dataesr/scientific-paragraphs-categorization huggingface dataset:
818823 , CARINE). H. N. N. gratefully acknowledges funding from the  DFG  under grant no. STR 596/21-1  (DaCapo). We acknowledge the  European Synchrotron Radiation Facility (ESRF)  for provision of synchrotron radiation facilities using beamlines ID01 and ID16B. We also acknowledge  DESY (Hamburg, Germany ), a member of the  Helmholtz Association HGF , for the provision of experimental facilities. Parts of this research were carried out at  PETRA III  using the  P10 Coherence Applications Beamline. Beamtime was allocated for proposal I- 20220139. We also thank  Dipl. -Ing. Sören Selve  and  Dr. Christian Günther  from the  Zentraleinrichtung für Elektronenmikroskopie (ZELMI ) at  Technische Universitaet Berlin (TUB ) for their support with FIB/SEM lamella preparation as well as STEM-EDX mappings.
= ± 1. 9 ´ 10 4 Oe. Rietveld refinement software MAUD (Material Analysis Using Diffraction)  [22]  was employed to obtain a Full profile analysis of XRD patterns that confirmed the formation of a single cubic spinel phase, no other phases were detected. XRD data were analyzed to compute structural parameters: experimental lattice parameter (aexp. ), cell volume (V), and X-ray density (ρxrd) as described in  [16, 23]. By using Williamson-Hall (W-H) method, grain diameter (DW-H) was obtained by incorporating both instrumental and strain broadening  [16, 24]. The XRD data of the standard LaB6 sample was used to obtain instrumental broadening. By using this method, x-ray diffraction peak broadening is given by the following expression: bhkl = bsize + bstrain. The actual peak broadening (b) is obtained by correcting the experimental peak broadening 'bex' and the instrumental broadening 'bin' as: b 2 = bex 2 bin 2.
Acknowledgments: Authors thank  Dr. M. Gupta   UGC-DAE CSR, Indore , for XRD measurements. SNK acknowledges gratefully for one month of " Invited Professor" stay at ENS Paris-Saclay ,  Cachan (France). This work is supported by  Seed  money grant: No. Dev/Seedmoney2. 0/2020-21/649 ,20 Jan. 2022 of  Devi Ahilya University, Indore (India). TT thanks the  Ministry of Education and Science of Ukraine  (project number  0121U109476 ) for financial support.
Life cycle assessment LCA, a normalised method (ISO 14040 (International Organization for Standardization, 2020)), is carried out from "cradle-to-grave" to determine environmental impacts. The International reference Life   (kg. m -3 ) cs (J. kg -1. K -1 ) λs (W. m -1. K -1 ) εs (kJ. m -2. K -1. s -1/2 ) LCA s (ca. year. (Ecoinvent, 2023)  and OpenLCA v1. 10 software  (OpenLCA, 2023). The analysis of the reference TES is based on Lalau's works  (Lalau et al. , 2016 (Lalau et al. , , 2021)). The TESM elaboration is based on  (Lalau et al. , 2021; Le Roux et al. , 2022a). In order to fairly compare the environmental benefits of a system to the reference case, the same service is specified. The functional unit is defined as follows: Provide a discharged thermal energy equal to that of the reference tank (Q * ds = (Q ds ) ES = 1567 kWh th /cycle), during its lifespan (25 years) considering 2 cycles a day and 15 days off a year for maintenance.
2. 3. 1. Step 1: combination of the seven pareto sets Fig. 1  shows the three-dimensional (3D) Pareto sets obtained by the multi-objective GA for bauxite (circles), alumina (plus signs), CFA (diamonds), CHCA (stars), cofalit (triangles), basalt (crosses) and quartzite (pentagons). The same marking code will be used in the following sections. Each Pareto set obtained with the multi-objective GA of Matlab® includes 18 optimal solutions among the initially generated population of 50 individuals. Machined ceramics (bauxite and alumina) have the worst environmental and economic performance. Recycled ceramics and natural rocks have the smallest ecological footprint and the lowest LCOE. As for the exergy efficiency (1 -N ex ), it is similar for all the TESM (between 96. 1 and 98. 0%).
Overall structure of the multi-criteria optimisation of thermocline storage The links between the physical, environmental and economic models and the decision-making methods are shown in Fig. 5. The three models, the optimisation algorithm and the decision-making method TOPSIS have been detailed in  (Le Roux et al. , 2021 , 2022b). To improve the selection of the best compromise solution, the Shannon entropy is combined with TOPSIS method. In TOPSIS method, a subjective weight is assigned to each criterion which is a problem. The Shannon entropy allows us to get rid of this subjective choice. For a HTF/TESM pair, Pareto set is obtained using the Matlab® multi-objective GA. The seven Pareto sets related to the seven HTF/TESM pairs studied are then combined. From this new set, a single Pareto set is constructed, consisting only of non-dominated solutions.
