dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION The performance of photovoltaic installations depends on climatic conditions such as temperature, irradiation. For this reason, their energy performance becomes an important subject of analysis for different locations of PV installations. In this study, the overall performance of the three different PV technologies (a-Si, mono-Si and poly-Si) was examined. AC electricity from the inverter output, final yield, performance index and capacity factor were evaluated under the same weather conditions with the same orientation angle. Using 2017 measurements taken and a comparison had been presented between 2017 measurement and other data in the literation. This study showed that the poly-Si module has the best performance under the Fez weather conditions. The overall annual efficiency of the PV system was found equal to 8.
The energy demand per kg of feedstock CO 2 supplied is 4. 04 MJ of natural gas supply and 1. 01 MJ of electricity. 16 he economic values of the products are assumed to be 60 h per ton for CO 2 , 71 380 h per ton for ammonia,  72  and 425 h per ton for bioethanol. 73 In all plants, electricity is supplied from the European grid and heat is supplied by the combustion of natural gas (both EU-28 mixes from GaBi database). 45.
Acknowledgements This research has been funded by the  Federal Ministry of Education and Research of Germany  in the project  Dream Resource  ( 033RC002 ). V. Sick acknowledges support from the  Global CO 2 Initiative. STM contribution is supported by the  Canada First Research Excellence Fund (CFREF). CESR contribution supported by  BMBF  project  CO 2 -WIN-Connect (033RC016B ). The authors would like to thank  Timothy J. Skone , P. E. ,  U. S. Department of Energy, National Energy Technology Laboratory  for his review and comments on the paper's methodological approach. All recommendations and findings are attributed to the authors only. Neither the methodology in this paper nor the input data are necessarily endorsed by the  European Commission  for use in future legislation.
MIMS SETUP, OPTIMIZATION AND DATA PROCESSING A typical MIMS setup is composed of five parts (Figure  1 ): (i), a reaction vessel containing the liquid medium/algal culture where gas exchange reactions take place; (ii), a membrane separating the liquid phase of the reaction vessel from the high vacuum line; FIGURE 1 | Schematic setup of a Membrane Inlet Mass Spectrometry. The biological sample (cell suspension, cell free extract, or enzyme containing medium) is introduced inside a thermo-regulated reaction vessel equipped with a gas permeable membrane. Dissolved gases contained in the liquid medium pass through the membrane to a high vacuum tubing. After passing through a cold trap protecting the mass spectrometer from water leaks, gases enter the ion source of a mass spectrometer. Upon ionization, charged molecules are accelerated by an electric field (E), deviated by a magnetic field (B), and collected by a detector.
Data Management Because the mass spectrometer is consuming gases, measured variations of gas concentrations must be corrected from the mass spectrometer consumption to determine actual gas exchange rates between the biological sample and the extracellular medium (Methods S2). Although this effect has been well described  (Berlier et al. , 1985; Kotiaho and Lauritsen, 2002; Konermann et al. , 2008)  its correction has been overlooked in the recent literature. After calculating gas concentrations inside the reaction vessel, gas exchange rates are calculated by correcting from the mass spectrometer consumption. Fluctuations of physical properties of the setup can result in variations of the flux of gas to the mass spectrometer and be an important source of noise. An additional normalization of gas exchange rates to a non-reactive gas (like N 2 or Ar) can advantageously correct such signal shifts or noise.
These artifacts are therefore highly limited although biggest ones remain (Figure  S2 ). Note that prior to any experiment, one must ensure the absence of gas leakage in the system (tubing, connections, inlets), which can be done by flushing helium outside the setup and following the m/z = 4 with the mass spectrometer. Details of the calculations have been described in Methods S1 and S2, and we have developed easy-to-run software that allows real time calculation and visualization of MIMS data (Downloadable at: https://github. com/francoisBurlacot/MIMS_Analysis).
Indeed, fluorescence measurements have often been used together with MIMS CO 2 and O 2 exchange measurements to for the same experiment. Cells were grown photoautotrophically in air, centrifuged and resuspended in fresh medium at a concentration of 20 µg Chl ml -1. Upon addition of 5mM HCO 3 -, 18 O 2 was injected inside the cell suspension, and the reaction vessel was closed. After 5 min of dark adaptation, green light was turned on (500 µmol photon m -2 s -1 ) for 10 min. Levels of 16 O 2 and 18 O 2 were recorded at respective m/z = 32 and 36. O 2 Uptake (red), O 2 Evolution (blue), and Net O 2 production (black) were calculated as described; cumulated gas exchange were calculated by directly integrating obtained exchange rates. To limit noise on the exchange rates graphic, data shown in (D) are integrated with a sliding average of 30 s wide.
