dataesr/scientific-paragraphs-categorization huggingface dataset:
Reliable Fe/Co occupancies were obtained from refining Co-source data solely. In other words, the in-house powder pattern has sufficient contrast to distinguish the neighbouring elements of Fe and Co, when taking into consideration the resonant scattering contributions f 0 and f 00. This may be expanded to other elements, e. g. Fe/Mn and Fe/ Ni, allowing in-house determination of site occupancies in spinel structures and other transition metal oxides with mixed occupancies, e. g. battery materials. Here neutron diffraction data improved the robustness of the model, even if only six Bragg peaks were included. Part 2 was focused on the reproducibility of the synthesis, data collection and refinements of the low-Q-range neutron data. Comparing multiple measurements on the same sample clearly showed that the low-Q-range neutron data collection and Rietveld refinements are highly reproducible.
Acknowledgements We gratefully acknowledge neutron beamtime at  PSI  and  IFE  and support by the beamline staff  Emmanuel Cane  ´vet and  Denis Sheptyakov  at  PSI  and Magnus Sørby at  IFE. Affiliation with the  ESS lighthouses SMART  and  Q-MAT  as well as the  Center for Integrated Materials Research (iMAT) at Aarhus University  is gratefully acknowledged.
(2019). -The biochemical and physiological responses to stress rely on modifications of the transcription patterns. The diversity of experimental conditions, including taxon, growth and stress conditions, although providing complementary data, often prevents the determination of common modules in the responses to different stresses. Ait-Mohamed et al. analyzed RNAseq datasets generated under varying stress using Weighted Gene Correlation Network Analysis and identified 28 modules of coexpressed genes that reveal the fundamental principles on which co-regulation of genes expression in P. tricornutum relies. -The obtention of efficient biomolecule production platforms: Despite the recognition that microalgae, including diatoms, synthesize many molecules of interest [e. g. ,  Mimouni et al. (2012) ] and the availability of tools for the genetic improvement of certain taxa  (George et al.
CONCLUSIONS The research on diatoms and Stramenopiles is very dynamic. Since the World War II, more than 24,000 publications have been published with a title containing either word. Circa 10% of these articles are dedicated to their metabolism (Figure  1 ). This theme issue groups 11 articles describing the most recent research on the topic. These new data have been nicely welcomed by the scientific community with more than 30,000 views (https://www. frontiersin. org/research-topics/11978/metabolic-regulation-ofdiatoms-and-other-chromalveolates -consulted on 2022 04 06) and 15 citations (WOS, all database, consulted on 2022 03 06).
mol -1. K -1 ] Ra Rayleigh number [-] T Temperature (K) t Time [s] U Speed [m. s -1 ] V Volume [m 3 ] Greek letters µ Dynamic viscosity [kg. m -1. s -1 ] ɛ Emissivity [-] α Absorptivity [-] β Dilatation coefficient [K -1 ] θ Wind direction [degree] σ Stephan-Boltzman constant [W. m -2. K -4 ] υ Kinematic viscosity [m 3. This paper addresses the development of a dynamic model able to predict the evolution of the temperature culture medium in a PBR system integrated with a building façade, depending on its overall configuration and the external environmental conditions. It investigates the SymBiO 2 -Box, a semi-industrial prototype on which experimental data has been collected at different seasons. The paper describes in detail the mathematical model and related parameters. An experimental validation with the SymBiO 2 -Box is then presented.
Online measurement and data acquisition system A weather station (Vantage Pro 2, Davis, USA) was used to measure the outside air temperature [°C], global radiation [W. m -2 ], wind speed [m. s -1 ] and direction, and relative air humidity (RH)  [%]. The sensor was vertically oriented towards the south, parallel to the PBR's optical surface. All data was collected in a text file and input to the thermal model (details below). K-type thermocouple probes were installed at different points in the SymBiO 2 -Box to measure the temperature of the steel plate, the ACC and the water circulating in the double jacket for thermal regulation of the culture. The temperature in the culture was monitored with a pH probe (InPRO 4800i SG 120/M300, Mettler Toledo, Germany), which was also used for pH regulation (M300 multiple channel transmitter Mettler Toledo, Germany).
