dataesr/scientific-paragraphs-categorization huggingface dataset:
Plasma sources used for CO 2 conversion As stated in the introduction, the practical applications of plasma-catalysis are predominantly gas decontamination and molecular synthesis. Despite their different end goals, these applications frequently share a common aspect, which is the use of a dielectric barrier discharge (DBD) as the plasma source and, more specifically, a ''packed bed DBD" in coaxial geometry. The extensive use of packed bed DBDs is because they are easy to implement, relatively cheap, and provide simple catalyst coupling. However, in the context of CO 2 conversion, DBDs often have the drawback of being unable to exploit the more efficient vibrationally excited dissociation. This is due to the high electric field induced by the Fig. 1. Number of publications on plasma-catalysis per year obtained from different popular search engines for maximum objectivity using the Publish or Perish software  [22].
However in plasma-catalysis, this effect could be compensated in some cases for instance because of a stronger vibrational excitation at reduced pressure enhancing the efficiency of surface reaction. The diversity of the considered plasma sources raises questions on the relevance of the operating parameters that can be compared between them, for instance, power, flow rate, pressure, etc. As such, we have included Section 3. 2 dedicated to the types of parameters included in the database so that sensible comparisons can be made between the different plasma sources. In all cases though, the reactivity of these plasma sources combines the effects of charged species, excited electronic and vibrational states, strong electric fields, and often strong spatio-temporal gradients, resulting in chemical reactivity fundamentally different from that obtained in thermal catalysis.
To be able to compare different plasma sources ideally, it would be necessary to know the energy distribution function of the electrons, or at least the electric field and the electron density. It would also be necessary to characterise the spatial inhomogeneity of the energy deposited. However, these quantities are in general difficult to determine, especially when the plasma is in direct contact with a catalyst. Alternatively, more macroscopic parameters such as power or specific energy input (SEI) and gas temperature should at least be known so that one can study the conversion performances collected in the database.
In plasma-catalysis, the flux of species to the catalyst surface is a complex mixture of excited states and radicals, whereas, in the other forms of catalysis, the flux of species reaching the surface is composed of stable molecules in their ground states. As a result, the tools developed for analysing performances and comparing results in thermal, electro-and photo-catalysis cannot be easily used on a plasma-catalytic system. From the great uniqueness and complexity of plasma-catalysis processes for CO 2 conversion comes the motivation to develop the CO2-PDB, which is presented in this publication. 3. The PIONEER database: positioning, graphical interface and type of data set.
Motivation for the creation of the CO2-PDB and positioning The usefulness of creating a database dedicated to plasmacatalysis for CO 2 conversion arises from two main points. Firstly, the complexity of plasma-catalyst interactions discussed in previous sections necessitates large amounts of data in order to identify trends which indicate fundamental mechanisms. Secondly, the number of works in the field is becoming large and, without comparison tools, potentially useful information can be missed. Therefore, the CO2-PDB is a first attempt to fulfil these two objectives by filling a gap with respect to existing tools in other scientific communities. The performance data on plasma-catalysis experiments gathered in the CO2-PDB represent a valuable contribution to the research field as no comparable database exists.
Even in conventional catalysis, we are unaware of a global performance database for given catalysts. The list of catalysis databases available online includes: the Open Materials Database  [42] , the Materials Project  [43] , AFLOW  [44] , the Open Quantum Materials Database  [45] , Aiida  [46] , the Catalysis-Hub  [47] , the Open Crystallography Database  [48] , NoMaD CoE  [49] , and the Computational Materials Repository  [50]. They come with high-quality interfaces, Python modules for data handling and access, and extensive data compared to the CO2-PDB. Nevertheless, these databases focus on data derived from computational methods (e. g. density functional theory) with the goal of discovering new materials, particularly catalysts. Comparatively, the CO2-PDB focuses on experimental performance data from plasma(-catalytic) CO 2 conversion.
On the plasma side, databases are even more scarce. A popular example is the LXCat database for electron and ion scattering cross sections and swarm parameters  [51] , which provides valuable input for plasma kinetic simulations. The extension to plasmacatalysis is however not straightforward. In this regard, it is worth mentioning the Korean data center for plasma properties that provides data for the plasma-wall interaction, and other aspects  [52]. Likewise the LXCat database, this database is meant to serve as input for modelling, and no performance data is included. Therefore, the CO2-PDB bridges the gap between large-scale/ big-data studies and well-controlled, ideal, comparable, smallscale studies. In fact, all the catalysis databases presented above follow the ''big-data" approach and usually come with some machine learning utility  [42] [43] [44] 47, 49, 50].
