dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational methods To account for the formation of the anhydride form of the Cu-metalated MOF evidenced experimentally upon light irradiation and formic acid adsorption, we adopted our previously constructed anhydride model for UiO-66-(COOH)2 labeled as UiO-66-(COO)2 with anhydride bridge formed between adjacent ligands  69. All the calculations for the reaction process were realized on a representative cluster model UiO-66-(COO)2 loaded with a single Cu as model system as shown in Figure  S1  and noted thereafter UiO-66-(COO)2-Cu. All DFT computations were performed by using the Vienna Ab-initio Simulation Package (VASP, version: 5. 4. 4)  70  with the Projector augmented-wave (PAW) method to describe the pseudopotential. The electron exchange-correlation functional was treated by the Perdew-Burke-Ernzerhof functional within the generalized gradient approximation (GGA) scheme  71.
Moreover, the optical band gap energies Eg for the free and copper metalated MOFs were determined from the absorbance data using the Tauc-plot method (fig S10B  ,  and C ), As expected, the calculated Eg for UiO66-(COOH)2 was 3. 9 eV. However, in the case of UiO-66-(COOH2)-Cu (Figure  10C ), the in-situ formation of Cu  (I)  oxa nanoparticles was evident by the narrow band gap observed (Eg=1. 84 eV) which corresponds to the band gap of Cu2O semiconductor  76. The charge transfer (CT) between the UiO66-COOH structure and the copper site is also investigated by absorption transient spectroscopy. The results presented in Figure  (S13A ) demonstrate no absorption transient (AT) of the post metalated MOF structure (UiO66-COOH)2 with a high photoluminescence (PL). However, UiO66-(COOH)2-Cu exhibits an AT decay (broad AT band with a maximum at 440 nm) with very low PL emission, confirming a CT between the MOF structure and the Cu centers.
Once assembled, the cell was mounted onto a house-built electrolyzer test station, which was controlled by LabView software. First, the cell was activated at room temperature with a current density of 25 mA cm -2 for 24 h, followed by a second phase activation with an increased current density of 100 mA cm -2 for another 12 h. Afterwards, the cell temperature was increased to 50 °C and the current density was increased to 1 A cm -2 for constant operation. The entire test was uninterrupted except that the current-voltage (i-V) curves were measured after 85 h operation and at the end of the test (486 h). During the durability test, the feeding stock 1 M KOH was completely replaced with freshly prepared solution every 72 h. 2. 5. DFT + U Computations. Spin-polarized DFT + U calculations were performed with VASP.
Research objectives This paper proposes a methodology to evaluate the uncertainty on the shiftable heating load when activating a group of buildings using heat pump systems. Facing these challenges, a probabilistic characterization methodology with a district database is proposed in this study. Different set point changes (duration and intensity) will be sent to the dwellings, in which the energy use for space heating will be modulated according to the constraints and flexibility tolerances of the users. The stochastic thermostat adjustment behavior of the occupants will be modelled with an agent-based approach. The influence of the input parameters on the flexibility potential will then be evaluated. The results of this study can be used for different purposes: quantifying the uncertainties for control, listing input parameters for a flexibility audit, evaluating the optimal scale of aggregation, providing guidelines on signal design to increase reliability, etc.
year): the envelope and system properties are defined in accordance with the current French building regulation (2012). Space heating is provided by heat pumps and water-based radiators; the old fabric district (mean consumption for heating of 100 kWh/m 2. year): the building properties are defined according to the typical characteristics of multi-storey residential buildings from the period 1982-1989, including renovations. Space heating is provided by direct electric convectors. In both cases, national databases are used to define the buildings properties (see Annex).
