dataesr/scientific-paragraphs-categorization huggingface dataset:
04 versus vC = 2. 55)  [91]. Also, the presence of P atoms in the carbonaceous matrix of the carbocatalyst serves as an active site and further regulates the local charge density of the carbonaceous matrix. These results support the conclusions reported by  [34]. Consequently, doping the N and P heteroatoms increases the number of active sites in the carbocatalyst matrix. It interacts with the PDS through an electron transfer phenomenon, generating the non-radical species 1 O 2 , which is responsible for the degradation of the organic pollutants. In addition, it has been reported that the two radical species SO 4. -and HO. bound to the surface of the carbocatalyst are less reactive than the free radicals produced by the radical route  [98]. Although having a larger dataset would support the results found and help to better understand the degradation mechanism involved in the system N, P-HC/PDS/RhB.
In  [16] , Powell et al. optimized the integration of storage in multi-energy networks by demonstrating the economic gains made by optimizing the use of storage. The study focused exclusively on operation with an MINLP approach to decide to operate some cooling sources or not. The authors proved that good storage operation could reduce costs by 16. 5%. Haikarainen et al. [17]  proposed an MILP model to optimize the structure and operation of a multi-energy network. One of the notable contributions was the use of pipelines that included both feed and return (to reduce heat losses), but the temperature levels were not considered, as in most of the linear approaches, which reduces the accuracy of renewable source integration. The calculations were performed via the Matlab interface and Cplex MILP solver in about 15 min.
Existing Tools to Promote Renewable Energies in DHNs The number of energy network analysis tools has steadily increased in recent years. Three areas are particularly studied, namely electricity networks, heating networks and transport networks. Many tools are designed to cover each of these three sectors. Some tools, such as TRNSYS  [26] , are made for accurate simulation and analysis, while others, such as energy plan  [27] , study systems at a larger scale. A review of existing tools was conducted by Conolly et al. [28]  in 2010 and more recently by Limpens et al. [29]. In their article, Hörsch et al. [29]  introduced the Python for Power System Analysis (PyPSA) tool that permits study of the production and transmission of electricity across all Europe, but thermal modeling is not robust yet.
On the other hand, 24 kinds of typical days for electric energy sold to the grid were taken into account. Another approach commonly used in the literature is the clustering method presented by F. Domínguez-Muñoz et al. [40]  to extract typical days for the heat and cold loads of a system. User intervention is required to determine the number of typical days in addition to the peak days for heating and cooling demand. Repetition of each typical day is determined with this method. The different methods described here all have their own criteria to check how close the TDs are to the real data and how to improve their similarities. The clustering method is able to classify the data that are the most similar. A combined heuristic and clustering method is employed here to reduce the dimension of the optimization model to a manageable size.
Results The results were obtained with the resolution strategy presented by Delubac et al. [9] , which combined a step-by-step resolution from a simplified model to the full complete case and a random initialization strategy to select the best optimum among all optimal results. The problem was formulated in Julia language  [46] ; its optimization package JuMP  [47]  and the NLP problem were solved with the open solver Ipopt. The resolution strategy was applied to solve the problem over 400 times in about 2 h with a convergence rate of approximately 30%.
X-ray absorption near-edge structure (XANES) XANES experiments were performed at the Fe K-edge (7112eV) at the BL22 -CLAESS beamline of the ALBA synchrotron (Spain) and at the BM23 beamline of the ESRF synchrotron (France). Samples were measured in fluorescence yield mode at low temperatures (77 K at ALBA, 10 K at ESRF) to avoid radiation damage in the cells. The monochromator used in both experiments was a double Si (111) crystal. XANES spectra were measured up to k = 9 Å -1. Reference samples such as horse spleen ferritin (HoSF, (Sigma-Aldrich, F7879)), magnetosomes, maghemite, goethite, and others were measured in transmission configuration. The experimental spectra were normalized using standard procedures for background subtraction and data normalization as implemented in the free software Athena of the IFEFFIT package.
