dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization The crystalline phase, proportions and particle sizes were examined with PXRD using a Bruker D8 Discover diffractometer (Bruker AXS, Karlsruhe, Germany) with CuKα1 radiation (λ = 0. 15, 406 nm) selected with a Ge (111) monochromator and LynxEye detector. Phases were identified using the crystallography open database  [21]  in Bruker EVA version 6. 0. Diffraction patterns were fitted using the Rietveld method in TOPAS version 6  [22]  to obtain phase weight percentages and crystallite sizes (Scherrer method with full profile fit, k = 0. 89). The peak shape was modeled using a fundamental parameters approach and the cubic lattice parameter, scale and Lorentzian size broadening were refined for three phases: copper (I) nitride (COD 1010167), cuprite (COD 1000063) and metallic copper (COD 9013014).
Density functional theory The atomic M-N-C structures and formation energy were obtained from Karmodak et al. , assuming a single sheet of graphene as the surrounding carbon structure. 41  Here, an *OH intermediate was added to the structures and spin polarised for the clean and *OH structures. For relaxation computations GPAW code was used, 127,128 with the following settings: (4, 4, 1) kpoint, a grid spacing of 0. 18, with the BEEF-vdW functional, 129 and relax the structures to below 0. 05 eV per atom. Aer relaxing the structures, the OH binding energy was calculated from the following equation:  DG *OH ¼ E *OH À E * þ 1 2 E H 2 À E H 2 O.
Acknowledgements The authors acknowledge nancial support from the  Engineering and Physical Sciences Research Council (EPSRC)  ( EP/ W031019/1  and  EP/S023259/1 ), the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 866402 ). A. P. thanks the  EPSRC Centre   for Doctoral Training in the Advanced Characterisation of Materials  (grant number  EP/ L015277/1 ). A. B. acknowledges support from the  Carlsberg foundation  (grant no. CF21-0114 ). J. B. acknowledges nancial support from  Imperial College London  through the  Imperial College Research Fellowship.
AFM-IR spectral maps were collected along with AFM topography and deflection images for three fixed wavenumbers of interest: 1600 cm -1 , 1708 cm -1 and 1742 cm -1 corresponding to free fatty acids in ionized forms/beta-glucans, free fatty acids (FFAs) and triacylglycerides (TAGs), respectively. All measurements were conducted in a contact mode, with 256 lasers pulses produced to be averaged as a resulting data pixel value. In total, 30 spectral maps (3 maps per each area, 5 areas per each sample, 2 samples), along with the same number of corresponding AFM topography and deflection maps were collected. It took around 30 min to measure one batch of AFM-IR image data (topography, deflection, absorption) per area.
Data analysis All spectroscopic data were analyzed using in-house developed algorithms written in Python. For the assessment of influence of different drying times on ATR-FTIR spectra of M. circinelloides, ATR-FTIR spectra were preprocessed in the following way: (1) Second derivative was calculated using a Savitzky-Golay (SG) filter  [45]  (window size = 15, order of a polynomial to be fitted = 2); (2) Extended multiplicative signal correction (EMSC)  [47]  was used to remove baseline shift and scaling effects, as well as first-and second-order polynomial dependency. Aforementioned pre-processing steps were applied to ATR-FTIR spectral data within the following spectral regions: from 4000 to 700 cm -1 (whole spectra), from 3050 to 2800 cm -1 and 1800 cm -1 to 1700 cm -1 (lipid region), 1700 cm -1 to 1200 cm -1 (combined protein and mixed region), and 1200 cm -1 to 700 cm -1 (polysaccharide and polyphosphate region).
Design matrixes were used to calculate estimates for both effects and their interactions. Resulting partitions were subjected to PCA together with the residual matrix comprising the information not assigned to any effect. For the comparison of HTS-and ATR-FTIR spectra, ATR-FTIR spectra of M. circinelloides corresponding to the first day of storage were used. The following preprocessing steps were employed: (1) An intersection of HTS-and ATR-FTIR wavenumbers was obtained by aligning, both, HTS-and ATR-FTIR datasets to perform peak-to-peak comparison; (2) EMSC correction (linear and quadratic effects) was applied to both datasets separately; (3) The most prominent peaks were selected using find_peaks method from the Signal Processing module of SciPy, Python's open-source library for scientific computing  [49].
