dataesr/scientific-paragraphs-categorization huggingface dataset:
The data were normalized to the intensity of the incoming incident energy and processed with the Athena software from the IFEFFIT package. For the EXAFS analysis, an E 0 value of 7715. 4 eV was used for the cobalt K-edge jump energy. SEM using a field emission gun was performed using a Zeiss Supra 40. Infrared spectra (IR) were recorded on a Bio-Rad FTS 175 C Fourier transform infrared spectrometer spectrophotometer. Ultraviolet (UV)-visible absorption spectra were obtained using a Shimadzu 2001 UV spectrophotometer. High resolution mass spectra were measured on an Agilent 6530 Accurate-Mass Q-TOF LC/MS spectrometer equipped with electrospray ionization source. NMR spectra were recorded in deuterated chloroform (CDCl 3 ) and THF-d 8 on a Varian 500 MHz spectrometer. Melting points were recorded on a Stuart SMP apparatus.
This is done by using greenhouse gas emissions factors, adapted from international or national sources. The initial survey covered 400 data entries for household consumption, their expenditure on consumption and socioeconomic data. This household consumption included all fields of personal consumption in housing, mobility, food and other consumption (such as clothing, furniture, electronics, etc. ). We did not explicitly consider public services like education and health. However, we did consider CO 2 emissions of expenditures for insurance, in order to capture some of these goods and services. Approximately 100 consumption data entries were converted to CO 2 e. The data allows for a deeper and more detailed description of household emission patterns as well as the simulation of different household emission pathways and their net cost implications for the households.
Method 3: Qualitative household interviews Qualitative semi-structured in-depth interviews were carried out with a sub-sample of households participating in the simulation game using a common interview guide aiming to explore their knowledge and perception of climate change and household mitigation options. Maximum variation sampling was used to recruit a diverse set of informants covering a diversity of socioeconomic characteristics such as age, gender, income, education, type of housing, location of housing, etc. [40]. In addition, recruitment of interviewees was informed by findings from methods 1 and 2, such as households with big or small initial footprints and high or low rates of reduction. The final sample size of each country sample was determined by the qualitative principle of data saturation  [41].
EU policies were assumed to be implemented at subsidiary levels of government and therefore not mapped separately. Policy data was gathered through the Odyssee-Mure database [49], official reports to international organizations and government databases from the national, regional and local levels of government. The resulting corpus included 250 policies that were coded along three dimensions in accordance with the policy categories used in the IPCC Fifth Assessment Report  [50] : (1) policy area (housing, mobility, food and other consumption); (2) anticipated policy mechanism (change patterns or volumes of consumption); and (3) policy instruments (economic instruments, information policies, public goods and services, and regulatory approaches).
Method 1: Mapping empirical household carbon footprints The FCS-Tool first calculated the carbon footprint of each participating household after detailed data about each households' consumption habits in the four sectors of Food, Mobility, Housing and Other Consumption were entered. The tool considered formulas to calculate the carbon footprint by combining the questionnaire inputs on consumption habits (e. g. the kilometers of car use per year and information about the type of car used) and the corresponding emission factors (e. g. CO 2 e emissions per kilometer for this specific type of car). The emission factors were either taken from a common database (the IMPACTS database of the French Environment and Energy Management Agency) or from other databases adapted to the national contexts whenever necessary (e.
Method 2: Conducting a mitigation simulation game with households At the beginning of our simulation game, which was conducted as a personal on-site interview with each household, we confronted the household with their current carbon footprint, calculated with data from the online questionnaire. Then we offered households an array of up to 65 climate change mitigation actions, which they could implement. Those mitigation actions were presented on "Action Cards" and included information about avoided CO2e emissions, positive or negative financial costs, and health impact if applicable (for examples of actions cards see Fig. A2 ). The number of actions varied for each household, as some actions were rated as "already done" (e. g. already vegetarian) or "not applicable" (e. g. not able to insulate a roof because of living as tenant on the ground floor).