Over time, the practices used to study plasma catalysis will become more comparable because the database will make it easier for everyone to be aware of the critical parameters needed to cross-check data from one system to another. New contributions from the entire community will improve the quality and completeness of the data sets in the database. Ultimately, this will make it possible to identify the physico-chemical properties that are really beneficial to plasma-catalyst coupling, in order to define new materials, probably quite different from the catalysts usually used in thermal catalysis, that are really capable of improving the performance of the plasma itself as well as taking advantage of the short-lived species generated by the plasma.
Scope of the database The research fields of plasma and catalysis feature an enormous versatility of characterisation that only expands when the two merge. Thus, any attempt to cover everything in one plasmacatalysis database is unlikely to succeed. For that reason, we  prioritise comparability and importance of included data over quantity, leading to a manageable set of data for each measurement series. An in-depth discussion of included data is given in the Supplementary Material while this section gives a brief overview and focuses on some selected metrics. Table  1  gives an overview of the amount and diversity of the data included in the CO2-PDB at the time of submission of this manuscript. In short, the CO2-PDB reports on so-called ''process parameters", i. e. x-values in Table  2 , against so-called ''performance parameters", i.
e. yvalues in Table  3 , which are backed up by metadata to facilitate the interpretation and comparison of trends. Note that all data sets in the CO2-PDB report on plasma experiments on CO 2 conversion, of which 2012 are plasma-catalytic experiments. No data of conventional thermal catalysis is included. Insights obtained in this section are used in Section 5 to give some best practice advice. The data collected in the CO2-PDB must have been previously published in a peer-reviewed journal in English. As such, the validity of the measurements is ensured by the classical peer reviewing process and is in no way the responsibility of the CO2-PDB authors. Beyond the simple identification of the article in which the data were published, the CO2-PDB metadata allows the characterisation of the gas mixture studied, the plasma source, the nature of the catalyst, and the way in which the catalyst is coupled to the plasma.
Each data set (i. e. set of (x; y) pairs that comprise a measurement, line, or equivalent) in the CO2-PDB is associated to all these parameters when the information is available in the published article. Fig. 2  shows the fraction of data sets in the CO2-PDB providing certain information focusing on the plasma in Fig. 2 (a) and on the catalyst in Fig. 2(b ). Hence, each bar in the chart corresponds to one column in the database. For Fig. 2 (a), all data sets in the CO2-PDB are considered as all studies include a plasma (i. e. 3041 data sets at the date of submission of this paper, see Table  1 ), while Fig. 2(b ) includes only plasma-catalytic studies (i. e. 2012 data sets). All data are considered important such that the closer a bar is to 100%, the better. Note that only a selection of all data columns in the CO2-PDB is shown, namely, those data columns that are crucial to each and every data set.
For instance, power always has to be deposited to ignite the plasma. Conversely, MW discharges do not rely on an applied voltage, which is not included in Fig. 2. From the comparison of Fig. 2  (a and b), it becomes apparent that in the general perspective of the database, the plasma is more exhaustively characterised than the catalyst. In fact, the average fraction of provided data for plasma is 89%, while it is only 45% for the catalyst. A possible rationale is that the field of plasmacatalysis is still relatively young, needs significant interdisciplinary efforts, and is still being led mostly by the plasma community, which has less experience in catalyst characterisation. Therefore, the characterisations provided for the catalysts are still not in the standard of other more established fields of catalysis.
From Fig. 2  (a), it can be seen that parameters that are best documented mainly concern what is put into a reactor and in what quantity, for example, gas composition, and flow rate. Conversely, the physical properties of the plasma studied are limited except for power  - Fig. 2. The fraction of data sets that provide information about individual plasma parameters with respect to the total number of sets in the CO2-PDB in (a). The fraction of data sets that provide information on catalyst parameters with respect to the total number of data sets including a catalyst in (b). and SEI. Parameters such as electron densities, electric fields, or vibrational temperatures would provide great value, but unfortunately these parameters are unknown in the vast majority of cases and therefore are not included as CO2-PDB metadata for the moment.
The database also foresees developments in the separation of the plasma reaction products. Few publications have dealt with separation of products, specifically oxygen atoms as they can participate in the back reaction to CO 2  [58, 59]. At the time of publication, it is hard to estimate which possible input data would be interesting for the database. So far, separation type and position are the only parameters included but this can change and extend according to future developments. More is discussed in the Supplementary Material. To summarise, the CO2-PDB comprises data extracted from a large and wide-ranging collection of peer-reviewed publications arranged into an easy-to-use format (Table  1 ). However, we have not included all possible items from the reported data. Instead, we only included data that we deemed important and comparable.