Removal of noisy and non-informative wavenumber regions For each dataset, certain wavenumbers were excluded to avoid noisy and non-informative absorbance signals present at certain wavenumbers from having a detrimental effect on the subsequent data analysis. As such, there is a strong water-absorbing region with no lipid-related signatures above 3200 cm -1 , and absorbance values below 1000 cm -1 contained negative values due to the acquisition errors. Therefore, a typical working range for every FPA-FTIR hyperspectral image was selected between 3200 and 1000 cm -1.
Removal of outliers Spectral data were examined for outliers in each dataset that corresponded to noisy spectra and dead pixels. For each spectral map, a data pixel was classified as an outlier if its value was more than three scaled median absolute deviations (MAD) away from the median absorbance value of the spectral map. Furthermore, pixels that maintained anomalously large values all over the hyperspectral dataset were also detected as outliers. Values of outlier pixels were replaced by the mean values of the neighboring nonoutlier pixels for each spectral map separately.
FPA-FTIR mage segmentation FPA-FTIR spectral images that correspond to the most prominent lipid-and protein-related peaks such as 3010 cm -1 , 2955 cm -1 , 2925 cm -1 , 2855 cm -1 , 1742 cm -1 , 1708 cm -1 , and 1650 cm -1 , were selected for subsequent image segmentation to separate hyphae pixels from non-informative slide background pixels. To do that, selected FPA-FTIR spectral data were scaled (StandardScaler from sklearn preprocessing) module with default parameters was employed) and then subjected to PCA analysis. Principal components corresponding to 99% of total variance were processed with k-means clustering, which utilized k-means++ initialization scheme to detect two clusters.
DSAE correction of FPA-FTIR spectral data and image comparison A descattering autoencoder  [49] , which was pretrained on a set of infrared spectra of M. circinelloides that were scatter-corrected by the Mie Extinction Extended Multiplicative Signal Correction (ME-EMSC) algorithm, was used to obtain scatterfree FPA-FTIR spectral data. Only spectra that correspond to fungal hyphae were processed by the DSAE to yield smoothed and scatter-free spectra. Resulting data were reshaped as images and plotted alongside each other. A scale was adjusted for each image separately to make the chemical differences within a spectral map more pronounced. Furthermore, a ratio map was calculated for each area of interest using spectral maps at 1742 cm -1 and 1650 cm -1.
For each area of interest, an AFM topography image at 1600 cm -1 served as a fixed image, and the other topography images-as moving ones. A rigid transformation that accounts for translation and rotation of the moving image relative to the fixed one was estimated using the register function of the StackReg Python package for intensity-based image registration. After that, moving topography images, along with the corresponding deflection and absorption images, were transformed according to the resulting geometric transformation using the transform function from the same package. d. Scaling: Negative topography values might occur due to the Z-position of the scanner not been adjusted properly during the scanning. To compensate for it, topography values were shifted towards zero so that all values became positive.
CRediT authorship contribution statement FL and RV conceptualized and formulated the original ideas for this work. MB carried out the research and investigation process, specifically performing the experiments, and data/evidence collection. The experimental part was supervised and validated by FL and RV. MB collected and formatted the experimental data and bibliography necessary for the writing of the manuscript. The preparation of the first draft of the manuscript was done by RV. FL and MB participated in the writing and correction of the manuscript.
Acknowledgements This study benefited from the support of the project  OxCyCat-CO 2 ANR-17-CE06-0009  of the  French Agence Nationale de la Recherche (ANR, France)  for the PhD fellowship of  Mr Matthieu Balas , and more generally for funding this work. The authors want also to acknowledge the  Centre National de la Recherche Scientifique (CNRS, France)  and  Sorbonne Université.
1 H-NMR Analysis and Degree of Substitution (DS) A 400 Hz Bruker 1 H NMR spectrometer was utilized to obtain the proton nuclear magnetic resonance ( 1 H NMR) spectra at 65 °C. The powder biopolymer was dissolved in D2O (deuterium oxide) at 25 °C. Subsequently, MestReNova software (Version 2. 9) was used to analyze the 1 H-NMR spectra of all samples. The calculation of the degree of substitution (DS) for the derivatives was conducted using 1 H-NMR analysis, according to the following Equation (1)  [22].
