dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: STEM-HAADF/EDS chemical analyses were performed in the frame of the  French Government Program of Investment for the Future (Programme d'Investissement d'Avenir-TEMPOS Equipex-ANR-10-EQPX-50, pole NanoTEM ). The authors would also like to acknowledge the  Centre Interdisciplinaire de Microscopie électronique de l'X (CIMEX).
Acknowledgments: The authors thank  Université de Haute-Alsace  and  CNRS  for providing facilities and funding as well as the characterization platforms of IS2M and LIMA. N. M. acknowledges  Université de Haute-Alsace and Lebanese University  for PhD fellowship. J. -P. G. and T. J. D. thanks  IUF  for financial support. The authors thank  Marion Nyznyk  for chemicals preparation.
Characterization and data analysis The selected carbon materials were thoroughly characterized using various techniques (e. g. , gas adsorption isotherms, elemental analysis, Raman spectroscopy). Details of the experimental protocols are shown in the Supplementary Information File (Tables  S1  and S2 , Figs. S1-S3 ). A hierarchical cluster analysis method was used to analyze Raman data, following Ward's clustering algorithm (minimum variance method) and the Euclidian distance measure to generate a dendrogram using a script programmed in Phyton with Scipy library selecting linkage function and choosing the Ward method  [51, 52]. The hierarchical cluster analysis was performed on the full Raman spectrum.
Energy consumption of the cathodes Fig. 6  shows the calculations of the energy consumption per gram of H 2 O 2 for the different carbon cathodes, using two different criteria:  (1)  constant applied electric charge of 0. 12 Ah L -1 (Fig. 6a ) and (  2 ) the production of 20 mg L -1 of H 2 O 2 (Fig. 6b ). These values of applied electric charge and H 2 O 2 production were selected to be able to carry out the comparison of the 22 carbon materials studied in this work (given their large differences in electrocatalytic performance). Data of the benchmark carbon felt electrode in two electrode configurations (with and without a PTFE coating) is included for a fair comparison. Overall, the energy consumption of the carbon cathodes was rather low (especially for those carbons classified in Groups I and II), and in all the cases lower than that of the carbon felt benchmark.
Conclusions We have investigated the dependence of the electrocatalytic activity of carbon cathodes through 2e-ORR to produce hydrogen peroxide, with the characteristics of the carbon materials, by analyzing a pool of 22 carbons with varied physicochemical and structural properties. Electrocatalytic tests have been performed in small (0. 2 cm 2 ) and large (4 cm 2 ) electrodes. Our data has shown that the electrocatalytic activity of carbon cathodes to produce hydrogen peroxide is a complex process that requires balanced characteristics in the electrodes to assure good electron transfer and O 2 mass transfer. While electrical conductivity of the carbons is important, the structural defects play a key role in their electrocatalytic activity, with less impact on composition and porosity. This dependence is more pronounced when the electrocatalytic tests are carried out in large electrodes and after various hours (ca.
Acknowledgements M. Muñoz-Morales  and  J. Llanos  wish to express their gratitude to  Junta de Comunidades de Castilla-La Mancha, Diputación de Albacete and UCLM  for the funding (grants  SBPLY/21/180501/000058  and  DIPUAB-2022   MU ÑOZMORALES  respectively). Conchi Ania  thanks the funding from the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  776816 ) and  Region Centre Val de Loire (PRESERVE , grant  EX010666 ). The authors acknowledge  Dr. Vautrin-Ul  and  Mme. Jebali  from  ICMN (Orléans ) for the access to contact angle and RRDE measurements.