Panel Filter important metadata and minimal annotation on applied normalisation, which is crucial for comparison. Alongside these buttons, there are two tabs for further control over the plot and data. The ''Plot setup" tab contains the widgets that control the appearance of the database plot. Using these widgets, it is possible to control the scale of the axes, the colour map of the plot and toggling automatically redrawing the plot after each filter change. Special attention should be given to the ''Group data by" widget, which allows the selection of one or more categorical columns by which the data are grouped and colour mapped in the plot. By allowing the user to create custom groupings, insights into how certain groups and categories of experiments compare against each other can be made, see Fig.
Advanced data inspection The main interface of the database tool provides convenient and powerful methods to filter, group, and visualise the data within the database, making it well suited to data exploration and comparison between publications. It however cannot replace all possible filtering and visualisation for a user's needs, and neither does it aim to. Furthermore, understanding nuances and outliers in the data requires a detailed comparison of metadata such as pressure, temperature and volume. For this reason, there is a secondary tab called ''advanced database inspection" in the main viewport, which offers a powerful pivot table interface (referenced as ''Data Table ".
going forth) to the user for data inspection. It gives full access to the data in the database but is limited to some extent in that it can-not represent array-like metadata, such as temperature ranges, as numeric array objects, but rather as string-like objects. Applied categorical filters in the main view are synchronised to the Data Table to support an explore-then-inspect workflow. However, please note that these filters do not work in the reverse direction. Furthermore, fine-grained control over data selection is enabled by extensive customisation of selected columns, filters, groupings, and aggregations across the database using drag-and-drop of column names. Using the toolbar on the bottom of the Data Table widget, it is possible to export the selected data or all data to several common formats such as, *.
csv and *. json, thus enabling the user to utilise the data in their own workflow after initial inspection, and control what data they need. As a final feature, there is the option on the top left of the widget to change the view from a tabular ''Datagrid" to other visualisations, such as, bar charts and scatter plots, which can be easily adapted using the column list and aggregations. This complements the feature set of the main interface, allowing further exploration, and represents the main way in which a copy of a subset of the database can be obtained. Examples of data extracted with the aid of the CO2-PDB app are shown and discussed in Section 4. 1.
A note on calculations, assumptions and features It is important to note that the database is not just a catalogue of data, but also a tool that allows quick comparisons of (normalised) data. However, there are some core assumptions and approximations that are made to compare as much data as possible across a wide range of conditions. For example, SEI is seen as a macroscopic process parameter that inputs power into a given amount of flow rate regardless of the plasma conditions. Within the database, SEI values directly extracted from publications appear alongside values calculated from reported powers and flow rates, and are labelled accordingly. Contrary to the direct calculation of SEI, the adjustment with respect to standard conditions is barely mentioned in the publications used to build the database.
Therefore, we can only assume that temperature and pressure inside the plasma or reactor are not considered when SEI or energy efficiencies are calculated in the majority of publications. Here, in the calculation of s res , the standard conditions are taken into account when adjusting for the discharge conditions. It is crucial to know how much time a molecule spends under plasma conditions. Similarly for the energy efficiency calculations, the correction of the SEI used here had to ponder temperature and pressure (values extracted from the database). Ultimately, the number of particles into which energy has been deposited is considered in the back end CO2-PDB app. Comparison of example graphs as they can be directly exported from the CO2-PDB app graphical user interface (left as-is, save for moving the legend) showcasing the CO 2 conversion (in %) versus the SEI (in J L À1 ) with data grouped by the ''main goal" and use of catalyst (either ''True" or ''False").
3. 1). Despite the versatility of the CO2-PDB, some precautions, discussed in Section 4. 3, need to be taken. Furthermore, Section 4. 4 illustrates the benefit of using some representation techniques, popular in statistics, to analyse large data sets and elucidate any synergistic effect of plasma-catalysis. Finally, Section 4. 5 provides an example of new analysis that can be performed with the CO2-PDB. Particularly, the focus is on the production of hydrocarbons C x H y in plasma-based dry reforming of methane, taken as illustration of a topic that is growing in interest. In the coming sections, there are plots using data contained in the database. In some special instances, these plots are the direct exports of the database app for demonstrative purpose, which is noted explicitly in their caption.
Comparison with graphs from previous review papers The plasma-catalysis community has already understood the importance of collecting a vast array of experimental results on plasma-based CO 2 conversion, and this is evidenced by the number of review articles available in the literature. In this section, the extraction of CO2-PDB data is compared with the collected data from two review papers  [60, 61] , with the aim of. 1. Assessing the reliability of the data already available in the CO2-PDB at the date of publication of this paper. 2. Checking the progress that has been made in the field since the publication of previous review papers. In fact, one of the missions of the CO2-PDB is to become the reference point for the CO 2 plasma-catalysis community, aiming at becoming the largest and most up-to-date database available.