Levels of 16 O 2 and 18 O 2 were recorded at respective m/z = 32 and 36. O 2 Uptake (red), O 2 Evolution (blue), and Net O 2 production (black) were calculated as described; cumulated gas exchange were calculated by directly integrating obtained exchange rates. To limit noise on the exchange rates graphic, data shown in (D) are integrated with a sliding average of 30 s wide.
ACKNOWLEDGMENTS The authors thank  Dr. Solène Moulin  for drawings used in Figures 4, 5, 7, and 8 and  Dr. Bernard Genty  for stimulating discussions. The authors acknowledge the  European Union Regional Developing Fund , the  Region Provence-Alpes-Cote d'Azur , the  French Ministry of Research , and the  CEA  for funding the HelioBiotec platform.
Acquisitions obtained by epifluorescence microscopy were saved under the Zeiss. czi format and imported with ImageJ 1. 48v using the BIO-FORMATS plugins 5. 7. 2. The scale was predefined in the. czi format, allowing the measurement of LD diameters. Measurements were based on segments drawn from one side to the other of LDs. Data were stored and saved in. txt format and imported with Rstudio 1. 1. 423v. Diameters were divided by two and transformed into radiuses. Statistical analysis was performed using the computing environment R (https://www. Rproject. org), with a significance level of 0. 01. Means' comparisons were evaluated using both a Student's test (n = number of cells analyzed) and a multiple one-way analysis of variance. The latter was performed with a Bonferonni correction for each day in order to estimate the reliability of the replications.
All experimental dataset were tested for normality test on the ANOVA residuals using the Shapiro-Wilk test  (Taeger and Kuhnt, 2014). When required, a Bartlett test of homogeneity of variances was also performed. Even with classical data transformations, the LD size in day 4 did not meet ANOVA assumptions and so, the nonparametric counterpart of the oneway analysis of variance, the Kruskall-Wallis test, was applied. For confocal microscopy imaging, 3 μL of stained P. tricornutum suspensions were used. Observations were carried out using a Zeiss LSM800 confocal laser scanning microscope equipped with a Zeiss Plan-APO objective (x63/1. 46, oil immersion) and enlarged four times. Nile Red fluorescence was monitored by excitation at 488 nm and capture zone ranging from 579 to 641 nm. Chlorophyll fluorescence was monitored by excitation at 488 nm and capture zone ranging from 650 to 700 nm.
FUNDING This work was supported by a  DRF Impulsion program  from  CEA (RMN-Biocar) , a  Flagship program  from the  CEA High Commissioner  and the  French National Research Agency  (Oceanomics  ANR-11-BTBR-0008 , GlycoAlps  ANR-15-IDEX-02 ,  GRAL  Labex  ANR-10-LABEX-04 , and  EUR CBS ANR-17-EURE-0003 ).
The solid solution state of the particles is attested by both EDX and diffraction analyses. EDX chemical mappings presented in Fig. 5 , 6 and EDX spectra in Fig. S5  (ESI) † show that both Ni and Pt species are uniformly spatially distributed in the volume and at the surface of the particles in all cases. In addition, this technique shows the absence of an oxide layer at the NP surface due to the homogeneous protective organic layer provided by the surfactants. In addition, in all cases, electron diffraction and PXRD (see Fig. S6  and Table  1  of ESI †) reveal that the particles are crystallized in a disordered FCC structure. No chemical order on the FCC lattice was observed, which is expected to be the stable state at equilibrium in the bulk system. As an example, PXRD data for sample B with targeted equiatomic composition are presented in Fig.
