dataesr/scientific-paragraphs-categorization huggingface dataset:
There are no data for binary alloys between Cu and Bi or ternary alloys between Pt, Cu and Bi but a metastable Cu-Bi alloy could be formed. 44, 45 Considering only the stable phases, Pt will preferentially alloy with Cu (and not with Bi) and Cu will be segregated from Bi; however the database compounds are calculated at 0 K and 0 atm and sputtering is a synthesis technique far-from-equilibrium. Therefore, the presence of metastable phases cannot be discarded. Due to differences in the growing process, properties such as crystallinity, lattice parameters and the nanostructure of the Ptbased clusters slightly change depending on the formation of mono-, bi-and tri-metallic clusters and these differences could affect their performances as electrocatalysts. For example, atom size differences between Pt and Cu in binary PtCu and ternary PtCuBi materials lead to differences in electronegativity that are responsible for d-band edge downshi,  36  with a possible change in the oxidative catalytic activity.
Acknowledgements This work was funded by the  French "Agence Nationale de la Recherche"  under grant  ANR-16-CE29-0007  ( ECO-PLAN  project) and by the  CNRS   PEPS  project  Dust4Pem. All the authors acknowledge  Olivier Weldling  and  Thierry Sauvage  from  CEMHTI laboratory (Orléans)  for the RBS analysis. The authors thank  Audrey Sauldubois  ( CME, Orléans ) and  Jaafar Ghanbaja  ( IJL, Nancy ) for the HRTEM measurements and  Thomas Lecas  and  Nicolas Dumuis  for technical support on the gas aggregation source.
The EXAFS data were collected up to 15 Å -1 , with a variable sampling step in energy (5 eV in the pre-edge region, 0. 5 eV in the XANES region, and Δk constant in the EXAFS region with 0. 03 Å -1 step), and an integration time of 3 s. Typically, 2-3 specimens of each mineral were examined, and 2-3 scans were averaged for each spectrum. Both Cu K-and Fe K-edge XAFS spectra were treated applying standard procedures implemented in the Demeter software package (version 0. 9. 26) based on the IFEFFIT program, version 1. 2. 12. 40 The k 2 -weighted Cu K-EXAFS oscillations were processed with the photoelectron wavevector k in the range 2. 3-11. 5 Å -1 , using a Hanning window with a sill width of 3 Å -1. The Fourier transformed R-space data were windowed in the range of 1. 0-2. 314 and 3. 082 Å for valleriites and chalcopyrite, respectively.
The photoelectron threshold energy E o was first fitted from the data and then kept constant. The EXAFS signals were simulated utilizing a single-path scattering approach. X-ray Photoelectron Spectroscopy. The photoelectron spectra presented here were recorded with a SPECS spectrometer equipped with a PHOIBOS 150 MCD9 hemispherical energy analyzer using Mg Kα radiation of the dual anode X-ray tube at an analyzer transmission energy of 20 eV for the survey spectra or 8 eV for narrow scans. The binding energies were calibrated against the C 1s line of the adventitious carbon (285. 0 eV); a low-energy electron source FG 20 (SPECS), applied to eliminate heterogeneous electrostatic charging, was operated with an electron energy of 0. 05 eV and a current of 10 μA. The spectra were fitted after subtraction of the Shirley-type nonlinear background using the Gauss-Lorentz peak shape using the CasaXPS software package.
Mossbauer Spectroscopy. Transmission Mossbauer experiments were carried out using an MC-1104Em spectrometer with a 57 Co(Rh) source; the source was kept at room temperature, while the absorber was either at the ambient temperature or cooled down to liquid nitrogen (78 K) and liquid helium (4. 2 K) temperatures. The powder samples (about 3 mg/cm 2 of Fe in thickness) sealed with Kapton tape were attached to an Al sample holder. Isomer shifts (IS) are given relative to α-iron at room temperature. Probabilities P of the quadrupole splitting P(QS) for doublets and of magnetic hyperfine fields P(H) for sextets were first determined from the experimental spectra. In the second stage, these data were used to generate a model spectrum and then to fit the experimental spectra varying the full set of parameters.
