (ENS Paris), Thibault Oujia (I2M, Marseille), and Keigo Matsuda (JAMSTEC).

Abstract
Lithium-ion (Li-ion) battery technology is significant for sustainability, reducing carbon dioxide emissions and preventing global warming. Therefore, the most important component of the battery should have a high energy density, stability and long charge/discharge cycle life to increase the performance of the anodes. Instead of graphite with a specific capacity of 372 mAh/g as the most known anode; By using 4200 mAh/g silicon (Si), the energy density of Li-ion batteries can be increased. But, commercial usage of Si anode is limited due to its high electronic resistance and low mechanical stability. For this reason, many battery manufacturers and electric vehicle automotive companies are involved in R&D activities to eliminate the disadvantages of Si anodes and take advantage of their advantageous features.
In this study, various transition metal oxide modified (MeO) (sandwich-like Si/MeO/Si thin film anodes were produced on copper substrate by Radio Frequency (Rf) Magnetic Sputtering method. Si-based thin-film anodes have properties that do not contain any binders, solvents and conductive chemicals that can reduce their specific capacity.
On the other hand, to reduce the excessive time and energy consumption while producing the thin film, it is necessary to develop and use models that can predict some of the characterization outputs of the thin film according to the production parameters. Moreover, the prediction of battery performance analyses (Specific capacity, cycle performance, capacity retention and Coulomb efficiency, discharge capacity, and charge/discharge profiles) is of great importance for the safe and efficient operation of Li-ion batteries, that is why these batteries are complex, time-varying, non-linear electrochemical system and their performance can change with many factors such as charge/discharge current, ageing and temperature changes.
The main aim of this study is to support and compare produced Si-based thin-film anodes and their battery half-cell characterization data and battery performance test studies, and to make their prediction with the help of ML to reduce energy, material, and time consumption.

Abstract
Hydrogels are biomaterials with a 3D network made with hydrophilic polymers. Its structure is desired for biological applications as it can hold large amounts of aqueous solvents and biological fluids. The conventional route for hydrogel preparation is to polymerize monomers by introducing initiators and crosslinking reagents to form the 3D network. But for the case of natural polymers like chitosan and cellulose, the 3D network can be made without the use of reagents and relies solely on the hydrogen bonding of the side chains in the polymer backbone. The use of natural polymers for hydrogel preparation is more desirable for biological applications because natural polymers have chemical features similar to the macromolecules in the extracellular matrix of the skin. However, with just hydrogen bonding, the hydrogel formed has poorer stability compared to the conventional route. To address this, natural polymers are modified with monomers to improve stability. Plasma treatment of polymeric solutions like starch and chitosan has already been explored in several studies. This results in the degradation of polymers. However, the plasma in liquid phases results not only in polymer degradation but also in the generation of more reactive species such as OH radicals. The production of more reactive species may also produce more chitosan macroradicals. Given that the plasma could create a rich environment of radicals, the study aims to explore if the formation of the chitosan-acrylic acid hydrogel network can be feasible with the use of plasma treatment.

Abstract
Recent development in the field of electron-induced fluorescence (EIF) techniques will be discussed, with a focus on the molecules relevant to the atmospheric pressure plasmas, such as N2, O2, H2O, N2O.

Abstract
For the past decade, the definition of biomaterials has transitioned from having inert materials for implantation in a living system, to an engineered substance which can interact with the living system. Materials such as metals, ceramics and polymers can generally be used to develop biomaterials. Polymeric biomaterials are one of the most easily manufactured biomaterial. Among this group, hydrogels are considered to be the most promising one for biomedical applications. Hydrogels resemble tissue-like functions because of their elasticity and water absorption ability. Depending on the type of polymer or crosslinking used, the structural integrity and physicochemical properties of hydrogels may be differentiated. In this study, a blend of natural and synthetic polymers, which are the chitosan and acrylic acid, was used to prepare the biocompatible hydrogels. The study also investigated the use of an atmospheric pressure plasma treatment to facilitate the hydrogel synthesis. The hydrogel forming ability and modification of atmospheric pressure plasma remain largely unexplored. The interaction of plasma in the chitosan and acrylic acid blend was examined in this study. Finally, the plasma-assisted hydrogels were compared with the pristine chitosan-acrylic acid hydrogels in terms of the physical, chemical, mechanical, and biological properties.