General approach A model describing thermal exchanges between different components of the system was set up, as described in the section below. This followed a similar approach to the one used by  (Goetz et al. , 2011) , who developed a thermal model for describing the temperature evolution under different operating conditions in a horizontal flat-panel photobioreactor. This model was able to simulate the time evolution of the culture medium temperature depending on environmental conditions generated by a meteorological database  (Meteonorm, www. meteonorm. com). As with  (Goetz et al. , 2011)  and  (Pruvost et al. , 2019) , the system was divided into sub-systems which were modeled through thermal balances. The model is described in the section below. The black indian ink experiments were used to calibrate the model parameters and verify most of the assumptions.
Similarly, a correction factor was introduced to represent the actual speed of the air flow in the ACC, and then consider where this speed was lower than wind speed measured outside. This was obtained by introducing a correction factor F wind. The two empirical parameters have been identified from the experimental data using two different datasets. The first one includes 115 measurements (from May 5th to May 10th) where the ACC was kept closed to estimate Floss. The second one includes 375 measurements (from May 10th to May 25th) with the ACC always open to estimate Fwind. The value of those parameters was optimized by the function fminsearch from MATLAB® software with the Mean Absolute Error (MAE, see below) of the ACC temperature as the criterion to be minimized. The optimized value was finally rounded 3 decimal places.
A value of 4. 000 W. K -1 and 0. 025 was obtained for Floss and Fwind respectively. The parameter's values were finally validated using two other datasets with 1318 (January 26 th to March 22 nd ; ACC closed) and 654 (March 24 th to April 25 th ; ACC opened) data points respectively. In the following, these parameters, which are dependent of the biofaçade design, were then set as constant for the rest of the study.
Model inputs Meteorological input data (solar radiation, wind speed, exterior ambient temperature and air humidity) were recorded during the experiments (see model validation), or sourced from the meteorological database (simulation section). In the latter case, weather data averaged over the previous 10 years in Nantes (47°13' 6. 136" N 1°33' 13. 036" W, France) were taken from a weather database  (Meteonorm, 2017). Note that q is the total radiation, i. e. the sum of direct and diffuse solar radiation.
Estimating energy consumption for thermal regulation To illustrate the impact of different ACC configurations on the temperature of the culture, various simulations were conducted for the same outdoor conditions. The simulated day was January 3, as obtained from a meteorological database  (Nantes, France). This day was specially retained as a case study due to the combination of high light received on the PBR surface and low exterior temperature (winter season with clear sky). The results are given in Fig. 6. They illustrate the influence of closing the ACC to reduce cooling of the PBR by the airflow inside. The culture temperatures were higher, almost 10 °C more than with the ACC kept open. The effect of introducing dynamic control of the ACC is also demonstrated. In our case, the ACC was open during the day between 10 am and 8 pm, then closed.
Acknowledgments This work was supported by the  FUI SYMBIO2  industrial project which aims to develop building façades with integrated photobioreactors for producing microalgae biomass in industrial and urban environments, while inducing symbiotic thermal and chemical exchanges with the support building. The project involved architects (X-TU), providers of technological solutions for waste treatment (Séché Environnement) and microalgae cultivation ( AlgoSource Technologies ), metal work specialists for design of the  PBR (Viry) , specialists in energy and environmental building integration (Oasiis, LHEEA), and an expert academic partner in PBR engineering (GEPEA, UMR CNRS).
Acknowledgements We gratefully acknowledge '' Valsovit  project" with the contribution of the  Emilia Romagna Region  through  POR FERS 2014-2020  funds ( European Regional Development Fund)  and the  University of Bologna. The company CAVIRO s. r. l (namely  Dr. Rosa Prati  and  Ing. Giovanni Marani ) deserve a special acknowledgement for furnishing the waste ethanol real mixtures. IR gratefully acknowledges the use of  HPC resources of the ''Pôle Scientifique de Modélisation Numérique" (PSMN)  of the  ENS-Lyon, France.
Selection of candidate reference genes. Twelve candidate genes were selected from literature and algae homologous sequences were then identified using BLASTn searches within the Klebsormidium nitens NIES-2285 genome project database v1. 1 (http:// www. plant morph ogene sis. bio. titech. ac. jp/ ~algae_ genome_ proje ct/ klebs ormid ium/), including the genome and the transcriptome of K. nitens (Table  1 ). Sequence with at least 70% identity between the species of origins and K. nitens were selected. Primers sequences were designed using Prim-er3Plus (http:// www. bioin forma tics. nl/ cgi-bin/ prime r3plus/ prime r3plus. cgi) (Table  1 ). Primer specificity was assessed via RT-qPCR, melting curve analysis (-dFluorescence/dTemperature) and 2% (w/v) agarose (> 99. 8%, Table  1. Candidate reference genes and associated primers set used in RT-qPCR.
