dataesr/scientific-paragraphs-categorization huggingface dataset:
These parameters are intentionally tracked to improve the capabilities of the database in future work. Currently, the available data allow users to discriminate the effect of the supports from the effect of the active phase added to supports. To that end, Fig. 13  shows again the CO 2 conversion rates and energy efficiency like Fig. 11 , but this time restricted to solely DBDs, whilst comparing the performances obtained in plasma either with the support alone (cyan), or with an active phase on the same support (green). The range of conversion rates in Fig. 13(a ) is correlated to the support used. In the case of alumina for example, the conversion does not vary significantly with the addition of an active phase, reaching a maximum of 40% conversion. Zeolites exhibit a larger range of conversion rates with a second peak above 50% once the material with the active phase is considered.
Suggestions of good practices The choice of metadata currently included in the CO2-PDB is an image of what is most commonly reported in the plasma-catalysis literature. As such, the CO2-PDB gives not only a comprehensive overview of the state of the art but also serves as template of what data should be provided to facilitate the comparison between studies. A critical assessment of this template might lead to the conclusion that there is the plasma characterisation on the one hand and the catalyst characterisation on the other. Within that narrative, Fig. 2  seems to show in general a more complete description of the plasma-related parameters than the catalyst-related ones. In reality, neither the plasma source nor the catalyst are easily characterised because of the presence of each other.
For one thing, lessons learned from the status quo of the CO2-PDB are recapitulated. In brief, we argue that if future studies would adhere to the template of metadata established by the CO2-PDB, a significant amount of comparability would be obtained. For another thing, with relatively simple methods elaborated on in the following, even more essential information could be exploited in a more systematic way. Note that these methods occasionally go into the details and beyond what is currently included in the database. The CO2-PDB is meant to evolve and grow though, allowing it to become a guideline for future research rather than a mere image of it.
In turn, this helps assessing whether a catalyst performance increase in the presence of a plasma originates in a gas temperature increase, or actual synergy  [121, 122]. Despite this potential, none of the publications in the CO2-PDB report the thermal conductivity of the used catalyst. As such, this parameter is not included in the database yet. In conclusion, we recommend a thorough thermal characterisation of the plasma-catalytic system, preferably involving the two following types of measurements. Firstly, the measurement of the gas temperature T g , preferably non-invasively in situ at the location of conversion under as many experimental conditions as possible, and secondly the determination of the thermal conductivity of the catalyst in order to estimate the temperature in the bed from the power dissipated and the outer surface temperature.
Conclusions There are a lot of nuances involved with any critical appraisal of plasma-catalytic CO 2 experiments. Comparing experiments conducted across a large range of conditions and configurations is already a non-trivial task, which is further complicated by the manner in how-and also which-parameters are reported in literature. A concerted effort has been made to compile the experimen-tal outcomes of a large body of literature in the field, along with descriptive metadata. This effort has been crystallised in the form of the CO 2 PIONEER Database (CO2-PDB), containing data from almost 200 publications spanning roughly two decades at the date of submission of this paper. The information contained within the CO2-PDB reproduces previously observed trends from review papers, which can be trivially replicated using the dedicated web application (https://db.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 813393. VG was partially funded by the  Portuguese FCT-Fundação para a Ciência e a Tecnologia , under projects  UIDB/50010/2020 ,  UIDP/50010/2020  and  PTDC/FIS-PLA/1616/2021.
Numerical resolution In order to evaluate the energy performance of the studied solar systems, we have used a heat balance between the different layers for each configuration. The energy balance Eqs. (  1 ), (  6 ),  (8)  for the photovoltaic module, and Eqs. (  9 ), (  11 ), (  13 ), (  16 ),  (21)  for the PV/T air collector. In transient regime, a numerical approach based on the fourth order Runge-Kutta iteration method (RK4), is used for the resolution of the governing equations. Then, the equations will be introduced into a program run on MatLAB language (Matlab-R2015a) in order to solve our system of equations, and also to validate our results. The Runge-Kutta 4th order method is a numerical analysis method for approximating the solution of a differential equation. This method is based on the principle of iterations, which signifies that a first approach to the solution is exploited to compute a more accurate second approach.