Experimental Section Cyclic voltammograms (CV) were obtained by use of CHI 750E potentiostat equipped with a standard three-electrode cell. The working electrode was a commercial 3 mm-diameter glassy carbon (GC) disk. The counter-electrode was a platinum wire and the reference electrode was Ag/Ag + (AgNO 3 10 mM) electrode in acetonitrile + 0. 1 M NBu 4 PF 6 in acetonitrile. Electrolysis were performed using a Solartron Analytical Instrument potentiostat (Modulab XM MTS) using XM-studio software. All experiments were performed under argon or CO 2 with a carbon felt working electrode attached to a glassy carbon rod. The reference electrode was an Ag/Ag + (AgNO 3 10 mM) in acetonitrile + 0. 1 M NBu 4 PF 6 with an acetonitrile + 0. 1 M NBu 4 PF 6 electrolyte bridge and the counter electrode a platinum wire in a bridge separated from the electrolytic cell by a glass frit.
Acknowledgements The  MITI  ( Mission pour les Initiatives Transverses et Interdisciplinaires) program  of the  CNRS  is gratefully acknowledged for RD doctoral financial support. This work was partially supported by the  Agence Nationale de la Recherche :  Labex ARCANE ,  CBH-EUR-GS ,  ANR-17-EURE-0003  and  ANR-22-CE07-0028-01. The  Initiatives de Recherche à Grenoble Alpes ( IRGA program ) is acknowledged for financial support. The  NanoBio ICMG  ( UAR 2607 ), is acknowledged for providing facilities for EPR analyses.
FE simulations of energy pile groups Saggu and Chakraborty (2016) conducted numerical studies of energy pile groups with spacings of 2. 5 and 4 m using the finite element software Abaqus. In these studies, the pile group size was 3 × 2, and the slab was considered to be composed of the same concrete as the pile. The slabs were 15. 5 m long, 1. 2 m thick, and 12. 9 m wide. All piles, 20 m long and 1 m in diameter, were wished into a single layer of dry Ottawa sand. During the simulations, an axial load of 6000 kN was continuously applied to the slab, and all the piles were first heated and then recovered. The temperature changes were 21 and 2. 8 • C at the heating and recovery ends, respectively. The parameters used in this study are listed in Table  3. A contact stiffness calculated as 401.
Variation of the Metabolome of the Three Life Stages Metabolome variations were analysed via a metabolomics study on the apolar extracts of five replicates of each of the three following life stages of A. armata: the tretrasporophyte (T), the gametophyte (G) and the gametophyte with developed cystocarpes (GC) collected in Banyuls-sur-Mer where the species is invasive. After data processing and filtering, the matrix obtained from the LC-ESI-HRMS analysis included 261 features. A PCA was performed to evaluate the global variation of algal metabolome. The two first principal components (PC) explained 56. 82% of the total variation. The scores plot showed a separation of GC from the two other stages along PC1, while T and G were separated along PC2 (Figure  1A ). Each stage harboured a different metabolomic fingerprint (p = 0.
Variation of the Metabolome of the Three Life Stages Metabolome variations were analysed via a metabolomics study on the apolar extracts of five replicates of each of the three following life stages of A. armata: the tretrasporophyte (T), the gametophyte (G) and the gametophyte with developed cystocarpes (GC) collected in Banyuls-sur-Mer where the species is invasive. After data processing and filtering, the matrix obtained from the LC-ESI -HRMS analysis included 261 features. A PCA was performed to evaluate the global variation of algal metabolome. The two first principal components (PC) explained 56. 82% of the total variation. The scores plot showed a separation of GC from the two other stages along PC1, while T and G were separated along PC2 (Figure  1A ). Each stage harboured a different metabolomic fingerprint (p = 0.
Analysis of Bacterial Communities Associated with A. armata Stages The variations of the bacterial communities associated with the same five replicates for each of the three algal stages for which metabolome and antibacterial activity were described in Sections 2. 1 and 2. 2 were then assessed. The metabarcoding sequencing of the 16S rRNA gene resulted in a dataset of 2,235,756 reads. After filtering and rarefying to the lowest number of sequences (24,710) (Table  S6 ; Figure  S11 ), the final dataset was finally composed of 3075 amplicon sequence variants (ASV) (Table  S7 ). The α-diversity indices were calculated. No significant differences were observed between the three stages for evenness (Table  2 ). In contrast, T displayed significantly higher values than the other algal stages for Chao1 and Shannon indices (Table  2 ).
