dataesr/scientific-paragraphs-categorization huggingface dataset:
System control The core of the system is controlled by a microprocessor. A microprocessor performs logical comparisons between system variables to set a status to actuators. It uses a status array to determine in which mode it should operate, and an output array to determine the actuators states in each mode. Dead bands ensure hysteresis and prevent the controller from switching from mode to mode too often. TRNSYS provides a microprocessor (Type 40), but a limitation of five comparators is hardcoded. The Fortran source code was modified to increase this limit and allow both status and output arrays to be given as external text files, gaining in convenience. The subsequent rebuilt Type 40 was validated against the example provided with TRNSYS documentation, yielding exactly the same results.
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Charles MARAGNA, Charlotte REY and Marc PERREAUX report financial support was provided by the French Agency for Ecological Transition. Charles MARAGNA reports a relationship with French Ministry of Economy Finance and Recovery that includes: non-financial support. Charles MARAGNA reports a relationship with VINCI Construction SAS that includes: consulting or advisory: Charles MARAGNA took part in two research projects entirely funded by EUROVIA, a subsidiary of VINCI Construction SAS and executed by BRGM. The projects aimed at optimizing the Underground Thermal Energy Storage associated with "Power Road", a solar thermal collector embedded in roadways developed by EUROVIA.
Acknowledgments This paper has been subsidized by the  ERANET cofund GEO-THERMICA  from the  European Commission  (grant number  731117 ),  ADEME (French Agency for Ecological Transition , grant number  1882C0016 ) and  RVO  (the Netherlands),  DETEC (Switzerland) ,  FZJ-PtJ (Germany) ,  EUDP (Denmark) ,  Rannis (Iceland) ,  VEA (Belgium) ,  FRCT (Portugal) , and  MINECO (Spain). More information can be found on www. heatstore. eu. This paper would not have been possible without their support.
Then, a hierarchical cluster analysis was used to group low-carbon innovations with similar characteristics. This type of multivariate clustering classifies similar objects into clusters in a way that each cluster is as much distinct as possible from the rest  [91, 92]. For each low-carbon innovation, we considered survey results for all three evaluation criteria: mitigation potential, timing of commercial availability (CTIs) or widespread adoption (ODIs), and risk of being delayed. The number of respondents, who could not respond and who assessed innovations will never take place, was also included to capture uncertainty factors of these transformative innovations. The data obtained from the survey was first normalised and then used to calculate the Euclidean distance between different low-carbon innovations.
Acknowledgements We express our deepest gratitude to the experts who responded to the survey for the time and thought they devoted. This work was supported by the  H2020 European Commission  Project " PARIS REINFORCE " under Grant Agreement No. 820846 , the  Horizon Europe European Commission  Project " IAM COMPACT " under Grant No. 101056306 , and the  Hellenic Foundation for Research and Innovation (HFRI)  and  General Secretariat for Research and Innovation (GSRI)  project  "ATOM" under Grant  Agreement No. HFRI-FM17-2566. The sole responsibility for the content of this paper lies with the authors; the paper does not necessarily reflect the opinions of the  European Commission.
Funding This research was funded by the  European Commission Horizon 2020 "PARIS REINFORCE" Research and Innovation Project  under Grant No. 820846  and  Horizon Europe "IAM COMPACT" Research and Innovation Project  under Grant No. 101056306 , as well as the  Hellenic Foundation for Research and Innovation (HFRI)  and  General Secretariat for Research and Innovation (GSRI)  project  "ATOM" under Grant  Agreement No. HFRI-FM17-2566.
Acknowledgments We thank  R. Checa  (CO 2 hydrogenation setup support),  Y. Aizac  (XRD),  N. Bonnet  &  P. Mascunan  ( ICP & XRF ),  L. Burel  ( TEM ),  M. Bugnet  (preliminary STEM observations),  P. Chartier  (Ti 3 AlC 2 synthesis), and  C. Molinet  (Pt/CeO 2 reference sample preparation). We acknowledge IRCATECH (ICP, XRF, XRD, XPS), CTµ (SEM), and CLYM (TEM & STEM) characterization platforms. We also thank  China Scholarship Council  [Grant  202008330293 ] for YY's PhD funding, and  ANR UltraCat  [ ANR-17-CE06-0008 ],  ANR   MXENECAT  [ ANR-18-CE08-014 ] &  ICL MXCat programs  for financial support. The IC2MP authors also acknowledge the financial support of the  European Union (ERDF) ,  Region Nouvelle Aquitaine  and the  French government 's " Investissements d′Avenir" program  ( EUR INTREE ,  ANR-18-EURE-0010 ).