Abstract
Ion mobility spectrometry (IMS) is a fast and sensitive technique of the detection of trace gases and chemical l compounds in different environments. The principles of the techniques, the recent advances and developments as well as some important applications of IMS technique in different fields, including the plasma physics, will be presented.

Abstract
Plasma treatment for surface modification has been extensively used as a result of its robustness, speed, and generally environmentally benign processes. These processes are carried out under different operating conditions that ranged from atmospheric pressure to high vacuum. Glow discharges, dielectric barrier discharges, and ion beams have been exploited to tailor the surface properties of materials for target applications and specific responses. Custom-built systems allowed for a further understanding of how plasma interacts with a surface. In our work, plasma-based processes provided alternative solutions to the modification and improvement of surfaces of locally available materials. These specially made plasma systems are used to modify materials for different applications. These include the deposition of decorative coatings for the furniture and creative industries. Use of plasma-modified natural fibers for cement reinforcement. Treatment of zeolitic materials to enhance the adsorption capacity. Fabrication of nanostructured plasmonic catalytic materials via plasma-induced reduction for wastewater treatment applications. Design of low-cost deposition systems. Development of low-energy ion source systems for surface modification and thin film growth. Optical and electrical characterizations of the plasma were used to correlate these process parameters with the resulting surface properties. Similarly, these results were also used to tune gas discharges for a targeted surface response, as well as to establish the repeatability and scalability of the process.

Abstract
Compounds such as Cu2ZnSnS4 (CZTS) have attracted the concern of investigators of photovoltaic applications due to their abundance of elements and their nontoxic and promising optical characteristics. The impact of the working pressure and the radiofrequency (RF) power on the stoichiometry, structure and optical properties of Cu2ZnSnS4 layers, which were fabricated by the magnetron RF sputtering of a single quaternary target, are examined using different techniques, such as energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Raman spectroscopy and spectrophotometry. The vacuum in the chamber decreased the number of sulfur atoms during the creation of the film. Then, increasing the working pressure from 20 mTorr to 100 mTorr improved the stoichiometry of the films. As the RF power increases from 75 W to 200 W, the atomic percentage of tin in the prepared film increases from 9.93% to 42.04%, and this increase is at the expense of the concentration of other elements.

Abstract
The European Union has adopted a European Green Deal, which foresees zero net emissions of greenhouse gases for all member states by 2050. As human beings are facing serious ecological problems, sustainable-development has got attention from more and more countries and has been treated as an economic and social development strategy. The development and utilization of renewable energy can promote the long-term sustainable development of local economies. In this context, energy utilities motivate the end users to take part in a sustainable energy transition through Demand Response management (DR). Residential consumers are thus encouraged by governments through incentives to install small scale renewable power generation units to build self-sustainability as well as support the grid by exporting their excess generation to grid. Such users are referred to as prosumers. This is very important for reducing the peak load of the power grid, the installed capacity of the system, and the operation cost. Because DR can help the integration of renewable energy sources characterized by their intermittency, it is believed that DR can reduce the carbon-intensity. In order to design such solutions, there is an increasing need of using Cloud computing with respect to its data-intensive application in real-time DR with advantages in high processing speed, unlimited data aggregation, cost-saving, security, and confidentiality. Thus, in this talk, we will discuss the implication of these new solutions for the green economy.

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.

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.

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).

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.