Acknowledgements We are indebted to  Hakim Amara  and  Christophe Bichara  for fruitful and inspiring discussions as well as for their constant support to this work. We also thank  Alice Castan ,  Salomé Forel ,  Jean-Sébastien Mérot ,  Sandra Mazerat  and  Diana Dragoe  for their assistance all through this work. The authors thank  ANR GiANT  (No. ANR-18-CE09-0014-04 ) and  ARF "Nano" of ONERA  for nancing this work. The present work has beneted from the electron microscopy facility of  Imagerie-Gif , (http:// www. i2bc. paris-saclay. fr),member of IBiSA (http:// www. ibisa. net),supported by " France-BioImaging " ( ANR-10-INBS-04-01 ), and the  Labex "Saclay Plant Science"  ( ANR-11-IDEX-0003-02 ). HRSTEM-EDX study was carried out within the MATMECA consortium, supported by the  ANR-10-EQPX-37  contract and has beneted from the facilities of the  Laboratory MSSMat  ( UMR CNRS 8579 ),  CentraleSupélec.
The mixture was vortexed for 2 minutes and then decanted. The upper phase contained the methylated fatty acids. The gas chromatography was an Agilent 6850 Series II, equipped with a autosampler G4513A and a flame ionization detector (FID). One µL of sample was injected. The column used was a polar column HP 88 (112-88A7E) of 100 m x 0. 25 mm X 0. 2 μm. The oven was set at an initial temperature of 140°C for 5 minutes followed by a ramp of 4°C/min up to 240°C. Finally, the temperature was maintained at 240°C for 5 minutes. Nitrogen was used as a carrier gas, with the injector and the FID temperatures fixed at 260°C and the split ratio at 1: 20. Acquisition and data processing were done using Chemstation software. The fatty acids methyl esters were identified and quantified using standards, methylated according to the same method (25 injected standards).
Mass of fatty acids was then reported to the dry mass of the original sample. The chlorophyll and carotenoids pigments were identified by high-pressure liquid chromatography (HPLC), using an Agilent 1100 Series, equipped with a module of pumps, an automatic injector and a Diodes Array Detector (DAD). The acquisition of the data and their processing was done using Chemstation software (version A. 08. 03). The pigments were extracted according to the method previously described and resuspended in dichloromethan : methanol (25:75, v:v) according to  Yuan et al. (1997). The free astaxanthin (Glentham), Lutein (Glentham), chlorophyll a (Sigma-Aldrich), chlorophyll b (Sigma-Aldrich) and βcarotene (Glentham) were used as standards. Other pigments were identified according to their retention times  (Yuan et al.
The technical routing map for the analytical framework of this study is illustrated in figure  1. The cities' statistics books were required to contain a complete set of standard information from 2000-2015, including energy consumption of 17 fossil fuels from 39 manufacturing sectors, industrial outputs of these manufacturing sectors, population and other socioeconomic data sets. Based on this criterion (method S1 (https://stacks. iop. org/ERL/15/ 074020/mmedia)), 99 cities nationwide were selected for the analysis in our study. We first estimated the city-level EICEs (equation S1, method S2) and combined the corresponding socioeconomic data set to build the analysis inventories. Second, we classified the 99 cities into four city groups (energy production [EP], heavy manufacturing [HM], light manufacturing [LM] and high-tech development [HD]) using Shan's clustering method  (Shan et al 2018a, method S3).
Structural uncertainty is based on uncertainties in economic growth, how energy use (including energy structure and intensity) changes with economic growth and industrial structure changes. We utilized Matlab7. 10. 0 to report the uncertainty of EICEs with the Monte Carlo simulation analysis for scenario 1 and scenario 5 (figure  S2 ). Despite the uncertainties, the scenarios illustrate the enormous potential for a mitigation wedge in the four city groups. The uncertainties change the magnitude of potential, but the underlying reasons for the mitigation wedge remain. Furthermore, decoupling relationships between economic growth and EICEs changes will become robust and reach strong levels before 2030 under scenario 5 compared with the fluctuating and weak decoupling relationships prior to 2015 (figure  10  , method S6).
Acknowledgments We thank the editor and reviewers for their valuable comments to improve the manuscript. We also  Y Su  et al thank  Professor Michael Batty  and  Professor Yichun Xie  for their help in polishing the English and the revision of the manuscript. This study was supported by the  National Natural Science Foundation of China  (Grant Nos. 41971275 ,  31971458  ), the  Guangdong Basic and Applied Basic Research Foundation  (Grant No. 2020A1515010910  ), the  'GDAS' project of Science and Technology Development  (Grant Nos. GDASYL-20200302001 ,  2020GDASYL-20200102002 ,  2019GDASYL-0103002 ), the  Special High-level Plan Project of Guangdong Province  (Grant No. 2016TQ03Z354  ), and the  Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)  (Grant No.