Gene name Gene symbol Gene ID Gene ID Homolog and species Eurobio Scientific, Courtaboeuf, France) gel electrophoresis of the qPCR products (Supplementary Fig. S1 ). The PCR amplification efficiency (E) for each primer pair were calculated using the LinRegPCR software  41. Analysis of gene expression stability and ranking of candidate reference genes. The stability of the candidate genes was evaluated in silico using four algorithms, GeNorm  35  , NormFinder 34 , BestKeeper  37  , and the ∆Cq method  36. GeNorm calculation, as Normfinder, are based on fold change values, whereas BestKeeper and the ∆Cq methods are based on raw Cq values. GeNorm calculates the average pairwise variation of gene expression of each candidate compared to the others. A stability value (M) is then assigned to each gene.
This M value must be lower than the threshold of 1. 5 set by the software developers. The most stable reference gene has the lowest M value. NormFinder calculates the stability value (SV) based on the overall expression variation of candidate genes for normalization, and also the variation between subgroups of samples. The BestKeeper algorithm calculates the stability of candidate reference genes based on the coefficient of variance and standard deviation of gene expression (CV). The reference gene with the lowest CV is considered as the most stable gene. The ΔCq method consists of comparing the relative expression of each pair of genes. All comparisons for one gene allowed calculation of mean standard deviation (mSD) that was used to assess the stability of the gene expression of the 12 candidates: low mSD value indicated a stable expression.
Therefore, a final ranking was determined using the R RankAggreg package which combines the different rankings by aggregation via a Cross-Entropy algorithm  38. Determination of stress marker genes. Several stress-related genes were selected based on their homologous function described in the literature in Arabidopsis thaliana. Algae homologous genes were identified by BLASTn method with the Klebsormidium nitens NIES-2285 genome project database v1. 1 including the genome and the transcriptome of K. nitens (Supplementary Table  S1 ). Relative gene expressions were analyzed using the common base method from Ganger  42. Statistical analyses were performed by Kruskal-Wallis test followed by Dunn test and p-value were adjusted by Holm method, between treated and control samples for salt and PEG stress, and between each point for heat shock.
1 ). Following the three treatments, the Cq values of the potential reference genes ranged from 15. 95 to 29. 46, with 90% ranging from 18. 00 to 26. 00 (Fig. 1 , Supplementary Dataset 1, 2). Among all the samples, the least abundant transcripts were those encoding eIF6, S26, and UBQ (mean Cq values over than 25. 00 in response to the three stresses), while H3 emerged as the most abundant transcripts (mean Cq values of 17. 03, 17. 09 and 17. 47 in response to PEG, salt, and heat stress, respectively; Fig. 1a-c ). Concerning the variability of the control and treated sample transcript levels, in response to heat shock L34 showed the lowest variability with Cq between 23. 14 and 24. 73, while UBQ mRNA content is the most affected (values ranging from 24. 20 to 27. 28; Fig. 1a ). Regarding salt stress, the lowest variability was found for CitS mRNA, while that of eIF6 is the most variable (Fig.
1b ; Supplementary Dataset 2). On the contrary, under PEG stress, eIF6 mRNA displayed the lower dispersal of the Cq value and S26 mRNA the highest one (Fig. 1c ; Supplementary Dataset 2). These Cq analyzes highlighted the need to identify reference genes adapted to each stress conditions. To obtain a more robust evaluation of candidates, we performed statistical tests to rank genes using four algorithms: NormFinder 34 , GeNorm  35  , the ΔCq calculation  36  (dCq), and BestKeeper  37.
NormFinder is the only one that considered variations in transcript levels within treatment. The dCq method focused only on the standard deviation of the pairwise difference between the Cq of each candidate gene. The information provided by these four methods were complementary. In order to limit biases in the selection of the optimal reference genes in our conditions, the rankings obtained from each algorithm were separately evaluated and summarized in Fig. 2. Although the classification criteria were different, the rankings obtained with each algorithm shared similarities within each treatment (Fig. 2 ). For example, ACT was ranked 1 by NormFinder, GeNorm and dCq, and 5 by BestKeeper in response to heat stress, while UBQ were ranked 12 by NormFinder, GeNorm and dCq, and 11 by BestKeeper.
