dataesr/scientific-paragraphs-categorization huggingface dataset:
10C ) that appears to be weakly coordinating (∼2. 6 Å) the Ni atom alone, where the distance to the Fe atom is too long (4. 0 Å) for it to be considered as a bridging ligand. Structure of CO-inhibited form. The diatomic electron density occupies an area similar in position to exogenous CO seen previously in the [NiFe]-hydrogenase from Desulfovibrio vulgaris Myazaki F: 104,105 Therefore to make a direct comparison, a crystal structure of the CO-inhibited form of R479K was obtained aer treating H 2 -reduced enzyme with 100% CO for 20 minutes. Differences in the electron density adjacent to the Ni atom suggested that CO occupies the pocket next to the Ni (pdb accession 6SZK, Fig. 11D  and Table  S3  †), although mixed occupancy remains possible. Signicantly, the electron density is distributed asymmetrically across the two atoms in the CO molecule with more residing on the O atom, similar to the distribution observed for the permanent CO ligand to the Fe atom (Fig.
Acknowledgements This research was supported by the  UK Biological and Biotechnological Sciences Research Council  Grants  BB/I022309  and  BB/L009722  to F. A. A. and  BB/R018413  to K. A. V. S. E. B. was supported by a  Holaday Scholarship  awarded by the  Dakota Foundation. L. K. was supported by a  Gateway Fellowship  provided by  RUB Research School , PLUS, nanced by the excellence initiative [ DFG GSC 98/3 ]. W. K. M. is supported by the  UK EPSRC  ( EP/L011972/1 , grant to CAESR, the  Centre for Advanced Spin Resonance). The authors would like to thank  Diamond Light Source  for beam time (proposals mx12346 and  sm21651 ) and the staff of beamlines i04-1 and B22 for assistance during data collection. P. R. M. thanks the  University of Oxford  for a  Glasstone Research Fellowship  and  Linacre College Oxford  for a  Junior Research Fellowship.
Analysis of vesicle permeability with image J software Fluorescence intensity analysis for vesicle permeability study was performed using Image J (software version 1. 53 k, National Institute of Healh, USA). The measured fluorescence intensities inside the vesicles were compared with the fluorescence intensity outside the vesicles on the confocal images. For each dextran molecular weight, at least 50 vesicles were analyzed on an 8 × 8 µm image and, in parallel, the vesicle population was analyzed on 25 × 25 µm images. Measurements were always performed on clear background segments and at a distance of at least 20% from the membrane to exclude possible interference with membrane luminescence. The relative fluorescence intensity within the vesicles was expressed as a percentage of the fluorescence intensity of the background.
Sample preparation for AFM measurements For AFM imaging in air, a volume of 5 µL of the suspension containing the reconstructed membrane vesicles was pipetted directly onto freshly cleaved mica and dried in a closed Petri dish for 1 h before imaging. AFM imaging of the vesicles was performed using a MultiMode Scanning Probe Microscope with Nanoscope IIIa controller (Bruker, Billerica USA) and a 125 µm Vertical Engagement (JV) scanner. Images were acquired in contact mode using a standard silicon nitride tip (DNP-10, Bruker, nominal frequency 18 kHz, nominal spring constant of 0. 06 N/m) with a scan resolution of 512 samples per line. Sampling rates were typically optimized to 1-2 Hz. Images were processed and analyzed using NanoScopeTM software (Digital Instruments, version V614r1). The force was kept at the lowest possible value to minimize interaction forces between the tip and the surface.
01 N/m), in 10 mM Tris-HCL with addition of 10 mM MgCl 2 buffer. Images were acquired at a resolution of 150 × 150 pixels, applying a force of <1 nN. In all cases, the spring constants of the cantilevers were determined using the thermal noise method prior to imaging  [27]. The obtained height images were then analyzed using Data Processing Software (Bruker, USA).
Mass spectrometry (MS) was performed using an Autoflex speed MALDI-TOF /TOF analyzer (Bruker, Germany). Mass spectra were obtained according to the method described in  [34]. Protein identification was performed using Protein-Scape software version 3. 0 (Bruker, Germany) with the search engine MASCOT. An in-house database of protein sequence data of D. tertiolecta was used. Protein sequence data were downloaded from the UniProt database (accessed March 19, 2022, no. of entry 12 786). Search parameters were set as follows: a missing trypsin cleavage, oxidation on methionine as a variable modification, precursor ion mass tolerance 50 ppm, and fragment ion mass tolerance 0. 6 Da. The protein-protein interaction network (PPI) of the identified proteins was generated using STRING version 11. 0 with a minimum confidence level of 0.