As there are no studies on the influence of the pore dimensions in the GDL on the distribution of the contact pressures between GDL/BPP, we are especially interested in some parameters (porosity of the GDL, thickness of the GDL and bending radius of the BPP) influence the contact pressure. To realize this experimental plan in good conditions and in reasonable timing of calculations (reasonable timing), we associated it with a 2D mechanical finite elements model of a fuel cell. The subsequent parts of this article are organized as follows: the finite element model using ABAQUS software introduced in section Material and method presents the 2D geometry of two half-channel and one rib of the cell, the behavior of each component and the loading conditions. The design of experiment for low porosity and high porosity is presented in section Results and discussion.
Material and method Fuel cell system electrical performance is very dependent on the contact pressure evolution, in particular, the ohmic losses related to the contact resistance  [26, 27, 36]. To optimize the fuel cell system and to improve its durability, the design of experiments of a simplified fuel cell stack assembly is studied numerically using ABAQUS software  [51]. In this section, we will present two approaches to simulate the behavior of the GDL under mechanical compression loading and the contact resistance between several stack components.
a) Mesostructured carbon paper model To consider the microstructure of the GDL, the pore can be modeled by periodic and random distribution using the computer-aided design accomplished using the python language script and MATLAB software  [38]. In this case, the GDL is performed by spherical periodic pore distribution in which the porosity can vary from 10 ε 57%. In this 2D model, quadrilateral nonlinear finite elements with eight nodes with reduced integration (CPEG8R) are used to mesh the fuel cells component.
Experimental procedure 2. 1 Experimental context The purpose of this study is to approach at best postal delivery conditions met with the vehicles operated during the Mobypost European project. Mobypost project aimed at developing a fleet of ten postal FCEVs and monitoring them during more than three years during day-to-day operation to create a rich database. The vehicle architecture is illustrated in Figure. The fuel cell system is integrated as a range extender in order to power the battery pack and charge it during stop phases. The objective of the study is to analyze and understand degradation mechanisms in order to improve system design and energy management that will lead to better durability of the future vehicles. That is the reason why the purpose of our long duration test is to reproduce at best the environmental and operating conditions of the fuel cell system integrated in this architecture.
Typically as it is shown in  [29, 30]  a postal delivery duration is about four hours during which the fuel cell system should provide quite a constant power to the battery pack all along the driving cycle. Likewise, ambient temperature mainly depends on the considered season in the year. According to the database created through the three years monitoring of the ten vehicles, the temperature in the vehicles have never been lower than 0°C. Moreover, the vehicles were parked in a garage where ambient temperature was controlled to be equal to 12°C in order to avoid freezing during winter.
Here, the nominal power is 1kW, it integrates an open cathode stack with forcedconvection and dead-end anode which is air cooled by fans and self-humidified by short-circuits. The nominal operating point is 50A, 62°C (stack temperature) and the ambient operating temperature range specified by the manufacturer is +5°C to 35°C. The system can operate autonomously and is able to communicate through CAN bus network its operating physical values such as fuel cell temperature, fuel cell current and output voltage. The control unit dedicated to monitor and control the test bench and to communicate with the fuel cell system is developed with LabVIEW software and based on a PXI National instrument hardware. The control unit assures the data logging with a sampling frequency of 1Hz, the current load demand, the safety controls, the data display true dedicated HMI.
e water uptake of neat NaCl above the hygroscopic point at 25 °C (3 data points) was correlated to E-AIM as it showed an experimental deviation (Figure  9(a) ). Several studies have examined that anatase TiO 2  [27]  and amorphous SiO 2  [57] [58] [59]  exhibit a moderate water uptake as the relative pressure increases. us, the steep isotherms of the NaCl-TiO 2 and NaCl-SiO 2 composites at high relative pressure imply that they were boosted by the deliquescence of NaCl (Figures  9(b ) and 9(c)). At stage 1, new graphs were obtained from the full range of relative pressure. ese graphs were plotted separately in order to comprehend the adsorption process behavior (Figures  9(d )-9(f )). Here, the temperature seems to be an active kinetics parameter influencing the water uptake at low relatively pressure, even though this stage is related to a monolayer adsorption mechanism.