For the energy efficiency, however, the dilution is taken into account. Therefore, for the sake of comparison, the same criterion has been applied to Fig. 5(b). Despite the similar trends displayed in Fig. 5 , the data sets considered in Snoeckx and Bogaerts  [60]  and in the CO2-PDB are different, with the latter including also data from recent publications, from 2017 on. Fig. 6  shows the number of publications in Fig. 6 (a) and and the corresponding number of data sets in Fig. 6(b) , included in the CO2-PDB (up to March 2023), as a function of the year of publication. Interestingly, almost half of the data included in the CO2-PDB is more recent than the review paper from Snoeckx and Bogaerts  [60]  (green), demonstrating the usefulness of an online database that is constantly updated. In 2021, Chen et al.
This highlights the fact that the database and visualisation tool proposed here (the CO2-PDB and the CO2-PDB App) is not a substitute for keeping a critical eye on the graphs generated as output. Indeed, when data seems to be out of line with the general trends, it is essential to always try to understand which parameter could be responsible for this difference. In the example of Fig. 4(b) , the extreme dilution of CO 2 in argon explains the difference observed for the data from  [67]. Beyond the choices made by the CO2-PDB App user, one should also not forget the problems inherent to the lack of data standardisation in the plasma-catalysis community which are discussed below.
Caveats and disclaimers Despite our best efforts to create a harmonised data set across a large selection of parameters, there are caveats and problematic data in the CO2-PDB. These issues are rooted in the way data are reported in literature and how they are entered into the CO2-PDB. The clear outline of the undertaken steps allows any user of the database to retrieve data and perform their own calculations. For illustration, an article by Martini et al. [102] , containing a comprehensive clarification of parameters used and assumptions made, is discussed here. In their work, they establish the conversion in the effluent by gas chromatography, even accounting for the change in molecule number density as a consequence of the conversion by using N 2 as an internal standard, whilst also performing in situ time-resolved measurements by laser spectroscopy.
So if the user tries to recalculate the Martini energy efficiency from the dissociation rate column and the mean temperature measured in situ at 1400 to 2500 K, this time the dotted magenta line is obtained in Fig. 9. Due to the higher temperature in the discharge compared to the exhaust, the calculated efficiency seems now con-siderably lower which is due to the inconsistency of the data used for this particular calculation. It is impossible to anticipate all the uses that can be made by users of the data extracted from the CO2-PDB but it is essential to always refer to the real meaning of each parameter (see in particular the Supplementary Material) before exploiting them. In conclusion, the discrepancy in reported and calculated values is highlighted to further address the implications that metadata in the database can have, and how it should be carefully considered alongside the data itself.
However, since the definition based on the standard reaction enthalpy valid for the pure CO 2 case can Fig. 10. Overview of energy efficiency of CO 2 conversion as function of CO 2 conversion (both in %) for different groupings of the data, where (a) shows grouping by the stated goal of the experiment and (b) shows grouping by the methodology/region used to determine the temperature. Comparing the energy cost of e. g. methanation and dry reforming of methane experiments to the reaction enthalpy of CO 2 splitting to calculate the ''efficiency" is flawed itself. However, a further role for how and where the temperature of the gas and/or plasma is established also can be expected. still be retrieved from the CO2-PDB data by way of filtering the database on only CO 2 splitting experiments, the other experimental goals are not excluded from the CO2-PDB, see Fig.
Influence of support and active phase shown through statistical data The advanced data extraction functionalities presented in Section 3. 3. 2 enable easy cross-comparisons with all database input parameters. In this section, examples of extraction graphs and their corresponding analyses are given. However, it is essential to keep in mind that all the insights available from a database rely on the quantity and value range of the data with respect to the parameters considered. The conclusions that can be drawn from a data extraction must therefore always be considered with a critical eye on the amount of data available for a specific combination of input parameters. It is obvious that the more data the database will contain thanks to the contribution of the whole community, the more trustworthy the conclusions that can be drawn from it will become.
12. Contrarily in glow discharges, the available data on zeolites are concentrated between 60 to 70% conversion and are solely responsible for the high conversion outcomes for catalyst-coupled glow discharges in Fig. 11. In this latter instance however, all the points represented correspond to a single publication  [103] , and therefore it cannot be concluded in a general manner that zeolites in glow discharges produce these significantly improved dissociation rates for lack of data, despite what these distributions suggest. The input parameters of the database allow one to push the analysis further, in particular thanks to the parameters describing the properties of the catalyst. At the time of writing there is still a paucity of data on the support shape, pore volume, or specific surface which precludes any trustworthy conclusions.