Conclusion Multi-objective optimisation is performed on an industrial thermocline TES according to exergy, environmental and economic criteria. A one-dimensional two-phase model is applied to compute the exergy efficiency. The environmental footprint is determined by LCA and each TES system provide the same discharge exergy over its lifetime. The LCOE is calculated by LCCA. Seven different solid fillers are compared: machined ceramics (bauxite and alumina), recycled ceramics obtained from waste (CFA, CHCA and cofalit) and natural rocks (basalt and quartzite). This study seeks to find the optimal geometry of the thermocline tank, through two dimensionless variables, for a given HTF and filler material. The Matlab® multi-objective GA is used to solve the problem. A Pareto set of optimised solutions is obtained for each filler material tested.
ton -1 ) Cycle Data system (ILCD) 2016 mid-point method is used to calculate the LCA indicators using EcoInvent v3. 7. 1 database Cs (€. kg -1 ) MC - 3005 1076 4. 0 3. 59 0. 92 0. 9 Bauxite MC - 3670 1023 21. 0 8. 88 0. 95 1. 6 Alumina Recycled 2600 1000 1. 8 2. 16 0. 12 0. 03 ceramic - CFA Recycled 2200 1050 1. 0 1. 52 0. 12 0. 03 ceramic - CHCA Recycled 3120 1025 1. 8 2. 37 0. 23 0. 03 ceramic - Cofalit Natural 2900 900 2. 0 2. 29 0. 01 0. 03 rock - Basalt Natural 2500 830 5. 7 3. 44 0. 00 0. 03 rock - Quartzite.
From the RRDE voltammograms, the number of exchanged electron (n) and hydroperoxide percent yield have been extracted and monitored as a function of potential (Figure  6 ). For all the catalysts, the ORR exhibits behaviors that are in fair agreement with a 2e -+ 2e -reduction pathway with HO 2 -as intermediates. HO 2 -is first formed then can undergo a further reduction to OH -at more negative potentials thanks to the good disproportionation activity of gold nanomaterials. [32, 39] owever, sharp differences can be pointed out among the different nanocatalysts. For Au-calixCOO -, Au-calixPropyl, Au-calixPEG 4 and Au-calixCF 3 , a low production of HO 2 -(≤11%) From ref. [27]; b) From ref. [20]; c) From ref. [26]; d) Estimated from MM2 energy minimization with ChemBio3D software; e) Taking into account the different values determined for E onset or E 1/2 for the different catalysts, we have considered the maximum yield production of H 2 O -in the mass-transport-controlled region.
The nanoparticles were washed 3 times by centrifugation and re-dispersed in acetonitrile, water, toluene or acetone depending on the nature of the calix [4]arene ligands, with a concentration equal to 2 mg Au mL -1. Caution! Although we have not encountered any problem, it is noted that diazonium salts derivatives are potentially explosive and should be handled with appropriate precautions. Transmission Electron Microscopy: Morphology and size distribution of the samples were investigated by high-resolution transmission electron microscopy (HRTEM), using a Jeol 2100 with an acceleration voltage of 200 kV on which the images were recorded on a GATAN Orius 200 CCD camera. The size distribution was estimated using ImageJ software, version 1. 52a. X-Ray Photoelectrons Spectroscopy: XPS data have been collected by a Kratos Axis Nova spectrometer using the Al Kα X-ray source working at 1486.
6 eV and using a spot size of 0. 7 × 0. 3 mm 2. Survey spectra (0-1000 eV) (Figure  S7 , Supporting Information) were acquired with an analyzer pass energy of 160 eV (0. 5 eV/step); high resolution spectra used a pass energy of 40 eV (0. 1 eV/step). Binding energies were referenced to C1s peak at 285 eV. The core level spectra were peak-fitted using the CasaXPS Software, Ltd. Version 2. 3. 18. U2 Tougaard or Shirley was used as background. The peaks areas were normalized by the manufacturer-supplied sensitivity factor (S C1s = 0. 278, S F1s = 1, S O1s = 0. 78, S S2p = 0. 668, and S Au4f = 6. 25). Since the XPS measurements cannot be made with the RRDE electrodes due to their height, deposit of AuNPs were performed onto screen-printed carbon electrode Dropsens. The modified electrodes subsequently underwent ORR stability experiments (1000 cycles at 500 mV s -1 from 1.