Treatment with exogenous H2S donors like NaHS in a neutral solution like water leads to about 20% H2S and 80% (HS-)  [67]. Treatment of TUM1 KO cells with 10 μM of NaHS (as exogenous hydrogen sulfide donor) reverted the growth deficit. NaHS was applied due to previous results showing that hydrogen sulfide stimulates cellular bioenergetics at lower concentrations  [68]  and the known involvement of TUM1 in hydrogen sulfide production in mitochondria  [69]. Conclusively, TUM1 KO cells might lower the rate of cell proliferation based on a lower hydrogen sulfide production in the mitochondria. MTT was added at 0. 5 mg/mL to the culture media and incubated at atmosphere of 5% CO2 for 3 h at 37 °C. The formazan dye formed was dissolved using the permeabilization solution and measured at 570 nm. Independent samples t-test with SPSS was performed as.
(E) Proliferation rate with (T) and without NaHS treatment of HEK293T cell lines. MTT was added at 0. 5 mg/mL to the culture media and incubated at atmosphere of 5% CO2 for 3 h at 37 °C. The formazan dye formed was dissolved using the permeabilization solution and measured at 570 nm. Independent samples t-test with SPSS was performed as.
(E) Proliferation rate with (T) and without NaHS treatment of HEK293T cell lines. MTT was added at 0. 5 mg/mL to the culture media and incubated at atmosphere of 5% CO 2 for 3 h at 37 • C. The formazan dye formed was dissolved using the permeabilization solution and measured at 570 nm. Independent samples t-test with SPSS was performed as indicated * p < 0. 05, ** p < 0. 01, *** p < 0. 005, **** p < 0. 001 (n = 3) (n represents number of biological replicates).
Proteins were visualized using POD-labeled secondary antibodies. Representative Image J intensity quantification of band intensity (G) CTU1 (H) CTU2 (I) URM1 Independent samples t-test with SPSS was performed as indicated ND; no statistical difference, * p < 0. 05, ** p < 0. 01, *** p < 0. 005, **** p < 0. 001 (n = 3).
DFigure 4. 4 Figure 4. Effect of NaHS on sulfite oxidase activity in HEK293T cell lines. (A) Sulfite oxidase activity in WT, TUM1 KO and MOCS3 KO (-/-) were determined using sodium sulfite as substrate; the reaction was monitored by the reduction of cytochrome c at 550 nm for 5 min. (B) Immunodetection of SO, MOCS3 and TUM1 using respective antibodies. (C) Immunodetection of other H 2 S biosynthesis enzyme CBS and CTH in WT, TUM1 KO, and MOCS3 KO cells (-/-) cell lines. Proteins were visualized using POD-labeled secondary antibodies. (D) Image J quantification of relative intensity of blot bands of CBS and CTH compared to Actin. Independent samples t-test with SPSS was performed as indicated ND; no statistical difference, * p < 0. 05, ** p < 0. 01, *** p < 0. 005, **** p < 0. 001, (n = 3).
24 Figure 6. 246 Figure 6. Invitro H2S production in HEK 293 T cell lines. Cells from WT, TUM1 KO, MOCS3 KO were lysed in NP-40 containing Tris buffer. Protein concentrations of resulting lysates were determined by the Bradford assay. H2S production was measured using Methylene blue assay. The activity was started by the addition of (A) 500 μM L-cysteine as substrate and incubated for 1 h (B) 1 mM 3-mercaptopyruvate as substrate and incubated for 15 min (C) with 2 mM propargylglycine (CTH inhibitor) treatment (D) with 2 mM aminooxyacetate (CBS inhibitor). Each reaction was stopped by simultaneous addition 20 mM-DMPD and 30 mM FeCl3 and incubated further for 20 min. The absorbance was measured at 670 nm. Independent samples t-test with SPSS was performed as indicated ND; no statistical difference, * p < 0.