Acknowledgments This work is supported by the  Croatian Science Foundation  projects "From algal cell surface properties to stress markers for aquatic ecosystems" ( IP-2018-01-5840 ), " Synthesis of Supramolecular Selfassembled Nanostructures for Construction of Advanced Functional Materials " ( IP-2018-01-6910 ), Croatia, and by the  Croatian-French program  "Cogito" partner  Hubert Curien  ( Campus France  n  • 46656ZC ).
Acknowledgements Part or all of the research described in this paper was performed at the  Canadian Light Source , a national research facility of the  University of Saskatchewan , which is supported by the  Canada Foundation for Innovation (CFI) , the  Natural Sciences and Engineering Research Council (NSERC) , the  National Research Council (NRC) , the  Canadian Institutes of Health Research (CIHR) , the  Government of Saskatchewan , and the  University of Saskatchewan. S. C. and M. G. gratefully acknowledge the  French National Research Agency (ANR)  for its support through project  I2MPAC , grant number  ANR-20-CE05-0018-01.
A mathematical model was implemented in MATLAB software and validated through a comparison with previously published research. The exergy analysis was carried out based on the second law of thermodynamics to evaluate the irreversibility of the plant and the subsystems. In this study, the components of the sub-systems were analyzed separately to identify and quantify the component that has a high loss of energy and exergy. According to the energy and exergy analyses, the highest source of irreversibility occurs in the reactor core with 50% of the total exergy destruction. However, turbines, steam generators, and condensers also contribute to energy loss. Further, the thermodynamic efficiency of the cogeneration plant was obtained as 35. 38%, which is more effective than other systems. In the ME-TVC-MED desalination unit, the main sources of energy losses are located in the evaporators and the thermo-compressor (about 50% and 36%, respectively).
The results showed that the costs of thermodynamic inefficiencies (the cost of exergetic destruction) for the optimized system components have been reduced compared to the corresponding cost of exergetic destruction in the base scenario plant. In addition, it has been confirmed that their optimization improved the thermodynamic and economic characteristics of the system. Khoshgoftar Manesh et al. [8, 9]  carried out a multi-objective optimization of a PWR nuclear power plant coupled to a multi-flash distillation (MSF) desalination system through GA and mixed-integer non-linear mathematical programming methods. In addition, a computer program has been developed in the MATLAB environment for a thermoeconomic analysis. The results demonstrated that the evolutionary algorithm (NSGA-II) could be systemically and elegantly applied to the PWR-MSF dual-purpose plant.
Conclusions In this paper, the energy and exergy efficiency of a PWR-ME-TVC-MED cogeneration plant for the simultaneous production of electrical energy and freshwater has been analyzed. The second law was used in defining exergy to reveal contributions of the components. The most recent validated data in the literature were used to determine the thermodynamic characteristics of seawater. A mathematical model was implemented in MATLAB software and validated based on a comparison with previously published research. The following are the key findings of this investigation: It has been shown that the nuclear reactor core is the main source of exergy loss in the cogeneration plant. Indeed, it is the worse component in the whole cogeneration system due to the irreversibility of the energy transformation.
15 to 523. 15 K and salt concentrations up to saturation whenever the ionic strength of the aqueous solutions is up to 6 mol/kg, which is below the solubility limits of many electrolytes. e-NRTL model has been successful and extensively employed in computer-aided calculations of industrial applications for aqueous and mixed-solvent electrolytes  [65] [66] [67]. This has been possible due to its implementation as property model in the Aspen Property/Aspen Plus program packages developed and distributed by the Aspen Technology Enterprise. Two versions of the e-NRTL model have been implemented in the referred program packages through several property methods (ELECNRTL, e-NRTL-RK, e-NRTL-SR, and e-NRTL-HF) that make use of the unsymmetrical original version  [68, 69]  of the model and the more recently symmetric one  [55].
e. , τ ij ∕ = τ ji. Furthermore, τ ij parameters reflect the temperature dependence of the corresponding interactions  [48, 70]. This dependence is expressed throughout the so-called binary interaction parameters in the conventional form described, for example, by the equations (1) and (2) in ref. [71]. In the previous work  [71] , the binary electrolyte-solvent interaction parameters of the e-NRTL model for different salts in water, using the experimental data reported in the literature, were computed, and validated. The results obtained with the refined sets of parameters along with those attained using the parameters contained in the Aspen Properties database were compared with the experimental values demonstrating that it is possible to improve the quality of the prediction of multicomponent systems with proper values of the binary electrolyte-solvent interaction parameters, without considering electrolyte-electrolyte interactions foreseen in the original e-NRTL model.