Correlation between Metabolite and Bacterial Compositions in Algal Stages The correlation between metabolite and bacterial compositions of the five replicates of each of the algal stages was assessed by a Mantel test and a multiblock PLS-DA (DIA-BLO). The Mantel test showed no significant covariation between both datasets (r = 0. 16, p > 0. 05). In contrast, the scores plot (Figure  S14 ) and correlation scores (0. 93 on dimension 1 and 0. 89 on dimension 2; Figure  S15 ) of the multiblock PLS-DA analysis between the datasets showed good congruence and covariation between the bacterial community and the metabolite compositions for each algal stage. The three algal stages were well discriminated with the built model (CER = 0. 06, p = 0. 001). The first dimension discriminated T from the other groups, while the second component discriminated GC from G (Figure  S14 ).
Discussion Asparagopsis armata and A. taxiformis are two red algae known for their production of halogenated molecules  [26] [27] [28] [29]. Several studies reported differences in the concentration of some targeted halogenated molecules between the different life stages of the genus [6,  16, 17]. In this study, we first aimed at exploring the metabolome and antibacterial activity variation of the different life stages of A. armata. Secondly, we performed a bioguided fractionation in order to identify the molecule(s) responsible for the antibacterial activity. Thirdly, we studied the variation of microbiota composition between the three algal stages, and finally, we assessed the complex interactions between the chemistry and the microbiota datasets.
The Overall Analyses Performed Suggest Complex Interactions between the Host and Its Microbiota Although the unsupervised analysis suggested no significant relationship between chemical and bacterial community compositions, the supervised multi-omics analysis allowed us to identify discriminant variables that correlated with each other in both datasets. The discriminant ASVs identified in the model were correlated with both halogenated and non-halogenated molecules. These ASVs were mostly present in a low abundance in the algal samples, meaning that more than just the most abundant bacteria are potentially involved in chemical interactions with the host. In this study, among ASVs negatively correlated with compounds 1, 2 and 7 and with other halogenated molecules, three were abundant in GC: a Saprospiraceae variant (Bacteroidota), a Campbellbacteria (Patescibacteria) variant and a 0000069-P22 variant (Patescibacteria).
Data Treatment Data acquisitions were performed using Xcalibur 4. 1. 31. 9 (Thermo Fisher Scientific, Waltham, MA, USA). Raw data were converted to mzML files with MSconvert (version 3. 0, from Proteowizard library, Palo Alto, CA, USA). mzML files were uploaded and processed using the Galaxy web platform (version 3. 3)  [56, 57]. The workflow used for data pre-processing and used parameters are published on the Galaxy Workflow4Metabolomics platform at https://workflow4metabolomics. usegalaxy. fr/u/christelle_parchemin/w/ workflowparcheminalgae (publication date: 22 October 2021) and are available in Supplementary Method S2. Briefly, the pre-processing consisted of chromatographic peak detection (Galaxy Version 3. 12+galaxy0, accessed on 2 July 2022), followed by a peak grouping, a loess/non-linear "PeakGroups" retention time adjustment, a peak filling and "CAMERA" peak annotation.
Sequence Analyses The DADA2 package  [78]  (truncLen = c(260,230); maxN = 0; maxEE = c(2,2); truncQ = 2) was used to define amplicon sequence variants (ASV) and computed taxonomic affiliations using Silva database (release 138, December 2019). The dataset was filtered for singletons. Rarefaction curves of species richness were computed using the {phyloseq} R package and the ggrare function. The rarefy_even_depth function was used to subsample datasets. The estimate_richness function was used to compute alpha diversity metrics (Chao1, evenness and Shannon). Lastly, the tax_glom function was used to obtain abundances at different taxonomic ranks (from genus to phylum).
