dataesr/scientific-paragraphs-categorization huggingface dataset:
Commercial semiconductor gas sensors are calibrated to several gases, and the calibration curves are available on the device's datasheet. These curves are logarithmic plots in which the y-axis is the sensor response, and the x-axis is the gas concentrations. These log-log plots are helpful because the power-law nature of the interaction between the gas and the semiconductor  [10]  can be readily read. In other words, one calibration curve is determined after measuring the device's electrical resistance upon several gas exposures in which the response, response time, and recovery time should be calculated for each dynamic response-recovery curve. Modern calibration systems in industry or academia measure several sensors' dynamic response recovery curves simultaneously, leading to massive resistance-time data tables that should be carefully analyzed to determine each device's calibration curves correctly.
Commercial gas sensors are often available with an interface that allows the user to monitor the concentration of a target gas, such as the GasLab data logging package or the SGX Sensortech Evaluation kit. Other tools are designed to analyze the data from sensors in general, not specifically for gas sensors as the Vernier Graphical Analysis software. To the best of our knowledge, there is only one analysis tool that was specifically designed for gas sensing applications, the DAV 3 E  [28]. This software allows a complete toolbox to analyze and calibrate multiple gas sensors that operate in temperature cycling mode, generating complex patterns and requires a rigorous approach to its proper analysis. Within this context, we report an open-source, user-friendly graphical interface (GUI) that provides a simple path to calculate the response, response time, and recovery time of 8 sensors measured simultaneously.
Distribution This software is open-source, and the source code can be accessed on GitHub on the following repository (https://github. com/delimabs/Gas -Sensor-Data-Analysis-System). A windows installer is also available, not requiring any previous installation of python or the libraries. Bug reports and suggestions can be given at https://github. com/delimabs/ Gas-Sensor-Data-Analysis-System/issues. The main layout of the software is shown in Fig. 2. Fig. S2  shows all dialog boxes that are used in the analysis process.
Algorithm and methodology As soon as the software is running, it creates six pandas Data Frames (DFs) addressed as rawDF, previewDF, visualizationDF, normalizationDF, propertiesDF, fitDF within the code. All of these data frames are initially empty and will be populated according to the user's workflow. Fig. S1  illustrates the main tasks possible within the workflow of this software. After the software is started, the user can access the open file dialog box by clicking on the "Open CSV file" button. In this box, Fig. S2(a) , the user should select the appropriate column separator character before opening the CSV file. Here, the system will check if the dataset has at least two columns, the first one is assumed as the time data, and all others will be assumed as the sensor signal from each sample.
The algorithm will count the number of columns in the dataset, and if the number is one, it will warn the user with an error message pointing out that the column separator is not correct. The user can also fill the input boxes with the time and channel units and their respective converting factors. This feature was designed for convenience since experimental datasets often present time in seconds or minutes. The sensor signal is frequently measured as electrical resistance, typically varying from kΩ to GΩ. In addition, some measurement setups can measure electrical current or voltage drop as the sensor signal. The preview button allows the user to check the dataset before plotting it. After choosing these parameters and verifying if everything is correct, the system is suitable to plot data from up to eight samples (maximum columns allowed is 8, and another one from the time).
This box is designed to handle possible invalid parameters, such as writing characters when choosing the time interval or leaving all channel checkboxes unchecked. The error-handling routine in these situations consists of popping up a window to warn the invalid parameters chosen and plotting the data on the reviewDF, respectively. By setting these parameters and clicking the plot button, the system fills the visualizationDF and plots it. After the visualization process is complete, the user can choose to compare the dataset by normalizing. For example, if the data set consists  of the electrical resistance data from four different samples, it is expected that each sample will exhibit its own electrical resistance range. Therefore, to compare, the user can choose a specific time in which the system will divide each column by its own value at that specific time.
