dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank the  European Synchrotron Radiation Facility (ESRF)  for providing the beamtime. This work was performed in part at the  Chalmers Materials Analysis Laboratory, CMAL. The work was carried out within the  Competence Centre for Catalysis , which is hosted by  Chalmers University of Technology  and financially supported by the  Swedish Energy Agency  and the member companies AB Volvo, ECAPS AB,  Johnson Matthey AB, Preem AB, Scania CV AB, Umicore Denmark ApS, and Volvo Car Corporation AB. This work was funded by the  Swedish Energy Agency  through the  FFI project  " Fundamental studies on the influence of water on oxidation catalysts for biogas applications " (No. 40274-1 ).
Synthesis and Solid-State Characterization 14a,b,e,15] The solids were analyzed by solid state IR spectroscopy using an ATR detector. IR spectra for complexes 1, 2 and 3 displayed the characteristic four-signal pattern of tetracarbonyl diimine systems in the 1800-2050 cm À 1 region, corresponding to v(CO) frequency modes (Table  S1 ). [16] Stretching energies did not significantly vary within the series, in agreement with X-ray data. The already-published XRD structures  [14a,d,f]  offer the possibility to analyze bond lengths and angles at solid state for 1, 2 and 3 (Tables  S2  and S3 ). The three molybdenum complexes exhibit a pseudo-octahedral geometry centered on the metal atom, with two axial and two equatorial carbonyl ligands. X-ray data suggest a shorter MoÀ C(O) axial bond for complex 3 in comparison to complex 1 (Scheme 1).
Spectroscopic Characterization 1 H NMR, FTIR and UV-Visible spectroscopies were carried out for the characterization of the synthesized complexes in solution. NMR spectroscopy allowed accurate assignment of the hydrogen atoms of the bpy, phen, and py-indz groups in the aromatic region (7-8 ppm, see Supplementary Information). As found at solid state, IR spectroscopy in tetrahydrofuran (THF) displayed four main bands in the 1800-2050 cm À 1 range corresponding to carbonyl stretching vibration modes (Figure  2 , black curves). The values for the energy are somewhat similar to those found with solids, despite being up-shifted by 30 cm À 1 for some frequency modes (Tables  1  and S1 ). 14c] In particular, the lowest values of wavenumber were found for complex 3 as in solid state. Moreover, stretching energies for complexes 1 and 2 are similar to reported data in organic solvents.
Conclusion In summary, the studied diimine [Mo(CO) 4 (L)] complexes have shown similar redox properties under inert atmosphere, but somewhat different behavior in presence of carbon dioxide. In particular, low scan-rate cyclic voltammetry has revealed that reactivity with CO 2 occurs already at the mono-reduced state. Spectroelectrochemical data supported by DFT calculations suggest that the tricarbonyl [Mo(CO) 3 (L)] * À species originating from [Mo(CO) 4 (L)] * À is the key reactive species for further CO 2 activation. Two different mechanistic pathways have been identified depending on the reactivity of the tricarbonyl monoreduced complexes with protons or carbon dioxide. Strikingly, the experimental data demonstrate that the rate of CO 2 conversion is ligand-dependent, the highest rate being obtained for the unsymmetrical py-indz Mo species.
Computational Method All DFT calculations were performed with the Orca package (version 3. 03)  [26]  using the PBE functional  [27]  and a mixed basis set (TZVP for H, C, N and O and LANL2DZ for Mo). [28] The calculations were accelerated with the resolution of identity (RI) approximation in conjunction with an appropriate auxiliary basis set. The geometry optimizations and frequency calculations were carried out using the conductor-like screening model (COSMO) to account for the solvation effects of THF. [29] Vibrational frequency analyses allowed us to confirm that the optimized geometries were actual minima (no imaginary frequencies) on the potential energy surface (PES) and to calculate the free energies. The calculations of the redox potentials using well established thermodynamic cycles require the use of the solvation energy of the electron in THF, which value is not known.