Abstract
Plasmas containing a single ion species are rather well understood. A convenient description can be provided in the frame of fluid dynamics under the additional assumption of quasi-neutrality. The latter assumption eliminates access to the natural boundary conditions at the wall. However, an effective new boundary condition is included in the fluid equations since all derivatives diverge when the ions reach their sound speed. This divergence marks the breakdown of the quasi-neutrality assumption. Conditions in plasmas containing multiple ion species are less obvious. As was shown by Benilov, also here a divergence exists. However, this divergence provides only one condition while N>1 conditions for the ions are required. These conditions will be derived. A fully-self consistent calculation requires in addition also calculation of the N ionization rates. Further, the relative plasma densities are calculated. The concept introduced here is quite recent and still in progress. Comments are very welcome.

Abstract
Many technological applications of low-temperature plasmas (LTPs) rely on the interaction of the plasma with the surrounding walls. Whereas plasma-surface interactions (PSIs) may be described by surface coefficients (e.g., emission), these are often effective, averaged over various physical processes. Detailed knowledge on the surface kinetics may be obtained by sophisticated diagnostics, modeling, or a combination. These are often limited due to acquisition or computational requirements. Moreover, a comprehensive understanding of LTPs and related PSIs must be inherently multi-scale. This holds specifically for plasma modeling, where a consistent description requires sub-models on individual levels. In this work, the applicability of machine learning surrogate models to depict PSIs is discussed in the context of metallic thin film sputter deposition. Different surface models are assessed in terms of quality and abundance of data, as well as reliable physical descriptors. Lower physical fidelity data based on the transport and range of ions in matter simulations provide insight into the steady surface state; higher physical fidelity reactive molecular dynamics data capture also the dependence of a changing surface state. Both data sets are exploited for the training of corresponding machine learning models. The applied model architectures – based on artificial neural networks – are reviewed and the resulting prediction metrics are assessed. It is concluded that the obtained data-driven surrogate models entail the fidelity of the original physical models. They allow for a reliable and consistent multi-scale model coupling at significantly reduced computational costs. Envisioned applications of this modeling procedure include different plasma processes, materials, and phenomena (e.g., plasma catalysis).

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 138690629 (TRR 87) and Project-ID 434434223 (SFB 1461).

Abstract
Recent research at the Institute for Plasma and Atomic Physics at Ruhr University Bochum (RUB) is introduced. Particularly, four research topics are discussed in more detail. These topics range from plasmas at low pressure (p < 1Pa) to atmospheric pressures and include experimental as well as theoretical aspects: 1) The inductively coupled array (INCA) discharge, 2) Describing local and non-local electron heating described by the Fokker-Planck equation, 3) Enhanced dynamic range for ion retarding field energy analyzers (RFEA), 4) ns-pulsed atmospheric pressure discharges (jets) in nitrogen and CO2 - the latter topic includes also a wide range of different diagnostics (EFISH, CARS, QCLAS, OES, V/I), PIC/MCC simulation, and modelling. The intention is to provide an overview and not an in-depth discussion of these topics. However, questions and comments are be welcome at any time during my present stay at Osaka University.

Abstract
A twin surface dielectric barrier discharge (SDBD), specially designed for the conversion of VOCs in synthetic air, has been previously studied regarding its fundamental plasma parameters, power efficiency, gas phase chemistry, gas dynamics, and conversion of frequently used hydrocarbons with and without catalyst [1-3]. However, the complex interaction of the different media and the underlying conversion mechanism is not yet fully understood.
Here, techniques such as flame ionization detectors and gas chromatography-mass spectrometry are used to gain insight into the occurring gas-phase chemistry, possible reaction pathways, and advantages of the presented discharge over comparable techniques. Optical absorption spectroscopy is used to measure absolute densities of selected reactive oxygen and nitrogen species to further elucidate the conversion mechanism based on these radicals. A mode-transition effect, also known from literature [4], can be observed for different volumetric flow rates and be replicated in both, the experiment and a complementary zero dimensional chemistry model. Finally, flow analysis by schlieren imaging is performed to illustrate the comparably high performance of the system, despite the low plasma to surrounding gas ratio.

• [1] B. Offerhaus et al., Plasma Processes and Polymers 14 (2019).
• [2] L. Schücke et al., Plasma Sources Science and Technology 29 (2020).
• [3] N. Peters et al., Plasma Processes and Polymers 18 (2021).
• [4] T. Shimizu et al., New Journal of Physics 14 (2012).