Introduction One of the notable facts about the long-term process of economic development is the Kuznets curve, also known as the inverted U-shape patterns of inequality  (Acemoglu & Robinson, 2002). Using both time series data of the United States, the United Kingdom and two states in Germany,  Kuznets (1955)  found that as countries developed, inequality first increased, peaked and then declined. Although there have been an intensive number of studies examining the empirical validity of the Kuznets curve for different countries and datasets, the empirical evidence is rather inconclusive  (Anand and Kanbur, 1993; Lindert, 1986; Morrisson, 1999). While historical investigations from Western European countries such as England, France, Germany and Sweden tend to support the Kuznets hypothesis, inequality has consistently experienced falls during the process of economic development in some Asian countries such Japan, South Korea and Taiwan  (Acemoglu and Robinson, 2002).
To best of our knowledge, there is limited evidence on inequalities in electricity in the literature and no evidence has been provided in Vietnam thus far, while empirical results would help better design the electricity access policy to ultimately foster sustainable development goals. At the empirical stage, we draw evidence from two dedicated datasets (the Vietnam Household Living Standard Survey and the Vietnam Population and Housing Census) which collect information for individuals (demographics, education, employment, health, and migration), households (durables, assets, production, income and expenditure, and participation in government programs). The availability of information about the household access to electricity is particularly advantageous as it allows for investigating the special distribution of households having access to electricity across districts and provinces.
Data We use two datasets in this study. The first data set is the 15-percent sample of the Vietnam Individual data consist of information on demographics, education, employment, health, and migration. Household data are on durables, assets, production, income and expenditure, and participation in government programs. Unlike the previous VHLSSs, the 2010 VHLSS contains data about not only expenditure on electricity but also the number of kilowatt hours (kWh) that households consumed in the last month. The 2010 VHLSS covered 9,399 households. In this study, we use the 2010 VHLSS, since it is closer to the 2009 VHPC. The small area estimation method is used to combine a household survey and a census which should be conducted in the same year or at least close years.
The common variables include household and individual characteristics, and area mean variables computed from the census. Area mean variables are the average of household characteristics by small areas such as a commune. For example, from the census we can calculate the average age of household heads at the commune level. In the second step, we regress electricity consumption on selected common variables using data from the 2010 VHLSS: , ) ln( , , , , , , , d c i d d c d c i d c i C X kWh           (1) where ) ln( , , dd c i kWh is the natural logarithm of per capita monthly electricity use (measured by the number of kWh) of household i in commune c in a specific district. The per capita consumption is equal to the total consumption of a household divided by the number of household members.
Powder diffraction files provided by the International Centre for Diffraction Data (ICDD PDF-2 database) contain several copper-cobalt mixed oxides with various Co and Cu content from Cu0. 95Co2. 05O4 (PDF 01-78-2177) to Cu0. 15Co2. 84O4 (PDF 01-78-2172). These oxides have very similar positions of diffraction lines with only small shifts caused by small difference in the Co and Cu cation sizes. Copper cations are assumed to occupy tetrahedral  [6, 17]  or octahedral positions in the spinel lattice with certain degree of inversion  [18]. Therefore, it is difficult to clearly determine from the powder XRD patterns, whether the spinel structure corresponds to Co3O4 or CuxCo3-x O4  [19]. In mixed Co-Cu spinels, the copper content is represented by x value ranging from 0 to 1  [18]. Values of x calculated for catalysts with Co:Cu molar ratios of 4:1, 1:1, and 1:4 correspond to 0.
On the other hand, some other authors attributed the diffraction peaks to Co-Cu spinel-like oxides without confronting the similarity with Co 3 O 4 peaks  [4, [19] [20] [21] [22]  or matched the peaks with Co 3 O 4 pattern. Regarding no presence of CuO, they expected incorporation of Cu 2+ cations into the spinel lattice and suggested formation of a mixed oxide  [6, 17, 23, 24]. From the XRD data only, it is difficult to distinguish whether the samples contain Co-Cu mixed oxides with spinel structure, Co 3 O 4 or a mixture of Co 3 O 4 with a Co-Cu mixed oxide  [20, 24, 25]. In our case, CuO was present in all Co-Cu catalysts. The quantification of the spinel and CuO contents in the CoCu41, CoCu11, and CoCu14 catalysts was based on the integral intensity of the corresponding diffraction peaks using HighScore Plus software.
