dataesr/scientific-paragraphs-categorization huggingface dataset:
The same substrates were used in electrochemical experiments. XPS fitting was performed in CasaXPS software. All data were referenced to the principal C 1s peak at 284. 8 eV after fitting. In the Ga 2p region, only the Ga 2p 3/2 peaks were used for fitting and quantification. In the Ga 3d region, both Ga-metal and Ga-oxide peaks were fitted with contributions from spin-orbit coupling; the Ga 3d 3/2 and Ga 3d 5/2 peak separations were fixed at 0. 46 eV, and the relative intensities were fixed at 0. 633. In the Cu 2p region the area under the peak at around 932. 5 eV was used for the quantification. In general, all peaks from a particular region were first fitted with equal line widths, and then this constraint was relaxed to refine the fit. The NPs solution concentration was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) performed on an Agilent 5100 device with a VistaChip II CCD detector.
For the experiments under N 2 atmosphere, the only difference was the gas source (N 2 , 99. 999 %, Carbagas); all the other details remained the same. The electrolysis experiments were performed in chronoamperometry mode. Electrochemical impedance spectroscopy (EIS) was implemented prior to the main chronoamperometry measurement to determine the electrochemical cell resistance (R cell ) and compensate for the ohmic losses. Four spectra were measured at the open-circuit potential, using 41 points between 1 MHz and 100 Hz, using a sinus amplitude of 20 mV and a pause time of 0. 6 s between each frequency. Software in-built into the potentiostat software (EC-lab) was used to apply the ohmic loss correction to further measurement.
XAS data processing and analysis Data extraction, normalization, and averaging to obtain the heat maps of XAS spectra evolution during operation were performed using the Demeter package (Athena and Artemis). 33  addition, the XAS data were reduced and normalized using the Prestopronto package that was also subsequently used to perform linear combination analyses. 34 Subsequent analysis of the extracted EXAFS data was performed using EXCURV (v. 9. 3). 35.
Considering the XPS is a surface technique, these data confirms that the as-synthesized CuGa NPs consist of an alloyed CuGa core with a Ga-rich shell, which forms during the synthesis and oxidizes upon exposure to air and/or the electrolyte, in agreement with the STEM-EDXS data (Figures  5, S10 ). We also carried out the same XPS analysis on the electrodes after 4 hours of CO2RR, which is reported in Figure  6B. Please note that Ga 3d could not be used for this purpose, as potassium in the electrolyte deposits on the electrode during CO2RR and the XPS signal of K 3p overlaps with the one of Ga 3d (washing the samples reduced the K 3p signal but did not remove it). Therefore, Ga 2p was used for the analysis. Because of the reduced penetration depth of the X-Ray in this energy range, only Ga oxide is detected in CuGa17 and CuGa37; the amount of Ga in CuGa4 is probably below the detection limit.
Operando X-Ray absorption characterization To gather complementary insight to those provided by the ex situ characterization, we performed operando XAS and monitored the catalyst state during electrolysis at -1. 2 VRHE. We note that the Ga K-edge signal for CuGa4 was too weak and noisy to yield any viable information (Figure  S15 ). However, the data were sufficient to confirm that Ga is present on the electrode, supporting STEM-EDXS and XPS observations that Ga is present in this sample under operation despite its low ca. 1 % content. The Ga K-edge spectra of both CuGa17 and CuGa37 are characterized by a rising edge at ca. 10368 eV both at ocp (Figure  S16 ) and during Because Ga is crystallized in the fcc (CuGa17) and Cu9Ga4-type (CuGa37) lattices for which there are no Ga and Ga2O3 standards, linear combination analysis (LCA) was not applicable for the quantification of the metallic ond oxidized components in the Ga K-edge.
ACKNOWLEDGEMENTS This work was primarily financed by the  Swiss National Science Foundation (SNSF)  for financial support from grant number  200021L_191997/1. The authors thank the  European Synchrotron Radiation Facility in Grenoble, France , for the provision of synchrotron radiation beam time at the Swiss-Norwegian beamline  BM31 , and  Petru Albertini  and  Dr. Wouter van Beek  for assistance. Dr. Natalia Gasilova ,  Kimoon Lee  and  Petru Albertini ,  Dr. Min Wang  are acknowledged for ICP-MS, ICP-OES and HPLC measurements.
