dataesr/scientific-paragraphs-categorization huggingface dataset:
Influence of Magnetic Mesoporous Silica Nanoparticles' Coverage on Their Interaction with Proteins and Cell Membranes To reduce the formation of the protein corona at the NPs' surfaces, NPs are generally covered by different layers. Numerous reported studies demonstrate that a polyethylene glycol (PEG) coating presents several advantages, like colloidal stability, inertia in biological media, and higher circulation time of NPs  [11, 21]. Another strategy consists of the deposition of a phospholipid bilayer on the inorganic NPs' surface, in order to create a biomimetic surface  [22, 23]. Fusion of liposomes to a spherical, high surface area, nanoporous silica core improves capacity, selectivity, and stability of   [17]  with permission from the Royal Society of Chemistry. (b) Protein-protein interactions are represented in a network developed using the NetworkAnalyst software  [18, 19] , based on InnateDB  [20].
6 mm Pt disk electrode. The counter electrode was either a graphite sheet (5 × 5 cm) glassy carbon sheet or a graphite rod (Alfa Aesar, Heysam, UK). We did not use Pt CE in experiments performed in Pt-free solutions. The reference electrode was an Ag/AgCl reference electrode (Fisher Scientific, Illkirch, France; E = 0. 197 V vs. NHE) used with a bridge filled with 0. 5 M NaCl with the pH adjusted to 2. 3, as in the electrodeposition solution. The standard rate constants, k 0 were determined by fitting experimental data to an electrochemical mechanism  [41]. The linear sweep voltammograms (LSVs) obtained for the H + reduction where simulated with the commercial software DigiElch version 7. FD (ElchSoft, Kleinromstedt, Germany) that allowed us to correct the LSVs for iR u drop and capacitive charging.
The k 0 value was varied until a good fit of the experimental data was obtained using previously reported values of diffusion coefficients of H + and H 2 as well as the proton concentration. (D H + = 7. 1 × 10 -5 cm 2 /s  [42]  and for H 2 , D H 2 = 3. 37 × 10 -5 cm 2 /s)  [43]. Pt/HOPG Electrode preparation: Full details are given elsewhere for the preparation of the Pt electrode on HOPG and  [19, 29]. Briefly, the HOPG was exposed to the solution, and the deposition was performed either on a 2-or 3-electrode cell. In the 2-electrode cell, Pt electrodeposition is performed by applying -3 V vs. the counter electrode for 200 s. This potential was applied immediately following an immersion time during which the solution was cooled to 4 • C while bubbled with nitrogen to remove oxygen. The solution was stirred at 900 rpm during electrodeposition to remove hydrogen bubbles evolving from the surface.
Electrodeposition Process Characterization Figure  1  shows the data of the electrodeposition on HOPG. The electrodeposition was performed in a two-electrode cell, with the first channel of the Bio-Logic potentiostat (floating configuration) with the potential simultaneously monitored with the second potentiostat channel. Initially, the electrode is immersed in the solution and kept under OCP conditions, which is typically around +1 V vs. Ag/AgCl (ca. 0. 8 V vs. the normal hydrogen electrode, NHE). After the immersion time, a constant potential of -3 V vs. the CE is applied, which corresponds to ca. -2 V vs. Ag/AgCl (-2. 2 V vs. NHE). At these potentials, the current is around 1 µA, which corresponds to ca. 2. 8 µA/cm 2. We focus our discussion on two aspects: (1) the initial OCP value and (2) the value of the applied potential (E app ) to evaluate the effect of eventual hydrogen formation.
V vs. NHE) used with a bridge filled with 0. 5 M NaCl with the pH adjusted to 2. 3, as in the electrodeposition solution. The standard rate constants, k 0 were determined by fitting experimental data to an electrochemical mechanism  [41]. The linear sweep voltammograms (LSVs) obtained for the H + reduction where simulated with the commercial software DigiElch version 7. FD (ElchSoft, Kleinromstedt, Germany) that allowed us to correct the LSVs for iRu drop and capacitive charging. The k 0 value was varied until a good fit of the experimental data was obtained using previously reported values of diffusion coefficients of H + and H2 as well as the proton concentration. (DH + = 7. 1 × 10 -5 cm 2 /s  [42]  and for H2, DH2 = 3. 37 × 10 -5 cm 2 /s)  [43]. Pt/HOPG Electrode preparation: Full details are given elsewhere for the preparation of the Pt electrode on HOPG and  [19, 29].