Using solvent-exfoliated, p-type WSe 2 nanosheets, a significant decrease of the electrocatalytic activity is observed for films formed by deposition-evaporation or drop casting onto WSe 2 (Fig. 2 ) substrates compared to films deposited onto FTO (Fig. S3 ). More specifically, this decrease is clearly observed in the mass loading range of 125-500 nanomoles cm -2 for complex solutions displaying a higher content of monomers (pH > 9. 25) or prepared in the lower pH range (pH < 8. 5). On films drop casted onto WSe 2 electrodes, the best electrocatalytic activities were recorded on electrodes formed from solutions prepared in the pH range, 8. 5 < pH < 9. 25. As previously reported, this pH range corresponds to the predominance domain of Mo dimers or trimers. XPS data performed on the corresponding dried films indicate a significant decrease of ~ 30 % of the terminal sulfide ratio, r S-ter , compared with films drop casted on FTO (Fig.
Acknowledgements. J. B. B. and L. G. F. V. thank  Conselho Nacional de Desenvolvimente Cientifico e Technologico (CNPQ) ,  Brazil , for the financial support through  CNPq grants  ( CNPq 201490/2015-3  and  INAMI/CNPq/MCT). I. T. M. is grateful for the computational resources provided by the high-performance computational facility of the  Babes-Bolyai University (MADECIP, POSCCE , COD SMIS 48801/1862 ) co-financed by the  European Regional Development Fund of the European Union. I. C. G. and R. P. thank the CALMIP for their generous allocation of computational resources (N° p0812). This work was also granted access to the HPC resources of CINES and IDRIS under the allocation 2018-A0040906649 made by  GENCI.
The average amount of thermal diffusivity was 9. 6×10 -7 m 2. s -. Busby  [32]  estimates the thermal properties of different types of soils by utilizing a database of soil temperature measurements taken at a depth of 1 m. The range of the thermal diffusivity values (5×10 -7 ≤α≤10 -6 m 2. s -  ) found by Busby  [32]  was consistent with the thermal diffusivity values determined in this study for soils with similar characteristics. Table  2 : Estimated thermal diffusivity α and thermal conductivity λ of compacted soil in the thermo-regulated metric scale container; r1 and r2 are the distance of the sensors from the axis of the container.
X = x - x max + x min 2 x max -x min 2 (21) With X the reduced dimensionless number, x the dimensionless number The weights of the ANN are obtained using the software NeurOne©. Because the evolutions of the void fraction according to the four dimensionless parameters are non-linear, the learning is done by a Levenberg-Marquardt (L-M) algorithm  [24]  with parameters initialized according to a normal law N(0, 0. 1) over about 70 iterations. The L-M algorithm consists in reducing a Sum Square Error (SSE) defined by Equation  (22). SSE                               Re V G Fr V G h * r *                , → w N,M                = 1 2 i=O i e 2 i = 1 2 i=O i ε f luent,i -ε ANN,i 2 (22) → w N,M the weight vector with the subscript N the number of layer (from 0 to N) and M the total number of weights per layer and O the number of outputs.
3D finite element modelling of an energy pile group A 3D time-dependent finite element model (Fig. 2a ) was built using the COMSOL Multiphysics Software  [25]  to investigate the long-term performance of energy piles, thus allowing the intermittent operation of the heat pump. In other words, in the presented model, the heat pump operates until the daily heating/ cooling demand of the building is met, following which the operation is automatically terminated until the next day. The intermittent operation of the heat pump allows the temperatures of the pile and soil to recover to some extent during the stoppage times, which is also the case for the actual geothermal operations of energy piles. The heatingecooling demands presented in the previous section were employed in the model for this purpose.
The model comprises of extremely fine and extra fine meshes of 902,104 elements in total to characterize the soil (590,509 elements) and pile (311,595 elements) domains. Tetrahedral, triangular, linear and vertex elements were employed to describe the finite element model. Regarding the pipes, Pipe Flow Module of COMSOL Multiphysics Software was employed, which idealizes the 3D flow within pipes to edge elements. The mesh for the pipes were defined by 8075 edge elements.