Photocatalytic Degradation The photocatalytic degradation of two different dyes, i. e. , basic fuchsin (generalpurpose grade, Fisher Chemical, Hampton, NH, USA) and methylene blue (high purity, biological stain, Thermos Scientific, Waltham, MA, USA) were investigated. Three different configurations were tested. The measurements were performed with a handheld Lovibond photometer (MD 600) which was calibrated against the UV-Vis spectrophotometer (UV-1600PC) as shown in the supporting data Figure  S1. The dye removal was tracked by measuring the absorbance at or close to the main absorption peaks (i. e. , 560 nm for basic fuchsin and 660 nm for methylene blue) at regular intervals for 3 h. The amount of residual dye in the solution is directly proportional to the absorbance. The removed amount can be calculated from the difference in the initial and residual dye concentrations taking into account the mass of the catalyst and the volume of the solution taken.
Funding: This research has been supported by the  European Regional Development Fund  project,  Archimedes foundation , grant number  TK134  " EQUiTANT ",  Eesti Maaülikool (EMÜ Bridge Funding  ( P200030TIBT )), and  T210013TIBT  " PARROT mobility program ". We thank the  EU-H2020 research and innovation program  under grant agreement no. 1029 supporting the  Transnational Access Activity within the framework NFFA-Europe.
The numerical predictions were compared to experimental data in addition to numerical computations based on an existing literature database, and good agreement has been found. The investigation also conducted a sensitivity analysis of process variables and membrane specifications on the VMD performance, as well as an assessment of the impact of temperature and concentration polarization. The obtained results showed that the permeation flux reached 18. 42 kg/m 2 •h at 35 g/L feed concentration, 65 • C feed temperature, 50 L/h feed flow rate, and 3 kPa vacuum pressure. Moreover, the findings revealed that the feed temperature was the most significant factor, while the feed flow rate was the least important in determining the permeation flux. Additionally, the findings suggested that the effectiveness of the VMD process heavily relies on the composition and permeability of the support materials.
integrate. odeint Python package solving a system of ordinary differential equations), and the theoretical temporal conversion of hydroxide with time is obtained, as reported in Figure  2. The surface accessibility factor of nickel(III), ε, used to plot these figures is equal to 2. 6%, suggesting then that a small amount of nickel(III) is available for the chemical reaction. Indeed, after a first cycle of adsorption, the urea molecules having a strong affinity to nickel(III) could induce a "screen effect", thus reducing the number of nickel(III) sites into the internal layers of the particles. Figure  2  also reports a sensitivity study with regard to the chemical kinetic constant, k χ : a maximum deviation of 20% is observed for the reaction times longer than 600 s. The validity of the kinetic rate law is then checked by considering several initial concentrations of urea and hydroxide ions.
| Urea electrolysis on nickel electrode: case results and comparison In order to evaluate the veracity of the model, the experimentally measured urea concentrations in the bulk for various electrolyzes for which high conversions are achieved, will be considered and compared to the ones predicted by the model. correlation is observed with the experimental points obtained in our previous work. 30 gure  7A  compares the predicted (continuous line) and experimental (dots) normalized urea concentrations in the bulk, obtained during electrolyzes at different KOH concentrations, using two S/ V ratios, where significant conversion rates of urea were reached. Data obtained for a S/V ratio of 8 are extracted from our previous work. 30 In addition to examining the validity of the model for an enhanced value of S/V ratio, an alkaline electrolysis was carried out using an anode surface of 26 cm 2 in 130 mL of electrolyte.
ACKNOWLEDGMENTS This work was supported by the  French National Research Agency  (proposal HYUREA  ANR-19-CE04-0009 ). The authors would like to thank  S. Mallet-Ladeira  ( CNRS, LCC, Toulouse ) for the DRX analysis and useful explanations about this method,  D. Pereira Do Carmo , master trainee ( LGC, Toulouse ) for her help concerning some experiments,  Pr. P. Bacchin  and  Dr. F. Chauvet  for their useful discussions concerning the numerical methods (LGC, Toulouse).