20 to -0. 35 V vs RHE) under the same experimental conditions used for ORR measurements with RRDE. For each sample, XPS spectra were recorded on three different locations, and we did not observe any difference. Thermogravimetric Analyses: The thermogravimetric analyses (TGA) were performed with a TA instruments Model SDT 2960. For this purpose, 4 to 6 mg of dry nanoparticles weighed in a ceramic crucible Perkin Elmer TGA undergo a linear ramp of increasing temperature from 30 to 900 °C at a rate of 10 °C min -1 under nitrogen atmosphere. The obtained results are then processed with the TA data analysis software. Electrochemical Analyses: The ORR activity was evaluated using a rotating Pt/GC ring-disk electrode (37% collection efficiency) controlled by a MSR Rotator from Pine Research. The GC electrode preparation (deposit of catalysts) is described in the supplementary part.
The LSV scans were recorded using an Autolab PGSTAT302N potentiostat-galvanostat (Metrohm) equipped with a BIPOT module, in a conventional three-electrode electrochemical cell at a scan rate of 10 mV s -1 in a 0. 1 m KOH solution saturated with O 2. A platinum grid and an Hg/HgO, (20% KOH) electrode were used as counter and reference electrodes, respectively. Voltammograms were recorded with the Metrohm Nova 2. 1 software. The potentials were not corrected from iR drop. The potentials are reported against the reversible hydrogen electrode according to E(RHE) = E(Hg/HgO) + 0. 059 pH + 0. 098. The number of electrons exchanged can be quantified using the disk and ring currents according to n I I N = +     4 I D D R (5) where I D is the modulus of the disk current, I R is the ring current corresponding to the oxidation of HO 2 -, and N is the collection efficiency.
Acknowledgements This work was supported by  Agence Nationale de la Recherche   ANR-21-CE50-MARCEL  project. Q. L. thanks the  French Ministry of Research  for financial support. The authors are grateful to  L. Rault  for the assistance in TEM experiments performed on THEMIS platform (CPER-FEDER 2007-2014). R. Lebullenger  ( University of Rennes 1 ) is thanked for his help in the TGA analyses. The authors are indebted to  L. Troian-Gautier  ( ULB ) for his help in the synthesis of calixarenes.
Thermodynamic Analysis The thermodynamic model of the proposed MED-TVC desalination system was developed in MATLAB 7. 9 software using mass and energy conservation equations according to the following assumptions: - The desalination plant operates in a steady state condition. - The effects have a similar temperature difference and the feed-flow rate is constant. - The distillate and vapor are salt-free. -A pump average efficiency of 75% is considered. In addition, the model includes the equations for calculating the physical properties of the seawater and the heat transfer coefficients. A detailed description of seawater properties' (enthalpy, entropy, chemical potential and density) calculation and the thermodynamic model developed for a combined cycle for the MED-TVC desalination system were previously presented by the authors  [17, 23].
As D. salina is haploid, we expected all derived cells of a given population to be genetically identical. Each of these isogenic populations were also subjected to the plasticity assay described above. Total RNA extraction and purification of 24 samples ((9 lines + 3 isogenic populations) × 2 salinities) was carried out using Nucleozol, following Macherey Nagel's protocol, and whole genomic DNA was isolated according to the phenol-chloroform purification and ethanol precipitation method of Sambrook and colleagues  [62]. Library construction (TruSeq RNA Library Preparation kit for RNA-seq and Swift Bioscience Accel-NGS Methyl-Seq DNA library Kit for WGB-seq) and high-throughput sequencing steps (Paired-End (PE) 2 × 150 bp, Illumina HiSeq) were performed by Genewiz (Leipzig, Germany). We performed all the bioinformatic preprocessing analyses using publicly available software implemented in the European UseGalaxy server  [63].
72  [68]  and subjected to adapter trimming and quality filtering using Trim Galore! version 0. 4. 3. 1  [69]. Additional 12 bp and 3 bp were also removed at the 5 0 and 3 0 extremity, respectively, to avoid bias not directly related to adapter sequences or basecall quality according to FastQC outputs, and only reads with a minimum length of 50 bp were retained. We used the reference nuclear (Dunsal1 v. 2, GenBank accession: GCA_002284615. 2), chloroplastic (GenBank accession: GQ250046), and mitochondrial (GenBank accession: GQ250045) genomes of D. salina strain CCAP 19/18 (closely related to CCAP 19/15 used here) for trimmed reads alignment, using HISAT2 version 2. 1. 0 [70]  with default parameters for PE reads and spliced alignment option. We finally quantified the number of reads per transcript with FeatureCounts version 2.