Acknowledgments This paper was written as part of the  HOPE (HOusehold Preferences  for reducing greenhouse gas Emissions) Project: www. hope-project. net. The  HOPE  project is supported by the following national funding bodies under the umbrella of the  Joint Program Initiative (JPI) Climate , a pan-European intergovernmental research platform: the  French National Research Agency  ( ANR-14-JCLI-0001-03 ), the  German Federal Ministry of Education and Research  ( 01UV1414A ), the  Research Council of Norway  ( 244 , 905/E10 ) and the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning  ( 214-2014-1717 ). One of the authors of this paper (Sovacool) is the Editor-in-Chief for Energy Research & Social Science. He was not involved in managing the peer review or editorial process for this article.
Mössbauer Spectroscopy Transmission 57 Fe Mössbauer spectra were collected with a 57 Co:Rh source at room temperature. The samples were cooled at 5 K in a helium-flow cryostat (SHI-850 Series, Janis). The velocity driver was operated with a triangular velocity waveform. The velocity scale was calibrated at room temperature with the magnetically split sextet of a high-purity α-Fe foil. The spectra were fitted by least-squares methods to appropriate combinations of Lorentzian profiles representing the quadrupole doublets, magnetic sextets, or octets using the program PC-Mos II  [28]. In this way, spectral parameters such as the hyperfine magnetic field (H) quadrupole splitting (∆), the isomer shift (δ), and the line width at half-maximum (Γ) of the different spectral components were determined for the whole series of spectra.
Numerical Simulations Numerical simulations were performed with a high Biot number (Bi = 10 4 ), characteristic of this diffusion-limited case. The partial differential equation system (Equations (  2 ), (  3 ),  (8) , and (  11 )) was transformed into an ordinary differential equation (ODE) system by spatial discretization with a second-order central difference method and a mesh of 100 nodes. The resulting ODE system was numerically solved using MATLAB (MathWorks).
Funding: This work has been carried out in the framework of the  Labex SERENADE  ( ANR-11-LABX-0064 ) funded by the  Investissements d'Avenir French Government program  managed by the  French National Research Agency (ANR)  and in the framework of the  French program  Mutations alimentaires 2019, cofinanced by  INRA  and  CNRS. Funding: This work has been carried out in the framework of the  Labex SERENADE  ( ANR-11-LABX-0064 ) funded by the  Investissements d'Avenir French Government program  managed by the  French National Research Agency (ANR)  and in the framework of the  French program  Mutations alimentaires 2019, cofinanced by  INRA  and  CNRS.
g -  iron at 20 • C and 100% RH. The reaction-diffusion model was able to reproduce the observed data of O 2 adsorption. This study suggests that there is significant potential for the use of this new oxygen-scavenging composite, MMT-Fe, which, if included in packaging under the correct anoxic conditions, might help to extend the shelf life of oxygen-sensitive food products. Supplementary Materials: Raw data of Table  2  and Figure  7  are available online at http://www. mdpi. com/2079-4991/9/9/1193/s1: and https://doi. org/10. 5281/zenodo. 3253103. Author Contributions: E. -M. K. -T. -L. performed the experiments and contributed to the writing of the draft; S. G. did the modeling and parameter optimization and contributed to the writing and proofreading of the manuscript; M. S. and L. S. performed the Mössbauer experiments and their analysis and contributed to the writing and proofreading of the manuscript; N.
Imaging was started at the same time the flow of Fe NPs suspension was started (2 μL/min). Particles flow was kept for 30 min, after which the cell was once again flushed with toluene for 1 h (10 μL/min) to remove the excess of particles. The current was measured on the fluorescent screen and was continuously 0. 165 nA. Afterward, the cell was thoroughly cleaned by flushing with a hexane/HCl mixture (0. 1 M, 0. 5 mL, 5 μL/min) and ethanol (5 mL, 10 μL/min). The program ImageJ was used to analyze images and count the number of Fe particles attaching to the CNFs. The Fe particle counting was performed every 15 frames for the attachment of Fe-NP to CNF and every 5 frames for Fe-NP attachment to CNF-Ox. As the resolution of the images was relatively poor, the counting was performed manually. Moreover, an experiment was performed to rule out the influence of the reducing effect of hydrogen radicals in the cell.