In order to determine the charge transfer resistance (Rct) between electrode and electrolyte, electrochemical impedance spectroscopy (EIS) was performed. Data were analyzed using Z view software using equivalent circuits as described in Fig. 7 (a) based on the results of the EIS test in 1. 0 M KOH solution. According to the Nyquist plot of the prepared electrocatalyst shown in Fig. 7 (a), all samples have similar solution resistances (R S ) since the test was conducted at a similar electrolyte concentration, i. e. , 1. 0 M KOH. Furthermore, pristine Co 3 O 4 has a higher estimated value of R ct of 470 and the most effective catalyst (CCR-0. 3) has a lower estimate of Rct of 58. 2. Data from the EIS were used to calculate double layer capacitance values (CPE dl ) that are mentioned in the summarized features Table  1.
Acknowledgments We also extend our sincere appreciation to the Researchers Supporting Project Number ( RSP2023R79 ) at  King Saud University, Riyadh, Saudi Arabia. B. V. thank the platform "Microscopies,  Microprobes and Metallography (3 M) " ( Institut Jean Lamour, IJL, Nancy, France ) for access to SEM facilities and  F. Alnjiman  for his valuable help. We also acknowledge partial funding of the  Ajman University , Grant ID: DGSR ref. 2022-IRG-HBS-5.
Furthermore, the charging mechanism efficiency and heat transfer characteristics of a three-stage cascaded latent heat thermal energy storage unit with longitudinal fins were examined by Elmghari et al. [13]  using "ANSYS Fluent"software. Their study demonstrated improved charging efficiency and better thermal performance with the proposed configuration. In a different application, a novel design of a milk chiller based on PCM for coolness storage of 12/24 hours was presented by Jilte et al. [14]. Their proposed system allowed for cooling even during non-availability of power by switching off the refrigeration after a certain interval. Moreover, Hu et al. [15]  proposed a spherical PCM capsule with embedded composite fins for latent thermal energy storage. Their numerical analysis revealed that the inclusion of composite metal fins promoted the melting process, with the punched fin configuration showing favorable results.
Numerical settings To establish a simplified calculation model, the initial conditions and boundary conditions are combined. Utilizing the "ANSYS-Fluent 2022 R1" software, the finite volume method (FVM) is employed for solving the governing equations, with discretization being applied to the three governing equations. The convergence criteria are set at 10 -6 , 10 -6 , and 10 -8 for mass, velocity, and energy, respectively. The handling of the coupling between pressure and velocity is carried out using the pressure correction algorithm. In the case of turbulent kinetic energy, specific dissipation rate, momentum thickness, and governing equations, the double-precision solution is enabled through the application of the second-order upwind scheme. The treatment of the liquid fraction and temperature during the melting process involves the utilization of the Voller and Swaminathan programs, ensuring accurate handling of the mushy region  [32, 33].
Notably, the Phase Change Material (PCM) undergoes solidification within its liquid phase, commencing at 65 ∘ C and progressing towards the boundary wall temperature of 20 ∘ C in accordance with experimental observations. It is asserted that the discernible influence of heat loss from the boundary wall remains negligible  [34]. In Eq. 9, the MRE is calculated by comparing each pair of corresponding values V exp,i and V num,i , and then taking the average of the relative differences for all pairs of data. This allows the mean relative error between numerical values and experimental values to be evaluated over the entire available dataset. where MRE is expressed as a percentage. It represents the average deviation between numerical values and experimental values, measured in percentage. n is the total number of corresponding pairs of numerical and experimental values.
It is represented by the number of data points for which both numerical and experimental values are available. V exp,i is the experimental value for the i-th data point. It is the value that was measured experimentally for a specific data point. V num,i is the numerical value for the i-th data point. It is the value that was obtained from a numerical simulation or model for the same specific data point. MRE = 1 n ∑ n i=1 ⃒ ⃒ ⃒ ⃒ V exp,i -V num,i V exp,i ⃒ ⃒ ⃒ ⃒ × 100 (9) Table  2  is presented, encompassing a comprehensive dataset containing spatial coordinates (X and Y) for both experimental (EXP) observations by Ismail et al. [34]  and our current CFD simulations, spanning time intervals of 30, 60, and 90 minutes across various experimental cases. The tabular structure serves to facilitate in-depth data analysis.