Electrodeposition Process Characterization Figure  1  shows the data of the electrodeposition on HOPG. The electrodeposition was performed in a two-electrode cell, with the first channel of the Bio-Logic potentiostat (floating configuration) with the potential simultaneously monitored with the second potentiostat channel. Initially, the electrode is immersed in the solution and kept under OCP conditions, which is typically around +1 V vs. Ag/AgCl (ca. 0. 8 V vs. the normal hydrogen electrode, NHE). After the immersion time, a constant potential of -3 V vs. the CE is applied, which corresponds to ca. -2 V vs. Ag/AgCl (-2. 2 V vs. NHE). At these potentials, the current is around 1 μA, which corresponds to ca. 2. 8 μA/cm 2. We focus our discussion on two aspects: (1) the initial OCP value and (2) the value of the applied potential (Eapp) to evaluate the effect of eventual hydrogen formation.
Transmission Electron Microscopy Imaging and Analysis of the Palladium NPs A JEOL 3010 high-resolution transmission electron microscope at 300 kV (JEOL, Tokyo, Japan) has been used for the HR-TEM studies. The number of NPs, size distribution, and surface coverage were determined over areas of 400 × 400 nm 2 using the ImageJ software (NIH, MD, USA). The average diameter and its standard deviation were determined by averaging ten analyses of areas of 400 × 400 nm 2.
Chemical Analysis XPS analysis was performed using a VG ESCALAB 250 spectrometer (ThermoFisher Scientific, Waltham, MA, USA) calibrated against the reference binding energies (BE) of clean Cu (932. 6 eV), Ag (368. 2 eV), and Au (84 eV) samples. Survey spectra and high-resolution spectra of the C 1s, B 1s, N 1s, O 1s, and Pd 3d core-level regions were collected at a take-off angle of 90 • and a pass energy of 100 and 20 eV, respectively, using an Al K α monochromated X-ray source (hν = 1486. 6 eV). Data processing was performed using the CasaXPS analysis software using a Shirley-type background, component peaks defined by BE, Full Width at Half Maximum (FWHM) and Gaussian/Lorentzian envelopes. BEs of the component peaks were corrected with reference to the C 1s peak for -CH 2 -CH 2bonds set at 285.
TEM measurements have been performed in order to obtain information on the nanomaterials prepared. The data shown in Figure  1a-c   This figure illustrates that the particles' nucleation involves successive steps. Most of the Pd NPs are formed during the first 100 cycles. TEM data analysis, shown in Figure  2 , revealed a surface density of ∼10 13 NPs/cm 2 and a surface coverage of 18% ± 9%. After 200 cycles, more NPs are formed all over the substrate and start to merge, reaching a surface coverage of 38% ± 6%. The surface density increased to 4 × 10 13 NPs/cm 2. After 300 cycles, most of the NPs are merged, which can be described as particle coalescence. The surface coverage after 300 cycles is around 62% ± 14%. The difference in gray color between the NPs in the TEM images is related to the diffraction contrast, indicating that the deposited NPs are crystalline.
Acknowledgments We thank  Alain Ayong Le Kama ,  Mook Bangalore ,  Patrice Dumas ,  Marianne Fay ,  Michael Hanemann ,  Frédéric Ghersi ,  Louis-Gaëtan Giraudet ,  Christian Gollier ,  Christophe de Gouvello ,  Céline Guivarch ,  Jean Charles Hourcade ,  Tamaro Kane ,  Oskar Lecuyer ,  Baptiste Perissin Fabert ,  Antonin Pottier ,  Lionel Ragot ,  Julie Rozenberg ,  François Salanié ,  Ankur Shah ,  Nicolas Stern ,  Mike Toman , several anonymous referees, seminar participants at Cired,  École Polytechnique ,  Université Paris Ouest, Paris Sorbonne, Toulouse School of Economics, Princeton University's Woodrow Wilson School , the  Joint World Bank -International Monetary Fund Seminar on Environment and Energy Topics , the  Conference on Pricing Climate Risk held at Center for Environmental Economics and Sustainability Policy of Arizona State University , and audiences at the  EAERE  and the  ASSA  and SURED conferences for useful comments and suggestions on various versions of this paper.
