Abstract: In this paper, simulations are performed to gain a better insight into the properties of a Cl2/Ar plasma, with and without O2, during plasma etching of Si. Both plasma and surface properties are calculated in a self-consistent manner. Special attention is paid to the behavior of etch products coming from the wafer or the walls, and how the chamber walls can affect the plasma and the resulting etch process. Two modeling cases are considered. In the first case, the reactor walls are defined as clean (Al2O3), whereas in the second case a SiO2 coating is introduced on the reactor walls before the etching process, so that oxygen will be sputtered from the walls and introduced into the plasma. For this reason, a detailed reaction set is presented for a Cl2/O2/Ar plasma containing etched species, as well as an extensive reaction set for surface processes, including physical and chemical sputtering, chemical etching and deposition processes. Density and flux profiles of various species are presented for a better understanding of the bulk plasma during the etching process. Detailed information is also given on the composition of the surfaces at various locations of the reactor, on the etch products in the plasma and on the surface loss probabilities of the plasma species at the walls, with different compositions. It is found that in the clean chamber, walls are mostly chlorinated (Al2Cl3), with a thin layer of etch products residing on the wall. In the coated chamber, an oxy-chloride layer is grown on the walls for a few nanometers during the etching process. The Cl atom wall loss probability is found to decrease significantly in the coated chamber, hence increasing the etch rate. SiCl2, SiCl4 and SiCl3 are found to be the main etch products in the plasma, with the fraction of SiCl2 being always slightly higher. The simulation results compare well with experimental data available from the literature.

Abstract: In this work, we study the abatement of dilute trichloroethylene (TCE) in air with a negative direct current corona discharge. A numerical model is used to theoretically investigate the underlying plasma chemistry for the removal of TCE, and a reaction pathway for the abatement of TCE is proposed. The Cl atom, mainly produced by dissociation of COCl, is one of the controlling species in the TCE destruction chemistry and contributes to the production of chlorine containing by-products. The effect of humidity on the removal efficiency is studied and a good agreement is found between experiments and the model for both dry (5 % relative humidity (RH)) and humid air (50 % RH). An increase of the relative humidity from 5 % to 50 % has a negative effect on the removal efficiency, decreasing by ±15 % in humid air. The main loss reactions for TCE are with ClO·, O· and CHCl2. Finally, the by-products and energy cost of TCE abatement are discussed.

Abstract: In this paper, a new implementation of the Metropolis Monte Carlo (MMC) algorithm is presented. When combining the MMC model with a molecular dynamics (MD) code, crystal growth by plasma-enhanced chemical vapor deposition can be simulated. As the MD part simulates impacts of growth species onto the surface on a time scale of picoseconds, the MMC algorithm simulates the slower adatom surface processes. The implementation includes a criterion for the selection of atoms that are allowed to be displaced during the simulation, and a criterion of after how many MMC cycles the simulation is stopped. We performed combined MD-MMC simulations for hydrocarbon species that are important for the growth of ultrananocrystalline diamond (UNCD) films at partially hydrogenated diamond surfaces, since this implementation is part of a study of the growth mechanisms of (ultra)nanocrystalline diamond films. Exemplary for adatom arrangements during the growth of UNCD, the adatom surface behavior of C and C2H2 at diamond (111)1 × 1, C and C4H2 at diamond (111)1 × 1 and C3 at diamond (100)2 × 1 has been investigated. For all cases, the diamond crystal structure is pursued under the influence of MMC simulation. Additional longer time-scale MD simulations put forward very similar structures, verifying the MMC algorithm. Nevertheless, the MMC simulation time is typically one order of magnitude shorter than the MD simulation time.