Acknowledgements NA, JR and SK acknowledge support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  802891 ). https://doi. org/10. 5194/egusphere -2023-1750  Preprint. Discussion started: 4 September 2023 c Author(s) 2023. CC BY 4. 0  License.
Evaluating the anti-corrosion performance of nanostructured coatings requires the utilization of high-resolution imaging techniques, such as FIB-SEM, STEM and nanotomography, as done in other areas of corrosion science  [100] [101] [102] [103] [104] [105] [106]. Notably the post-mortem assessment of morphological and/or compositional changes of the coating and the imaging of trapped corrosion products in its pore network (as in Fig. 8c ) could bring valuable insight into the protective properties. The characterization of iridium (oxide) layers protecting a titanium PTL before and after electrolysis operation with STEM-EDX mapping shown in Fig. 6b  and the X-ray computed tomography of Nb/Ti coated stainless steel PTL are valuable illustrations of such analyses  [60, 64]. The ex situ insights collected with those methods would complete the in situ electrochemical data (potential-current density curves, potentiostatic/galvanostatic curves, impedance spectra) recorded during electrolysis for gaining a deeper understanding of the coating performance.
c) N 2 pyhsisorption isotherm recorded at 77 K, d) H 2 O vapour physisorption isotherm recorded at 298 K, e) solid-state UV-Vis spectrum and f) solidstate steady state excitation (dotted line, emission at 600 nm) and emission spectra (solid line, excitation at 378 nm) recorded under inert atmosphere. (Figure  S16 ). From Mott Schottky analysis of the EIS data (Figure  3b ), the conduction band position is estimated to be at À 3. 56 eV (À 0. 88 V vs NHE, Figure  3c ). Considering the indirect bandgap of 2. 24 eV, the valence band is aligned at À 5. 80 eV (1. 36 V vs NHE). Compared to the redox potential of the two half-reactions of artificial water splitting, a driving force of approx. 260 meV for HER and at least 50 meV for triethanolamine oxidation in water results (Figure  3c ). Photocatalytic hydrogen evolution reaction.
Acknowledgements M. A. -F. , E. A. Q. , J. C. and R. P. gratefully thank the  SINCHEM Joint Doctorate programme selected under the Erasmus Mundus Action 1 Programme  ( FPA 2013-0037 ). M. A. -F and R. P acknowledge the  Fuel Science Centre  ( EXC 2186 , ID:  390919832 ) funded by the  German Excellence Initiative. D. D. gratefully thanks the  German Federal Environmental Foundation  for financial support (grant number:  20019/629-32 ). F. M. W. gratefully acknowledges financial support from  CNRS  through  Momentum 2018 excellence grant. The authors are grateful to  Chantal Lorentz  for recording solid state NMR spectra and to  Ashta C. Ghosh  for determination of TEOA redox potential. Open Access funding enabled and organized by Projekt DEAL.
Statistical Analysis All the detoxification tests as well as the cultivation assays were performed in triplicate, and results were expressed as mean value with the associated standard deviation. A oneway analysis of variance (ANOVA) followed by Tukey's post hoc analysis was conducted to detect statistical differences between the different experimental groups, using a 95% level of confidence. The statistical analysis was performed using the OriginPro 2022 software, version 9. 9. 0. 220.
platensis culture (data not shown). Thus, the aim of the experiment was to compare the growth performance between the two types of digestate diluted at different dilution rates as well as two control groups. The OD at 680 nmwas continuously measured over 10 days in order to evaluate the growth profiles of all the conditions. Similar growth tendency of S. platensis was observed in both the raw digestate (RD) and the treated digestate (TD) conditions tested at three different dilution factors (Figure  2A, B ). Higher growth responses were generated when increasing the dilution factor of the cultivation media for both the raw and pretreated digestate. Similarly, the lag phase of S. platensis was affected by the dilution factor of the raw and pretreated digestate, with a longer lag phase observed the more the digestate was concentrated.
Acknowledgements AKM and DS acknowledge  Khalifa University Abu Dhabi  for the generous support of this research. This research is supported by  ASPIRE , the technology program management pillar of  Abu Dhabi 's  Advanced Technology Research Council (ATRC) , via the  ASPIRE AARE 436  ( AARE20-123 ). M. A. A. thanks the  Materials Chemistry Consortium  for computational resources on YOUNG ( EP/P020194 ).