The evaluated Co 3 O 4 to CuO weight ratios were used to calculate corresponding Co:Cu molar ratios (Table  2 ). In the CoCu41 sample, the Co:Cu molar ratio evaluated from powder XRD data is considerably higher than the expected value (i. e. , that adjusted in nitrate solutions used for the precursors synthesis), indicating that less copper was identified by XRD analysis. This finding could be explained by incorporation of copper cations into the spinel lattice or by formation of amorphous copper species, which were not detected by XRD. The crystallite size evaluated from the powder XRD data reflected structure ordering of the studied catalysts. The FWHM (full width in half maximum) values were evaluated from the powder XRD patterns, and the crystallite sizes of spinel-type oxides were calculated as a mean coherence length in  [311]  direction; the corresponding (311) spinel diffraction line is located around 43.
179 nm) in the range from 10 to 80 • 2θ (step size 0. 02 • ). The qualitative analysis was performed using HighScore Plus 4. 8 software package (PANalytical). The lattice parameters were obtained from cell refinement of attributed phase after default line profile fitting in HighScore Plus software (PANanalytical, Malvern, Almelo, The Netherlands, 2018). The full width in half maximum (FWHM) values and sizes were evaluated using the same software. The crystallite sizes of Co 3 O 4 -like oxides were calculated as a mean coherence length in [311] direction (the most intense diffraction line of cubic spinel); the crystallite sizes of CuO (tenorite) were calculated as a mean coherence length in [-202] direction. Surface composition of Co-Cu oxides was analyzed using the X-ray photoelectron spectroscopy (XPS).
The measurements were carried out using AXIS Ultra DLD Kratos spectrometer equipped with a monochromatic Al Kα source (hν = 1486. 6 eV) operating at 180 W. The XPS spectra were recorded at ambient temperature, while using an ultra-high vacuum system (base pressure 10 -9 mbar). The Co, Cu, O, N, Na, and C elements were detected. The calibration for charging effect was carried out based on binding energy of adventitious carbon C 1s peak located at 284. 8 eV. The XPS data evaluation was performed using CasaXPS software. Surface area and textural properties of the catalysts were determined from nitrogen adsorption-desorption isotherms at 77 K using Micromeritics Tristar II Surface Areas and Porosity apparatus (Norcross, Atlanta, GA, Micromeritics, USA). Surface area S BET was calculated by the BET method.
Pore size and volume distribution were calculated using the BJH method. The samples (about 0. 20 g) were outgassed in vacuum at 150 • C prior to data collection. Temperature-programmed reduction (H 2 -TPR) measurements were carried out using AutoChem II 2920 V3. 05 equipment (Micromeritics) with a H 2 /Ar mixture (5 mol. % H 2 ), flow rate 50 mL min -1 and linear temperature increase 10 • C min -1 in temperature range from 25 to 700 • C. The amount of analyzed sample was approximately 15 mg. Raman spectra were recorded on Horiba Xplora spectrometer. The 785 nm laser was used for excitation, the power on the sample was adjusted by 1% filter. The laser beam was focused on the surface of the sample using 100 × microscope objective (NA 0. 9). Due to various stability of the samples, the acquisition times were used as follows: 30 s for Co, 600 s for Cu, and 1800 s for CoCu41, CoCu11, and CoCu14 oxide samples.
Conclusions The Co-Cu oxide catalysts with various Co:Cu molar ratios were prepared by calcination of coprecipitated precursors and compared with the single-component Co and Cu oxides. Both powder XRD patterns and Raman spectra indicated that the Co-Cu catalysts contained a mixture of Co 3 O 4 and CuO. The surface area of the single-component oxides showed a marked difference (27 and 5 m 2 g -1 was found for the and Cu catalysts, respectively), while the surface area of all Co-Cu catalysts was very similar (18-21 m 2 g -1 ). Temperature-programmed reduction by hydrogen proved that CuO promoted the reduction of Co 3 O 4 in the Co-Cu oxides. The CoCu41 catalyst with a Co:Cu molar ratio of 4:1 was reduced at the lowest temperature, likely due to a high dispersion of CuO. The CuO content evaluated from powder XRD data was significantly lower in comparison with bulk Co:Cu molar ratio determined by AAS; it indicated the formation of XRD-amorphous copper species.