Acknowledgements Nicolas Le Poul is grateful to  Gaël Le Roux  ( UBO ) for his participation to the building of the IR spectroelectrochemical cell. Financial support was provided by  UBO  for a PhD grant ( Carlos Garcia Bellido ), by  FEDER UE Interreg Atlantic Area (HYLANTIC ,  EAPA204/2016 ) for a post-doctoral fellowship ( Lucía Álvarez Miguel ) and by  ANR  (CATHOMIX,. ). This project is co-financed by the  Interreg Atlantic Area Programme  through the  European Regional Development Fund.
These results are not presented here, as they show the same general trends than for the representative example of W/W-Al 2 O 3 /Al 2 O 3. Finally, despite the fact that thermal absorbers are made of stainless steel  [3, 28] , iron (Fe) was selected as substrate, since it is the main component of steel, and no consensual data for steel was found in optical databases. Its thickness was fixed to 1 mm.
Materials spectral complex refractive indices To calculate R(λ), A(λ) and E(λ), the spectral complex refractive indices of the stack constitutive materials (Fig. 1 ) are needed. They were found in the literature: for Al 2 O 3 , from Boidin et al. [31] ; for W, from Rakic et al. [32] ; for Fe, from M. R. Querry  [33]. These particular studies were selected because they cover a large spectral domain, from the solar range 280-4000 nm, to the IR range (for radiative losses calculation). They also present a good accuracy in the solar range, needed for a good estimation of solar performance  [34]. Also, the data was measured on actual thin film samples fabricated by deposition techniques similar to that used in CSP industries. The data was linearly interpolated with a wavelength step of 5 nm. The spectral complex refractive indices of the W-Al 2 O 3 composite layer (Fig.
Simulation and optimization method Simulations were computed using a numerical code written on Scilab software  [44]. The code can calculate spectral and total optical values of the W/W-Al 2 O 3 /Al 2 O 3 stack (Fig. 1 ) from spectral complex refractive indices, using the Transfer Matrix Method. It also contains an optimization algorithm to optimize the composition of the cermet layer and thickness of each layer to achieve a suitable reflectance spectrum giving access to the maximal heliothermal conversion efficiency at specific operation conditions. The optimization target is the maximization of heliothermal efficiency R h for different sets of parameters, i. e. , concentration ratios C and absorber temperatures T A. C was varied from 10 to 300 with a step of 10. T A was comprised between 323 and 873 K (50-600 • C) with a step of 25 K.
1-4 mW). The modulation of amplitude (1-5 G) was adjusted for the same reason. The lowtemperature measurements were made with cryogenic systems. Specific surface area and total pore volume of the materials were measured on ASAP 2020 (Micromeritics) surface area and porosimetry system. Extended X-ray absorption fine structure (EXAFS) spectra were acquired at ELETTRA, using XAFS beamline (experiment code: 20145422)  [55]  at room temperature at the tungsten LIII-edge. A pair of Si(111) crystals were used as monochromator and the harmonics were rejected by a detuning of the second crystal. The spectra were recorded in the transmission mode between 9. 9 and 11. 43 keV. Three scans were collected for each sample. Each data set was collected simultaneously with a W metal foil reference (11206. 7 eV), and was later aligned according to that reference (maximum in the first derivative of the W foil first peak).
AC-magnetic and calorimetric characterization High-frequency hysteresis loops were measured in a homemade setup, 5 placing 0. 5 mL of sample in a 6 mm diameter sample holder. The magnetic eld frequency was 50 kHz with the amplitude ranging from 10 to 60 mT. In addition, the time evolution of the hysteresis loops was characterized by extracting the data of the cycles at different measurement times, from 5 ms to 10 s. The sample concentration was around 30 mg mL À1 for samples MAG-12 and MAG-30, and around 50 mg mL À1 for sample MAG-50. The specic loss power was calculated as the product of the hysteresis area (A) and the eld frequency (f): SLP ¼ Af. Calorimetric characterization was performed in the same homemade hysteresis loop meter measuring the temperature increase simultaneously with the hysteresis loop using two ber optic thermometers (Reex 4, Neoptix) set at different sample heights.