The first issue discussed arises when true values are close to or equal to zero. Such situations arise, for example, when dealing with energy used for heating in temperate climates, where heating may not be used during (some periods of) summer. Another possibility is large amplitude changes over the evaluated time series, requiring units that result in values close to zero for part of the data. In such a case, a "value by value" normalisation, such as is done for MAPE, can lead to instability or undefined values. One way to avoid such problems is to normalise the results on a higher temporal granularity, for example, daily. Such normalisation is commonly done using the mean of the reference data over the whole time series, for example, for CVRMSE or NMBE. However, while such an approach avoids the above problem and can therefore handle data with values close to or equal to zero, a problem arises for data with varying magnitude.
Such (seasonally) varying magnitude is common when evaluating energy use data and can lead to biased results, as periods with high magnitude are likely to have a higher weight. For instance, when evaluating the accuracy of a building space heating demand model with CVRMSE, the results will vary greatly (it can be several orders of magnitude) if computing the metric on a monthly basis (normalization by the monthly average), a yearly basis (normalization by the yearly average), or computing the CVRMSE for each month with normalization by the yearly average. For a constant model bias, the CVRMSE is much higher in the periods when the average is low (i. e. , during the summer periods for the considered example case) and much lower in the periods when the average is high (i. e. , during the winter periods).
Similarly, shifting the average over the entire analysis period will significantly change the CVRMSE results. For example, for the same model, increasing the overall/average heating demand (introducing a constant bias for both the reference and the tested model) will improve (decrease) the CVRMSE while the Coefficient of Determination (R 2 ) will not change. This issue was also discussed in Johra et al. , 2021  [186]  and can be mitigated if data are normalised on an appropriate temporal granularity, for example, daily, before aggregation. This issue also highlights the potential disadvantage of using metrics such as RMSE that are based on a certain distance (in the unit of the data or some modified unit of data) without considering the possible varying size of the data. A similar issue arises when evaluating the performance of a model over different time series, for example, different buildings or different types of quantity (energy or temperature).
For instance, the IEA compiled performance variables in their hosted IEA PVPS Performance Database  [9]  and Jordan et al. made available open-source databases on measured degradation rates. [6, 10]  From two separate analyses, a typical reference performance ratio of around 76 % was reported  [11, 12]  and an average performance loss rate in the order of magnitude of 0. 5 % have been highlighted. [10, 11]  All these studies suggest performance benchmarks based on historical PV systems, and yet none offer correction factors to adjust those expectations in the context of climate change. By means of climates scenarios, PV potential has been investigated around the world  [13]. [14]  Historical trends have been identified in degradation models with an increase in degradation rate following the increase of the world ambient temperature on the ERA5 dataset.
In order to build up climate projections, 15 models from the EURO-CORDEX-11 ensemble  [18, 19]  were retrieved and are further described in Table 6 in Appendix A. 1. The RCP 8. 5 scenario, which is one of the most pessimistic, corresponds to an additional radiative forcing of 8. 5 W/m2 in 2100 and has been chosen for the projections to study the worst-case degradation configuration. Nonetheless, the outputs of the EURO-CORDEX models are often far from real conditions  [20]  and need to be modified according to the location with bias-correction methods taking the ERA5 dataset  [21]  as a reference on 1981-2019.
Decomposition model Decomposing the Global Horizontal Irradiance (GHI) into Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI) is a crucial step to estimate the irradiance received by the photovoltaic installation. The DIRINT model  [28]  is adopted to decompose the irradiance and needs as inputs the absolute airmass AM abs , the angle of incidence AOI and the extraterrestrial Direct Normal Irradiance DN I extra to deduce the clearness index. The Spencer model  [29]  enables to calculate the DN I extra with the solar constant equal to 1370 W/m 2. The relative airmass AM rel is calculated by the Kasten model  [30]  and the altitude of the installation is deducted from the EU-DEM database. [31]  The absolute airmass AM abs is then deducted from the relative airmass AM rel and the pressure P with AM abs = AM rel P 101325.