This feature allows fast comparison between different samples because all normalized data will then be plotted together. The normalization option is available by clicking on the normalization button in the dock menu that opens the normalization dialog box, shown in Fig. S2(c ). After setting the visualization parameters, it is also possible to calculate the response, response-time, and recovery-time for each exposure-recovery cycle from the analyzed dataset. To perform this calculation, the user should click on the dock widget's "Calc. Resp. " button to access the response dialog box, shown in Fig. S2(d). Each sample dataset comprises multiple exposure cycles in a typical sensor calibration routine, similar to the one pointed out in Fig. 1. Each cycle yields one response value, one response-time, and one recovery-time.
Fig. 2  presents the software interface, and it shows a dataset of four samples composed of four different exposure cycles. If the user manually calculates the response, response-time, and recovery-time for each sample cycle, that would result in 48 operations. The response dialog box will automatically perform these calculations after entering the concentration value, exposure time, end of exposure time, and end of recovery time. These values are subjective, depending on the experimental setup and configuration, but a good estimative is pointed out by arrows in Fig. 1. To calculate the response, response time, and recovery time, the user has to click on the calculate button after filling the input box with the time of start and end of exposure and the time of the end of recovery. The calculation of these three parameters is the core of the algorithm of this software.
By clicking the plot button, the user should see the plot area like in Fig. 3(a). To compare the data from the samples quickly, the user can access the normalization menu, enter 50 in the input dialog box, and click the normalize button. Now, the plot area should be similar to the one shown in Fig. 3(b). The user can access the response analysis menu by clicking the "calc resp" button in the dock menu. It is important to note that this dataset consists of four cycles of exposure to NO 2 during 10 minutes, with the concentration of each cycle of 0. 5, 1, 2, and 5 ppm, respectively. This measurement recorded the electrical resistance over time of four different rGO-based sensors, which exhibited a p-type behavior towards this strong oxidizing gas. The recovery period of each cycle was set to 50 minutes.
3 (e) and (f). All data and fit information can be exported to a specific location. The system will generate ". dat" files that will use the same column separator as the initial data file. These files are also available in our GitHub repository for comparison purposes in the results folder within the zip file. The second zip file, "dataSample_2", consists of data set measured to evaluate the stability of ZnO-based sensors for O 3 detection. This data set consists of measuring the response-recovery cycle of two sensors prepared together in a sputtering chamber, and they were kept at 200 C for one month. Each sensor was submitted to several sequences of O 3 exposures in three different samples. In this data set, channels 1, 2, and 3 correspond to the data from one sensor measured on weeks 1, 2, and 3, respectively.
Channels 4, 5, and 6, corresponds to the data of another sensor measured on weeks 1, 2, and 3. This dataset can also be downloaded from our GitHub repository. In this case, the stabilization period lasted 12 hours. Each sample was exposed to O 3 for 15 minutes and recovered for 60 minutes. In order to perform the analysis of this dataset, the user can open the "dataSample_2", set column separator to tab, the number of channels 6, divide the time data by 60, and the channels by 1000, then the time will be given in minutes and the sensor data in kΩ. By clicking the visualization button, the user can set the visualization parameters to start at 670 and to end at 2000 min. These values were chosen to get the start time of the first exposure time at 50 minutes (12 hours of stabilization ¼ 720 min-670 min).
The second exposure cycle starts at 125, ends at 140, and the recovery ends at 200. This dataset has 17 exposure cycles, and the resulting curve of response versus concentration is shown in Fig. 4(c). By fitting this dataset, the user should see the curve shown in Fig. 4(d). As these samples were prepared in the same batch and exposed to O 3 in the same conditions, it is expected that they would exhibit similar sensing behavior. For the third example, Fig. 5 , the user can open the "data-Sample_3. dat", set the time factor to 60, unit to min, channels' factor to 1e6 (1 Â 10 6 ), and channel units to MΩ. This dataset was obtained after measuring the dynamic resistance of a polyaniline film upon controlled ammonia exposures ranging from 50 to 500 ppm. In this dataset, the user set the visualization to start at 3 minutes and check the box "set initial time" to zero.