Water contact angle Wettability of membranes was examined through water contact angle measurements. A Dataphysics OCA15 plus goniometer and a SCA20 software (DataPhysics Instruments GmbH, Germany) were employed. First, the membranes in the Na + form were immersed in a 1 M HCl solution for 24 h at 60 °C to replace Na + with H +. Finally, the membranes were dried under vacuum for 6 h at 60 °C before the test. The water contact angle was obtained using a sessile drop technique. In the procedure, a 3 lL of deionized water was dropped onto the dried membrane surface. At least five measurements were performed on each membrane.
The test was performed with humidified hydrogen (SHE, anode) and nitrogen (cathode) at a flow rate of 200 mLÁmin À1 to supply the system. Data were collected every 10 °C. The amplitude voltage was 10 mV, and the frequency was in the range between 10 Hz and 10 5 Hz. A DC bias potential of 0. 45 V was used to record the spectra. Each sample was measured at least five times after it had obtained a constant value to ensure good data reproducibility. The experimental values resulted from the EIS measurements were analyzed using the software Z-View analysis impedance (Scribner Associates, Inc. , Southern Pines, NC, USA). The proton conductivity of the membrane was determined from the resistance value (intersecting point in the abscissa axis at Nyquist diagram) through the Eq. (3).
Acknowledgment This work has been supported by  Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER/UE)  the project  MINECO  ( PID-2019-106662RB-C43 ). We also wish to thank the  Regional Government (Comunidad de Madrid)  through three Projects: 1)  DROMADER- CM  ( Y2020/NMT6584 ) 2)  PEM4ENERGY -CM-UC3M  and 3) the  Multiannual  agreement with  UC3M  ('' Excelencia para el Profesorado Universitario" -EPUC3M04 ) -Fifth regional  research plan  2016-2020. We also thank the  IAAB  from  Universidad Carlos III de Madrid  for funding the mobility grant to  Université Grenoble-Alpes.
Source and definition Our main dataset comprises an unbalanced panel of 9700 French firms observed yearly from 2001 to 2015, representative of the entire manufacturing sector. 6  We obtain the dataset by merging three datasets: a trade dataset, an energy use dataset and a fiscal dataset, described below. International trade data are provided by the French customs authorities. The data consist of firm-level information on exported products, including information on the country of destination for 1995-2015. This data set includes exported value and exported quantity (in tons), which allows us to compute export unit value as a proxy for the average export price. Export unit values are not prices and value can change due to adjustments in the product mix. Nevertheless, they are often used to approximate export prices  (Berthou and Fontagné, 2016).
We therefore aggregate the energy data from the plant level to the firm level. This aggregation is straightforward for single-plant firms. For multi-plant firms, we would need data for all plants. To check whether this is the case, we proceed as follows. First, we compute the sum of employees for the plants for which energy data is available, using the list of manufacturing establishments (INSEE). Second, we compare the sum of the plants to the total number of the firm's employees. If we cover at least 85% of the firm's total number of employees, we consider that the sum of energy expenditure and use of its plants is a measure of the firm's total energy expenditure and use. The 85% threshold represents a trade-off between (i) minimizing the error in measuring the firms' total energy use and (ii) maximizing the number of observations.
Increasing the threshold decreases the error in measuring firms' total energy use, but also leads to a loss of the number of firms in our sample. For instance, with a 90% threshold we have 19% fewer firms. Using a very high ratio risks losing firms with establishments such as holding or other office work that do not consume large quantities of energy and would never be sampled in EACEI. Below, we conduct robustness checks to test the sensitivity to the choice of threshold. EACEI covers all plants with more than 250 employees. Other plants having between 20 and 249 employees are sampled through a two-level stratification based on the employment class and the plant location. Plants with fewer than 20 employees are not included in the sample. The response rate is very high. 8  At the firm level, data on the number of employees, output, and the firm's main economic activity come from the census provided by the French Ministry of Finance.
