Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)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),CO_(2)/CH_(4)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 plasma-catalytic 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.

Meta-analysis of CO_(2) conversion,energy efficiency,and other performance data of plasma-catalysis reactors with the open access PIONEER database

This paper brings the comparison of performances of CO_(2)conversion by plasma and plasma-assisted catalysis based on the data collected from literature in this field,organised in an open access online database.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 CO_(2)conversion by plasma-catalysis.The creation of this database and database user interface is motivated by the fact that plasma-catalysis is a fast-growing field for all CO_(2)conversion 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 however,the accumulated data within the CO_(2)plasma-catalysis community has become large enough to warrant 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 CO_(2)conversion performances by plasma-catalysis open to the whole community.This database has been initiated in the framework of a H_(2)0_(2)0 European project and is called the“PIONEER Data Base”.The database gathers a large amount of CO_(2)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 CO_(2)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.

Brain-wide N2cG compensation permits glycoprotein-deleted rabies virus to trace neural circuits across multiple synapses

Rabies-viruses-based retrograde tracers can spread across multiple synapses in a retrograde direction in the nervous system of rodents and primates,making them powerful tools for determining the structure and function of the complicated neural circuits of the brain.However,they have some limitations,such as posing high risks to human health and the inability to retrograde trans-synaptic label inputs from genetically-de¯ned starter neurons.Here,we established a new retrograde trans-multi-synaptic tracing method through brain-wide rabies virus glycoprotein(RVG)compensation,followed by glycoprotein-deleted rabies virus(RV-△G)infection in specific brain regions.Furthermore,in combination with the avian tumor virus receptor A(TVA)controlled by a cell-type-specific promoter,we found that EnvA-pseudotyped RV-△G can mediate e±cient retrograde trans-multi-synaptic transduction from cell-type-specific starter neurons.This study provides new alternative methods for neuroscience researchers to analyze the input neural networks of rodents and nonhuman primates.