Measurement of DSSCs performance DSSCs performance was measured using a 50-W xenon lamp (PRO-LITE), and the intensity was measured using a multimetric SPM72 solar meter. The distance between the light and the DSSC was 20 cm, and the intensity of the lamp was adjusted using a lighting control dimmer. The V oc and I sc generated by DSSC under varying intensities (0-1000 W/ m 2 ignoring the effect of the temperature) were determined using an Agilent 34461A multimeter equipped with Keysight BenchVue software. Measurement of I sc and V oc produced by DSSCs at each light intensity was carried out for 3 min. The equivalent circuits of DSSCs were determined using an AMETEX solartron equipped with Modulab MTS software.
Data driven approaches: Machine learning and statistical models Data driven approaches have been successsfully applied to estimate the PEMFC state of health since it can be performed without a deep understanding of the aging process. Furthermore, data driven approaches are able to deal with systems having several interactions and features acting on their performance. Indeed, data based approaches can model the parameters' correlations and the operating conditions effects. Different data based prognostics approaches exist in the literature such as Adaptive Neuro-Fuzzy Inference System (ANFIS)  [30] , Summation Wavelet-Extreme Learning Machine (SW-ELM)  [31] , Echo State Network (ESN)  [10] , ELM, etc. These approaches require the availability of a large datasets. Statistical and black box models were widely applied to PEMFC prognostics.
The authors proposed in  [32]  an autoregressive integrated moving average (ARIMA) model and a polynomial model to predict the PEMFC power. The data used for prediction came from two different fuel cells ran under two different load profiles (a constant load profile and a variable one) for 1000 h. The data was processed using a discrete wavelet transform (DWT) in order to decrease the observations number (168 h of operation were represented by 21 h). A comparison of the polynomial and ARIMA model was done with and without DWT, and the authors confirmed that the best results were obtained using a combined ARIMA-DWT. Then the RUL was estimated using this model and the failure threshold was set at a loss of 5. 5% of the initial power. The authors highlighted the size decrease of the required database for the prognostics but they did not comment about a potential information loss when compressing data.
The method was applied on three datasets, carried out under a constant load profile. The proposed method performance was also compared to ANFIS. The authors confirm that the method they have proposed is more accurate when predicting the voltage, with a training base lower than the used one to train the ANFIS model. A PEMFC performance prediction model using a supervised learning technique, the relevance vector machine (RVM), was proposed in Wu et al. [37]. Experimental aging data was used to construct an RVM based on the voltage signals of a PEMFC. This model was then confronted with degradation data from two experimental tests of a 1. 2 kW fuel cell. The results obtained were then compared to a conventional support vector machine (SVM). RVM and SVM are data-based approaches both based on the concept of kernel functions.
The authors noticed the influence of the dataset and the voltage recovery phases effect on the accuracy. The authors affirmed that the accuracy decreased when using LSTM, compared to G-LSTM especially at the ripple parts of the voltage. However G-LSTM was able to predict the voltage trend accurately despite the voltage ripples. The effect of changing the sliding windows sizes was introduced, and the authors confirmed that small sliding windows gave smaller voltage prediction errors. The Echo State Network has good performances in terms of computational complexity and convergence rate. A comparison between several ANN (feed-forward, ANFIS and cascade neural network) in a voltage prediction task was performed by the authors in a previous work  [41]. ESN showed better results than the others in terms of accuracy and errors' dispersion.
Knocking down the locks linked to the hybridization of both data and model-based methodologies is not systematic  [19]. It is still necessary to have a physical understanding of the system to: (i) model the degradation phenomena in a physical way, and (ii) know how to recognize where the lack of knowledge exists in order to replace the physical model by a Black Box model. In addition, to set the part of the model performed by a black box, it is still necessary to have an experimental dataset. Ultimately, the realization of a hybrid prognosis model therefore requires physical knowledge of the system, experimental data, but also advanced knowledge in Prognostics and Health Management (PHM) in order to combine the two previous methods intelligently. The authors in  [47]  proposed a hybrid prognosis approach.