2%, the CR is 3 (30/10 bar) and the hot fluid is at 85ºC. To summarize, previous studies are focused in the best combination of alloys at different stages of compression while searching the enhancement of the kinetics through geometry optimization. Unfortunately, several MHHC works disregard many important thermodynamic features such as sloping plateau and hysteresis during the metal-hydrogen reaction or the use of real H 2 equation of state (EOS). Moreover, they only considered the combination of a few materials, though the thermodynamics of many MH are reported in the literature  [5, 17]. On this regard, Voskuilen et al. [18]  developed an interesting approach by creating a fairly conscious data base of MH and the use of a simple numerical model to select a combination of materials for a two-hydride thermal system, specifically a heat pump.
In the first part of the study, thermophysical data from literature and operational parameters of the compressor are considered to select the proper materials trough a simple model based on ideal thermodynamics. Then, the selected materials were synthesized and characterized chemically, structurally and thermodynamically. Finally, an algorithm that considers the realistic thermodynamic models and real gas H 2 EOS is implemented to define and optimize some parameters of the MHHC system (i. e. , volume and alloy mass of each stage) to deliver the greatest quantity of hydrogen compressed at the highest compression ratio.
Materials and methodology First of all, the thermophysical properties of more than 200 alloys from a data base  [18]  and literature studies  were considered to implement a selection program for a three-stage MHHC. The operational parameters of the system were considered as described in section 2. 1. The selected materials were synthesized and characterized as detailed in section 2. 2. Finally, the Pressure-composition Isotherms (P-c-I) determined experimentally are the main input of a Matlab TM program that uses realistic thermodynamic features of the hydrides and real EOS to design each stage of compression so that the final compression ratio and number of moles compressed can be optimized, as it is described in section 2. 3.
Realistic thermodynamic simulation approach Fig. 6  shows the thermodynamic simulation of the coupling between the three stages of a realistic MHHC compressor, i. e. after considering hysteresis and sloping plateau effects. The figure shows the operation points (O. P. ) for absorption (solid squares) at 23ºC and desorption (solid circles) at a given HT that fulfills the operational conditions described in section 2. 3. HT was initially fixed to 110 °C for the three stages. P-c-I absorption curves at 23°C are experimental data (open symbols). P-c-I desorption curves at HT are simulations using Fang model (Fig. 5 ) at the temperature where the operation conditions are satisfied. The dot lines with arrows indicate the thermodynamic path followed by the MHHC compressor. Several aspects of this simulation are worth discussing.
Conclusions Three AB 2 compounds (M1: Ti 0. 85 Zr 0. 15 Mn 1. 33 V 0. 3 , M2: Ti 0. 8 Zr 0. 2 Mn 1. 2 Cr 0. 6 V 0. 2 , M3: Ti 0. 9 Zr 0. 1 Mn 1. 47 Cr 0. 4 V 0. 2 ) have been selected from literature thermodynamic data in order to be used in a three stage MHHC. A realistic thermodynamic model that uses experimental thermodynamic data and real equation of state for H 2 gas were used to provide a more precise simulation of the MHHC operation parameters. This model allows to determine the optimal operational temperature, alloy mass and vessel volume at each stage. The realistic thermodynamic features of hydride formation and decomposition (sloping plateau, hysteresis and dependence of α+β miscibility gap with temperatures) have an important effect on the optimal values of these operational parameters.
Acknowledgements The authors thank the  Spanish MINECO/FEDER  for financial support under contract ( MAT2015-65203-R ) and  Mr. F. Moreno  and  F. Couturas  for technical assistance. One of the authors (A. R. Galvis E) would like to thank the  Colombian Government  and the  Administrative Department of Science, Technology and Innovation -Colciencias-,  for the financial support under contract  568.
Experimental procedure Air temperature was gathered from a meteorological station located near the pilot plant that recorded data every 30 minutes. The average air temperature during the experiment was 24. 3ºC, oscillating between 12. 6ºC and 34. 3ºC (Figure  1 ). Photosynthetically Active Radiation (PAR) was measured by a probe (Sky Instruments PAR Quantum Sensor) located on the surface of one tank recording data every five minutes. Water physical parameters (water temperature and pH) were continually measured by a pH probe (InPro 426i, Mettler Toledo, CH) and recorded every 5 sec. The pH probe was calibrated every week with standard pH solutions (4 and 7). For microalgal growth monitoring, samples of the mixed liquor were taken from each tank three times per week at 10 am and observed with a microscope (BX 60, Olympus) and algae cells concentration was determined by means of the Thoma cell counting chamber at a 40X magnification.