Among the different databases used (in blue in Fig. 4 ) a selection is made according to the case study in order to obtain a representative dataset. From this selection, preprocessing is performed to convert the data into usable inputs for building energy models, some of them being represented in Fig. 5. When possible, these inputs are added to the UBEM as normal distributions, with the mean (m) and standard deviation (r) computed from databases (see Annex). In general, normal distributions appeared as being well representative of the inputs variability observed in the database. For distributions that do not follow a normal distribution, see for example the number of occupants in each dwelling in Fig. 5b , a random selection of inputs is performed. In Fig. 5 , the bins represents the discrete sampling of the distributions, while the lines represent the kernel density estimate of the distribution  [28].
Buildings For each building, the composition of the exterior and interior walls, the windows, the floor, and the roof is defined according to the current building regulation  [30]  or the energy audit  [31]  databases. These databases are analyzed in order to obtain a mean value (m) and a standard deviation (r) for the distribution of each parameter. Space heating is provided by air-to-water heat pumps for the new district and by electric convectors for the old fabric district. Air-to-water heat pumps are variable speed. The coefficient of per-  formance (COP) of the heat pumps is based on a polynomial regression from the nominal COP to estimate the thermal power output as a function of the temperature difference between the sink (i. e. the building) and the source (i. e. ambient air temperature).
This hourly profile is repeated for each day (Fig. 3 ). Thus, the hourly set point schedule is defined for each household in a pre-process with respect to the dynamic thermal simulation, while the manual thermostat adjustment behavior is dynamically simulated. Defining an operation schedule does not necessarily imply that each household is using a night set point temperature or a setback temperature. The probability of using either a night set point temperature or a setback temperature is equal to 80 % based on the PHEBUS dataset  [41]. The T setpoint, day distribution is also based on the PHEBUS dataset (Fig. 5 ). Each occupant adapts its default clothing level based on the T setpoint, day in order to obtain a neutral PMV value. The default clothing insulation and the default set point temperature can then be modified by the occupants over the course of the dynamic simulation (see Section 3.
Appliances The appliances are randomly allocated to the households based on the appliances' ownership rate conditioned on the household size and the professional category and calculated using aggregated data from the PHEBUS dataset  [41]. As the conditional distributions were not available, the capacity and energy class of the appliances cannot be conditioned on the household size and the professional category. The marginal distributions of the capacity of the appliances were available from the ADEME survey campaign  [69]. The marginal distributions of the energy class of the appliances were built from the marginal distributions of the age of the appliances combined with sales data  [66]. The appliances' electricity load curves are randomly assigned to the activity starting times based on the capacity and the selected energy class of the equipment (ranging from A+++ to C).
Modeling flexibility from occupants Occupant thermostat adjustments can occur because of rejection of DR events, change of metabolic rate, or thermal discomfort due to mismatch between schedule and presence (Fig. 6 ). The thermostat adjustment behavior is modeled using an agent-based approach: each member of the household is represented as an agent with a set of attributes (status, clothing, and metabolic rate) and a set of possible adaptive actions (set point and clothing adjustment). User interaction data from about 9,000 connected Canadian thermostats included in the Donate Your Data (DYD) dataset  [43]  are used to calibrate the thermostat adjustments model  [21]. The adaptive principle  [44]  is assumed to be determining the manual overriding behavior. For modeling the agent's adaptive behavior, we use a particular type of agent: the Belief-Desire-Intention (BDI) agent  [45].
the clothing p adj, clothing (before), with a mean value of 10. 5 % and an observed range of 3-18% based on the calibration with the DYD dataset, 2. the set point temperature p adj, SP (afterward), with a mean value of 3. 5 % and an observed range of 1-6%. Thus, it is assumed that the adjustment of the clothing insulation is the preferred adaptation strategy. When the agent decides to adjust its clothing, he does it by either increasing or decreasing the clothing of DCLO = 0. 1 clo where 0. 1 clo is, for example, the clothing insulation change made when passing from a thin long-sleeved sweater to a thick longsleeved sweater  [46]. While, when the occupant decides to adjust the set point of DT setpoint , he does it to restore thermal neutrality (i. e. towards a PMV $ 0). The T setpoint + DT setpoint has a lower limit equal to T setpoint,day À1°C during warm exposures and an upper limit equal to T setpoint,day + 6 °C during cold exposures, based on observations of the DYD dataset.