1 H-NMR Analysis and Degree of Substitution (DS) A 400 Hz Bruker 1 H NMR spectrometer was utilized to obtain the proton nuclear magnetic resonance ( 1 H NMR) spectra at 65 • C. The powder biopolymer was dissolved in D 2 O (deuterium oxide) at 25 • C. Subsequently, MestReNova software (Version 2. 9) was used to analyze the 1 H-NMR spectra of all samples. The calculation of the degree of substitution (DS) for the derivatives was conducted using 1 H-NMR analysis, according to the following Equation (1)  [22]. DS = ACH 3 + ACH 2 (2(n -1) + 3) × AH anomeric (1) In the given formula, A represents the area under the picks corresponding to CH 3 , CH 2 groups, and the anomeric proton (H). Meanwhile, n refers to the carbon atom number in the alkyl chain, which in this case is 8.
Scanning Electron Microscopy (SEM) Scanning electron micrographs (SEM) of KC and KCRs were taken using scanning electron microscopy (FEI Quanta 250) at 25 • C. The accelerating voltage used ranged from 10 kV to 12. 5 kV, with SEM images taken at various magnification levels including 1000×, 6000×, and 16,000×. The analysis was carried out using xT microscopy software (Version 4. 1. 0. 1864).
Kalair et al. [37]  conducted a comprehensive analysis using the TRNSYS software to simulate an evacuated glass tube SWHS. Their study focused on determining the optimal configurations and sizes of key components, such as heat exchangers, solar collectors, and tank volumes, to achieve efficient yearly performance. Through their parametric analysis, they concluded that for a single-family dwelling's heating demands to be met effectively by the system, it should have a collector area of 6 m 2 along with a tank volume and height of 0. 42 m 3 and 1. 2 m, respectively. In addition, Al-Mamun et al. [38]  conducted a comprehensive review on SWHSs which examines the design aspects of their major components, including the solar thermal collector, storage tank, heat exchanger, heat transfer fluid, and absorber plate.
A smooth copper tube with an internal and external diameter of 9. 5 mm and 12 mm, respectively, and a total length of 1000 mm was used to construct the solar collector. Using experimental data, a TRNSYS model was created, simulated, and validated. During the studies, temperature and flow rate data were gathered simultaneously to evaluate the effectiveness of the solar collector. The water flow rate was kept at 0. 75 L/min, while the solar collector's intake temperature stayed steady at 37. performance of a SWHS equipped with a flat plate collector  [77]. The study focused on evaluating different flow rates and their impact on achieving higher water temperatures. The results indicated that at a flow rate of 5. 3 L/min, the maximum temperature reached was 51. 4 • C while at 6. 5 L/min it was slightly lower at 49 • C.
Consequently, based on these studies, it can be concluded that for optimal performance in terms of attaining higher water temperatures, lower flow rates are favorable. Through the use of a parallel tube configuration, T. R. Desisa  [78]  concentrated on experimentally examining the heat transfer properties of a solar flat-plate collector (SFPC) using Al2O3/water as the working fluid. The effectiveness of the SFPC was assessed using nanoparticles with mean diameters ranging from 15 to 50 nm and at various mass flow rates (0. 1, 0. 2, 0. 3, and 0. 4 kg/min). Additionally, using ANSYS Fluent 17. 1 software, a computational fluid dynamics (CFD) model was used to numerically investigate steady and laminar flow in the SFPC while taking into account nanofluids (CuO/water, Ti2O/water, and Si2O/water) that were not tested in the trials.
The author examined the coefficient of heat transfer, represented by the Nusselt number, for various nanofluids compared to the SFPC using water alone. At a mass flow rate of 0. 2 kg/min, the Nusselt number increases by 4. 1423 %, 3. 3429 %, 3. 3011 %, and 0. 6887 % for 3 % volume concentration of CuO, Al2O3, Ti2O, and Si2O, respectively. CuO and Si2O nanoparticles exhibit the highest and lowest improvements, respectively, compared to other nanoparticles. Furthermore, the study indicated that the pressure drop remains negligible under all tested conditions. Sint et al. conducted a theoretical analysis to examine the efficiency of solar flat plate collectors that utilize nano-fluids  [79]. The researchers developed a mathematical model and used a MATLAB code to calculate the efficiency of these collectors specifically designed for domestic hot water heating systems in a city located in Myanmar, taking into consideration local weather conditions.