8 µL of formamide and 0. 2 µL of internal standard GeneScan ROX (Applied Biosystems). Samples were denatured for 5 min at 95°C and immediately cooled in ice. Capillary Electrophoresis Single-Strand Conformation Polymorphism (CE-SSCP) electrophoresis was performed using an ABI prism 3130 genetic analyzer (Applied Biosystems). The separation was performed in 50 cm long capillary tubes filled with a non-denaturing 5. 6% conformation analysis polymer (Applied Biosystems). The elution was carried out at 12 kV and 32°C for 64 min. CE-SSCP raw data were analyzed using GeneScan software (Applied Biosystems) and the StatFingerprints R library  (Michelland et al. 2009 ). All statistical analyses, including rank correlations, were performed using R (R Core Team 2013).
Structured Non-dominated Sorting Genetic Algorithm In this paper, a structured non-dominated sorting genetic algorithm was developed to solve multi-objective building design problems with hierarchical variables. It is a modified version of NSGA-II and its development was inspired from the following studies:  Dasgupta and McGregor (1993) ,  Molfetas (2006) ,  Wang, Rivard, and Zmeureanu (2005). The algorithm which we named sNSGA is presented in this section. It has the capacity to deal with structured variables and is specifically adapted to the case of BPO. Figure  2  shows a schematic representation of a genetic algorithm and distinctions between sNSGA and NSGA-II are detailed in the next sections. Most of the steps are processed from a script written in Python (using DEAP framework) except for steps 2 and 5 that are related to the evaluation of the objective functions, which are processed by TRNSYS in our case (energy simulation software).
The parameters defining each individual in step 1 are transferred to TRNSYS to perform an energy simulation. The results are sent back to the script for the evaluation of the fitness functions. Steps 3 to 8 describe the genetic evolution procedure. During this process, the fitter individuals will reproduce and create an offspring population ready for a new evaluation. The evolution stops after a pre-defined number of generations Gen, as defined in Table  4. Based on the thorough analysis of Section 5, it will be shown that the value of Gen that was chosen was sufficient to provide a good convergence to the optimal solutions.
DEAP, an evolutionary framework for rapid prototyping of new algorithm Many optimization tools are available in order to support BPO. Attia et al. (2013)  have sorted 12 tools in order of importance from an exhaustive review of 165 publications in the domain. The results show that GenOpt and Matlab are the two most commonly used. With similar features as the one included in GenOpt and Matlab, DEAP is an open-source platform designed for a fast prototyping of new algorithms  (Rainville et al. 2012). New toolboxes, genetic operators and algorithms can easily be created or the users can use the ones from a database. Moreover, it is compatible with SCOOP, a Python module allowing concurrent parallel programming on various environments. It allows speeding up the optimization process which can be crucial when the number of design variables is high.
It allows creating a construction by using data from many European countries from the ESUCO database. For the purpose of this study, only the impacts from phases A1 to A3 are considered in order to determine 2. They include the raw materials supply, transport and manufacturing. SBS also supplies the environmental impacts of a wide variety of heating systems and water radiator. It provides a generic value, not for a specific brand and it changes according to the size of the equipment. Table  2. Table  3  present a summary of the carbon footprint of some of the most impactful optimization variables of the present study. Environmental impact of energy sources are extracted from the GaBi database SP33. For electricity in Germany in 2017, the global warming potential (GWP 100 years) per kWh is 0.
toruloides? An available, curated and annotated genome database would help researchers worldwide to work with this yeast. The genes involved in the pathways leading to the production of lipids and carotenoids have been recently described, and the tools to overexpress and delete genes have been developed. Would it be possible to further increase production titers by performing metabolic engineering on these pathways? Is it possible to create high yields of new products that are not naturally produced in R. toruloides (e. g. , alkanes, terpenes, dicarboxylic acids, etc. ) by expressing heterologous enzymes? Can R. toruloides be engineered to use low-cost substrates that it naturally is unable to consume, or be improved in its natural substrate assimilation capacities? This could reduce the cost of the processes, which can make a difference for an economically viable industrial setup.