The second proposed mechanism (i. e. , excretion to the surrounding environment) is confirmed by the progression of the total iron mass of the cell populations determined by ICP-AES. As observed in Figure 2B, F I G U R E 1 (A) Histograms representing the side scattered light as a function of time in A549 and RAW 264. 7. (B) TEM images of A549 and RAW 264. 7, 2 h and 8 days after magnetosome uptake. Yellow arrows point at magnetosome clusters and cellular nuclei are marked N. F I G U R E 2 (A) Progression of the number of cells as a function of time for A549 and RAW 264. 7. Data represent the mean ± standard deviation, n = 3. The line is an exponential fit to the data. (B) Total iron mass measured by ICP-AES of the whole cell population of A549 and RAW 264. 7 along time after magnetosome uptake.
I G U R E 3 (A,B) Hysteresis loops of A549 and RAW 264. 7 cell populations along time. (C) Ratio between the saturation magnetic moment values at a certain time point, m s (t), and after 2 h of magnetosome uptake, m s (2 h). Data represent the mean ± standard deviation, n = 3. The line represents a guide for the eye.
In addition to the spectrum for magnetosomes (magnetite), which indicates the starting point, and HoSF (ferrihydrite), other references were tested: maghemite (γ-Fe 2 O 3 ), goethite (α-FeO(OH)), and hematite (α-Fe 2 O 3 ). Only three references were needed for the best fitting: magnetosomes (magnetite), maghemite, and HoSF (ferrihydrite), or goethite. An example of a linear combination fitting of A549 and RAW 264. 7 8 days after magnetosome uptake is presented in Figure4C,F. The linear combination fittings of all measured spectra along with the experimental data are represented in the Supporting Information (FigureS1).
ACKNOWLEDGMENTS This work was supported by the grant  PID2020-115704RB-C31  funded by  MCIN/AEI/10. 13039/501100011033and  by the grant  IT1479-22  funded by the  Basque Government. L. G. would like to thank the financial support from the grant  PRE2018-083255  funded by  MCIN/AEI/10. 13039/501100011033and  by  European Union NextGenerationEU/PRTR. G. G. acknowledges financial support from the grant  FJC 2020-044866-I.
F I G U R E 3 (A,B) Hysteresis loops of A549 and RAW 264. 7 cell populations along time. (C) Ratio between the saturation magnetic moment values at a certain time point, m s (t), and after 2 h of magnetosome uptake, m s (2 h). Data represent the mean ± standard deviation, n = 3. The line represents a guide for the eye.
Acknowledgement D. V. , K. Q. , 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 ).
Process data such as duties, energy, material streams, and waste useful for environmental analysis were retrieved by means of Aspen tools. Process simulation is indeed well-established tool to collect useful data that could be exploited by a wide range analysis. Saebea et al. (2019)  developed a process simulation of bio-dimethyl ether synthesis from biogas tri-reforming to evaluate the CO 2 utilization of the system  (Saebea et al. , 2019). Then, in 2022, Fedeli et al. performed a conceptual design for bio-dimethyl ether (DME) synthesis from biogasaimed at the environmental impact assessment of the  purification section  (Fedeli et al. , 2022). Oliveria dos Santos et al.
Materials and methods The three technologies are rigorously simulated in Aspen HYSYS to collect process data for the subsequent environmental assessment. Operating conditions and process flowsheet related to the CHP process are taken from the literature  (Dalpaz et al. , 2020) , since it could be considered a well-established technology. However, DME and methanol production input is taken by the simulation of biogas-to-chemicals performed in this work. Biogas properties are retrieved from the work of Fedeli and Manenti, which analysed a feedstock from energy crops. The average H 2 S content in the gaseous mixture is 200 ppm  (Fedeli and Manenti, 2022). The process simulation for this study was performed both with Aspen HYSYS and Aspen Plus software. Further details could be found in the following three sections that present (i) process simulation, (ii) LCA analysis, and (iii) AHP methodology respectively.
Process simulation In each technology the process layout accounts for a purification section with equivalent design since the feedstock properties, the inlet flowrate and the operating conditions are the same. Biogas is treated with caustic scrubbing first in order to remove H 2 S impurities that could damage the downstreams units by means of corrosion and catalyst poisoning  (Chen et al. , 2022). Fig. 2  depicts the simplified flowsheet of the scrubbing unit that was rigorously simulated in Aspen Plus with Electrolyte Non Random Two Liquid (ELECNRTL) thermodynamic model. Soave-Redlich-Kwong (SRK) model is selected for the gas phase behaviour. To model this section the ASPEN plus software was used since it is more appropriate to handle electrolytic systems than Aspen HYSYS. The simulation results were then transferred as input parameters in Aspen HYSYS.