Analysis of previous works on SWHSs Since its inception, SWHS has been the subject of numerous studies aimed at improving its performance and maintaining optimal operation. Therefore, the purpose of this section is to review and analyze several research studies that have been conducted on SWHS over the years. Table  1  provides a summary of the various studies that have been carried out on SWHS. Based on the data presented in Table  1 , it is evident that there is a substantial body of research focused on SWH technology. These studies encompass a wide range of approaches, including literature reviews, modelling of SWH systems, analytical investigations, parametric analyses, case studies, numerical simulations, and experimental testing. The overarching objective of these research endeavors is to explore SWH technology comprehensively, aiming to enhance its performance and elucidate its implications for economic and environmental considerations.
Transmission Electron Microscopy Transmission Electron Microscopy (TEM) is used to analyse the size, the size distribution, and the shape of SPIONs. The TEM is a CM200-FEI operating at 200 kV (point resolution 0. 27 nm). A drop of the solution was deposit on a copper grid with a carbon film and dried to evaporate the solvent. After the analysis, the size distribution is calculated using free software ImageJ.
The r 2 value is 58 s -1. mmol -. L, 88 s -1. mmol -. L and 158 s -1. mmol -. L for SPIONs_CA1, SPIONs_CA2 and SPIONs_CA3 respectively. These values are in accordance with other SPIONs with equivalent diameters  [47, 48]. From SPIONs_CA1 to SPIONs_CA3, the increase in r 2 values is mainly due to the increase in their hydrodynamic diameters because their saturation magnetizations are of the same order. To complete the relaxation data of these nanoparticles, the r 1 value (longitudinal relaxation) is measured in the same conditions (same concentrations at 60 MHz) and is equal to 22 s -1. mmol -. L, 31 s -1. mmol -1. L and 23 s -1. mmol -. L for SPIONs_CA1, CA2 and CA3 respectively (Fig. S9 ). The values of r i =1, 2 are compared with the values of Endorem®, a commercial contrast agent composed of a superparamagnetic iron oxide core (around 5 nm) and an organic layer of dextran.
Acknowledgements The authors gratefully acknowledge the  "Plateforme de RMN de l′Institut Jean Barriol, Université de Lorraine ". In addition, the authors knowledge the center of Microscopy,  X-Gamma  and  Magnetism of Institute Jean Lamour. Supported by the " SONOMA " project co-funded by " FEDER-FSE Lorraine et Massif des Vosges "  2014-2020 , a  European Union Program.
Theoretical production is calculated using the average irradiance over one-year G average arriving at the solar panels with the associated inclination and orientation, multiplied by the total peak power of the installation. Through the ''JRC Photovoltaic Geographical Information System (PVGIS) (European Commission," n. d. ), the irradiance is recoverable for any location, inclination and orientation in Europe and North Africa following the calculation methods detailed by Amillo et al. (2014),  Huld et al. (2012 ), (IEEE Photovoltaic Specialists Conference, n. d. ) 40 2014 , Denver, and Urraca et al. (2018b). In France, irradiance varies from 1. 253 kWh/m 2 /year in the North to 1. 937 kWh/m 2 /year in the South. The data from SARAH  (Müller et al. , 2015) , CMSAF  (Mueller et al. , 2009) , ERA5 and COSMO  (Urraca et al.
Acknowledgments This work is part of the  UNIVEERS  project, co-financed by the  European Union  within the framework of the FEDER-FSE 2014-2020 and by the  Nouvelle-Aquitaine region  [grant number  2016-001948420 ]. The authors are grateful to the BDPV and PVGIS sites for making their data available; otherwise, the study would not have been possible.
To assess the goodness of the ts, both the R factor (% R) and the reduced c 2 (c v 2 ) were minimized, ensuring that the data were not over-t. An increase in the number of variables is generally expected to improve the R factor ; however c v 2 may go through a minimum and then increase, which is an indication that the model is over-tting the data. 40 The best t models were determined using a grid search with xed values for path coordination numbers by employing Larch, the Python implementation of Artemis. 41 he X-ray photoelectron spectroscopy (XPS) experiments were carried out using a Kratos Axis Ultra DLD spectrometer, equipped with a monochromatic Al Ka X-ray source (1486. 6 eV) operating at 225 W (15 kV, 15 mA). The instrument base pressure was 5 × 10 -10 torr and a charge neutralizing system was used for all acquisitions.