An accurate PV output forecasting method would be of great help to grid operators and would minimize costs while enabling more integration of variable renewable energy (RE) in the grid. The resulting statistical model should then be applied in future works on collected datasets at several particular temperate climatic sites for validation. A mathematical model  [29]  using satellite data has also been proposed to determine PV performance in continental scale. The two models should be compared in a future work. Statistically we studied a linear relation analysis of time series data collected over a period of time and investigated mainly the dependent variable of power output P on explanatory variables such as solar irradiance G and temperature T. For that we first determined the stationary character or not of each time series of solar irradiance and temperature as this will set the statistical method to be used.
Ideally all residuals should be small and unstructured meaning that the regression analysis has been successful in explaining the essential part variation of the dependent variable. We first apply the Goldfeld-Quandt (GQ) test when heteroscedastic variance is related to variables in the regression model then the Durbin Watson (DW) test to detect serial correlation in the residuals. In this present study, the GQ test is well verified but DW test is still doubtful and not very conclusive. Outliers are suspected in the model. Therefore, a more recent procedure such as the Engle & Granger (EG) method is suggested to determine the most appropriate model. Our model is formulated on the basis of PV real conditions statistical approach and is more realistic than steady approach models. For this study, the small samples of one year daily means data is retrieved among the 7 years measurements from the GREEN lab of Physics department of University of Lorraine in Metz.
Solar irradiance frequency distribution test Solar energy is harnessed with solar PV converting sunlight directly into electricity. Thus the energy output of PV system depends on the input solar irradiance distribution. In this section, the solar data of a peculiar site is systematically recorded and analyzed to determine whether the distribution of solar irradiance frequency distribution follows a predetermined distribution such as Weibull distribution which is then used to predict performance and energy output as for wind turbine 34. Although 8 years of solar irradiance data have been recorded from the GREEN Platform of University of Lorraine and due to data similarities between years, only one year data is used for the test. Otherwise the 8 years data that is being sampled every 10 minutes would need huge processing time.
The goldfeld quandt test The blue curve of Figure  6  is the residual of P with no outliers and is oscillating in a range of values that act as good estimates. This range is also known as the confidence interval and is represented by the dotted blue lines. GQ have argued that the error term may not satisfy the ordinary least squares assumptions and may itself be heteroscedastic. The GQ test is accomplished by undertaking separate least squares analyses on two subsets of the original dataset: these subsets are specified so that the observations for which the pre-identified explanatory variable takes the lowest values are in one subset, with higher values in the other. The test statistic or F-test used is the ratio of the mean square residual (MSR) errors for the regressions on the two subsets given by Eq 7 where MSR max is the highest value in a subset and MSR min is the lowest value.
The role of (h)GSH was also analyzed in the nitrogen-fixing zone. Downregulation of the γECS gene by RNA interference using the nodule nitrogen-fixing zone-specific NCR001 promoter resulted in significantly lower biological nitrogen fixation (BNF) associated with a significant reduction in the expression of nodule specific genes. This lower (h)GSH content was correlated with a reduction in the nodule size. Conversely, γECS overexpression using the same promoter resulted in an elevated GSH content associated with increased BNF and significantly higher expression of the sucrose synthase-1 and leghemoglobin genes. Taken together, these data show that the plant (h)GSH content of the nodule nitrogen-fixing zone modulates the efficiency of the BNF process, demonstrating their important role in the regulation of this process  [47].
Expression of glutaredoxins in M. truncatula nodules. Gene accession numbers are indicated in the table. Gene annotation is based on candidate orthologues and interprodomain signature. The different columns correspond to root and nodule whole organ analysis (Root and Nodule) and to the nodule zones: meristematic zone (I), distal infection zone (IId), proximal infection zone (IIp), infection/fixation interzone (IZ II-III) and nitrogen-fixation zone (III). The numbers in the different columns correspond to Total Reads ribominus. All RNA-seq read values were normalized.
[50]ession of S. meliloti genes from the Grx and Trx systems in M. truncatula nodules and regulation in free-living bacteria. Gene accession numbers are indicated in the table. Gene annotation is based on candidate orthologs and interprodomain signature. The values corresponding to gene expression in root nodules are, from left to right, total reads from laser-capture microdissection (LCM) and their distribution in each zone (%), as reported by Roux and colleagues[50]. All RNA-seq read values were normalized. The full organs were 10-day old nodules. IZ, interzone; ZIII, zone III; FI, fraction I; FIId, distal fraction II; FIIp, proximal fraction II.