Tables S1 to S4 list the electrolyte-solvent interaction parameters pairs for each system. In the cases of the quaternary mixtures, the equilibrium constants for the solubility of single electrolytes in water were also determined by either using the e-NRTL model and taking the values reported in each of the papers referenced. Tables S6 to S11 list the electrolyte-electrolyte interaction parameters pairs for each system. Experimental data of both the ternary and the quaternary systems considered in the current work were taken from literature as shown in Table  1. The results of the SLE calculations for the quaternary systems (three salts + water) were consigned using the Jänecke dry-salt indexes: J(salt i) = w salt i w salt 1 + w salt 2 + w salt 3 × 100 ( 24 ) where w salt j is the mass fraction of salt j in solution.
Validation of the binary parameters Although in the previous paper  [71] , the quality of the regressions accomplished for obtaining the refined binary electrolyte-water interaction parameters was statistically demonstrated, in the current work some binary systems are used as validation of the parameters used here. Due to the high quality of the binary parameters stored in the Aspen Properties databases (release 11) and listed in reference  [71] , the predictions realized with Aspen Plus are also shown for comparison. All the remainder calculations were performed with a home-made MS Excel-VBA application described previously  [71].
Impact of the values of binary electrolyte-water interaction parameters, τ ij Fig. s 1-6 show the predicted molality mean ionic activity of some of the pure salts considered in this study (KCl, NaBr, MgBr 2 , Li 2 SO 4 , LiCl and LiBr, respectively) vs. the molality of the salt in the solutions previously computed  [71]. The comparison of the predictions with different binary parameters (those obtained previously  [71]  and the stored in the Aspen Property databases) demonstrates that it is possible improving the quality of the prediction of multicomponent systems with proper values of the binary electrolyte interaction parameters, without considering electrolyte-electrolyte interactions foreseen in the original e-NRTL model. As already described in the previous paper  [71] , not all the electrolyte-water and water-electrolyte interaction pairs, τ salt,water and τ water,salt , included in the Aspen Property database are able to correctly predict the experimental data.
Liquid-solid equilibria. Quaternary systems The predictions of quaternary systems (three salts + water) with procedures e-NRTL BIN and e-NRTL BIN + EPairs are shown in Fig. s  12-17 according to the value of the Jänecke dry-salt indexes. Tables S6 to S11 list the electrolyte-electrolyte interaction parameter pairs used for each system in the second calculations. As a rule, both procedures predict in similar way the experimental data regardless the values of the electrolyte-electrolyte pair interaction parameters. No improvement on the quality of the prediction was Fig. 1. Comparison of experimental  [74]  and predicted mean ionic activity coefficients of KCl at 298. 15 K. Experimental represents the experimental data; e-NRTL BIN is the prediction made with the refined parameters obtained in  [71] ; and e-NRTL Aspen Plus is the estimation done with the parameters stored in the Aspen Properties database.
[74]  and predicted mean ionic activity coefficients of NaBr at 298. 15 K. Experimental represents the experimental data; e-NRTL BIN is the prediction made with the refined parameters obtained in  [71] ; and e-NRTL Aspen Plus is the estimation done with the parameters stored in the Aspen Properties database. [74]  and predicted mean ionic activity coefficients of MgBr 2 at 298. 15 K. Experimental represents the experimental data; e-NRTL BIN is the prediction made with the refined parameters obtained in  [71] ; and e-NRTL Aspen Plus is the estimation done with the parameters stored in the Aspen Properties database. when considered non-zero values for these parameters. In the case of system NaCl+KCl+SrCl 2 +H 2 O the previous observation is also satisfied but the prediction reached with both procedures was a little bit worse.