Acknowledgments We acknowledge the  European Research Council ,  EU   FP7   ERC Advanced Grant  no. 338846. J. Zečević  acknowledges financial support by  Netherlands Organization for Scientific Research (NWO) ,  Veni  Grant no. 722. 015. 010. Andrea Ferreira De Abreu  is thanked for the Raman measurements,  Remco Dalebout  is thanks for nitrogen physisorption measurements and  Coen Mulder  is thanked for ICP-AES measurements.
However, such storage is costly given the current state of technology and yields relatively low returns. The purpose of this paper is to evaluate the cost of large-scale storage of intermittent electricity in a 50% nuclear power mix, which is the target of the French energy policy by year 2035. Let us recall that the nuclear share was 75% in 2018 and 72% in 2015 that is our reference year. The study will be conducted using hourly data provided by RTE (the French Transmission System Operator) for 2015. The paper is organized as follows. Section 2 explains the adverse effects due to the massive injection of renewables when electricity storage is not available. Section 3 presents a model that calculates the additional cost of electricity production when the share of nuclear generation is reduced to 50% and uses, in compensation, renewable energy (wind and solar) that can be stored either by batteries or by power-to-gas.
2 If the inequality is verified, there is enough excess electricity to produce the gas needed by G2P; if not, the electricity mix is unable to produce enough energy to ensure self-sufficiency, and electricity will have to be imported. The P2G (Figure  S4 ) and the G2P (Figure  S5 ) optimization algorithms aim at reducing the installed capacities. The programs were written in language R (R core team, 2018) and used the following libraries: zoo  (Zeileis and Grothendieck, 2005) , doParallel  (Weston, 2017) , parallel (R core team, 2018) and ggplot2  (Wickham, 2011). As stated earlier, the key parameters of the simulation are the battery capacities (CBatt), the solar fraction in the renewable energy mix (fSun) and the overall energy efficiency of P2G-G2P (YP2GG2P). We simulate the energy mix for 7 values of CBatt ranging from 0 to 150 GWh, 41 values of fSun ranging from 0.
Gas and electricity network To calculate the cost of an electricity network, we based our estimation on the turnover of Enedis, the French entity responsible for the electricity grid. Typical peak power that Enedis deals with is 90 GW. By dividing Enedis turnover by typical peak power, we obtain 147 M€/GWpeak. We thus assume that the additive cost of the electrical network per GWpeak above 90 GW will lie in the 50-150 M€/GWpeak price range. For gas network usage we based our estimations on data from GRDF, the entity responsible for the gas network in France. According to its data, the gas transport cost is 5 €/MWhPCS and the gas storage cost is 3. 6 €/MWhPCS. We thus estimated that the transport and storage cost of the gas produced by P2G installations and consumed by G2P installations is 8.
Surface composition of pure phases after activation step In this section, typical results of XPS analysis of the activated surfaces are presented for the different studied materials. For each surface 5 to 7 points were acquired and the data were compared in order to verify that the spectra are representative. One should remember that all pure phases were polished prior to alkaline degreasing and activation step in order to minimize the surface roughness. The activator being traced by Ti, total Ti concentration on different model phases and its chemical forms were evaluated by XPS after alkaline degreasing and immersion in the activator bath. The spatial distribution on the coating was not possible to verify neither by XPS, nor by SEM-EDS because the size of the nuclei is less than several nm and the total quantity of Ti is significantly less than the detection limit of EDS.
Surface coverage evolution with time and temperature of PCC treatment To evaluate the surface coverage by phosphate crystals, on the secondary electron images the crystals (for low covering) or uncovered zones (in case of high covering) were colored manually and the fractions of pixels in colored and uncolored surfaces for each image were estimated using a color-thresholding method in ImageJ software. For each exposure condition present in Fig. 5 , the surfaces of several samples were first screened with medium resolution by SEM in order to define typical and representative zones of several mm 2. Then, for these zones, higher resolution images like presented in Fig. 5   2) Although different ZnAlMg materials and pure phases demonstrated differences in PCC grain morphology and PCC layer growth kinetics (see section 3.