Qualitative validation of the model Validating the results of UBEM tools is a challenging task due to the lack of standardized data, the lack of information and the complexity of the tools. Comparison with measured data cannot be performed because only half of the Atlantech district has been built to date. Therefore, the model validation focused on comparison of the thermal model, the input parameters and the simulation results with external references. The thermal model of the DIMOSIM tool was compared with the results of the benchmark tests BESTEST  [47]  (free-running and heating cases) and DESTEST  [48]. DIMOSIM shows good agreement with the other tools, both in terms of temperature and energy  [25]. Given the large number of input parameters required for the design of the district (around 15 000 for this district), the control of these parameters with typical values is of main importance.
To determine the most influential parameters, we conducted a 100-trajectories and 4-grid jump test with the parameters listed in Table  1. The grid jump is the number of samples into which each parameter is divided. The convergence of the results was checked and found to be well suited for the identification of the influential input parameters. In Table  1 , the parameters (i) are well defined and the determination of (m, r) was done by database analysis (part 3 and Annex). The mean of the input is changed between m-Dm and m+Dm based on the standard error of the mean (Dm=2 Â r/ p n, with n = 98 buildings). Diversity in the district will be provided by the standard deviation (r) of the distribution, which remains unchanged. The parameters (ii) are derived from a limited set of data, therefore, for these parameters we have defined a 10 % uncertainty on the mean, or an ad hoc value of this uncertainty.
Old/new district comparison The objective of this last study is to evaluate the importance of the building typology on the flexibility. We downgraded the building envelope properties according to the energy audit database (see Annex) giving the median values and standard deviations of U wall , U windows , U floor and U roof for buildings constructed during the period 1982-1989  [30]. These average values were set to 0. 6, 2. 98, 0. 4 and 0. 33 W/(m 2 K) respectively. It can be noticed that the standard deviations are larger than for the new district (Table  2 ). It should be highlighted that normal distributions might not be the best representation for partly-renovated building fabrics (log-normal distribution could be better suited  [59] ). However, it was decided to use normal distribution for the sake of repeatability.
Despite the broad scope of the study, some limitations should be highlighted. First of all, a district with a different compacity or with more variety of envelopes and systems might alter the conclusions. The relatively good knowledge of input data (from national databases), the homogeneity of systems and the large size of the district favored a strong decrease of some uncertainties. The evaluation of flexibility would be more challenging if some district properties are ill-known (such as construction year or type of heating system). However, this barrier should be overcome with the widespread publication of open-data on building characteristics. Furthermore, the influence of the district size has only been partly addressed in this study. An evaluation for a larger district (674 dwellings, Fig.
Conclusions We have investigated the flexibility potential of a district by estimating its energy consumption from a mono-zone UBEM. Flexibility (or DR events) were defined as a reduction in the set point from 6 pm, which is classically an overloaded period of the electrical grid in winter in France. The occupants, building characteristics and flexibility signal were set in the model according to datasets representing a current French case study. The acceptance of flexibility was modeled by a thermal comfort-based model of occupant thermostat adjustments. Finally, the flexibility potential was defined as the mean shifted power during DR event, and its calculation has been performed for a district of 337 dwellings. The positive effect of electrical load aggregation was highlighted in the prediction of the load at district level, with a decrease of the uncertainty for groups of more than 50 dwellings.
It appears to be one of the most influential parameters, so it must be carefully selected by the aggregator to optimize the flexibility portfolio. Considering DR event duration, it appears that a long-DR event leads to a decrease in the shiftable power. A tradeoff between the power decrease and the duration of activation should thus be defined to optimally activate flexibility. Occupants appear to play a key role in flexibility. The dynamic modelling of occupant behavior was carried out and allows the quantification of the occupant rejection phenomenon. The behavior of occupants must be carefully taken into account when estimating the flexibility potential. Finally, the characteristics of the buildings did not show a too large uncertainty in the resulting flexibility at district level. Indeed, the aggregation effect was sufficient enough to dampen the diversity observed in the building databases.