Statistical analyses Differential DNA methylation and gene expression. We used Bioconductor's methylKit package  [73]  to identify DMRs, i. e. , nonoverlapping 100 bp windows with methylation levels that varied significantly among evolutionary treatments or between salinities within each treatments. The significance of calculated methylation differences was determined using Fisher's exact tests. We used the BH adjustment of P values (FDR < 0. 05) and methylation difference cutoffs of 20%. Similarly, the differential gene expression analyses were performed with the Bioconductor's package DESeq2 version 1. 30. 1  [74]. We identified DE transcripts among the evolutionary treatments, salinity, and their interaction, by building a general linear model as implemented in DESeq2. We used the Wald test when comparing 2 salinities, and transcripts with FDR < 0.
05 (P values after BH adjustment) and |log 2 FC| > 1 were considered as DE. Significance of evolution (predictability ρ 2 ) and evolution of plasticity (ρ 2 × salinity interaction) were assessed using likelihood ratio tests (LRTs) comparing models with and without the corresponding terms  [74]. We then used the GO assignments from Leung and colleagues  [23]  to classify the functions of D. salina transcripts and to functionally annotate the identified DE transcripts. Enriched GO terms of the DE transcripts were identified using the 'classic' algorithm from the topGO R package  [75]  and based on p-value generated using Fisher's exact method, for the 3 GO categories, i. e. , molecular function, cellular component, and biological process. GO terms were then sorted by decreasing order of evidence within each category, based on the GO enrichment test P value after BH adjustment, and we showed the most probable gene function candidates with a threshold of FDR � 0.
1. Evolution of the degree plasticity at multiple levels. Since phenotypic plasticity can evolve as an adaptation to fluctuating and predictable environment  [3] [4] [5] [6] [7] , we wished to quantify to what extent environmental predictability during experimental evolution contributed to key mechanisms underlying phenotypic plasticity. We first applied a RDA  [76] , computed with the function rda() from the vegan R package  [77] , to quantify the proportion of the total epigenetic and gene expression variation that are significantly explained by the evolutionary treatments (predictability ρ 2 ) and assay environment (salinity). We performed a RDA using the table of DNA methylation levels of 100 bp windows regions, or the table of rlog transformed transcript count, as response variables, and the evolutionary treatments (predictability ρ 2 ), salinity, and the ρ 2 × salinity interaction, as explanatory variables, with population identity as covariates to account for paired samples between salinities.
We treated the target ρ 2 (low, intermediate, or high) and salinity (low or high) as categorical variables. We quantified the contribution to the total genetic variation using the adjusted R 2 and tested its significance by ANOVA-like permutation tests using 999 randomizations of the data  [78]. For both RNA-seq and WGB-seq data, we presented the results of the RDA ordinations as a biplot, to visualize the variation among samples along its major axes. We performed all multivariate statistical analyses with the vegan R package  [77]. To investigate how the overall degree of plasticity at multiple levels evolved in our experiment, we followed the protocol described in Leung and colleagues  [5] , which we here applied to DNA methylation level, gene expression, and cells morphological variation.
(Note that the morphological measurements were replicated here, rather than just reproducing values from Leung and colleagues  [5]. ) First, we assessed the degree of plasticity of each experimental population by computing the Euclidean distance between the multivariate means from the CpGmethylation level table, the rlog transformed transcript count table, or the raw cells morphological data measured, at low ([NaCl] = 0. 8 M) versus high ([NaCl] = 4. 0 M) salinity. The realized autocorrelation of a given time series can vary, to some extent, from its long-term stationary expectation, because of the randomness of the stochastic process in finite time. For a more quantitative relationship between plasticity and environmental predictability, we thus used the realized (rather than the target) ρ 2 as index of environmental predictability (as also done in  [5] ).
Acknowledgements The authors extend their sincere appreciations to Researchers Supporting Project number ( RSP2023R79 ),  King Saud University, Riyadh ,  Saudi Arabia , for partial funding of this work. B. V. and E. M would like to also thank the platform "Microscopies, Microprobes and  Metallography  ( 3M )" at the  Institut Jean Lamour (IJL, Nancy, France)  for TEM and SEM facilities. A. V. acknowledges the  Kempe Foundation  and the  Knut och Alice Wallenberg Foundation  for nancial support. AIM thanks the  Ministry of Science and Innovation of Spain  (Project  PID2021-126235OB-C32 ) for the nancial support.