By entering these parameters, the user's plot should be similar to the one shown in Fig. 5 (a). Here, each exposure cycle lasted 15 minutes, and the recovery cycle lasted 45 minutes. By filling the response parameters in the dialog box shown in Fig. S2(d ) (accessed by clicking the Calc. Resp. button), the user can then fit the data with a power-law function. Finally, in the settings menu, the user can check the sensitivity checkbox, which will also perform a linear regression between the concentration values and the response values when the power-law fit button is clicked. The sensitivity will then be the slope of this fit, and it will be shown in the legend of the figure, as shown in Fig. 5(b). This information will also be available to export in the Fit Info data frame.
Acknowledgments This work was based upon financial support from the  Sao Paulo Research Foundation  (grants  2018/07517-2 ,  2019/22899-1 ). The authors also wish to acknowledge two other Brazilian national agencies,  Conselho Nacional de Desenvolvimento Científico e Tecnol ogico (CNPq)  and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
Ab initio simulations Full computational details can be found in the literature,  39, 45  previously unpublished results are given in the ESI. † The Gaussian 09 soware package was used for the ab initio simulations in this work. 46 Symmetry processing was bypassed for all calculations. The SCF convergence criterion was set to at least 10 À9 on the density matrix and 10 À7 on the energy. A pruned integration grid with 99 radial shells and 590 angular points per shell was used. Two-dimensional potential energy scans were performed at the MP2/cc-pVTZ//B3LYP-GD3BJ/6-311+G(d,p) level of theory. All electrons were included for the single point energies of stationary points (full MP2), whereas only valence electrons were used for the 2D potential energy surface scans (frozen core MP2) to save computational time.
Physicochemical properties Physicochemical properties were measured as described previously. 45 Consistency of the physicochemical characterization with literature data was conrmed for [C 4 C 1 im][NTf 2 ] and showed good agreement, see ESI. † Prior to all chemical measurements, the ionic liquids were dried in high vacuum with stirring for at least two days and handled using Schlenk techniques, glove box and NMR port. Information about water content is given in the ESI. †.
Viscosity Shear dependent viscosity h was measured on a Physica MCR301 rheometer (Anton Paar, Austria) on a vibration isolated table using a setup with cone-plate geometry. The cone had a nominal diameter of 49. 95 mm and a distance between its tip and the plate of 0. 101 mm. All measurements were conducted under nitrogen atmosphere to avoid water uptake. Aer thermal equilibration the viscosities of the samples were measured by applying 30 different shear rates from 5 to 80 s À1 with linear spacing. For each shear rate the data over 15 seconds was averaged. As ionic liquids at all temperatures showed no shear rate dependence (Newtonian uid behaviour), the results for each measured temperature were averaged. The temperature dependent viscosity curves were constructed by repeating this procedure in 5 K steps from 298.
Molecular dynamics simulations Polarisable MD simulations were run using the LAMMPS  49  soware package, version 7 Aug 2019, ttdamp branch (the temperature grouped Nosé-Hoover thermostat (tgNH) is available in the USER-DRUDE package since version 24 Dec 2020). 50, 51 6] [57]  The procedure of force eld extension to [CHTf 2 ] À anion is described in ESI. † The initial LAMMPS data les were created using ool (https:// github. com/paduagroup/ool) and Packmol 58 with 512 cations and 512 anions in a periodic cubic box, explicit polarisation effects were introduced using polariser and scaleLJ scripts (https://github. com/paduagroup/clandpol). Details on the simulation setup are given in the ESI. † The prealpha soware package was used for processing of the trajectory and for the calculation of the intermittent dihedral autocorrelation function, the mean squared and mean quartic displacements, rotational autocorrelation functions, charge arm distributions, and conformer distribution histograms.
Source code, executable, input les and manual are available on Github (https://github. com/FPhilippi/prealpha). 0] [61]  The dynamic charge-charge correlation structure factor S charge (k,t) is utilised to characterise relaxation dynamics in the ILs, and the details of this calculation are provided in ESI. † The code for dynamic charge-charge correlation structure factors is available on Github (https://github. com/jmcdaniel43/Structure_factor_FFT).