Multi-Omics The correlation between the variation of the metabolome and the variation of the bacterial community composition of each of the five replicates of the three algal stages was calculated via a Mantel test using Spearman correlation and a permutation test (mantel and vegdist {Vegan}). Correlation between the two datasets was further explored using the multiblock model DIABLO from the MixOmics package  [80]. DIABLO allows the creation of a model that maximizes the covariance between datasets in order to identify the most correlated and discriminating variables according to a defined factor. Here, the model allows the identification of most correlated ASVs and metabolites involved in the discrimination of each algal stage. Optimal number of components (2) was determined with the perf() function.
Metabarcoding 4. 7. 1. DNA Extraction, PCR and Sequencing DNA extractions were conducted on approximately 1 cm 2 of each of the five replicates of the three algal stages using the ZymoBIOMICS DNA Miniprep Kit (ref. D4300, ZYMO RESEARCH, Torrance, CA, USA) according to the manufacturer's protocol. The 16S rRNA gene of bacterial communities was amplified and sequenced using the variable V3V4 loops (341F: 5 -CCTACGGGNGGCWGCAG-3 ; 805R: 5 -GACTACHVGGGTATCTAATCC-3 ) [77]. Paired-end sequencing (300 bp read length) was performed at Dalhousie University (Integrated Microbiota Resource, Halifax, NS, Canada) on the MiSeq system (Illumina) using v3 chemistry according to the manufacturer's protocol. Raw sequence data are available in the Sequence Read Archive database (BioProject ID PRJNA948342).
Acknowledgments: This work was supported by the  Occitanie Region, France. The LC-Q/Orbitrap methods, developments and analyses were performed using the  Biodiversité et Biotechnologies Marines (Bio2Mar) facilities-Métabolites Secondaires Xénobiotiques Métabolomique Environnementale (MSXM)  platform at the  Université de Perpignan Via Domitia  (https://bio2mar-msxm. univ-perp. fr/).
4. 7. 7 Metabarcoding 4. 7. 1. DNA Extraction, PCR and Sequencing DNA extractions were conducted on approximately 1 cm 2 of each of the five replicates of the three algal stages using the ZymoBIOMICS DNA Miniprep Kit (ref. D4300, ZYMO RESEARCH, Torrance, CA, USA) according to the manufacturer's protocol. The 16S rRNA gene of bacterial communities was amplified and sequenced using the variable V3V4 loops (341F: 5 -CCTACGGGNGGCWGCAG-3 ; 805R: 5 -GACTACHVGGGTATCTAATCC-3 ) [77]. Paired-end sequencing (300 bp read length) was performed at Dalhousie University (Integrated Microbiota Resource, Halifax, NS, Canada) on the MiSeq system (Illumina) using v3 chemistry according to the manufacturer's protocol. Raw sequence data are available in the Sequence Read Archive database (BioProject ID PRJNA948342).
Acknowledgements This work was supported by the  French National Research Agency  ( CHARMMMAT ,  ANR-11-LABX-0039 ; fellowships to S. M. ),  CNRS ,  University Paris-Saclay  the  International Isotope Society-European Division  ( IIS-ED ), the  European Union 's  Horizon 2020 research and innovation program  under the  European Research Council  ( ERC-2019-COG-864576 ) and  Marie Sklodowska-Curie  ( GA N°675071 ). The authors thank  A. Goudet ,  S. Lebrequier  and  D. -A. Buisson  ( DRF-JOLIOT-SCBM, CEA ) for the excellent analytical support.
Acknowledgement We thank the participants to the  THETS Conference at Goldsmiths University (London, Sept. 2019), to the  Triangle Seminar (Lyon, Oct. 2019), to the AEE Conference on energy efficiency history and forecasting (online, March 2021) and to the  BETA-CHPE Seminar (Strasbourg, April 2021 ) for their insightful feedback on a previous version of this paper. Special thanks go to  Christophe Bonnery ,  Jean-Daniel Boyer ,  Nicolas Chaigneau ,  Patrick Criqui ,  Claude Diebolt ,  Pedro G. Duarte ,  Ragip Ege ,  Marion Gaspard ,  Rémy Guichardaz ,  Herrade Igersheim ,  Magali Jaoul-Grammare ,  Faustine Perrin ,  Thomas Turnbull ,  Lucas Vivier  and the editors and reviewers of this journal. The final stage of the research was supported by the  European Union  ( ERC ETRANHET StG # 101040475 ).