In the proposed layout three different sections can be distinguished: (i) biogas reforming, (ii) methanol/DME production, and (iii) purification section. The first section, where biogas is reformed with steam to produce syngas, is the same for both production pathways. Biogas is fed into the system, and it is compressed with a three-stage compressor up to 1. 5 MPa. The pressurized stream is then mixed with MP steam coming from an internal loop. The mixture passes in a process-process heat exchanger where it is pre-heated before entering the steam reforming tubes. This unit is modelled by means of the Aspen HYSYS isothermal Plug Flow Reactor (PFR) module with Xu-Froment kinetics  (Xu and Froment, 1989). Inside the catalytic tubes, the following reactions occur (Eqs. (  12 ) and (  13 )): CH 4 + H 2 O ↔ CO + 3H 2 (12) CO + H 2 O ↔ CO 2 + 4H 2 (13) CH 4 + 2H 2 O ↔ CO 2 + 4H 2 (14) The operating conditions of the reformer unit are 950 • C and 1.
LCA analysis Based on the simulations, the LCA analysis of the three processes was then performed employing SimaPRO 9. 3 software. A cradle-to-gate lifecycle model was selected as the method to analyse the systems. This methodology assesses the product footprint until it leaves the factory gates before it is transported to the consumer. 1 m 3 /h of biogas with an average composition of 60% methane and 40% carbon dioxide at atmospheric conditions is used in this LCA study as the functional unit. Nine (9) LCA inventories were created to model the different processes to better organize the data and to avoid a single inventory file with too many inputs and outputs. The biogas purification is a common step for the three technologies. In this section, fertiliser production has been considered as avoided product lowering the impact of this technology.
Indeed, the digestate coming from anaerobic digestion is rich in nitrogen, phosphorous, and potassium  (Grandas Tavera et al. , 2023). Table  1  describes the different inventories and technologies used to make the LCA assessments properly. In order to have the same geographical context as the biogas, the Italian national grid was taken as the reference for electricity intake. The Italian fossil fuel mix states 79%, according to the official report of the IEA agency (IEA and Italy, 2020). Input and output data magnitude were retrieved from the data generated in the process simulation while most of the inventory inputs/outputs were taken from SimaPro databases of the European Market. A detailed description of the inventory is shown in Appendix Section A. Since bio-syngas production involves the combustion of natural gas to thermally sustain the reactions, this option was added to the market for heating for natural gas.
AHP methodology The analytic hierarchy process method was employed in this study to determine the optimal alternative for biogas utilization. The AHP algorithm was implemented in MATLAB 2019b with the below-mentioned method. When performing the analysis, several factors were taken into account, such as (i) Climate change, (ii) Ecotoxicity, (iii) Human toxicity, (iv) Land use, (v) Water use, and (vi) Revenues. The reason why these categories were selected is that they involve environmental, economic, and social concerns with the final purpose of identifying the process with the best overall performance. Environmental and social factors are the output of the LCA assessment, while economic ones are retrieved from the work of  Negri et al. (2022)   (Negri et al. , 2022)  where the economic assessments of different biogas utilization were evaluated.
Result and discussion The three investigated processes have different outputs. The CHP cycle generates electricity and heat, while the others produce DME, and methanol as better described in Table  3. The two chemical syntheses are analysed thanks to the evaluation of biogas molar conversion, H 2 molar conversion and chemical yield. Table  4  shows the main key performance indicators of the DME/methanol synthesis, including the design of the distillation columns. These performance indicators are not shown for biogas cogeneration since neither reactions nor purification steps are present. The computed conversion of methane in the reforming unit is 93% for both processes due to the same layout. This value shows good compliance with the literature data  (Chen et al. , 2019). The conversion of hydrogen as well is the same for both processes since the two technologies share the same methanol reactor.