Experimental database: Micro combined heat and power generation mission profile The experimental data comes from the European project SAPPHIRE "System Automation of PEMFCs with Prognostics and Health management for Improved Reliability and Economy"  [68, 69]. The objective of the study consists in observing the PEMFC behaviour on a long term test, particularly under a micro-cogeneration µCHP load profile. The test includes the realization of static and dynamic characterizations: polarization curves and Electrochemical Impedance Spectroscopy to evaluate the PEMFC degradation. A five cells PEMFC with a 100 cm 2 surface area is used to carry out these tests and the test operating conditions are described in Table  1. Since the studied µCHP load profile realized in the framework of the SAPPHIRE project presents a repetitive cycle, only a part of this load profile is shown in Fig.
Markov chains The idea of repeating the known part of the load profile presented previously implies the following assumption: the PEMFC is expected to experiment exactly the same periodic events during its entire operation. This hypothesis can be strong for real life applications, even for stationnary applications. Thus, the generation of various load profiles from the available part of the database makes it possible to check our algorithm responses to different solicitations. These different created profiles introduce randomness in the forecasting part, making it possible to evaluate the robustness and accuracy of our algorithm in case of load profile change. Therefore, in order to make our algorithm more realistic, a Markov chain profile generation is proposed. A Markov chain is a mathematical process for describing state-tostate transitions of a system according to probabilistic rules.
X(1)→X(2)→X(3)→⋯→X(t)→X(t + 1)→⋯ From the equation above, the Markov property means that the movement from X (t) to X (t + 1) depends only on X (t), the current state, and not on previous states. Let's take the example shown in Fig. 12. In this purely random example, we start from a state 1 at time t. Statistical analyzes of previous data made it possible to establish that, when starting from a state 1, there is: • P(1→1) = 25% probability of remaining in the same state at the next moment, • P(1→2) = 35% probability to go to state 2 at the next instant, • P(1→3) = 40% probability to go to state 3 at the next instant. Normally, these probabilities are defined from a preliminary study of an existing time series, which will serve as a model. Then, after having defined a first state, a random variable R v (rand under Matlab®) will define the following states and generate a cycle.
An example of a Markov chain load profile is shown in Fig. 13. The deviation between the real load profile and the generated profile by Markov chain has been evaluated by calculating the NRMSE. 10 profiles have been generated and the NRMSE has been calculated for each one of them. This error varies between 26% and 27% when 50% of the collected data is dedicated to the training basis. For the same learning basis, repeating the same load profile cyclically gives a NRMSE of 28%. This result is expected since the cycle repetition is a particular case of Markov chain load profile generation. The advantage of using the Markov chain is the possibility to generate multiple load profiles. The generated load profiles integrate the effect of random while remaining faithful to the real load profile.
After draw various components with the SolidWorks software, the unit was 3D printed with a ymer (Figure  2 ). An isometric view of the AGMD cell is shown in Figure  3. This AGMD mod an air gap (air layer) whose thickness is adjustable defined by the thickness of th crossed filaments which separates the two compartments of the cell. The hot feed is in direct contact with the membrane. The condensing plate separates the air the cooling channel. Hot and cold channels have front dimensions of 142 mm by and thicknesses of 26 mm and 35 mm, respectively. In the hot channel, wate through a 3. 9 mm hydraulic diameter channel. The air gap contains a drain and a An isometric view of the AGMD cell is shown in Figure  3. This AGMD module has an air gap (air layer) whose thickness is adjustable defined by the thickness of the spacer crossed filaments which separates the two compartments of the cell.