Acknowledgments The authors are grateful to the  Institut de Chimie of the CNRS  and  CEA Le Ripault, France  for financial support by supplying us with free materials,  TOSOH Finechemicals  and  INC (Shiba, Japan) , and  SynQuest (Alachua, USA ) that made this research possible. The authors thank  Ecole Nationale Supérieure de chimie de Montpellier (ENSCM)  and the  Université Libanaise (UL). The authors also thank  Prof. Bernard Boutevin  (ENSCM),  Dr. Vincent Besse ,  Dr. Sandy Gosset  (ENSCM) and  Dr. Daoud Naoufal  (UL).
This paper aims to validate a demo scale plant scrubber technology through experimental campaign and development of a digital twin. Thus, it is useful to evaluate the H 2 S absorption process in a biogas production plant for analysis and optimization purposes. The absorber unit removes H 2 S through the chemical absorption via sodium hydroxide (NaOH) as wet agent (30% w/w). The column treats 300 Nm 3 /h of biogas, whose inlet H 2 S concentration ranges from 1000 to 3000 ppm. Field measurements are conducted to investigate the H 2 S removal efficiency. An experimental dataset is collected, processed and used as input on Aspen PLUS suite to develop the digital twin. This model is helpful to generate a large dataset and simulate operating conditions different from the demoscale plant. The process simulation is then exploited to perform a sensitivity analysis to figure out main variables influencing the H 2 S removal efficiency.
Experimental campaigns are useful to measure and test the process efficiency in nominal conditions. A dataset of new experimental data is collected from the demo scale plant. To investigate the behavior of the plant in perturbed conditions, such as H 2 S fluctuations, a digital twin is realized. Moreover, digital twin is able to reproduce a large deviations of H 2 S concentration. The process is rigorously simulated in Aspen PLUS using as model input the experimental results. One of the most promising technologies for smart manufacturing and Industry 4. 0 is digital twin (DT). Both academia and industry acknowledge the importance of DTs. A digital twin (DT) is a physical product, a virtual product, and a connection between physical and virtual products that connects the physical and digital worlds.
Digital twin modelling The experimental campaign data are filtered and used as input for the detailed process simulation. The outliers are manually removed in case of physical unfeasibility and inconsistency. Commercial package tool Aspen PLUS® is employed for digital twin modelling. Electrolyte NRTL model has been adopted, whereas the Redlich-Kwong equation has been employed for the vapor phase. These thermodynamic methods are suitable to represent the liquid phase non-idealities as suggested by Cherif et al. [33]. A rate-based calculation type is selected despite the equilibrium-stage theory being extensively employed in the past years by chemical companies. The selected approach is more suitable to handle solid formation and chemical reactions occurring. Component properties such as activity coefficients, heat capacity, viscosity and density have been directly evaluated by Aspen Plus tool.
Henry's constants for CH 4 CO 2 , O 2 and H 2 S are retrieved from the National Institute of Standards and Technology (NIST) databases. The temperature dependence of Henry's constant used by Aspen Plus® is represented by Eq. (  2 ). Coefficients a ij , b ij , c ij , d ij , e ij and the upper and lower temperature limits for each system are reported in the Appendix (Table  A 1 ). The reacting system involves equilibrium, dissociation and kinetic reactions that are also reported in the Appendix. The selected set of reactions counts for thirteen reactions. Additional side reactions are not considered to limit the model complexity. Concerning the equilibrium reactions, Aspen Plus® uses the expression reported in Eq. (3) to estimate the equilibrium constants. Power-law equation (Eq. (  4 )) has been utilised to define the reaction rate of kinetically controlled reactions (R12 and R13) in Appendix.
In this case, by keeping constant NaOH flowrate and concentration, efficiency passes from 50% to 60%. Based on collected experimental results, the highest efficiency (i. e. 70%) is achieved in trial #5 with the following operating conditions. -NaOH concentration: 50% -NaOH flowrate: 8 L/h -Freshwater flow:160 L/h Therefore, this test is adopted as a base-case scenario to model the digital twin. All the data collected during trial #5 are summarized in the tables below, i. e. , process stream inlet composition and operating conditions. These data are used as input for the simulation. H 2 S inlet concentration is characterized with the averaged value of the different field measurements. The output removal efficiency of the model is 71. 2%, which is quite near the result measured during field experiments.
