dataesr/scientific-paragraphs-categorization huggingface dataset:
Only the subset of remaining articles predicted to be most likely to be relevant are then screened manually. We set out the screening process we followed in detail below, and summarise in figure  2. First, to ensure screening consistency between reviewers and a common understanding of inclusion/exclusion criteria, four authors screened the same set of 100 articles independently at the beginning of the process. The screening results were compared and reviewers jointly discussed consistency in the light of inclusion/exclusion criteria (table  2 ). After the consistency check, the reviewers screened a further 508 records independently, resulting in an initial random sample of 608 documents. We added 41 articles identified a priori as pertinent for our topic in order to provide more 'relevant' labels in our training dataset.
Step 4: coding the evidence and production of evidence database We developed a codebook (included as Supplementary material 3 to this article) for systematic extraction of information. In our codebook we reported results for each study in rows. We define a study as an instance where geographical and/or sectoral carbon lock-in is reported (either qualitatively or quantitatively). Overall, we found a total of 431 studies across the 226 included publications. For each study we extracted the meta-data (Authors, Title, Date, DOI) of the corresponding publication. We further recorded qualitative data on the study context such as the scale of analysis, the geographic areas and the sectors studied. For each quantification of carbon lock-in, we also extracted the numerical indicators related to carbon lock-in and their corresponding values.
It is the year in the mitigation scenario when emissions start to be different from emissions in the baseline scenario (i. e. the scenario with no climate policy). For static analyses based on retirement schedules of the existing stock, we considered the year of the dataset as the starting year of emissions reduction. The NDC scenario refers to a trajectory of emissions reductions consistent with the national determined contributions until 2030 and accelerated emissions reductions after 2030 to meet the long term temperature target. is implemented, the higher the expected stranded assets are. Even if the climate policy is implemented before 2020, the short-term level of stringency is a determining parameter. Compared to the scenarios assuming a strong emissions reduction as early as 2015 and consistent with a 2 • C target, global cumulative stranded coal power assets are expected to be higher by a factor 2-3 for National Determined Contributions (NDC) pathways (these NDC pathways correspond to scenarios where mitigation policies until 2030 correspond to the NDCs from Paris Agreement on Climate Change and where emissions reductions accelerate after 2030 to meet the long term temperature target).
Conclusion Long-lived capital-stocks, such as infrastructure and buildings, induce path-dependent evolution and therefore constitute a constraint for the rapid reduction of greenhouse gas emissions. Here, we systematically map the literature about carbon lock-in. To do so, we use machine learning to identify 226 relevant publications for a set of 38 095 articles from the Web of Science and Scopus databases. We extracted qualitative and quantitative information for each publication. The synthesis of this information reveals common analytical approaches, as well as geographical or sectoral specificities. We find that research on carbon lock-in has mainly focused on power generation. We also identify a bias toward developed countries, which represent the majority of our sample. Comparing the share of global emissions in 2018 and the share of publications for each country, we also highlight understudied countries such as Russian Federation or China (even though the latter has been the most studied country in the literature, it represents a smaller share of publications than its share in global emissions) and overstudied countries such as the United Kingdom of Great Britain and Northern Ireland, Australia, Sweden or Germany.
Acknowledgments Vivien Fisch-Romito  acknowledges funding from the Long-term modelling chair for sustainable development (Ponts Paristech-Mines Paristech). Jan Minx  acknowledges funding from the  German Federal Ministry of Education and Research  within the  PEGASOS  project (Grant Reference: 01LA1826A). Céline Guivarch acknowledges funding provided by the  NAVIGATE  project ( H2020/2019-2023 , Grant Agreement Number  821124 ) of the  European Commission.
Acknowledgement This work was supported by the platform of Biophysics of I2BC supported by  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05-05 , the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  675006  ( SE2B ), and  Gilibert  project  S-LZ-19-3. We would like to thank  Dr. Nami Yamano  (The  OCU Advanced Research Institute for Natural Science and Technology, Osaka City University, Japan ; present address,  Ren Min University of China, Beijing, P. R. China)  for her contribution to prepare a pigment, siphonaxanthin. We also thank a past master course student  Mr. Ryuichi Kano  ( Graduate School of Science, Osaka City University ) for his contribution to cultivate Codium fragile. We also thank  Dr.