A 0. 01 g precision scale was also connected to the Labview(National Instruments, Austin, TX, USA) data acquisition system which records the mass of permeate collected continuously. The propylene membrane has a 0. 25 µm thickness, a 55% porosity, and a 0. 064 µm pore size. The tests were carried out with a cold flow of 1 L/min, a hot flow of 0. 6 L/min, and an air-gap thickness of 1. 04 mm. The effect of coolant temperature on permeate flux is less important than the effect of hot feed water temperature, as expected  [48]. This behavior can be observed in Figure  5  with a variation of the hot water supply temperature from 25 to 55 °C. The shape of the curves respects the usual behavior of the effect of the hot water supply temperature on the permeate flux studied in the literature  [9, 15].
A 0. 01 g precision scale was also connected to the Labview(National Instruments, Austin, TX, USA) data acquisition system which records the mass of permeate collected continuously. The propylene membrane has a 0. 25 µm thickness, a 55% porosity, and a 0. 064 µm pore size. The tests were carried out with a cold flow of 1 L/min, a hot flow of 0. 6 L/min, and an air-gap thickness of 1. 04 mm. The effect of coolant temperature on permeate flux is less important than the effect of hot feed water temperature, as expected  [48]. This behavior can be observed in Figure  5  with a variation of the hot water supply temperature from 25 to 55 • C. The shape of the curves respects the usual behavior of the effect of the hot water supply temperature on the permeate flux studied in the literature  [9, 15].
In West Macedonia (Greece), CO 2 accounts as one of the largest contributors of greenhouse gas emissions related to the activity of the regional coal power plants located in Ptolemaida. The necessity to mitigate CO 2 emissions to prevent climate change under the Paris Agreement's framework remains an ongoing and demanding challenge. It requires implementing crucial environmentally sustainable technologies to provide balanced solutions between the short-term needs for dependency on fossil fuels and the requirements to move towards the energy transition era. The challenge to utilise and store CO 2 emissions will require actions aiming to contribute to a Europe-wide CCUS infrastructure. The Horizon 2020 European Project "STRATEGY CCUS" examines the potential for CO 2 storage in the Mesohellenic Trough from past available data deploying the USDOE methodology.
Each of these is situated in Portugal, Spain, Croatia, Greece, Romania, and France, with two regions. The scenarios identify CO 2 transport corridors between local CCUS clusters of industry, including the possibility of connecting to the North Sea CCUS infrastructure, reducing costs, and contributing to a Europe-wide CCUS infrastructure. Greece and especially West Macedonia is part of the project and are being investigated for the potential to deploy CCUS technologies to reduce carbon emissions during its energy transition from a high to a low carbon energy production system. Data regarding carbon emissions, related industrial infrastructure, potential industrial carbon utilisation, geological storage potential was collected from: (a) Public Power Corporation, (b) Greek Ministry of Energy, (c) available databases of the European Environment Agency (d) published scientific literature and (e) field research.
The first one provided a qualitative assessment of suitability to enhance the capacity estimate. Suitability covers all technical aspects of storage from reservoir capacity and quality to seals, faults and wells. The appraisal consisted of a Boston Square Analysis (BSA) score for both attribute suitability and data quality. The second fold provides a classification based on a pyramid approach. Capacity estimates were ranked using a quantitative resource pyramid approach consisting of four tiers that reflect the increasing maturity of data and understanding related to potential storage capacity from regional first approximations to targeted storage sites candidates. The requirements for each tier reflect this maturation  [60]. A tiered approach is a risk assessment progressing from relatively simple to more complex systems, reducing the evaluation's uncertainty when moving to higher tiers  [61].
e. , P90 (90% of reservoir have porosity greater than this value). The CO 2 density at reservoir storage conditions ρ res is calculated using temperature and pressure variables. The temperature at 1500 m and 2000 m depth was estimated from available geothermal gradients for Greece  [72]. Pressure was calculated by multiplying the average overburden density of the overlying rocks with depth. This conservative approach was adopted, as a first estimation, due to the fact that current data of seal fraction pressure is not as yet available. Using the previously mentioned parameters, the ρ res was determined using the Online Calculation Carbon dioxide (peacesoftware. de) software  [77]. Table  6  summarises the characteristics of potential onshore storage units' features in West Macedonia for a conservative scenario of a 0.