Following salt stress, H3 was ranked 3 by NormFinder, 1 by GeNorm and dCq, and 2 by BestKeeper while eIF3G was ranked 12 by NormFinder, GeNorm and BestKeeper, and 11 by dCq. Regarding PEG treatment, eIF6 appeared to rank 1 in the rankings of NormFinder, dCq and BestKeeper, and to rank 3 in the ranking of GeNorm, whereas H4 was ranked 12 by each algorithm (Fig. 2 ). The rankings described above highlighted the difference in stability of the transcripts and therefore the importance of choosing reference genes which expression was independent of the experimental conditions. While the rank of some candidate genes was similar whatever the algorithm, for others it differed. For instance, discrepancy appeared for L34. It was found to be the most stable gene by BestKeeper under heat stress conditions, while it was only ranked 7, 6, and 7 by Nor-mFinder, GeNorm and dCq, respectively.
Also, H3 was reported as stable under PEG treatment by GeNorm (rank 1), NormFinder (rank 3), and dCq (rank 4), while it emerged as unstable with BestKeeper at rank 10 (Fig. 2 ). As mentioned above, each method of calculation was complementary and, in order to combine these different rankings together and to overcome any contradictions between some algorithms, the R package RankAggreg  38  was used. This method allowed us to reorganize the ranks independently of the origin of the data  38  , thus making it possible to compare the results from the four algorithms. RankAggreg led to the determination of an optimal ranking list in each stress condition (Fig. 3 ). Thus, in case of heat stress, the most stable expressed gene was ACT (Fig. 3a ), the best one for salt stress was H3 (Fig. 3b ), while eIF6 displayed the most stability following PEGinduced dehydration (Fig.
Funding This work was supported by the  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation ,  Investissements d' Avenir program , project  ISITE-BFC NOISELESS  (contract  ANR-15-IDEX-0003 ; Grant  NOISELESS-RA18041. AEC. IS ), the  Agence Nationale de la Recherche , project  ALGAE-NOS  (Grant  ANR-18-CE20-0022-02 ).
Acknowledgement This work has been financially supported by the  Institut Universitaire de France  (for M. Taverna, senior member) and the  Agence Nationale de la Recherche  ( ANR, France)  with the grant no. ANR-18-CE29-0005-01. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programm  under the  marie Skolodowska-Curie grant agreemrnt No 896313. The doctoral scholarship for  Marco Morani  was supported by the doctoral school 2MIB ( Sciences Chimiques: Molécules, Matériaux, Instrumentation et Biosystèmes) -University Paris Saclay.
Deposit microstructure characterization Microstructural observations were performed with scanning electron microscopy with a field emission gun (SEM-FEG): MEB/FIB Helios 600i -EDS. Thicknesses of the deposit were measured from SEM cross-section observations. Epoxy resin impregnation was performed under pressure in the deposits to make a contrast agent for porosity determination. Samples were polished by using the Ar + ion beam of a cross-section polisher device (JEOL IB-19510 CP). Porosity measurements were performed by SEM image analysis with ImageJ software.
Results of REA in comparison to primary energy analysis In the following the results of three types of primary energy analysis are shown and compared with the results of REA and GHG analysis. The aim of the following comparison is to demonstrate how the choice of analysis methodology influences the results of energy system assessment and the following conclusions and why therefore REA is necessary to ensure a viable path to stopping climate change. To assess the pre-chain losses of primary energy consumption (PEC) for extraction, construction and transport of the used final energy three different factors were used. 1. Cumulated primary energy consumption (CPEC) uses the specific Cumulated Energy Consumption to represent pre-chain losses. It is the most scientifically accurate factor of the three considered and can be found in databases.
Powder X-ray diffraction For the as synthetized powder, Powder X-ray Diffraction (PXD) analysis was performed with a X'Pert Bragg-Brentano diffractometer, equipped with a Cu-Ka radiation and a X'celerator detector. The PXD patterns were acquired in air with an acquisition time of 1100 s per step, steps of 0. 016 , from 35 to 120 in 2q. After activation and cycling, the powders were analysed with a X'Pert Pro diffractometer in Debye-Scherrer geometry, equipped with Cu-Ka source. The powder samples were packed in glass capillaries with a diameter of 0. 8 mm in a glovebox under Ar atmosphere. Measurements have been performed from 30 to 120 in 2q, steps of 0. 016 and a time per step of 400 s. Qualitative analyses were performed with the software X-Pert High Score, while Rietveld refinements of the crystal structures were carried out with the Maud software  [38].