All measurements were performed at constant potentials of -1. 2 V, -1. 1V, -1. 0 V and -0. 9 V vs. RHE. Time-resolved spectra under CO2RR conditions were acquired every 12-15 min until no further changes were observed. Data alignment and normalization of the X-ray absorption near edge structure (XANES) spectra were carried out using the Athena software. Fitting of the Cu K-edge extended X-ray absorption fine structure (EXAFS) spectra χ(k)k 2 of the as-prepared catalysts was carried out in R-space in the range from Rmin = 1 Å up to Rmax = 2. 1 Å, while for the catalysts in the reduced state, Rmin = 1. 0 Å to Rmax = 3. 0 Å were used. The Fourier transforms were carried out in the k-range from 3. 0 Å -1 to 10. 0 Å -1 with a kweighting of 1, 2 and 3. Fitting parameters were the coordination numbers N, interatomic distances R, disorder factors σ 2 for Cu-O and Cu-Cu paths, as well as the corrections to the photoelectron reference energies ΔE0.
The S0 2 factors obtained from the deconvoluted EXAFS data of the references were found to be 0. 87 ± 0. 02 for Cu foil, 0. 32 ± 0. 01 for Cu2O. Computational details. All density functional theory (DFT) calculations were carried out in the Vienna Ab-initio Simulation Package (VASP) code with the projector augmented-wave (PAW) method. The exchange-correlation energy was treated using a general gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE).
Acknowledgments D. V. , K. Q. , and H. L. W. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 804320 ). K. Q. and Y. Z. acknowledge financial support from the  China Postdoctoral Science Foundation  ( 2018M633127 ) and the  Natural Science Foundation of Guangdong Province  ( 2018A030310602 ). J. L. acknowledge financial support by the  National Natural Science Foundation of China  ( 21808134 ). We thank.
A 0. 25 cm 2 plastic mask was used to limit the area of the cell receiving illumination during the IMPS measurements. EIS measurements under illumination were performed by applying a potential equal to the observed open-circuit voltage under constant illumination by white, blue (480 nm), and red (650 nm) Thorlabs LEDs over a wide range of DC light intensities. A 10 mV perturbation in the 10 -1 to 10 5 Hz range was applied. The NOVA 2. 1. software was used to generate the data. Z-view equivalent circuit modeling software (Scribner) was used to fit the spectra. A flow chart of the EIS measurements of the NPI cells to ensure that all the different experiments were performed on both the activated and deactivated forms of the dye can be observed in Figure  10. The EIS measurements under dark conditions were conducted ensuring that no external source of light interfered with the experiment and by applying an external voltage from 0.
■ ACKNOWLEDGMENTS This work was funded by the  Ministerio de Ciencia e Innovación of Spain, Agencia Estatal de Investigación (AEI)  and  EU (FEDER)  under grants  PID2019-110430GB-C22  and  PCI2019-111839-2  (SCALEUP) and  Junta de Andalucia  under grant  SOLARFORCE  ( UPO-1259175 ). The authors gratefully acknowledge support from the  Ministerio de Universidades and Universidad Pablo de Olavide  through the  Beatriz Galindo program  under project  BEAGAL 18/00077  and grant  BGP s18/00060. A. J. R. thanks the  Spanish Ministry of Education, Culture and Sports  for its supports via a PhD grant ( FPU2017-03684 ). R. D. acknowledges  ANR  for funding through the  ODYCE  project (grant agreement number  ANR-14-OHRI-0003-01 ) and the  European Research Council. V. M. M. thanks the  French Embassy in Kenya through Campus France for a scholarship grant.
Governing equations In this work, COMSOL Multiphysics software was used  [52] , in which a dynamic two dimensional (2D) model of heat transfer through the hollow brick is solved using the finite element method. Two configurations are studied in which the holes of the brick contain PCM or air. The time step (Dt) of simulation and mesh refinement influence, and the validation of the model, were carried out in our previous work  [47]. Finally, simulations are performed with a triangular mesh of 3088 elements, time step of Dt = 360 s, and a value of 10 -6 for the iteration convergence criterion. To evaluate heat flux evolutions and temperatures inside our hollow brick, we considered the following hypotheses: -Bricks have constant thermophysical properties; -Bricks are dry (non-evaporative heat source); -Contact between PCM/air and brick hole surfaces is ideal; -No convection within the melted phase of PCM; -Range of narrow-phase change temperature is 1 °C; -No thermal mass and ground radiation.