The simulation of the EPR spectrum was performed with the Easyspin software (http:// www. easys pin. org/). Crystallization, data collection and structure determination. Holo-MmNcsA at 10 mg/mL was crystallized under anaerobic conditions by the hanging drop procedure by mixing 1 µL protein with 1 µL reser-voir solution containing 0. 1 M HEPES pH 7. 5, 0. 2 M ammonium sulfate, 18% (w/v) PEG 4000, 10% isopropanol at 19 °C. X-ray data were collected on a single crystal at 100 K at the SOLEIL synchrotron (Saint Aubin, France) on the PROXIMA-2 beamline. Data were indexed, processed and scaled with XDS  97. Noticeable anisotropy in the diffraction was taken into account, and corrected by the STARANISO program, accessible by the server http:// stara niso. globa lphas ing. org. The P. horikoshii apo-TtuA protein (PDB code 3VRH), in which the C-terminal end was truncated and amino acids different from those of NcsA were changed to alanine, was used as a model to solve the structure by molecular replacement with PHASER  98.
Acknowledgements We thank SOLEIL for provision of synchrotron radiation facilities,  Martin Savko  for assistance in using beamline PROXIMA-2, the  French EPR CNRS Facility ,  Infrastructure de Recherche Renard  ( IR 3443 ), the  Vanderbilt Center for Structural Biology  for providing the pBG102 plasmid,  Thibault Fogeron  and  Yun Xu-Li  for help in the synthesis of (N-acryloylamino)phenyl)mercuric chloride,  Ludovic Pecqueur  for maintenance of the glove boxes,  Simon Arragain  and  Mohamed Atta  for fruitful discussions.
Funding This work was supported by the  Fondation de l'Orangerie for individual Philanthropy  and its donors as well as the  French State Program 'Investissements d' Avenir' (Grants "LABEX DYNAMO ",  ANR-11-LABX-0011  and " Integrative Biology of Emerging Infectious Diseases ",  ANR-10-LABX-62-IBEID ), the  CNRS  and the  Institut Pasteur.
Acknowledgements The authors acknowledge support from  CNRS ,  Agence Nationale de la Recherche  (grants  ANR-14-CE05-0010 ,  ANR-15-CE05-0020 ,  ANR-17-CE11-0027 ,  ANR-18-CE05-0029 ), and  Region PACA ,. The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" program. M. M. , C. L. , and V. F. are members of the  French Bioinorganic Chemistry group  (http://frenchbic. cnrs. fr). We also acknowledge support from  Fundação para a Ciência e Tecnologia  (grant  PTDC/BII-BBF/2050/2020  and fellowship  DFA/BD/7897/2020 ),  MOST-MICRO-ITQB unit  ( UIDB/04612/2020  and  UIDP/04612/ 2020 ) and Associated Laboratory  LS4FUTURE  ( LA/P/ 0087/2020 ), and support from the  MOSBRI  project, which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement  N°101004806.