Scanning Electron Microscopy The study of the microstructure on the as synthetized sample was performed with the powder embedded in a conductive resin and polished, while morphological investigation was performed on loose powder. Analysis was carried out using a Field Emission Gun -Scanning Electron Microscopy (FEG-SEM) instrument Tescan 9000. The loose powder was also studied by performing Energy Dispersive X-ray Spectroscopy (EDX) measurements at different energy, i. e. 2, 5 and 15 keV, to perform Monte Carlo simulations with the software CASINO  [39]. These were done to simulate the penetration depth of the beam inside the material. Finally, samples after cycling with H 2 as loose and embedded powders were studied with a SEM instrument Tescan Vega. On embedded samples, EDX elemental measurements were acquired at 20 keV.
5 keV and its decrease is observed by increasing the applied beam energy, confirming a change in composition between the surface and the matrix. The thickness of the passive layer was investigated by a Monte Carlo simulation with the software CASINO  [39] , estimating the penetration depth of the electronic beam as a function of the applied energy. An example of results of the electron penetration inside the material is displayed in Fig. S11. As shown there, when an incident beam penetrates inside the material, the emitted signal comes from various depths. However, the more the beam penetrates inside the material, the lower the emitted signal is, passing from 90% to 5%. Thus, it was selected to consider the corresponding depth of the 90% of the emitted energy, since it represents the zone from which the majority of the information regarding the material comes from.
Acknowledgements HyCARE project leading to this publication has received funding from the  Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership)  under Grant Agreement No 826352. This Joint Undertaking receives 2 support from the  European Union 's  Horizon 2020 Research and Innovation program ,  Hydrogen Europe  and  Hydrogen Europe Research. The authors wish to thank  E. Leroy  for EPMA analysis,  F. Couturas  for his help with hydrogenation experiments and  G. Fiore  for the performing of the FEG-SEM analysis. This work honours the memory of  Michel Latroche , a highly respected scientist and colleague, who substantially contributed to this work and passed away in December 2021.
X-ray powder diffraction measurements All samples, bare-core NPs and CS-NPs, are probed by X-ray diffraction (XRD) to follow the structural modifications resulting from the substitution of Mn cations to Zn ones. The measurements are carried out in powder form, which is obtained after the evaporation of the liquid phase (solvent) of magnetic fluids. The experimental data are collected at D12A-XRD1 beamline of Brazilian Synchrotron Light Source, with a monochromatic beam with 6. 99 keV (λ = 1. 77155 Å) in the 2θ range of 15°-120°, at room temperature, with an approximate area of 3 mm 2. Diffraction patterns are obtained typically within intervals scanned with an angle step of 0. 1º. In addition, in the case of ZM2, ZM5, and ZM8 samples, XRD is also performed right after the coprecipitation step (before the surface treatment).
After total evaporation of the liquid, a thin layer of NPs (usually less than 100 nm thick) is formed over the substrate. Then, the sample is subjected to microscopy, and the obtained micrographs are analyzed using ImageJ © software. After TEM images are collected, we select a specific area of the micrograph of each sample to perform the electron diffraction measurements, obtaining a bi-dimensional ring pattern. Each ring is related to a family of crystallographic plans of the sample that scatters the incident beam. We used Diffraction Ring Profiler © software  58  to investigate the obtained 2D patterns. Therefore, we obtain electron diffraction spectra, and we determine the inter-planar distances and index the rings by comparing the inter-planar distances with ICDD data.
Acknowledgments This work was supported by the  Brazilian agencies CNPq ,  CAPES , and  FAP/DF. We greatly acknowledge the bilateral program between  French-Brazilian universities SU (ex UPMC)/France  and  UnB/Brazil  through the contracts  PICS/CNRS  nº  5939/11 ,  CAPES/COFECUB  nº  714/11  and nº Ph959/20. The authors are also greatly indebted to LNLS for the beam-time obtained on the D12A-XRD1 beamline (X-ray diffraction) and to the  TEM Platform Faculty of Sciences -Sorbonne Université -SU/France.