Acknowledgements This work has received funding from the  Joint Bilateral Agreement CNR (Italy)/Royal Society (UK)  - Biennial Programme   2022-2023prot. number  0082091-2021. We thank  Nicholas Carboni  for preparing our samples for DMTA in the glovebox. We would like to thank  Lisa Haigh ,  Imperial College London , for mass spectrometry. Computational resources provided by the  Imperial College Research Computing Service  are gratefully acknowledged https://doi. org/10. 14469/hpc/2232. This work is based on the PhD thesis of FP. FP acknowledges funding by the  President 's PhD scholarships. KG thanks  IDEX Lyon Fellowship  ( ANR-16-IDEX-005 ) for the nancial support.
Vials filled with pure ethanol were used to calibrate the transmission of light in the Crystal16 equipment for a clear solution. Subsequently, the prepared vials containing MFA slurries with the original raw material (Form 1) in ethanol-water mixtures were added to the machine and a single temperature cycle was performed. The clear point temperature at which 100% light transmission was reached was taken as the saturation temperature of the sample. A larger number of measurement samples were prepared for this model compound. Fitting of experimental data was done using the MatLab Curve Fitting tool and by using the scipy. optimize curve_fit routine within Python. Occasionally, samples displayed larger than 1 °C difference between clear point temperatures measured in subsequent cycles and these measurements were then discarded, and fresh samples were used in a re-run.
Subsequently, the prepared vials containing MFA slurries with the original raw material (Form 1) in ethanol-water mixtures were added to the machine and a single temperature cycle was performed. The clear point temperature at which 100% light transmission was reached was taken as the saturation temperature of the sample. A larger number of measurement samples were prepared for this model compound. Fitting of experimental data was done using the MatLab Curve Fitting tool and by using the scipy. optimize curve_fit routine within Python. Sodium bromate (NaBrO 3 , Figure  2 ) is a water-soluble salt. Dissolved in water, it is achiral but it crystallizes in the chiral space group P2 1 3  [28]. Similar to some other sodium salts  [29]  the solubility of NaBrO 3 in water is high, 367 mg/g at 25 • C, and it increases with temperature to 505 mg/g at 40 • C (Figure  3a ).
Antisolvent Crystallization Phase Diagrams For the system of NaBrO 3 in water/ethanol, the fitted parameters A and B to Equation (2) at each antisolvent fraction are shown in Figure  5. The parameters A and B change substantially with the antisolvent fraction x AS. The other systems show a similar behaviour. Utilizing the observed behaviour of the solubility as a function of temperature and antisolvent fraction a single empirical equation is proposed based on Equation (2), in which the antisolvent fraction dependence is of parameters A and B is captured by simple polynomials: ln x = n ∑ i=0 a i x i AS 1 T + n ∑ i=0 b i x i AS (3) Table  1. Fitting parameters and their standard errors obtained from the model Equation (3) using the entire dataset for a combination of compound, solvent and antisolvent.
The standard deviation using three parameters is not substantially increasing for both systems. For the LOV system, with the more complex antisolvent fraction dependent solubility behaviour, the model, using n = 2, performs reasonably with a σlnx = 1. 7%. The ability of the model to capture the change in antisolvent and temperature is shown by the solid lines in the lnx-1/T diagrams in Figure  4. Table  1. Fitting parameters and their standard errors obtained from the model Equation (3) using the entire dataset for a combination of compound, solvent and antisolvent. The relative standard deviations σlnx and σx from Equation (4) for each system are also provided.
Compound A [K] x AS  [-]  Figure  5. The points are the parameters A (blue) and B (red) for the Na 2 BrO 3 system from the fits of the datasets at each antisolvent fraction x AS to Equation (  2 ). The lines are the antisolvent faction dependent parameters A (blue) and B (red) determined from a fit of the entire dataset of the Na 2 BrO 3 system to Equation (3) with n = 2. The parameters can be found in Table  1. The goodness of the fit for the model is determined for each system using Equation (4) for the relative standard deviation σ z in % with N the total number of experimental points used, with z = x or lnx: σ z = 100 × ∑ N i=1 z i -z pred z i 2 N -1 (4) An σ lnx = 1% means that the resulting parameters describe the lnx data with on average 68% of the datapoints having less than a 1% deviation from the model lnx value.