Acknowledgements The authors acknowledge the technical support given by  Fernando Giacomone  and nancial support by the  Spanish Ministery of Science and Innovation ,  PTA2015-10497-I. I. M acknowledge NEXT funding for the mobility grant. The authors also acknowledge to the  Ministerio de Ciencia, Innovación y Universidades of the Spanish Goverment  and the  Comunidad de Madrid  for the nancial support.
While writing this Review, Chen, Britt, and co-workers reported an investigation of the different steps of HydG reaction using density functional theory calculation. 62 In this work, they suggest that p-cresyl• would abstract a hydrogen atom from 2-iminoacetate that would be key to the fragmentation into cyanide and a •COO -  radical. Yet, such a proposed 2-iminoacetyl radical has not been detected while the p-cresyl• EPR signal decayed over time. 53 Hence, further experimental evidence is needed to validate the proposal. HydE: A Radical SAM Cysteine Desulfurase? Despite a less than 20% amino acid sequence identity with biotin synthase, in 2001, HydE primary structures were misidentified as BioB proteins in most of the databases. Meanwhile, HydE remained without a known substrate or function until 2020, fueling speculations about its role in [FeFe]-hydrogenase maturation, even suggesting at some point that it was not essential to the assembly of the [2Fe] H subcluster.
65 Yet, despite our many attempts, no structure of complexes between these molecules and the protein was obtained. Meanwhile, we performed an extensive structurebased molecular docking screening with various databases, including the KEGG E. coli metabolite database, to obtain leads for HydE substrate identification. The main challenge came from the fact that molecular docking programs are designed to maximize scores for high affinity ligands, which is usually not the case for substrates. Nevertheless, among the putative ligands, several molecules led to HydE-compound structures allowing the identification of thiazolidine derivatives as non-natural substrates. 66 Cocrystallizing HydE with various thiazolidine, thiazinane, and pyrrolidine derivatives unveiled a site distinct from sites S1-3 and that seems to strongly favor sulfur atoms.
Among the many crystal structures of TmHydE available in the Protein Data Bank and corresponding to different space groups and different crystal packing, this stretch was observed in all but one structure, where it is disordered, leaving the cavity wide open, thus suggesting a leaving path for the HydE end product to be delivered to HydF. Likewise, a putative channel that may involve the auxiliary cluster, when present, was proposed to afford complex-B delivery from HydG to HydE (Figure  14 ). Therefore, the overall HydE architecture supports the enzyme functions as a nanofactory assembly line, similar to the case of HydG. The questions regarding the true HydE product, its production mechanism involving C5′-S bond breaking, and substrate/product delivery from/to partners remain to be addressed.
Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. Therefore, this article provides data that can be used to create a simple zero order energy system model for DR Congo, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost) for 2020-2050.
Data accessibility With the article and in a repository. Repository name: Zenodo. Data identi cation number: v1. 0. 0. Direct URL to data: https://doi. org/10. 5281/zenodo. 4737640 Value of the data These data can be used to develop national energy system models to inform national energy investment outlooks and policy plans, as well as provide insights on the evolution of the electricity supply system under different trajectories. The data are useful for country analysts, policy makers and the broader scienti c community, as a zero-order starting point for model development. These data could be used to examine a range of possible energy system pathways, in addition to the examples given in this study, to provide further insights on the evolution of the country's power system. The data can be used both for conducting an analysis of the power system but also for capacity building activities.
Data Description The data provided in this paper can be used as input data to develop an energy system model for DR Congo. As an illustration, these data were used to develop an energy system model using the cost-optimization tool OSeMOSYS for the period 2015-2050. For reference, that model is described in Appendix A and its data les are available as Supplementary Materials. Figure  1  shows a zero-order model of the production of electricity by technology over the period 2020 to 2050 for a least cost energy future, repeated from the appendix. This is purely illustrative. Using the data described in this article, the analyst can reproduce this, as well as many other scenarios, such as net-zero by 2050, in a variety of energy planning toolkits. The data provided were collected from publicly available sources, including the reports of international organizations, journal articles and existing model databases.
The total power generation capacity in DR Congo is estimated at 3139. 6 MW in 2018  [3, 4, 5, 6 ]. The estimated existing power generation capacity is detailed in Table  1  below  [3, 4, 5, 6]. The methods used to calculate these estimates are described in more detail in Section 2. 1. Data on the installation year of each power plant can be found in the country dataset published on Zenodo.