*This study was funded by the German Research Foundation (DFG) with the CRC 1316 project A7.
*Co-authors: Arisa Bodnar, Niklas Friedrichs, Alexander Böddecker, Niklas Peters, Andrew R. Gibson, Martin Muhler and Peter Awakowicz

Abstract
During the last decade, Convolutional neural networks (CNN) have revolutionized the already prolific Artificial Intelligence field, surpassing classical neural networks in most applications including face recognition, classification, clustering, and so on. Properties like parameter sharing ensure this great accuracy while decreasing the needed number of parameters and computation time. In this introductory talk, we'll try to give a comprehensive view of the CNN basis as well as principal blocks, connections, and components. From there, the most known architectures will be presented. This talk will end with examples of how modern blocks can improve the aforementioned architectures. From mathematics background to recent CNN's components, efforts have been made to give interpretations and intuitive explanations.

Abstract
In PECVD (plasma polymerization being a part of it), we have many knobs: reactants (gas feed) and discharge characteristics (type of discharge, substrate potential, pressure, power, pulsing). Comparison of the film properties for one of the usual varied parameters such as discharge power can be difficult. Considering the most used discharge, the low-pressure RF CCP, the questions remain about the role of ions, their energy, density, and chemistry in the particular experiment. The comparison among the experimental set-ups is hindered by the varied substrate potential and the pressure (collisional / collisionless regimes) or not well-defined transitions between the discharge modes (CCP/ICP or α/γ of CCP). In my talk, I will present our results on plasma polymerization of cyclopropylamine (CPA) mixed with Ar in three different CCP reactor set-ups and compare them with other experiments to prepare amine plasma polymers. I will show the variations of the film chemistry (nitrogen percentage, NH2 groups, nitriles etc.) and discuss the question of different observed trends with increasing discharge power. Based on a usual film characterization (e. g., XPS, FTIR), without testing a specific function, it is hard to say what films are the best for the intended application. Thus, I will try to answer the question if we optimize the film properties to the right values.

Abstract
This talk includes a basic introduction to the plasma-related medical applications that are currently in use, including argon plasma coagulation (APC), sterilization, and surface modification. My work about plasma polymerized coating for biosensor fabrication will also be reported. The aerosol-assisted dielectric-barrier-discharge atmospheric-pressure plasma deposition (AAAPPD) involves depositing plasma-polymerized ethylene (ppE) with grafted hydroxyl functional groups and embedding the protein in the ppE in one step, making the protein entrapment faster than conventional methods and without using reagents. The immunostaining result of AAAPPD protein was close to that of covalent-bonded protein. This method is a rapid and reagent-free method to entrap proteins on different substrates for biosensor fabrication.

Abstract
This is the sequel to the seminar presented last week. that we started last week will continue in this seminar. As we discussed last week, we compared the effects of plasma with thermal therapy on lung cancer with malignant pleural effusion. This study finds out that the plasma can selectively kill lung cancer cells and the benign cells remain its viability. Besides, thermal therapy kills both cancer cells and benign cells. To investigate what is the plasma factor that inhibits cancer cells, we investigated the effects of plasma-generated short-lived species, long-lived species, and electric fields on skin melanoma and basal cell carcinoma cells (A2058 cells, BCC cells) and normal cells (BJ cells, Detroit 551 cells) and found that the short-lived species do make selective inhibition to the benign and malignant cells. The second part of my study is that we mix water aerosol with plasma jet at downstream region makes the plasma jet generate more • OH. We designed different mixing chambers and adjusted the water aerosol flow rate to maximize the • OH generated by plasma jet for biological applications. We also constructed an impedance matching circuit for a partial-discharge calibrated (PDC) atmospheric-pressure plane-to-plane DBD equivalent circuit. The last part of my work is that we used machine learning to distinguish the discharge current of different plasma. The plasma discharge can be different depending on the conditions, and the resulting discharge current has quite different electrical features. Hence, a real-time and cost-effective diagnosis of atmospheric-pressure plasma discharge can be possibly provided via the current classification with deep learning models.