After optimization, we thus selected the activated Ru/Al 2 O 3 as a hydrogenation catalyst (0. 1 wt% active metal to Acetophenone), used at 115 C for 2 h, under 50 bar of H 2. 2. Analysis of the selected Ru/Al 2 O 3 catalyst for the hydrogenation of Acetophenone The Ru/Al 2 O 3 catalyst was analysed by XPS at three different steps: as supplied, after activation and after hydrogenation. The Ru 3d and Ru 3p core levels were recorded (Fig. 4a  and ESI S9) so as to estimate the oxidation state of the catalyst. For data treatment, a Shirley background was applied for both spectra and fitting of the Ru 3d core level spectra was achieved by using three pairs of pseudo-voigt function, a linear combination of a Gaussian and Lorentzian. The ratio between the 3d doublet area and the distance between the doublet peaks were fixed at 2 and 4.
Catalyst surface analysis by XPS The catalysts were analysed by XPS after three different steps: as received, after activation and after reaction (hydrogenation or dehydrogenation). A monochromatic beam (X-ray source Al Ka 1486. 6 eV) of 100 mm in diameter and 24. 9 W of power was focused on the surface of the samples. High resolution core level analyses were performed using a pass energy of 23. 9 eV, which corresponds to an energy resolution of 0. 6 eV. All XPS measurements were carried out under ultrahigh vacuum conditions (7 Â 10 À8 Pa). Each core level peak was recorded within ten scans with a scan rate of 0. 1 eV/s. The binding energy calibration was performed using the C 1s peak shifted at 285 eV. The core level binding energies were recorded within an error of ±0. 1 eV. Curve fitting and background subtraction were accomplished using Casa XPS software.
Acknowledgments We thank  Christophe Brouard  ( CEA Grenoble ) for the preparation and analysis of samples by  ICP-OES. We thank  Nathalie Pelissier  ( CEA Grenoble ) for the preparation and analysis of samples by  TEM-EDS. We thank  Dr. Raphael Ramos  ( CEA Grenoble ) for the preparation, analysis and interpretation of samples by Raman spectroscopy. We thank  Dr. Anass Benayad  ( CEA Grenoble ) for his help in the interpretation of samples by XPS. This work was supported by the  CEA , the  CNRS , the  University Paris-Saclay, CINES (HPC Computing  time on Occigen, grant no. A010806494 ), the  European Research Council  ( ERC Consolidator Grant  Agreement no. 818260 ) and the European project funding from the  Fuel Cells and Hydrogen 2 Joint Undertaking  under grant agreement No  101007223.
The ground is warmed up to a much higher temperature than its initial temperature (e. g. from 12 • C to 50-80 • C). Conversely, in a Low Temperature BTES (LT-BTES) a reversible Heat Pump (HP) makes the ground temperature oscillate by 10-15 • C around its initial value to produce heating and cooling  [5]. LT-BTES is out of the scope of this paper. A bunch of HT-BTES demo sites are operational, including Solar Drake Landing, Okotoks, Canada  [6] , Neckarsulm, Germany  [7] , Emmaboda, Sweden  [8] , Braedstrup, Denmark  [9]. A comprehensive overview of these demo sites can be found in Refs. [9, 10]. Numerical models have been widely used to assess the thermal behaviour of BTES and their integration. Many studies used TRNSYS, a graphics-based software for the simulation of transient systems where modules (so-called "Types") are connected  [11].
System modelling The system is modelled in TRNSYS  [11]  with a time step of Δt = 7. 5 min over 7 years. The integration and convergence tolerances are set to 10 -3. To ensure reliable results, the number of time steps without convergence could not exceed 100 without throwing an error. Because of this restrictive setting, the allowed number of iterations per time step was set to a very high value (20 000). MATLAB®  [33]  is used to prepare and call the deck files (text files describing the model), post-process TRNSYS output files and to perform the sensitivity analysis.
Solar thermal collectors (STC) and solar tank The STC are modelled with the Type 1346. The STC efficiency η STC is computed as: η STC = P φ rad S = a 0 -a 1 T in -T amb φ rad -a 2 ( T in -T amb φ rad ) 2 (4) Where. -P: thermal power delivered by the STC [W] -φ rad : incoming solar radiation on the STC [W. m -2 ] -T in : inlet temperature [ • C] -T amb : ambiant temperature [ • C] -S: STC surface [m 2 ] -a 0 , a 1 and a 2 three empirical coefficients The data originates the technical documentation of VITOSOL 100-FM (plan), type SV1F (cf. Table  2 ). Note that the RT2012 climatic file provides solar visible radiations as Direct normal beam radiation and Diffuse radiation on horizontal surface. Type16k was used to convert these to Incident radiation, Total horizontal radiation and Horizontal diffuse radiation as inputs of Type 1346.