Electricity Supply System Data Data on DR Congo's existing on-grid power generation capacity, presented in Table  1 , were extracted from the PLEXOS World dataset  [3, 4, 5]  using scripts from OSeMOSYS global model generator  [24]. PLEXOS World provides estimated capacities and commissioning dates by power plant, based on the World Resources Institute Global Power Plant database  [5]. These data were used to estimate installed capacity in future years based on the operational life data in Table  2. Data on DR Congo's off-grid renewable energy capacity were sourced from yearly capacity statistics produced by IRENA  [6]. Cost, e ciency and operational life data in Table  2  were collected from reports by IRENA  [7, 8, 9] , which provide generic estimates for these parameters by technology. These reports also provide projections of future costs for renewable energy technologies.
These data are presented in Table  3  and Figure  2 , where it was assumed that costs fall linearly between the data points provided by IRENA and that costs remain constant beyond 2040 when the IRENA forecasts end (except for offshore wind, where the IRENA forecast continues to 2050). Country-speci c capacity factors for solar PV, onshore wind and hydropower were sourced from Renewables Ninja and the PLEXOS-World 2015 Model Dataset  [3, 10, 11]. These sources provide hourly capacity factors for 2015 for solar PV and wind, and 15-year averages monthly capacity factors for hydropower, the average values of which are presented in Table  2. These data were also used to estimate capacity factors for 8 time slices used in the OSeMOSYS model (see detail in Annex 1). Capacity factors for other technologies were sourced from reports by IRENA [8,12], which provide generic estimates for each technology.
Emissions Factors and Domestic Reserves factors were collected from the IPCC Emission Factor Database  [17] , which provides carbon emissions factors by fuel. Domestic renewable energy potentials for solar PV, CSP and wind were collected from an IRENA-KTH working paper  [18] , which provides estimates of potential yearly generation by country in Africa. Other renewable energy potentials were sourced from a regional report by IRENA  [19]  and the World Small Hydropower Development Report  [20] , which provide estimated potentials in MW by country. Estimated domestic fossil fuel reserves are from the websites of The World Bank and US EIA  [21, 22] , which provide estimates of reserves by country.
The dataset includes the techno-economic parameters of supply-side technologies, installed capacities, emission factors and nal electricity demands. Below shows the different items and their description, in order of appearance, presented in this article. Item Description of Content Table 1 A table showing the estimated installed capacity of different power plant types in DR Congo for 2015-2018 Table 2 A table showing techno-economic parameters for electricity generation technologies Table 3 A table showing capital cost projections for renewable energy technologies up to 2050 Figure 2 A graph showing capital cost projections for renewable energy technologies from 2015-2050 Table 4 A table showing cost and performance parameters for power transmission and distribution technologies Table 5 A table showing cost and performance data for re nery technologies Table 6 A table showing fuel price projections up to 2050 Figure 3 A graph showing fuel price projections from 2015-2050 Table 7 A table showing carbon dioxide emissions factors by fuel Table 8 A table showing estimated renewable energy potential in DR Congo Table 9 A table showing estimated fossil fuel reserves in DR Congo Figure 4 A graph showing a nal electricity demand projection for DR Congo from 2015-2070 1.
These cost data include projected cost reductions for renewable energy technologies, which are presented in Table 3. The cost and performance of parameters of fossil electricity generation technologies are assumed constant over the modelling period. In this analysis only xed power plant costs are considered, which capture variable operation and maintenance costs. Country-speci c capacity factors for solar PV, wind and hydropower technologies in DR Congo were sourced from Renewables Ninja and the PLEXOS-World 2015 Model Dataset [3, 10, 11]. Capacity factors for other technologies were sourced from the International Renewable Energy Agency [8, 12]   and are applicable to all of Africa. Average capacity factors were calculated for each technology and presented in the table below, with daytime (6am -6pm) averages presented for solar PV technologies.