Abstract
We compared the effects of plasma with thermal therapy on lung cancer with malignant pleural effusion. This study find out that the plasma can selectively kill lung cancer cells and the benign cells remain its viability. Besides, the thermal therapy kills both cancer cell and benign cells. To investigate what is the plasma factor that inhibits cancer cells, we investigated the effects of plasma-generated short-lived species, long-lived species, and electric fields on skin melanoma and basal cell carcinoma cells (A2058 cells, BCC cells) and normal cells (BJ cells, Detroit 551 cells) and found that the short-lived species do make selective inhibition to the benign and malignant cells. The second part of my study is that we mix water aerosol with plasma jet at downstream region makes the plasma jet generate more • OH. We designed different mixing chambers and adjusting the water aerosol flow rate maximize the • OH generated by plasma jet for biological applications. We also constructed an impedance matching circuit for a partial-discharge calibrated (PDC) atmospheric-pressure plane-to-plane DBD equivalent circuit. The last part of my work is that we used machine learning to distinguish the discharge current of different plasma. The plasma discharge can be different depending on the conditions, and the resulting discharge current has quite different electrical features. Hence, a real-time and cost-effective diagnosis of atmospheric-pressure plasma discharge can be possibly provided via current classification with deep learning model.

Abstract
During deposition, modification, and etching of thin films and nanomaterials in reactive plasmas, a large number of active species can interact with the sample simultaneously. This includes reactive neutrals created by fragmentation of the feed gas, positive ions and electrons created by electron-impact ionization of the feed gas and fragments, excited states (in particular the long-lived metastable species), and UV photons due to the spontaneous de-excitation of excited atoms and molecules. In order to provide insights into the dominant role of each active species in specific plasma-based processes of advanced materials, a unique system has been established combining beams of neutral atoms, positive ions, UV photons and a magnetron plasma. Furthermore, this setup is equipped with a unique ensemble of in plasma surface characterization tools. The reactor chamber is attached to an ion beam line of a 1.7 MV Tandetron accelerator generating a beam at grazing incidence that allows Rutherford Backscattering Spectrometry (RBS) to be carried out at high resolution near the surface. Elastic recoil detection (ERD) can also be used for standard ERD detection of H, but high resolution of surface H is available through nuclear reaction analysis. It In parallel, an optical port facing the substrate allows to perform Raman spectroscopy of the samples during plasma modification of the substrate. This system enables fast monitoring of a given Raman peak over nine points scattered on a surface of 2 mm2 without inter­ference from the light emitted by the plasma. An example of a possible experiment involving monolayer graphene will be presented. Raman measurements in real-time shed light on the influence of low-energy ions on monolayer CVD graphene and how self-healing takes place immediately after irradiation. The mechanism responsible will be also be detailed to show that carbons adatoms resulting from vacancy creation are essential for graphene self-healing.

Abstract
Raman spectroscopy is an efficient method to characterize the graphene structure. The technique gives distinctive features for pristine, damaged, and even doped graphene. Nonetheless, especially when graphene is grown on a polycrystalline substrate, strong discrepancies may appear on the macroscopic scale. Moreover, in the case of plasma irradiation of graphene, it is essential to understand the impact of the small heterogeneities in pristine graphene (local defects, grain boundaries, etc.) on the resulting graphene structure after treatment. Hyperspectral Raman Imaging (RIMA for Raman Imaging) is a powerful method enabling the capture of qualitative as well as quantitative data on a macroscopic scale. Grain Boundaries (GBs) reveal themselves being more resistant to plasma treatment than pristine graphene domains. After careful consideration of Raman parameters, it appears clearly that preferential self-healing of GBs and its surrounding is taking place, a phenomenon observed in 3D materials, yet to be observed in graphene. This mechanism is governed by carbon adatoms generated from impacts of low-energy argon ions with graphene film. Under constant irradiation from exited species (ions, metastable, VUV photons), carbon adatoms can easily migrate on graphene surface and, in particular, alongside GBs. Hence, defects created at GBs or present nearby might be healed by the adatoms influx. Furthermore, another plasma conditions shown that energy fluence from Argon metastable deexcitation can be linked to an enhanced defect migration and self-healing at GBs [4]. Finally, the previous study in Argon plasma enables the determination of ideal operating conditions for Argon plasma with B2H6 trace. The exposition of graphene to such plasma reveals boron in-plane substitution combined with low-level hydrogenation and defect generation.