Abstract: e investigate atmospheric pressure microwave (MW) plasma (2.45 GHz) conversion in CO2 and CH4 mixtures (i.e., dry reforming of methane, DRM) focusing on reaction performance and carbon formation. Promising energy costs of ~2.8–3.0 eV/molecule or ~11.1–11.9 kJ/L are amongst the best performance to date considering the current state-of-the-art for plasma-based DRM for all types of plasma. The conversion is in the range of ~46–49% and ~55–67% for CO2 and CH4, respectively, producing primarily syngas (i.e., H2 and CO) with H2/CO ratios of ~0.6–1 at CH4 fractions ranging from 30% to 45%. Water is the largest byproduct with levels ranging ~7–14% in the exhaust. Carbon particles visibly impact the plasma at higher CH4 fractions (> 30%), where they become heated and incandescent. Particle luminosity increases with increasing CH4 fractions, with the plasma becoming unstable near a 1:1 mixture (i.e., > 45% CH4). Electron microscopy of the carbon material reveals an agglomerated morphology of pure carbon nanoparticles. The mean particle size is determined as ~20 nm, free of any metal contamination, consistent with the electrode-less MW design.

Abstract: Direct oxidation of methane to methanol (DOMTM) is attractive for the increasing industrial demand of feedstock. In this review, the latest advances in heterogeneous catalysis and plasma catalysis for DOMTM are summarized, with the aim to pinpoint the differences between both, and to provide some insights into their reaction mechanisms, as well as the implications for future development of highly selective catalysts for DOMTM.

Abstract: This paper brings the comparison of performances of CO2 conversion by plasma and plasma-assisted catalysis based on the data collected from literature in this field, organised in an open access online data-base. This tool is open to all users to carry out their own analyses, but also to contributors who wish to add their data to the database in order to improve the relevance of the comparisons made, and ultimately to improve the efficiency of CO2 conversion by plasma-catalysis. The creation of this database and data-base user interface is motivated by the fact that plasma-catalysis is a fast-growing field for all CO2 con-version processes, be it methanation, dry reforming of methane, methanolisation, or others. As a result of this rapid increase, there is a need for a set of standard procedures to rigorously compare performances of different systems. However, this is currently not possible because the fundamental mechanisms of plasma-catalysis are still too poorly understood to define these standard procedures. Fortunately how-ever, the accumulated data within the CO2 plasma-catalysis community has become large enough to war-rant so-called “big data” studies more familiar in the fields of medicine and the social sciences. To enable comparisons between multiple data sets and make future research more effective, this work proposes the first database on CO2 conversion performances by plasma-catalysis open to the whole community. This database has been initiated in the framework of a H2020 European project and is called the “PIONEER DataBase”. The database gathers a large amount of CO2 conversion performance data such as conversion rate, energy efficiency, and selectivity for numerous plasma sources coupled with or without a catalyst. Each data set is associated with metadata describing the gas mixture, the plasma source, the nature of the catalyst, and the form of coupling with the plasma. Beyond the database itself, a data extraction tool with direct visualisation features or advanced filtering functionalities has been developed and is available online to the public. The simple and fast visualisation of the state of the art puts new results into context, identifies literal gaps in data, and consequently points towards promising research routes. More advanced data extraction illustrates the impact that the database can have in the understanding of plasma-catalyst coupling. Lessons learned from the review of a large amount of literature during the setup of the database lead to best practice advice to increase comparability between future CO2 plasma-catalytic studies. Finally, the community is strongly encouraged to contribute to the database not only to increase the visibility of their data but also the relevance of the comparisons allowed by this tool. (c) 2023 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. This is an open access article under the CC BY license (http://creati- vecommons.org/licenses/by/4.0/).

Abstract: Porous materials are commonly used in microelectronics, as they can meet the demand for continuously shrinking electronic feature dimensions. However, they are facing severe challenges in plasma etching, due to plasma induced damage. In this paper, we present both the plasma characteristics and surface processing during the etching of porous materials. We explain how the damage occurs in the porous material during plasma etching for a wide range of chuck temperatures and the responsible mechanism for plasma damage-free etching at cryogenic temperature, by a combination of experiments and numerical modeling.

Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of
51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.