Cu catalysts showed diffraction peaks corresponding to both CuO and Co 3 O 4 , the formation of a mixture of spinel-type Co 3 O 4 and CuO during calcination of coprecipitated Co-Cu precursors can be expected. Powder diffraction files provided by the International Centre for Diffraction Data (ICDD PDF-2 database) contain several copper-cobalt mixed oxides with various Co and Cu content from Cu 0. 95 Co 2. 05 O 4 (PDF 01-78-2177) to Cu 0. 15 Co 2. 84 O 4 (PDF 01-78-2172). These oxides have very similar positions of diffraction lines with only small shifts caused by small difference in the Co and Cu cation sizes. Copper cations are assumed to occupy tetrahedral Powder XRD pattern of the Cu catalyst corresponded to CuO (tenorite, PDF 04-015-5877), with characteristic diffraction lines at 34. 2, 38.
Data reduction was performed using DIALS  65  and Aimless. 66  Initial phase estimates were generated by molecular replacement within Phaser 67 using the Hyd-2 pdb le 6EHQ 12 as a search model. The electron-density maps were inspected using COOT,  68  residue 479 was manually changed to lysine and crystallographically ordered water molecules assigned. The resulting model was rened using REFMAC5 (ref. 69) followed by additional rounds of manual rebuilding and renement until no further improvements were possible. Structure analysis was performed using a combination of CastP, 70 Caver2 (ref. 71) and Pymol. 72.
EPR spectroscopy Continuous-wave EPR spectroscopy was carried out on asisolated R479K samples at concentrations of 90-400 mM in 100 mM MES, 100 mM NaCl, 10% glycerol, pH 6. 0. An X-band (9. 1-9. 9 GHz) Bruker EMX spectrometer equipped with a lowtemperature helium ow cryostat (Oxford Instruments) was used to make measurements at 10 K and 80 K. Thin-walled quartz EPR tubes (Fluorochem) were used, high precision (1. 6 mm) tubes for the 400 mM sample and standard 4 mm tubes for all other samples. Data were collected at a microwave power of 2. 00 mW and a modulation amplitude of 1. 0 mT. Spectra were averaged over >12 hours for the 400 mM sample. EPR simulations were performed using the EasySpin 5. 2. 25 routines for a Matlab scripting environment (The Mathworks, Natick, NJ). 73.
Data were processed using home-written routines in MATLAB™ and OPUS soware (Bruker). The gures were prepared in Origin graphical soware (OriginLabCorp). All samples were stored under liquid nitrogen or at -80 °C in a sealed anaerobic vial until use. Prior to measurements, asisolated samples were reductively activated: large volume dilute samples (>50 mL, ∼100 mM) were ushed with humidied 100% H 2 for 24 hours in an anaerobic glovebox (Belle Technologies, O 2 <3 ppm), whereas small-volume, concentrated samples (<10 mL at ∼1 mM) were activated under 4 bar H 2 (Büchi Tinyclave pressure vessel) inside an anaerobic glovebox (Glove Box Technology Ltd, <3 ppm O 2 ) for 19 hours. The various gas treatments of samples for identication of the diatomic ligand included an in-line ultra-high capacity O 2 scrubber (Restek™ Super-Clean™) and were performed as explained in Results.
All gases were obtained from BOC (H 2 >99. 99% purity, O 2 99. 5% purity, CO 99. 97% purity and Ar 99. 998% purity). In-crystallo microspectroscopic electrochemical IR experiments were performed using single crystals of R479K enzyme under electrochemical control as previously described. 74  Prior to any experiments, regardless of the oxidised/reduced state of the samples, the crystals were reductively activated, electrochemically, in a microspectroscopic electrochemical cell at ca. -600 mV until no further changes were observed in the v CO and v CN bands in the IR spectra over a period of approximately 10 min. Typically, this activation period was at least 1 h. Unless otherwise stated, all in-crystallo microspectroscopic electrochemical data were collected using a Vertex 80v FTIR spectrometer with a Hyperion 3000 IR microscope (Bruker) at Diamond Light Source, MIRIAM beam line B22.