Computational methods DFT calculations were done using the ORCA 5. 0. 3 software  64. The geometry optimization calculations were performed using the PBE functional, the SDD pseudopotential (with 19 valence-electrons treated explicitly) for the metal atoms, and the def2-TZVP basis set for carbon and oxygen atoms. Dispersion corrections were added to the functional used in the D3 framework proposed by Grimme with the addition of the Becke-Johnson damping (D3BJ) in all cases  65. ðCO 2 À Þ ad M (M for Au and Cu) has been designed as a model of a metallic NP in interaction with one CO 2 •-radical with solvent effects represented by a dielectric continuum model. The orientation and nature of the interaction between the radical and NP have been probed by several quantum chemical analyses. Numerical frequencies were calculated to ensure the structures corresponded to energy minima.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  ( 11975122 ,  22006067 , and  21906083 ),  Jiangsu Province Science Fund for Distinguished Young Scholars  ( BK20230032 ),  Fundamental Research Funds for the Central Universities  (No. NE2020006 ),  Scientific and Technological Innovation Special Fund for Carbon Peak and Carbon Neutrality of Jiangsu Province  ( BK20220026 ), and  Jiangsu Funding Program   for Excellent Postdoctoral Talent  ( 2022ZB197 ). We thank  Jean-Philippe Larbre  for his help during the experiments at ELYSE.
The acquisition time was 60 s with an accumulation of two spectra (average spectral resolution of 0. 4 cm -1 in the range of 80-850 cm -1 ). GIXRD scans were performed on a Malvern PANalytical Aeris diffractometer (Cu radiation) working at 30 kV (10 mA) and setting a grazing incidence angle of 0. 8°. High-resolution transmission electron microscopy (HRTEM) and highangle annular dark-field imaging (HAADF) studies were carried out in a Thermo Scientific TALOS F200X G2 analytical microscope working at an accelerating voltage of 200 kV. A Gatan Imaging Filter (GIF) Continuum system fitted in the Talos microscope was used for spatially-resolved EELS analysis in scanning (STEM) mode. STEM-EELS 2D spectrum image (SI) data were acquired using a 2. 5 mm diameter aperture and 0. 05 eV/channel energy dispersion.
Acknowledgements  A. J. Santos  would like to thank the  University of Cádiz  and the  Spanish Ministerio de Universidades  for the concession of a "Margarita Salas" postdoctoral fellowship funded by the  European Union -NextGenerationEU  ( 2021-067 # 9663/PN/MS-RECUAL/CD ). University of Cádiz and IMEYMAT are also acknowledged by financing the mutual facilities available at the  UCA R&D Central Services (SC-ICYT) , the  UCA  project references " PUENTE PR2020-003  and  PR2022-027 " and " OTRI AT2019/032 ", and the  IMEYMAT  project reference " LÍNEAS PRIORITARIAS   PLP2021120-1 ". This work was supported by the  Spanish State R&D project (Retos y Generación de Conocimiento)  ref. PID2020-114418RBI00. The regional government of Andalusia with  FEDER  co-funding also participates through the projects  AT-5983 Trewa 1157178  and  FEDER-UCA18-10788.
J. J. Jiménez  and F. M. Morales acknowledge " Fondo Social Europeo y la Consejería de Transformación Económica, Industria, Conocimiento y Universidades " of this same institution ( 2021-001/PAI/PAIDI2020/CD ). This work was partly supported by the  French RENATECH network ,  FEMTO -ST technological facility, by the  Region Bourgogne-Franche-Comté  and by  EIPHI Graduate School  (Contract " ANR-17-EURE-0002 ").
Materials characterization Powder X-Ray diffraction (XRD) patterns were acquired on a Bruker D8 Advance diffractometer with a Cu Kα source equipped with a Lynxeye one-dimensional detector. The diffractometer operated at 40 kV and 40 mA with a Cu Kα source with wavelength of 1. 54 Å. Bright-field transmission electron microscopy (TEM) images of Cu and CuGa NPs were acquired on a Thermo-Fisher Tecnai-Spirit at 120 kV. 10 µL of as-synthesized NPs were dropcasted on a carbon coated copper TEM grid (Ted Pella, Inc. ) to prepare the sample for imaging. Particle size distribution was evaluated with the use of ImageJ software from datasets of 300 particles from three different images each. High-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) coupled with energy-dispersive X-ray spectroscopy (EDXS) was performed using a FEI Tecnai Osiris TEM in STEM mode with an accelerating voltage of 200 kV.