Data collection A comprehensive search strategy was applied to compile the study cohort from different sources (for details, see Figure  1 ). A systematic search of the PubMed database yielded 44 published articles with 174 patients. Three patient registries were queried: the international PodoNet registry 8 for children with primary steroid-resistant nephrotic syndrome,  3  the Chinese Children Genetic Kidney Disease Database 9 (CCGKDD); and the German Network for Mitochondrial Disorders (mitoNET 10 ). 11 A total of 20, 17, and 7 new cases, respectively, were identified in these registries, and updated information was provided on 20,13, and 2 previously published cases. Finally, invitations to an online survey were sent to all members of the European Rare Kidney Disease Network (ERKNet), the PodoNet Consortium, the ESCAPE Clinical Research Network, and the European and Asian Societies for Pediatric Nephrology (ESPN and AsPN).
Through this effort, 34 novel and 32 previously published patients, in 24 countries, were identified and documented. All patient-related data were collected in a completely deidentified manner. Names, initials, birth dates, and hospital-specific patient identifiers were not retrieved. Center identifiers were deleted from the database after checking for duplicate entries was completed, and only the country or residence was retained for demographic studies. Also, times and ages (e. g. , age at diagnosis, time since diagnosis) were reported instead of calendar dates. As a result of these measures, all analyses were performed on completely anonymized datasets.
Verification and unification of genetic diagnoses All molecular diagnoses were re-evaluated by an expert geneticist, following the best practice recommendations of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for the interpretation of sequence variants. 12 Variants classified as (likely) pathogenetic, per ACMG criteria, were considered causative and were included in the study. Two COQ2 variants with conflicting interpretations of pathogenicity according to the ClinVar database-namely NM_015697. 8:c. 288dupC [rs759310292] and c. 683A>G [rs121918232]-were considered disease-causing due to their increased prevalence among affected individuals, compared to that among the general population, their presence in trans with another (likely) pathogenic variant in patients with highly specific phenotype, and the absence of homozygous cases in control databases.
A previously published case with a homozygous COQ6 NM_182476. 3:c. 41G>A [rs17094161] variant was excluded from the analysis, as the variant was reclassified as benign because it is relatively frequent, is also in a homozygous state, in general populations, has been reported as benign in the CliniVar database, and is located c l i n i c a l i n v e s t i g a t i o n.
Statistical analysis Descriptive data are presented as median and interquartile range. Patient and kidney survival rates were calculated using Kaplan-Meier lifetable analysis, with log-rank testing for analysis of significant differences. Fisher's exact test was performed for genotype-based pairwise comparisons, with 2 Â 2 and 2 Â 3 contingency tables of dichotomized data. Intra-and interfamilial phenotype variability was assessed by the coefficient of variation (CV ¼ SD/mean) of the age at kidney-disease onset. A value of P < 0. 05 was considered statistically significant. Statistical analyses were performed using Prism, version 8, data analysis software system (GraphPad Software).
ACKNOWLEDGMENTS This paper was made possible by a short-term Fellowship offered by the  ERA  (European Renal Association). In addition, this project has been supported by the  European Reference Network for Rare Kidney Diseases (ERKNet) , the  PodoNet Network for Podocyte Disorders , and the  German (mitoNET)  and  European Networks for Mitochondrial Disorders (GENOMIT). ERKNet is co-funded by the  European Union  within the framework of the  Third Health Programme  " ERN-2016 -Framework Partnership Agreement 2017-2021. "PodoNet and mitoNET/GENOMIT have received funding from the  German Ministry of Education and Research , and PodoNet from the  EU   7th Framework Programme  (EURenOmics; grant  2012-305608 ) and the  German Research Foundation  ( Scha 477/11-1 ). Founder effect analyses were supported by the  Polish Ministry of Science and Education  ( 2017/25/B/NZ2/00519 ).