S6 † ) buffer exchanged and the potential is slowly (0. 1 mV s -1 ) lowered from the highly oxidizing potential (approx. +0. 36 V). Other conditions: 30 °C, u = 1000 rpm, pH 6. 0, 100% H 2 at 1000 sccm. The Hyd-2 and R509K data have been smoothed with the Savitzky-Golay method, 41 points per window using Origin software, due to lower signal-to-noise. Exposure of R479K to O 2 (42 mM) at a potential where there is no obvious anaerobic inactivation (-0. 215 V) also showed minimal effect on H 2 oxidation ability (Fig. 6D ). To examine more closely the apparently 'robust' defence that R479K offers against transient exposure to O 2 , we tested the effects of prolonged exposure to O 2 during turnover (H 2 oxidation) and non-turnover conditions (Fig. 7 )the non-turnover experiments (argon/O 2 in the absence of H 2 ) more closely resembling conditions the enzymes would experience during isolation and purication.
659 V for 1 or 10 min to reductively activate aerobicallygenerated species, and then back to -0. 1 V to check for gain in H 2 oxidation activity. The fractional recovery (%) of H 2 oxidation activity after removal of O 2 and following potential steps is denoted in blue (relative to the initial current obtained in 100% H 2 ). Note: for Panel B data between approx. 0-1000 s were not auto-saved by the Nova software and have been manually extracted and digitised using Origin software. In PFE, because the enzyme coverage of the electrode used in the temperature dependence experiments is unknown, the intercept of the y axis cannot be used to calculate DS ‡ , instead we rely on the value of DH ‡ and the difference in steady-state rates measured in solution (Table  1 ) to estimate the difference in activation entropy for the two enzymes (DDS ‡ ) at a given potential (see ESI †).
The headspace gas was then swapped for 100% D 2 (BOC, 100% purity) and the same potential steps applied. The whole process was repeated several times to allow for lm loss correction and the average current value was determined for the last approx. 1 minute of data points collected at each potential step. The KIE was determined from the current ratio measured for H 2 vs. D 2. Generally, at each temperature the KIE increased with potential, and at each potential the KIE decreased with increasing temperature (Table  S2  †). The KIE values ranged between 1-1. 25 depending on potential and temperature.
Structures of Hyd-2 R479K The structure of the aerobically puried 'as isolated' R479K variant (pdb accession 6SYO, Table  S3  †) was solved to a resolution of 1. 25 Å (Fig. 10B  and 11A ). The active site is fully formed with minimal change in the position of sidechains relative to the native enzyme, except residue K479 (Fig. 10C ), suggesting any changes in kinetic parameters are a consequence of replacing the guanidinium group with a primary amine. The K479 side chain adopts an atypical conformation by rotation about the Cg-Cd bond, allowing Nz to contribute in the hydrogen bond network between the Od1 atom of the D103 and Od2 atom of D544. The rotated conformation increases both the distance between the head group of residue 479 and the Niatom (5. 2 Å) and the diameter of the terminus of the hydrophobic channel immediately adjacent to the active site (2.
11B , pdb accession 6SZD, Table  S3  †). In a more aggressive procedure, a solution of R479K at 5 mg mL -1 was diluted 2 : 1 with a 1 mM solution of electrochemically reduced methyl viologen then purged with 100% H 2 for 40 hours. Steady-state solution assays showed that R479K activated in this manner displayed approximately 5-fold higher activity, however, inspection of electron density maps suggested this method was no more effective at removing the bound diatomic ligand than simply exposing to H 2 for 20 hours. The increase in steady-state rate upon additional electrochemical reduction is likely due to activation of other slow-to-reactivate states that are not crystallographically resolved. The orientation and geometries of the metal atoms and amino acids in the reduced structures were unchanged from those of the as-isolated structure (RMSD 0.
04 Å), with the most signicant difference being removal of the diatomic species. Reexposure of the reduced sample to pure O 2 for 1 hour resulted in large increases in electron density for the bound diatomic species, the occupancy level returning to approximately 80% (pdb accession 6SYX, Fig. 11C  and Table  S3  †). The X-Y bond length is approximately 1. 2 Å. The X-atom is located 1. 85 Å from the Ni atom and the Y-atom is positioned so that it can form hydrogen bonds to the side chain Nz atom of K479 and a crystallographically ordered water molecule. The resulting stable complex has a Ni-X-Y angle of 163°. A structurally identical complex was also formed upon anaerobic oxidation with DCIP, but with the diatomic ligand showing full occupancy, in agreement with IR-spectroscopy analysis (see below).