However, a lack of kinetic implementation is reported in the literature for DME synthesis. Even if process simulation performances are not the main point of this work, they are nevertheless required to assess and compare the environmental footprint of the three technologies. The cradle-to-gate diagrams of the biogas utilizations are attached in the Appendix section. Several impact categories are analysed for each technology and the obtained results are expressed with Point (Pt) as a unit of measure in compliance with SimaPro software unit to display the resulting impact. The Pt unit is defined as a dimensionless value in the environmental indicator method that represents one thousand of the one-year environmental burden experienced by the average European population  (Morsali, 2017). Sixteen vectors with a length equal to 3 are generated and each of them represents an LCA category.
Photovoltaic (PV) modules in service undergo more or less severe degradation depending on their operating environments, ages and technologies. In this work, we investigated the coupled influence of the climatic conditions of operation and of the degree of deterioration of a PV module on its energy production. We considered four silicon PV modules characterized in standard test conditions. The PV conversion is modeled by a single diode model taking into account the presence of a fault. Matlab/Simulink software was used to calculate the energy supplied at a constant load for the PV module with and without defects. The ratio between the energy produced with fault and without fault allowed to quantify the percentage of loss. This loss was plotted according to the degrees of degradation of the short-circuit current I sc , the open-circuit voltage V oc , the series resistance R s and the shunt resistance R sh.
m À2 , K I is the current temperature coefficient (in A. K À1 ), T = T À T n is the temperature difference with T n = 25 °C; I 0 is the saturation current  [26, 27] : I 0 ¼ I sc;n þ K I DT exp V oc;n þK V T ð Þ a:V t À 1 ð10Þ where I sc,n and V oc,n are the values of the short-circuit current (in A) and the open-circuit voltage (in V) under STC conditions, K V is the voltage temperature coefficient (in V. K À1 ), and a is the ideality factor. It can be noted that I pv and I 0 are temperature dependent  [22, 26, 27]. Calculations were performed using a tool named SOPHEE (standing for Solar PV Energy Estimation) realized with the Matlab language and involving a "Matlab GUI" interface  [28]. The interface is displayed in Figure  5. The values provided in the datasheet of one PV module are entered in the interface with the value of the load and also the ones of the climatic parameters over a day (file with irradiance and temperature profiles).
Equations (  7 )À(10) are solved accordingly using blocks interconnected by mathematical operators in the Simulink platform. The degraded input values can be introduced as well. The tool enables to display the evolution of the power calculated according to equation (3), the mean power and the energy supplied to the load by integrating the power following equation (  4 ) with fitting curves. For our study, the calculated energies were then recorded on another file so as to calculate the energy loss according to equation  (5). The SOPHEE tool can also be applied to simulate the degradation of the performance of a PV module according to the years of operating, introducing aging laws into the interface (see Fig. 5 ). We considered for the simulations and comparisons three polycrystalline silicon modules (of types KC200GT, MSX60 and PW1560) and one monocrystalline silicon (of type Q6LM) having well-detailed datasheets and for which the single diode model had been validated in previous studies.
A good agreement between the single diode model results and the curves under STC was shown for irradiances between 400 W. m -2 and 1000 W. m -2 and cell temperatures between 25°C and 75 °C for these four PV modules  [22]. The SOPHEE tool provided us with similar results for the initial cases (non-defective PV modules). The technical parameters necessary for the simulation of the four PV modules displayed in Table  1  were extracted from their datasheets. The values of the degradation levels D indicated in Table  2  for the four X parameters were entered. A 16-hour day is assumed for the calculations of the generated energies. For the input data, two sets of values were defined. First, the irradiance G was varied from 100 to 1000 W. m À2 with a step of 200 W. m À2 while considering a module temperature T = 25 °C.
Raw catalytic CV data All experiments were carried out in buffered aqueous solutions. Based on previously reported data,  [5c, d]  a temperature of 40 °C was selected to measure catalytic currents that are both sizeable at low scan rates (down to 0. 05 V s À 1 ) and low enough to be overcome at fast scan rate (5 V s À 1 and upwards). 9b] The CVs are recorded at a glassy carbon electrode (surface area S = 0. 02 cm 2 ) in 0. 05 M 2-(Nmorpholino)ethanosulfonate (MES) buffer added with 0. 05 M Na 2 SO 4 (unless stated otherwise) or in 0. 1 M sulfuric acid solutions, at a series of pHs varying from 1 to 9 in the absence of H 2 or in the presence of 1 atm. H 2. The moderately low water solubility of the catalyst, and hence the small concentration used (50 or 100 μM), results in CV responses showing a substantial double layer capacitive charging current on top of which appears the faradaic responses of interest.