Data To perform our probabilistic forecasts, we used data from two sites located in France. For each case, we have ground-based GHI measurements from pyranometers, as well as satellite-derived GHI maps on radius larger than 50 km around the locations of the sensors. The characteristics of both sites and the period for which data is available are summarized in Table  1. The satellite-derived data used are time-series of GHI maps with a native time step of 15 min and a spatial resolution of approximately 3 × 4. 5 km. This satellite dataset has been extracted from the database HelioClim-3 version 5, based on the Heliosat-2 method applied on images from the sensor SEVIRI of Meteosat Second Generation (0 • Service). This database available online at http://www. soda-pro. com (last access: 23 June 2021) and is used both for research and industrial developments.
It can be used for historical analysis, starting at February 2014 up to the current day, and is also available at near real-time with a time lag of less than few minutes. Numerous validation and performance analyses of HelioClim-3 for different regions and climates have been performed  [51] [52] [53] [54]  and demonstrate the good reliability of this database to capture the spatial and temporal variability of the surface solar irradiance. The ground-based measurements of GHI used as reference are first quality-checked at their native time resolution: 1-min and 5-min time steps, respectively, for Carpentras and Signes. This quality check first uses an automatic procedure based on upper and lower limits for extremely rare intervals, as described by Reference  [55] , and then on a visual inspection.
These time-series of GHI are then averaged to 15-min to be consistent with the satellite data acquired every 15-min. Only data corresponding to a sun elevation angle larger than 10 • is considered both for ground measurements and satellite estimations. Due to the native time resolution of the considered data, forecasts were completed for 15-min intervals, up to a 1-h horizon. However, it is important to note that, just as in the deterministic CMV-based models, the Pr-CMV is very flexible: without being constrained by the resolution of the data, it can be adapted to any choice of time horizons. The CDF of the Heliosat-2 estimation errors are obtained based on a whole year used for the training period (2015), different for the whole year 2016 used for the performance analyses, in order to have a dataset representative for the four seasons.
Scanning transmission electron microscopy (STEM) experiments were carried out using a probe aberration corrected Titan (Thermo Fisher Scientific) equipped with a high-brightness field emission gun. While the electron gun was operated at 300 keV for acquisition of high-angle annular dark field (HAADF, acceptance angle 47. 9 mrad) STEM images to obtain maximum spatial resolution (convergence angle 25 mrad), the high energy was lowered to 80 keV for electron energy-loss spectroscopy (EELS) to minimize beam damage, to increase the EELS signal and to improve energy resolution (~1 eV). The Gatan imaging filter (GIF, Gatan Inc) was operated at a dispersion of 0. 2 eV /px in order to simultaneously analyze OK, FeL and CoL edges with a collection angle of 119 mrad. EELS spectra and spectrum images (SI) were treated using a custom Matlab software including principal component analysis (PCA) for noise reduction.
The spectra were fitted by means of the MOSFIT program  46  involving asymmetrical lines and lines with Lorentzian profiles, and an α-Fe foil was used as the calibration sample. The values of isomer shift are quoted relative to that of α-Fe at 300 K. SQUID magnetometry. Magnetic measurements were performed in using a Superconducting Quantum Interference Device (SQUID) magnetometer (Quantum Design MPMS-XL 5). Temperature dependent zero-field cooled (ZFC) and field cooled (FC) magnetization curves were recorded as follows: the sample was introduced in the SQUID at room temperature and cooled down to 5 K with no applied magnetic field and after applying a careful degaussing procedure. Then, a magnetic field of 7. 5 mT was applied, and the ZFC magnetization curve was recorded upon heating from 5 to 400 K.