Description of Engine Bench The tests were carried out on a three-cylinder spark-ignition engine. It is a turbocharged direct-injection engine. Its characteristics are specified in Table  1. It was braked by a dynamic HORIBA HT 250 bench managed by a SPARC control unit. The engine was instrumented with type K temperature sensors, 0-3 bar static pressure sensors, two Kistler type 4049B dynamic pressure sensors for the intake and the exhaust (frequency = 60 kHz), one HBM type 40 torquemeter (frequency = 10 kHz), three AVL type ZI33 in-cylinder pressure sensors (frequency = 150 kHz), and one ETAS ES430 air-fuel ratio sensor (frequency = 2 kHz). The INCA Software (version 7. 1. 10/3) controlled the engine ECU. All the sensors were connected to a National Instrument fast acquisition box. The STARS software from HORIBA made it possible to automatically control the dynamic bench, and it could create different cycles.
The PR-L804 fan from Dynair cooled the engine radiator, and the Fumex FB110 fan cooled the intake heat exchanger. Both fans were controlled by STARS software, which modified their rotation speeds according to the speed of the vehicle during a cycle. This equipment created test conditions close to the real ones. The pollutant emissions CO and HC were measured with a 3200 CAPELEC device, the NOx emission with an ECM NOxCANt sensor, and particle emission with a Pegasor Particle Sensor (PPS). The setup is shown in Figure  2.
Building a Blowby Simulation Model As a first step, it is necessary to model the blowby gases of the engine described in Table  1. In this way, the Gamma Technology simulation software, GT-suite 2021, which has a "RingPack" module (specially created to model piston/cylinder/ring friction and to model the pressures and flows of gases circulating through the rings and piston grooves) was used. Only a single cylinder simulation model was created. Indeed, the objective was to create a simulation model of the phenomenon of oil scavenging by the blow-by gases according to the value of the final gap of the first two rings of one of the cylinders of the engine used. This single cylinder has all the technical characteristics of the engine used, such as the cylinder and piston diameters, the length of the connecting rod, the depth, height, and location of the piston grooves and the rings.
This difference will generate a significant variation of the engine torque. Berthome, et al. ,  [13]  exposed that particulate emissions vary greatly from test to test despite mastering both engine and bench parameters. It is, therefore, necessary to accumulate data and to proceed with a statistician approach to get a trend out, and thus evaluate the impact of the idling time between two transients. To obtain as much data as possible, 100 accelerations (strictly identical from the point of view of the control and test conditions) are carried out successively after reaching thermal stability and respecting a time of idling between each test for each configuration. The demonstration of the similarity of these transients is not the subject of this article and has already been validated in a previous article by Berthome, et al.
This has the effect of pushing the positive ions towards the external surface of the trap in order to discharge them. If the trapping voltage increases, then the lighter ionized particles are absorbed by the trap. It is possible to choose a sample of particles according to their size thanks to the choice of the trapping voltage. It can vary from 0 V to 1000 V according to the manufacturer data  [51]. A trapping voltage of 400 V was used to measure particles with a size above or equal to 23 nm. This trapping voltage was selected because it corresponds to the minimum size required by the regulatory standards  [1]. This technology is very reactive because it performs measurements at the frequency of 100 Hz. However, it is necessary to make the assumption of mean diameter, standard deviation, and fractal dimension in order to determine the number and the mass flow rate of the particles  [52, 53].
Acknowledgments: This Special Issue has been initiated as an activity of the  IEEE Technical Committee on Impedance Spectroscopy  ( TC-2 ) within the  IEEE Instrumentation and Measurement Society during the International Workshop on Impedance Spectroscopy (IWIS) , which takes place every year at  Chemnitz University of Technology  [10]. We thank all the authors for their valuable contributions and the peer reviewers for their tremendous efforts in reviewing the submissions to this Special Issue. The journal team are thanked for their tireless editorial support, quick responses, and management, which contributed to the high satisfaction of all contributors and the success of the Special Issue.