μ i are firm fixed effects, γ t are year dummies, and u it is the error term. The fixed-effects estimator allows us to control for time invariant and firm specific characteristics that may be correlated with the energy price index or the outcome variables. This method captures differences across firms operating in industries that vary substantially in terms of energy intensity. 15 The year dummies γ t control for consumer demand and fuel price fluctuations affecting all French firms' outcomes as well as the fuel prices used to compute the energy price index. The year dummies also capture the average exchange rate effect. 16 Robust standard errors clustered at the firm level are computed. This model is firstly applied to estimate average elasticities over the whole database. It is then applied to different groups of export markets with contrasting features: EU, non-OECD, and low energy price countries.
This leads to different samples: in our database: 8319 French firms export to EU markets, 7927 to non-OECD markets and 5965 to the countries with low energy prices. The first model does not allow a distinction to be made between firms' different reactions to energy price increases. The literature suggests several characteristics which could play a role in a firm's response, especially size and energy intensity  (Sadorsky, 2008; Rammer et al. , 2017). The response may differ because the increase in energy cost should have a greater impact on energy-intensive firms, whose production costs largely depend on the energy prices. Table  3  shows that only 24% of SMEs are energy intensive, as opposed to 40% of large firms. Moreover, the energy cost is higher in large firms. At the same time, large firms have a greater capacity to negotiate and optimize the energy price variation.
Heterogeneity between firms The findings for the average firm may vary as a function of firms' attributes. Table  7  contains the results for model (2), testing for different export responses to energy price variation depending on specific features of the firms. Two key characteristics identified in the literature are the size and the energy intensity of firms  (Srinivasan and Archana, 2011; Rammer et al. , 2017). 21 In the estimation database, SMEs are 16% less likely to be energy intensive and their energy cost is 22% higher. We focus on the estimates based on the control function estimator. 22 Model (2) has three endogenous variables. Consequently, the control function estimator requires three instrumental variables: FEPI, FEPI in interaction with the SME dummy, and FEPI in interaction with the energy intensive dummy.
Characterizations The X-ray diffraction (XRD) patterns were obtained on a Bruker diffractometer with Cu Ka emission, and crystalline phases were identified using standard ICDD files. Transmission electron microscopy (TEM) was performed on a JEOL 2010 device with an accelerating voltage of 200 keV. The samples were dispersed in ethanol by ultrasound and put onto a holey carbon filament on a copper grid sample holder. The analysis of images was carried out using Digital Micrograph Gatan software. Thermogravimetric analysis (TGA) was carried out on a SETARAM device. A weighted amount of sample powder (5-10 mg) was placed in an alumina crucible and heated in nitrogen flow from room temperature to 800 °C at a 10 °C min À1 rate. Raman spectra were obtained on a LabRam HR spectrometer (Horiba-Jobin Yvon).
The samples were put in a homemade sample holder under argon. Ar laser with k = 532 nm and power of 1 mW was applied as excitation source. The spectra were accumulated during different periods of time, from 10 to 1800 sec. X-ray photoelectron spectroscopy (XPS) was performed on a Kratos Axis Ultra DLD spectrometer, using as X-ray source the monochromatic Al Ka radiation (1486. 6 eV), operating at 180 W, 12 kV, 15 mA. The samples were crushed into powder and pressed onto a conductive carbon tape under argon atmosphere. The sur-vey spectra were registered in the interval 1200-0 eV with a pass energy of 160 eV. High-resolution spectra, pass energy of 40 eV, were obtained for O 1s, Mo 3d + S 2s, C 1s, S 2p. The spectra were treated with CasaXPS software. For the spectra alignment, the C 1s photoemission peak of adventitious carbon was located at 284.
8 eV. X-ray absorption spectra (XAS) were obtained at the ROCK beamline of SOLEIL synchrotron. Prior to measurements the powders were pressed into 6 mm diameter pellets and put on the sample holder between two Kapton layers. The spectra were recorded at the Mo K edge using a Si(2 2 0) channel-cut monochromator. The preliminary procedures such as merging and alignment of the spectra were performed with the ATHENA software  [25]. Normalized Extended X-ray Absorption Fine Structure (EXAFS) spectra were fitted using FEFF generated paths  [26]  with the VIPER program  [27] , in the k and R spaces. Electrochemical hydrogen evolution reaction (HER) was performed in a three-electrode electrochemical cell in Ar-saturated 0. 5 M H 2 SO 4 electrolyte at room temperature. Glassy carbon rotating electrode was used as a working electrode, saturated calomel electrode as a reference electrode and graphite rod as a counter electrode.