Although the Fe3- O4 shell is very thin and discontinuous, it has a significant influence on the magnetic properties of CS nanoparticles. In comparison, pure CoFe2O4 nanoparticles with similar size to that of CSS display close value of TB to ours, although a much lower amount of Co atoms was incorporated. Furthermore, the softness of the Fe3- O4 shell resulted in the decrease of HC and MR while MS was preserved. Such a high control of the structure of the nanoparticles is particularly interesting to modulate their magnetic properties, thus extending their potential to a wide range of applications. ASSOCIATED CONTENT Supporting Information. Additional data related to STEM-HAADF micrographs, EELS spectra, EELS-SI mapping, GPA, FTIR, granulometry, magnetism, XMCD spectra. The Supporting Information is available free of charge on the ACS Publications website.
Results and Discussion The structure and morphology of the Pt/C, Pt/RGO, and Pt/RGO(s) catalysts were determined through TEM analysis. Figure  1  shows that instead of some agglomeration, the Pt nanoparticles of all of the studied catalysts were quite small and finely dispersed over the surface of the carbon substrate. Their size distributions (see insets of Figure  1 ) were obtained using the well-known ImageJ software  [46]. The analysis was based on no less than 100 particles for each catalyst. The size of most Pt nanoparticles (ca. 80%) was between 3 and 5 nm. The mean particle sizes of the Pt/C, Pt/RGO, and Pt/RGO(s) catalysts were 3. 6, 3. 9, and 3. 95 nm, respectively. Particle agglomeration made it difficult to precisely evaluate the nanoparticle size, thus XRD analysis was used to find the Pt crystallite size (see below).
3. 60, 3. 55, and 3. 50 nm, respectively. It should be mentioned that the mean size of the RGOsupported Pt nanoparticles is slightly higher in comparison with the Vulcan XC-72 supported ones, possibly because of some agglomeration of RGO during its reduction (thermal or chemical)  [39]. The simultaneous reduction of precursors did not significantly affect the mean Pt crystallite size (measured by XRD) or the mean nanoparticles size (d TEM ) deduced from the analysis of the TEM images (see combined Pt crystallites/nanoparticles sizes data in Table  1 ). CasaXPS software  [50]  was used to analyze the experimental data and decompose the lines into constituent components. The Gaussian-Lorentzian sum functions were used to interpret the spectra of the individual lines. The National Institute of Standards and Technology (NIST) laboratory database was used to determine the positions and shapes of the lines  [51].
The powdered samples were pressed into carbon scotch tape and were transferred to the vacuum chamber of a spectrometer with a base pressure of 3 × 10 -9 mbar. All of the spectra were measured in fixed analyzer transmission mode, with a pass energy of 120 and 40 eV for the survey spectra and separated lines, respectively. To analyze the experimental data and decompose the lines into constituent components, CasaXPS software (v. 2. 3. 23, Casa Software Ltd. , Teignmouth, UK) was used  [50]. The NIST laboratory database (v. 4. 1, National Institute of Standards and Technology, Gaithersburg, MD, USA) was used to determine the position of the lines  [52].
Introduction On 1 October 2018, the Integrated Single Electricity Market (I-SEM) went operational. It is now the enduring operational electricity market for both the Republic of Ireland and Northern Ireland  [1]. EirGrid plc and SONI Ltd. respectively act as the Single Electricity Market Operator (SEMO) for the island of Ireland. This market structure was drafted to integrate the all-island electricity market with European electricity markets  [2]. Observing SEMO market data from the first half of 2021, the energy prices reflect a record season in the European electricity markets and Ireland was no exception. During this period, the average electricity prices in the Irish SEMO SPOT market were around EUR 81/MWh with a deviation of approximately EUR 15/MWh. These values are relatively high figures for the usual price levels at that time of year, which in the previous two years fluctuated at around EUR 43/MWh.
Data Domain literature suggest chronological index parameters, lagged variables, and data relating to seasonality as strong potential model inputs  [15]. Additionally, exogenous variables such as generation capacity, load profiles, and ambient weather conditions have already been identified as suitable variables to explain electricity price dynamics  [16, 17]. The impact of external variables on the Irish day-ahead I-SEM spot prices are comprehensively investigated here by examining their correlations using the Pearson Correlation Coefficient (PCC). Variables tested include air temperature, wind speed, wind direction [18], oil prices (https://github. com/datasets/oil-prices [1] , accessed on 1 May 2021), and natural gas prices (https://www. eia. gov/dnav/ng/hist/rngwhhdD. htm [2] , accessed on 1 May 2021).