5418 Å), Kα2 being subtracted with Bruker Diffrac. Eva software for figures. XRD patterns were collected over the 2-80°2θ range, using 0. 4 s counting time per 0. 01°2θ step at room temperature. The powdered talc skeletal density (ρs) was measured at 25°C using an Ultrapycnometer 1000 -Quantachrome Corporation pycnometer using ultra-high purity helium (Air Liquide / 99. 999%). A field emission scanning electron microscope (FESEM) Inspect F50 equipment (FEI Instruments) was used to assess particle morphology.
The working of GCE was exhibiting an area of 3 mm and it was used to deposit the catalytic material. The urea stock solution was prepared using a solution of 0. 1 M NaOH. Various electrochemical methods were used to analyze the results, including cyclic voltammetry, electrochemical impedance spectroscopy, and chronoamperometry. As part of the scan rate study, CVs were measured at various scan rates. The purpose of this study was to examine electrochemical impedance spectroscopy (EIS) at 100 kHz to 0. 1 Hz, a sinusoidal potential of 10 mV, and a biasing potential of 0. 5 V. The raw EIS data were simulated using Z-view software, and well-fitting results were obtained. In order to determine the interference pattern and stability of the modified electrode, chronoamperometry was used. We conducted all measurements at room temperature.
Acknowledgments: Authors acknowledge the higher education commission Pakistan for partial support under the project ( NRPU/8350 ). The authors extend their sincere appreciation to Researchers Supporting Project number ( RSP2023R79 ),  King Saud University, Riyadh ,  Saudi Arabia , for partial funding of this work. We also acknowledge partial funding from  Ajman University , Grant ID:  2022-IRG-HBS-5 ,  2022-RTG-HBS-02. Brigitte Vigolog  would like to also thank the platform "Microscopies, Microprobes and  Metallography  ( 3M )" at the  Institut Jean Lamour (IJL, Nancy, France)  for  TEM  and  SEM  facilities.
Turbulent carrier flow To study polymer stretching in a turbulent flow, we use a database of Lagrangian trajectories from a DNS of homogeneous isotropic incompressible turbulence (at Taylor-microscale Reynolds number Re 𝜆 ≈ 111), generated at ICTS, Bangalore  (James & Ray 2017). The DNS solves the incompressible Navier-Stokes equations, discretized on a periodic cube, using a standard fullydealiased pseudo-spectral method with 512 3 grid points. Time integration is performed using a second-order slaved Adams-Bashforth scheme. The motion of 9 × 10 5 tracers is calculated using a second-order Runge-Kutta method for time integration; the fluid velocity at the location of a tracer is obtained from its value on the grid using trilinear interpolation. The velocity gradient ∇𝒖 is calculated along these trajectories and stored at intervals of 0.
Acknowledgments. The authors are grateful to  Samriddhi Sankar Ray  for sharing his database of Lagrangian trajectories in homogeneous and isotropic turbulence. D. V. would like to thank the  Isaac Newton Institute for Mathematical Sciences  for support and hospitality during the programme 'Mathematical aspects of turbulence: where do we stand?' when work on this paper was undertaken. J. R. P is similarly grateful for the hospitality provided by the  International Centre for Theoretical Sciences (ICTS) , Bangalore. D. V. and J. R. P. also thank the  OPAL infrastructure , the  Center for High-Performance Computing of Université Côte d'Azur , and the  ICTS, Bangalore , for computational resources. Simulations were also performed on the  IIT Bombay  workstation Aragorn. Funding. This work was supported by  IRCC, IIT Bombay  (J.
R. P. , seed grant);  DST-SERB  (J. R. P. , grant number  SRG/2021/001185 );  Fédération de Recherche Wolfgang Doeblin  (J. R. P. ); the  Agence Nationale de la Recherche  (D. V. , grant numbers  ANR-21-CE30-0040-01 ,  ANR-15-IDEX-01 );  EPSRC  (D. V. , grant number  EP/R014604/1 ); the  Simons Foundation  (D. V. ); and the  Indo-French Centre for Applied Mathematics IFCAM  (J. R. P. and D. V. ).
Acknowledgements The authors acknowledge the  Department of BEIS  in the framework of the  Low Carbon Hydrogen Supply 2: Stream 1 Phase 1 Competition  ( TRN 5044/04/2021 ) ( RECYCLE ,  HYS2137 ) for providing funding and support to the development of this study. Vincenzo Spallina  would like to thank also the  UKCCSRC  (part of the  UKRI EPSRC)  for partly funding this study.