Analytical Device and Parameters Cellulose membranes were analyzed by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/X-EDS) (FEI Quanta 200). The SEM/X-EDS instrument was equipped with a secondary electron detector (SED) to perform surface analysis of the shape and size of the particles; a backscattered electron detector (BSED) allowed atomic differentiation in terms of atomic weight (Z), while X-EDS provided the elementary composition of the particles. Combining the data obtained by the use of these three detectors yielded information about the texture, size distribution, shape and chemical composition of the particles trapped in the filters.
96 cm -1 data spacing). The spectra were analyzed and presented (including integration, differentiation and determination of peak positions) using a specialized Thermo software (Omnic). The XPS spectra were taken using a Kratos Axis spectrometer, equipped with an aluminum monochromater for a 1486. 6 eV source working at 120 W. The powder samples were pressed into 6 mm diameter pellets. The binding energies were corrected with respect to C 1s of 284. 6 eV and the binding energies were estimated within ±0. 2 eV. Photocatalytic tests. The photocatalytic conversion of methane was carried out in a homemade stainless-steel batch reactor (volume, ~250 mL) with a quartz window on the top of the reactor (Supplementary Fig. 11 ). The light source was 400 W Xe lamp (Newport). The Quantum Efficiency (QE) was determined by using a Hamamatsu spot light sources LC8-06 Hg-Xe lamps emitting between 240 and 600 nm and equipped with a quartz light-guide to deliver a stable and uniform illumination of the sample.
Acknowledgements We thank  Laurence Burylo ,  Pardis Simon , and  Martine Frère  for help with XRD and XPS measurements. X. Y. thanks the  Chinese scholarship council  for providing him a stipend for PhD studies in France. Chevreul Institute  ( FR 2638 ),  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation, Hauts-de-France Region  and  FEDER  are acknowledged for supporting and funding partially this work.
S1)  [50, 51]. The absorption kinetics were measured by a volumetric method with a HVPA-200 Sievert apparatus from Micromeritics. The absorptions were performed at 100, 200 and 300 °C under 30 bar of H2 and the absorption rates were calculated at 80 % of the maximum capacity of the sample. Before the isotherms, the powders were outgassed under vacuum at temperatures between 200 and 300 °C (the lowest temperature possible based on the TPD data to ensure the complete desorption of hydrogen). Finally, in order to study the reversibility of the formed storage systems, hydrogen absorption/desorption cycles were performed under three different temperature conditions: -"Hard" conditions: desorption under vacuum at 300 °C and absorption under 30 bars of H2 at 300 °C, -"Medium" conditions: desorption under vacuum at 225 °C and absorption under 30 bars of H2 at 200 °C, -"Mild" conditions: desorption under vacuum at 200 °C and absorption under 30 bars of H2 at 100 °C.
Acknowledgements This work was supported by the  French Agence Nationale de la Recherche  [Projet  ANR-15-CE29-0023-01 ]. The authors are thankful for the scientific services of IRCELYON, particularly  Y. Aizac  for the XRD analyses,  L. Burel  for the SEM and TEM analyses and  C. Lorentz  for the NMR analyses. Dr C. Dupuis  from  McPhy Company  is acknowledged for fruitful discussions.
Operating parameters The experiments were performed to examine the influence of the following parameters: Calibrations of the different configurations were realized using a flux bar located inside the cavity. This bar is painted with a Lambertian white coating. A camera placed in front of the experimental setup (at 18 m) imaged the flux bar brightness, with the different grey levels of the image corresponding to different flux densities. Flux densities were then determined using a reference flux meter. The spatial resolution of the camera was 1 mm/pixel and the time exposure of the camera was 30 µs. The direct normal irradiation reference was 1000 W/m 2 , it means that the given flux profile are scaled to a DNI of 1000 W/m 2. Further results such as power extracted by particles and data to calculate the heat transfer coefficient and the thermal efficiencies are also scaled to this DNI reference.
Wall temperature distribution On-sun temperature measurements with welded thermocouples give data on temperature distribution and profile but they are affected by uncertainty because thermocouples are placed under concentrated solar flux and the thermal contacts between the thermocouple wires and the wall are not perfect. Nevertheless, these measurements are useful to compare temperature distribution. A critical analysis on such temperature measurements is proposed in paragraph 5. The wall front surface temperature (highest temperature of the irradiated zone) has been measured at several heights (cf. Fig. 3 ), consequently, the surface temperature profile could be plotted as a function of the incident solar flux for a given particle mass flow rate. The Fig. 8  shows the plot of the front surface temperature as a function of tube height for a particle mass flow rate of 83 ( ± 2) kg/m 2 •s that is close to the nominal flow rate that will be used at pilot scale (3 MW th solar receiver).