e. , with the following approximation: d{ } d ≈ +1 - ∆ (18) With: { } = { } (19) leading to the following system solved in Matlab ® : 1 ∆ [ ] + [Λ ] { } = 1 ∆ [ ]{ } + { } (20) [ ] is a capacitance matrix [J•m -1 •K -1 ], [Λ ] a conductance matrix [W•m -1 •K -1 ] and { } a vector [W•m -1 ] defined as: [Λ ] = ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎡ 1 - 1 0 0 0 - 1 1 + 1 - 1 0 0 0 0 - + 0 2 0 0 - - 0 0 0 ̇ , ̇ , ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎤ (21) [ ] = ⎣ ⎢ ⎢ ⎢ ⎡ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ⎦ ⎥ ⎥ ⎥ ⎤ (22) { } = ⎩ ⎪ ⎨ ⎪ ⎧ 0 + 0 ⎭ ⎪ ⎬ ⎪ ⎫ (23) The typical execution time of the numerical model is a fraction of a second. Note that the model considers that all temperatures are constant along the pile and neglects the axial heat transfer. However, since the yearly variations of temperatures affect the ground up to a few meters, additional research is still required on how these changes, which result in non-uniform initial ground temperature T0 measurements, could affect the TRT result.
Analysis Approach The initial temperature was determined from the early TRT phases, before heat was injected to the ground. This circulation phase lasted 9 min for set B and 60 min for set C. Averaging the measured temperature yields T0 = 24. 97 °C for set B and 14. 23 °C for set C. For set B the value was confirmed to be appropriate with reference to three temperature sensors in a nearby borehole. Less than 0. 5 °C variation in ground temperature was observed over 18 m depth. In this paper, the RMSE (Equation (  4 )) was minimized with the Matlab ® software. { } was determined with the fmincon function, using the SQP (sequential quadratic programming) solver. The active-set solver was tested, but appeared to stop before converging to a local minimum of ({ }) in some cases, providing unreliable results.
A more realistic explanation is that set C measurements do not need to be corrected since specific care has been taken to insulate the pipes. For set B, the perturbation of the fluid temperature, estimated as a deviation from the fitted Jacob approximation of the ILS (Equation (  3 )), can reach c. a. 0. 4 °C, while it remains below 0. 1 °C for set A. The engineer sizing a ground-source heat pump is concerned with the ability of the models representing every system component to forecast the evolution of the fluid temperature at small, medium and large time scales. The capability of the purely resistive model coupled to the ILS (Equation (  1 )) or the ICS (Equation (  11 )) has been assessed for both experimental datasets (Figure  6 ). The TRNSYS Type 557, which is based on the duct storage model (DST) for ground heat exchangers  [25] , has been included in the comparison as well.
The DST uses superposition of three elements: a heat balance on the fluid, a local resistive process close to an individual pipe (so-called "duct") rather than the borehole, and a global process. 3 ) is also represented as it is used for the interpretation, though it may lead to a significant underestimation of the temperature at small time scales due to its logarithmic behavior. After 1 h of thermal load the ILS model (Equation (  1 )) overestimates the temperature of the fluid Tfl by ≈5. 0 °C for set B and by ≈3. 3 °C for set C. For both datasets, neglecting the thermal inertia of the concrete leads to an underestimation of the pile heat transfer capability, and therefore possibly a cost-ineffective geothermal equipment of many more piles than required. Indeed, the heat pump could exchange a larger amount of thermal energy with the underground than a purely resistive model would forecast.
Interpretation with the New Resistive-Capacitive (RC) Model Figure  7  shows the back-analyzed thermal conductivity for the two datasets using the RC model coupled to the infinite cylindrical source. For set B, the conductivity oscillates in the range 3. 1-3. 3 W•K -1 •m -1 as long as t < 103. 4 h, so before the power shutdown (Figure  7c ). λm exhibits smaller variations than the classical interpretation, suggesting that the new approach is better capable of dealing with daily perturbations in the measured temperature since it uses temporal superposition. However, though the RC model has the ability to deal with time-varying temperature changes, it fails to properly estimate the ground conductivity when encompassing data after the power shutdown, λm then soaring to the range 3. 7-3. 9 W•K -1 •m -1.