This process used 80% of the available data, i. e. , 277 days. Evaluation on test set (constituting 20% of total data instances for the period 1 January to 12 December 2019 that are chronologically consecutive, i. e. , 69 days). Model training, cross validation (cv), and hyperparameter tuning on randomly sampled training data for the period 30 September 2018 to 12 December 2019. The training and validation process involved 394 days of data. Evaluation on test set constituting 10% of randomly sampled data instances for the period 30 September 2018-12 December 2019, i. e. , 44 days. For data metrics, 30 iterations of the latter two steps are performed-an averaged thereof taken as the final score. The models were implemented in Python 3 using the scikit-learn  [26] , XGBoost  [27] , and LightGBM  [28]  libraries.
These Python packages are built on C libraries via Cython for more efficient execution times  [26]. With 24 (hourly prices) values to predict, each model makes 24 predictions simultaneously. To achieve this, 24 separate model instances were created during model training, i. e. , one for each hour of D + 1. Models and transformers such as scalers were only fitted to training data to prevent leakage (experimental code/dataset is available upon request).
Evaluation To demonstrate the volatility of the dataset, a univariate analysis of the electricity prices was performed for the evaluation period of interest, 1 January-12 December 2019. This saw min/max values of-EUR 11. 86 and EUR 365, respectively. Figure  2  illustrates a Probability Density Function (PDF) of electricity prices for the same period, which indicates a higher than usual mean of circa EUR 50, as discussed earlier in the paper. From the best features determined, detailed in part in Section 3. 1, the Taguchi method, a process/product optimization method that is based on planning, conducting, and evalu-ating results of matrix experiments  [29] , was then employed to test the impact of each of the selected features and their impact on GBM, XGBM, and LGBM model performance for multi-step ahead prediction.
Computational details In this study, the DFT-based Vienna ab initio simulation package (VASP) was employed to perform calculations  [30, 31] , using Perdew, Burke, and Ernzerhof (PBE) method for treating exchange and correlation. The valence electrons were described with a planewave basis set, with an optimized cut-off energy of 400 eV  [32, 33]. Nuclei and core electrons were treated with pseudopotentials of the projector augmented wave type. To improve the description of the long-range interactions, the DFT-D3 method of Grimme as implemented in VASP was employed  [34]. The Kohn-Sham equations were solved self-consistently until the energy difference between cycles becomes lower than 10 À4 eV. Methfessel-Paxton smearing with σ ¼ 0. 1 eV was used  [35]. The ionic positions were optimized until all forces were smaller than 0.
Physical characterization includes grazing incidence X-ray diffraction (GIXRD) performed on a Bruker diffractometer tted with a Cu X-ray source (l K ¼ 1. 5418 Å), energy-dispersive X-ray spectroscopy (EDX) using a Bruker QUANTAX X-ray detector mounted in a Zeiss Supra 40 scanning electron microscope and transmission electron microscopy (TEM) investigations using a JEM-ARM 200F Cold FEG TEM/STEM operating at 200 kV, equipped with a spherical aberration (Cs) probe and image correctors (point resolution 0. 12 nm in TEM mode and 0. 078 nm in STEM mode). The experimental diffraction signals from GIXRD and TEM are compared with calculated XRD patterns using VESTA soware. 27 Crystal structures are obtained from the Materials Project (MP) database where each structure has a unique identication number in the form mp-xxxx.
7b ) and the distribution of each element, Pt, Cu and Bi, in the clusters (Fig. 7c-e, respectively ). On the one hand, Pt and Cu are homogeneously distributed along each cluster, which is in accordance with the formation of a PtCu alloy. On the other hand, Bi is mainly distributed on the borders of the clusters and on the grain-boundaries. This could give us the insight that ternary PtCuBi clusters present the structure of an alloyed PtCu core surrounded by a Bi shell. Molecular dynamic simulations have already conrmed such a kind of core-shell structure for the Pt-Bi system grown using a GAS, having its origins in stronger Pt-Bi bonding energy than for Pt-Pt. 8 2] [43]  The MP database  28  indicates that three stable alloys can be found for the Pt-Cu system, while two stable alloys are present for the Pt-Bi system.