Consumption-based carbon assessments are essential for evaluating climate change responsibility. However, the results vary greatly depending on the scope chosen, making them confusing to non-expert readers and hindering their use for policymaking. This study aims to shed light on all the possible ways to calculate carbon footprints and on the implications of choosing one method over another. Integrating emissions associated with capital goods in the global multi-regional input-output database EXIOBASE, we calculate carbon footprints for 49 world regions between 1995 and 2019 and build an interactive, open access, visual web application representing all possible scopes as Sankey diagrams. We show that the ranking of carbon footprints across regions, and thus the national responsibility for ecological breakdown, change signi cantly depending on the scope chosen.
Full Text Introduction International climate negotiations rely on the concept of common but differentiated responsibility  1. Currently, these negotiations are mostly based on territorial greenhouse gases (GHG) emissions. As emissions in one country can enable the consumption of goods and services in another  2  , a fair distribution of responsibility for emissions is essential to halt climate change. The development of global multi-regional input-output databases, tracking global economic exchanges, has made it possible to include offshored emissions in what is known as the consumption-based (CB) account, as opposed to the production-based (PB) account which is limited to local emissions  3. CB accounting is not su cient to assess responsibilities  4, 5  but provides a useful complementary metric which could be used in climate negotiations.
In the CB approach, emissions associated with the production of capital goods remain in the country where the infrastructure was built  6. For example, if a car is manufactured in China but purchased in Europe, the emissions during the car's production process will be imputed to Europe but the car factory's production footprint will be imputed to China. An emerging body of literature addresses this problem by endogenizing capital in input-output databases  7. With this approach, for each car sold by a Chinese factory to a European consumer, part of the factory's footprint is imputed to the European consumer. This method brings footprint calculations closer to life cycle assessments and thereby makes them more accurate. However, the task is complex and calculating footprints with endogenous capital is not yet as straightforward as calculating traditional CB footprint.
Scopes We use EXIOBASE3  10  -a global multi-regional input-output database-to calculate GHG footprints using the PB approach, the CB approach and the CB approach with capital endogenized. The 10 different levels identi ed are represented by vertical dotted lines on the Sankey diagram of France in 2019 (Fig. 1 ) and are described thereafter. Diagrams for all other regions and years are available at https://sankey. theshiftproject. org. The rst level corresponds to the GHG disaggregation between carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O) and sulfur hexa uoride (SF 6 ). The sum of the four equals the total amount of GHG related to the studied region, ie. the sum of territorial emissions and imports. Levels 2, 3 and 4 correspond to PB accounts disaggregated between: regions (level 2), territorial or import (level 3) and emission sector (level 4).
The different steps are developed below and are available in the source code provided with the paper. To select the relevant ows for a region (France for example), we start by taking the CB footprint of France (partly from France, partly from other regions), we add the emissions in France which are allocated to the CB footprint of other regions (allowing to build the export nodes and to complete the part of the CB footprint of France). We then add the imports and exports related to the endogenization of capital. In particular, some of the ows imported to create GFCF in France are re-exported during capital endogenization, which includes the additional node "re-exported imports". Once all the ows have been selected, they are grouped in a table where each ow corresponds to a row and each column to the different steps.
To split them between housing and mobility, we use the IPCC data available for 36 regions  19. For the remaining regions, housing and mobility are each assumed to represent 50% of the total, in line with what is observed for countries with data. 14 of the 900 values obtained in the IPCC database are incompatible with EXIOBASE (residential emissions represent more than 90% of total direct household emissions). We therefore saturate these values at 90%. The column corresponding to level 4 of the created table allows to de ne a color code so that the color of each ow corresponds to its emission sector in the PB approach. The ows are then aggregated by color to avoid duplicating ows of the same color between the same nodes. The diagram is then built with the python plotly library. The code to build and put online the application to visualize the results is available with this article.