Photobioreactor The cultivation of Limnospira indica was ensured in HECTOR (see Figures  3A, B ), an airlift photobioreactor with a useful capacity of 170 L and a culture thickness of 6 cm. It was equipped with a LED lighting system, pH, temperature and dissolved oxygen probes connected to its own control system. The control software (C-BIO2) recorded each minute the monitored parameters (pH, temperature, dissolved oxygen, light intensity) and controlled the fixed parameters (pH, temperature, light intensity). In this way, automatic regulation of the culture conditions was ensured according to the setpoints fixed by the operator (i. e. , for temperature, pH, and light intensity). Temperature regulation was performed by circulation of industrial water in the internal heat exchanger. Regulation of pH was performed by injections of CO 2 from a gas bottle into the gas inlet line.
Wind system The specifications of G114-2. 0 wind turbines (shown in Table  2 )  (Gamesa, 2014)  were employed in this study, alongside the wind data extracted from the PVGIS software  (EU Science Hub, 2020; Huld et al. , 2012). As wind speed is variable, the generated electrical energy from wind turbines (WTs) fluctuates to a great extent. Wind speed (u h ) at hub height (z) can be calculated as u h = u 1 ( Z Z 1 ) ζ based on the wind speed data (u 1 ) at a height (z 1 ) given the shear coefficient (ζ ). Afterward, the hourly electric energy produced from the wind farm (E WT ,el ) is determined as E WT ,el = N WT × ⎧ ⎨ ⎩ 0, u h < u C or u h > u F a + b (u h ) ω , u c ≤ u h ≤ u R P e,R , u R < u h ≤ u F (3) where N WT is the number of WTs, u C , u F , and u R are the cut-in, cut-off, and rated wind speeds, and P e,R is the rated WT power.
In Eq. (  29 ), f 1 , f 2 , and f 3 stand for the three objective functions of the three aforementioned response variables (F RES , F RES,th , and COE). The values of these functions are determined based on Eqs. (  8 ), (11), and (  21 ), respectively, which requires solving the entire aforedescribed numerical framework. Maximizing the functions f 1 and f 2 would enrich the match between energy demand and the RES supply by tuning the capacities of the renewable energy systems, whilst minimizing the function f 3 would guarantee to lower the deficits in the economic aspect. In this study, the ParetoSearch toolbox in MATLAB was utilized for optimization since it is cost-effective in terms of the computational burden  (Madić and Radovanović, 2014; Wetter and Wright, 2003). The studied area (Estonia) was gridded into 710 points, covering sub-areas with uniform intervals of latitude and longitude, to find the optimal installation site of RES.
Before testing, a homogeneous slurry was obtained by continuously stirring in an ultrasonic bath for 20-30 minutes. Each catalyst was dropped with 5 mL onto the cleaned GCE surface (with 3 mm contact area) and dried at room temperature using an air blower. The mass of catalyst deposited onto GCE working electrode was determined to be roughly 0. 2 mg. First LSV was used to examine the polarization curves for water oxidation. The EIS was used to examine the charge transfer resistance for each catalyst, with experimental conditions of sweeping frequency ranging from 100 kHz to 0. 1 Hz, a sinusoidal potential of 5 mV, and 1. 45 V versus RHE (water oxidation onset potential). The EIS raw data was simulated by Z-view soware. The durability of the as-prepared catalyst was tested for 48 h at three different current densities of 20, 40, and 60 mA cm -2.
Acknowledgements B. V. thank the platform " Microscopies, Microprobes and Metallography (3 M) " ( Institut Jean Lamour, IJL, Nancy, France ) for access to SEM facilities and  F. Alnjiman  for his valuable help. We also acknowledge partial funding of the  Ajman University , Grant ID: DGSR ref. 2022-IRG-HBS-5 , and we would like to thank the platform of  National Natural Science Foundation of China  ( NSFC 51402065  and  51603053 ).