Abstract
Integration of distributed renewable generation technologies is an important issue in power networks. Distributed energy storage (ES) systems, although seen as a tool to mitigate the stress on local networks, tend to be operated only to minimize the energy cost of their direct owner. In this talk, cooperative game theory together with Artificial Intelligence tools is used to construct an energy grand coalition, in which ES system operations are optimized to minimize the coalitional energy cost.

Abstract
The structure and the formation process of a Cu-Zn surface alloy formed on Cu(997) were investigated by machine-learning molecular dynamics (MLMD). The force-field required in the MD simulations is built by means of Gaussian Process (GP) regression aided with an on-the-fly active learning scheme. The simulation reveals a detailed atomistic picture of the long-time scale formation of Cu-Zn alloy on Cu(997) surface which was intractable in experiments. The surface alloying is initiated at the terrace near the step edge, highlighting the importance of step edges in the surface alloying. The rationalization of alloying behavior is performed based on statistics and activation energies of various elementary events that occur during the simulations. The dominant alloying mechanisms are found to be hopping descend and exchange descend near the step edge.

Abstract
As mentioned in the Nobel Prize in Chemistry 2019 press release: “Lithium-ion batteries have revolutionised our lives… They have laid the foundation of a wireless, fossil fuel-free society, and are of the greatest benefit to humankind.” With this in mind, we (i) identified an optimised building block for our previously reported multi-layered Si-based Li-ion battery anodes [1], (ii) built it with a cheap, one-pot, green, and scalable cluster beam deposition method, and (iii) elucidated with large-scale atomistic computer simulations the underlying physical mechanism leading to its superior mechanical and electrochemical performance.
The Nano-Vault is a novel sculptured-thin-film nanostructure synthesised with the help of nanoparticles grown and deposited in the gas phase [2]. The name alludes to the civil engineering definition of a multi-arch structure sustained on columns, characterised by its high elastic modulus. As a result, Si anodes in Li-ion batteries with vaulted structures simultaneously show high mechanical stability and low lithium consumption during formation of solid electrolyte interface, addressing the two main challenges for Si anode commercialisation. This optimal electrochemical performance is associated with a distinct transition in mechanical behaviour at the exact moment when individual Si columns merge to form closed arches (but not beyond that point, with further growth of amorphous Si film on top).
The introduction of nano-vault and arch action brings many new possibilities in the design of new materials for batteries, but also, potentially, for other applications in which the surface is under variable and strong stress action.

Abstract
Smoke from a fire; a stormy cloud: both naturally occurring phenomena due to aggregation of molecules from the gas phase. In this seminar we will focus on a special type of “cloud”, that of inorganic nanoparticles grown from some sort of physical vapour deposition (PVD) method. To design and fabricate sophisticated nanoparticles for specific (nano)technological applications, molecular dynamics simulations offer an invaluable tool, as they can probe various simultaneous processes during nucleation and growth in atomistic detail. Nanomatter can be dramatically different from bulk matter, as physical properties do not always scale down to the nano-regime. Why? Which fundamental properties showcase this difference? In this seminar we will also identify some cases where nanomatter behaves “weirdly” (i.e., differently from our every-day experience), and choose one as the most characteristic; we will also try to understand why.

Keywords: atomistic modelling, nanoparticles, cluster beam deposition, nucleation & growth, magnetron sputtering

[1] P. Grammatikopoulos, Current Opinion in Chemical Engineering, 2019, 23, 164.