Acknowledgement The researchers would like to acknowledge the  Water Technology Initiative (WTI)  scheme,  Department of Science and Technology (DST), New Delhi  under  INSPIRE faculty award -2018  ( MS-146 ),  India , and the  French Government (Eiffel Excellence Scholarship 2022)  for providing the financial support for this research work. The  CNRS , the  Chevreul Institute  ( FR 2638 ), the  Ministère de l'Enseignement Supérieur et de la Recherche , the  Région Hauts-de-France , the  FEDER , and the MEL are acknowledged for supporting this work. Région Hauts-de-France  and  Fonds Européen de Développement Régional (FEDER)  are gratefully acknowledged for funding the  MFP-3D microscope under Program "Chemistry and Materials for a Sustainable Growth ". Dr. Pardis Simon  is acknowledged for XPS measurements.
Acknowledgments:  Karla Begonia Cervantes Diaz  acknowledges  CONACYT Mexico  for the grant financing her PhD thesis in France. The authors would like to thank  D. Cot  and  B. Rebière  from  IEM Montpellier  for SEM observations and EDX analyses and  Christophe Escape  for reflectance spectroscopy measurements performed at  CNRS Font Romeu (PROMES).
The software makes it possible to select domes geometries that make it possible to carry out geodesic trajectories, which we wish to apply to carbon fibers. It also makes it possible to select the construction of tanks with a single base. The result is simulation of winding, type of patterns made and all the parameters useful for manufacturing. The tank is, therefore, virtually coiled as can be seen below (Figure  13 ): The simulation generates a tank geometry, that is, the geometry of the mandrel, that is, the liner, and the configuration of the composite layers as well as the winding parameters. Text files can be used to create the geometry and the laminate as we will see later. These files are those that interest us in order to carry out the modeling. Cadwind also provides all the mandrel rotation speed and displacement parameters for the carriage unwinding the fiber and the parameters used directly in the programming of the tank manufacturing process.
In the data exported from Cadwind, we extract files that define the laminates according to r and z as well as the initial geometry of the mandrel, that is to say the liner. We enter the profile of the dome point by point in the Catia sketch, which we smooth. In addition, the base is defined using a Cosmosworks module. The composite winding is represented by a shell corresponding to the outer surface of the liner and base assembly as can be seen in the images on the following page. The CAD files will be imported into the finite element software to serve as support for the mesh (Figure  14 ). HyperMesh is a meshing tool of Hyperworks suite, a tool acquired by ENSAM for this project. Modeling of the tank has two main objectives: to tend toward an optimization of the geometry of this one in order to minimize the mass by allowing respect of specifications in terms of mechanical resistance and to demonstrate the feasibility of this modeling under HyperMesh (then the processing in static study of this model under OptiStruct), and thus to validate the acquisition of software by the laboratory for applications to composites in particular.
Acknowledgements D. B. gratefully acknowledges the  EU  through the  MSCA-ITN-ETN  (project:  PHOTOTRAIN , no. 722591 ),  MSCA-RISE  (project:  INFUSION , no. 734834 ; VIT, no. 101008237 ), and the  University of Vienna  for generous financial support. We thank the NMR and Mass service centers at the  Faculty of Chemistry of the University of Vienna  for the spectroscopic and spectrometric characterization of the products prepared in this work. In particular, we thank  Ass. Prof. H. Khalig  for the constant support in the NMR characterization and interpretation of the data. We thank  The Core Facility Multimodal Imaging of the Faculty of Chemistry  for the confocal microscope pictures. We acknowledge  Raimund Schubert  for manufacturing the custom glass microreactor.
Ex Situ X-ray Total Scattering Experiments. Samples were prepared by the temporal sampling of reaction aliquots into 3 mm NMR tubes sealed under argon atmosphere. We also used 2 mm glass capillaries from Hilgenberg for sampling, but those were prone to breaking upon heating to 80 °C and in general did not result in higher data quality. The samples were measured either using beamline P21. 1 at DESY in Hamburg, Germany or using beamline ID15A at ESRF in Grenoble, France. 54 At ESRF, data were collected at 80 °C (using a nitrogen cryostream), in rapid acquisition mode, using a 2D Pilatus CdTe 2M detector (1679 × 1475 pixels and 172 × 172 μm pixel size) with a sample-to-detector distance of 264 mm. The incident wavelength of the X-rays was λ = 0. 1441 Å (66. 05 keV). Calibration of the experimental setup was performed using a Silicon standard sample.