Conclusion A single element fluidized particle-in-tube solar absorber was tested on-sun in order to collect data for scaling-up this type of particle solar receiver developed by CNRS. Experiments were performed with a one meter long finned tube to improve heat transfer between the fluidized suspension and the absorber tube wall. Three incident solar flux distributions and particle mass flux density in the range 25-110 kg/m 2 •s have been tested to simulate realistic operation conditions. Tube surface temperature distribution has been measured as a function of the solar flux density and the particle mass flow rate. Particles were heated from ambient up to 400 and 140 °C for a particle mass flux density of 30 and 110 kg/m 2 •s respectively. Under low solar flux the power extracted by the particle suspension reaches 17.
Data Sources and Cleaning Genome and protein sequences of S. japonica and C. okamuranus are freely available, respectively, in references  [26]  and  [3]. These algae are usually grown in non-axenic media, and the presence of contaminating sequences was demonstrated in the published genome of S. japonica  [27]. Therefore, Taxoblast analysis (version 1. 21beta) was carried out to discriminate prokaryote and eukaryote in order to filter out all prokaryotic sequences from the genome of S. japonica. Blast analyses were performed using diamond blastx (version 0. 9. 18)  [28]  against the nr database (downloaded on 13 August 2016) and the nodes. dmp (downloaded on 23 March 2018) from https://ftp. ncbi. nlm. nih. gov/pub/taxonomy/. The cleaned version of the S. japonica genome is available under the following link: http://abims.
These intermediate networks were then merged, analyzed both qualitatively (topological analysis) and quantitatively (constraint-based analysis), refined by manual curation and enriched with metabolic profiling data extracted from the literature (see Table  S2  and Table  S3  of Supplementary Materials). To reconstruct the intermediate annotation-based network, predicted coding regions in transcripts were functionally annotated using the Trinotate pipeline (version 3. 0. 1) [29]. An internal Trinotate script extracted the Gene Ontology Terms (GOT)  [30, 31]. The Kyoto Encyclopedia of Genes and Genomes identifiers (KEGG)  [32]  from the annotation file were used to retrieve the EC numbers associated with the genes. This step was performed via the KEGG-APi REST (REpresentational State Transfer) application programming interface (http://www.
kegg. jp/kegg/rest/keggapi. html). The annotation concerning S. japonica was enriched by further analyses carried out with Kobas  [33]  and Blast2GO  [34]  with an e-value cutoff of 1e-05. A file created in the GenBank format, containing all the available data, was then generated using the following script: https://github. com/ArnaudBelcour/gbk_from_gff. This GenBank file was used as an input to the PathoLogic software from the Pathway Tools suite (version 20. 5, default settings)  [35]. The database containing the information from the annotation was then exported in attribute-value flat files, which were necessary for further analysis in the AuReMe workspace. To reconstruct the intermediate orthology-supported network, templates from 3 model organisms were used: Arabidopsis thaliana  [36] , E.
siliculosus  [24] , and Nannochloropsis salina  [37]. Those templates were chosen according to the quality of their GSMNs or their phylogenetic proximity to the 2 studied algae. Orthology searches between the 2 studied algae, and these 3 templates were carried out using the OrthoMCL (version 1. 4)  [38]  and Inparanoid (version 4. 0)  [39]. The results of the latter were combined using the pantograph (version 0. 2)  [37]. Since the S. japonica GSMN was the first to be generated, it was added to the 3 previous templates during the reconstruction of the C. okamuranus GSMN. As the A. thaliana and N. salina data refer to the KEGG  [32]  and BIGG  [40]  databases, respectively, a mapping operation, intrinsic to AuReMe, against the MetaCyc database (version 20. 5)  [41] [42] [43]  was performed to standardize the identifiers with the MetaNetX dictionary  [44].