As mentioned before, equation  (5)  shows the used crystallization probability function, which is calibrated by an experimental study  [43]. F cry (t s , I n ) = 1 -e -ts. In where ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ I nuc (T s , f ) = K 1. e -f. K 2 ΔT 2 s f ( L adj ) = 1 - L adj K 3 (5) Fig. 41  gives K 2 = 7250K -2 and K 3 = 0. 7 for the minimal deviation of 140 W. Both models are linked to each other by the heat flow rates from PCM to HTF. The heat flow rates are generated by the 3D model and collected in two datasets. The former contains data as the phase change occurs and the latter contains data when PCM remains liquid. The heat flow rates are then used as source term in the 1D tube model. As shown above, the researchers tend to use different methods to include supercooling in numerical simulations. Simplifying the model is an important issue used to decrease the time and complexity of the simulation.
Implementing all known parameters/datasets in the simulations coupled to a statistical tool is still difficult, because of the models complexity and the simulation time that are restricted by the available computational power. Therefore, researchers take approximations, assumptions and neglect parameters to decrease this complexity. So, it is recommended to use micro-capsulations to increase the heat transfer area and avoid sudden release of huge amount of latent heat. The whole knowledge of PCMs behavior should help in choosing a better PCM that can be a combination of several PCMs to obtain the desired melting temperature. For example, paraffins have high latent heat with a melting temperature near the indoor comfort temperature, which is needed in thermal energy storage in building walls; however, their thermal conductivity is low.
It provides the opportunity to decarbonize industry, buildings and transportation as well as to provide flexibility to the electricity grid through fuel cell technology  [6, 7]. Likewise, the development of hydrogen sector can strengthen the economy by creating thousands of jobs and adding billions of dollars to gross domestic product (GDP), enabling the integration of renewable energy into the power system, reducing dependency on imported fossil fuels, and lowering the carbon footprint [8e10]. In the literature, studies on hydrogen production and storage have received increasing attention during the last twenty years. The variation of yearly published works in Scopus database is plotted in Fig. 1. These data are generated using "hydrogen energy production and storage" as keywords in Scopus Website.
S3 ). The dA/dt value obtained this way is plotted in Fig. 2  a, and it represents the speed of photoactivation. If the process is caused by absorption of a single photon, one would expect that doubling the irradiation intensity should result in a doubled rate of OCP R accumulation (so dA/dt should also double). This can be visualized in a log-log plot, where this dependence should result in a linear fit with slope equal to 1. Linear fits in Fig. 2  a (dashed line) show that both C-tagged and N-tagged OCPs are characterized by a slope higher than 1 (meaning that doubling light intensity causes more than doubled speed of photoactivation), with a larger slope determined for C-tagged OCP. The C-tagged dataset was measured twice, with 550 (green) and 580 nm (red) probing, to ensure that the slope value is reproducible.
5 eV). Curve fitting was performed using a combined Gaussian and Lorentzian line profile after Shirley's background subtraction by Casa XPS software. Thermogravimetric analysis (TGA-DTG) was performed with a Setaram Setsys evolution 1750 by using dry air as the carrier gas. Temperature ramp of 5 °C. min -  from room temperature to 900 °C was used during the measurement. Micro-Raman spectroscopy was carried out with a Jobin Yvon LabRAM HR800 equipped with a CCD detector, cooled at -70 °C. A 633 nm laser wavelength was used as the incident beam focused on the sample with microscope through a wide length 9 50 objective. For each sample, at least 3 spectra were recorded from different area on the sample. For each spectrum, a background was subtracted and the maximum intensity of the D and the G bands were used to calculate the D intensity over the G intensity ratio, I D /I G.