Suitability is scored by expert judgement. High values indicate good attributes such as high capacity, high reservoir porosity and permeability, an effective seal, an absence of problematic faulting, fracturing or well issues; low scores flag a prospect for review. Data quality (Figures  A1  and A2 ) indicates strengths and gaps in the evidence base  [71]. It was possible to classify attributes such as storage capacity, migration risk, level of fracturing and seal quality, which are in the medium to high suitability (index 2 and 3), and medium to high data quality (1-3) (Figure  6b ). Nevertheless, there is a lack of information to apply the BSA on some critical attributes, such as injectivity, number and conditions of existing wells, location and conditions for monitoring or intervention in case of leakage.
Fieldwork, drilling and seismic data interpretation will be essential steps to upgrade the CLSF pyramid rank and to improve the BSA classification, and its use for comparison between storage supporting decision-making. The Boston Square Analysis (BSA, Figure  6b ) represents the broader assessment for data quality (x-axis) and suitability of attributes (y-axis). This approach allows for a qualitative analysis for data suitability and data quality (both evaluated as an index 1, 2 and 3) of a suite of parameters (see Appendix A) from reservoir capacity and seal quality to injectivity and the presence of faults. Attributes leading to high and low scores in data quality describe the type of data (seismic, core, logging, and literature), their quantity and uncertainty. Suitability is scored by expert judgement.
Summary and Conclusions CO 2 is one of the most common GHGs emissions in West Macedonia (11-14% v/v of the total GHGs emissions). These emissions are mostly associated with the activity of coal power plants located in the region of Ptolemaida. The national strategy for a phaseout of coal power plants envisages that a single coal power plant will be operational by 2028. Future operation will depend on the utilisation of biomass as a renewable source of energy. In either case, CO 2 emissions will need to be captured and either utilised or stored. Currently (2021), under the Green Deal regime and the European energy transition fund there is an accelerated decarbonisation of the Greek electrical energy mix with the country's power plans being revised. During the H 2020 European Project "STRATEGY CCUS", the potential for CO 2 storage in the Mesohellenic Trough was re-evaluated from past available data deploying the USDOE methodology.
Addressing these gaps should be based on laboratory experiments, numerical simulations and field measurements. At the current stage, CO 2 storage in Greece remains in Tier 1 status. The current study provides a theoretical approach based on literature data and calculations regarding the CO 2 storage capacity within sedimentary formations of the Mesohellenic Trough (Western Macedonia, Greece). In this context, research uncertainties, such as porosimetry, depth estimations and storage capacity calculations are assigned to the lack of primary experimental data. The assessed storage is not matured sufficiently to meet this demand and need to be reappraised to deliver sufficient storage in the coming years and fulfill the country's net zero ambitions. Further research is going to be deployed in order to provide detailed mineralogical, petrological, geochemical, petrophysical and geological data.
These data will be combined with experimental results (such as CO 2 storage experiments) in order to propose comprehensive CO 2 storage scenarios. Based on the IEA Energy Technology Perspectives forecast  [81]  for CCUS contributions to mitigation, Europe requires injection rates of 52 million tonnes per year from 2025 to 96 million tonnes by 2030 to meet established targets. In the region of West Macedonia, further research will be held in order to provide matured results from theoretical to matched level regarding the CSLF.
Annual methanol production of the proposed FARWIND system design Since energy ships are mobile, their route schedules can be dynamically optimized based on weather forecasts in order to maximize energy production. This was performed using a modified version of the weather-routing software QTVLM  (Abd-Jamil et al. , 2019). The coordinates of the starting and arrival point are: N 50. 5; W 18. 9 (approximately 1,000 km from the port of Brest, France). Over the three years 2015, 2016 and 2017, it was found that an average capacity factor of over 75% can be achieved. That estimate does not consider downtime due to maintenance (availability). According to  (Sheng, 2013)  and  (Pfaffel, 2017) , the failure rate of wind turbines is in the order of one failure per annum. Given the greater complexity of the energy ship system (additional energy conversion subsystems in comparison to a wind turbine e.