dataesr/scientific-paragraphs-categorization huggingface dataset:
The cross sections of selected regions on the samples were obtained after the deposition of a thin layer of platinum on the surface (the first layer, 0. 2 μm thick, was deposited using an electron beam at 2 kV and 0. 4 nA, the second layer, 1 μm thick, was deposited using Ga FIB beam at 30 kV and 0. 24 nA), followed by cutting the coating using Ga FIB beam at 30 kV and 9. 4 nA. The surface was polished with a Ga beam at 30 kV and 0. 4 nA in the last step. Imaging along the cross section was performed by SEM using SE mode at 15 kV. The top surface and crosssection compositions were determined by EDS using an Oxford † Instruments AZtec system with an X-max SDD 50 mm 2 (silicon drift detector). The EDS analyses were performed at 15 kV, 5 kV, or 3 kV in a point analysis. The data were normalized to atomic percentages (at%).
The amount of carbon was excluded from the quantitative analysis. EDS mapping was recorded at 15 kV. The CASINO † program (version v. 2. 51, University of Sherbrooke, Canada), a single scattering Monte Carlo simulation of electron trajectory in solid, specifically designed for low energy beam interaction was used to generate many recorded signals (x-rays and backscattered electrons) in a SEM. The surface characterization of elemental composition and chemical state of the elements was performed by XPS using Thermo Electron ESCALAB 250 † spectrometer, with a monochromated Al Kα x-ray source (hν = 1,486. 6 eV). The base pressure in the analytical chamber was maintained at 10 -9 mbar. The spectrometer was calibrated using Au 4f 7/2 at 84. 1 eV. The take-off angle of analyzed photoelectrons was 90°.
(ii) LPR curves were then recorded in the range from -10 mV to 10 mV vs. E oc using a 0. 1 mV/s potential scan rate. Polarization resistance (R p ) was determined as the slope of the fitted potential (E) vs. current density (j) curve using Nova 2. 1 † software. (iii) Electrochemical impedance spectra (EIS) were recorded at E oc in the frequency (f) range from 100 kHz to 0. 01 Hz using an AC potential amplitude of 10 mV (rms). (iv) Potentiodynamic polarization curve was recorded in the potential region starting from -250 mV vs. E oc in the anodic direction until the current density reached 0. 001 A/cm 2. Corrosion current density (j corr ) and corrosion potential (E corr ) were determined using Nova 2. 1 software using extrapolation of the linear portion of polarization curves, i. e. , Tafel extrapolation, based on the mixed potential theory.
ACKNOWLEDGMENTS This work is a part of the  M-ERA. NET  project entitled "Design of corrosion resistant coatings targeted for versatile applications" (acronym COR_ID). The financial support of the project by  MESS  ( Ministry of Education, Science, and Sport  of the Republic of Slovenia) and  ANR  (The  French National Research Agency ) is acknowledged. This work is also a part of the bilateral Proteus program between Slovenia and France entitled " DCOIN: Disentangling COrrosion and its INhibition" and INCOR: INterfaces relevant for CORrosion and its inhibition ", financed by the  Slovenian Research Agency (SRA)  and the  ANR  (Grant No. BI-FR/21-22-008 ). The financial support from the  SRA  (research core funding No. P2-0393  and  P1-0134 ) is acknowledged. Région Ile-de-France  is acknowledged for partial funding of the XPS equipment.
In this view, the interaction coefficients (Table  3 ) demonstrated linkage between SIM and PL concentrations for DOX entrapped concentration plotted in Figure  3E. It can be noted that the highest DOX entrapped concentration was achieved at low levels of PL and SIM These mathematical relationships established between input and output provide the scientific basis for defining the design space for SIM-DOX-LCL formulation  [18, 21]. The design space and the optimal formulation for SIM-DOX-LCL were generated using the optimization function of Modde 13 software after establishing the target ranges for each response. As illustrated in Figure  5 , the design space (green area) suggests that the achievement of a liposomal formulation that meets the QTPP is conditioned by the use of increased levels of SIM and DOX, attesting to the criticality of these two factors.
These mathematical relationships established between input and output provide the scientific basis for defining the design space for SIM-DOX-LCL formulation  [18, 21]. The design space and the optimal formulation for SIM-DOX-LCL were generated using the optimization function of Modde 13 software after establishing the target ranges for each response. As illustrated in Figure  5 , the design space (green area) suggests that the achievement of a liposomal formulation that meets the QTPP is conditioned by the use of increased levels of SIM and DOX, attesting to the criticality of these two factors. The quantitative formulation of the optimized SIM-DOX-LCL is presented in Table  4  along with the predicted values and the experimental results, showing that these two values are very close. In other words, it can be said that SIM-DOX-LCL optimal formulation complies with the QTPP, since the experimental results meet the target ranges.
In this case, the cell viability was less than 50% for all drug combinations, making it difficult to estimate the IC 50 (Table  6 ). The long circulating liposomes loaded with DOX (DOX-LCL) were observed to increase the IC 50 of DOX on both cell lines, possibly due to the prolonged drug release  [14, 74]. In the case of SIM-LCL formulation, a two-fold decrease in the IC 50 of SIM on T47D-KBluc cell line was noted. On the other hand, the results obtained on the A549 cell line highlighted that there is only a small difference in the IC 50 value of SIM-LCL and free SIM. It should be mentioned that empty liposomes (E-LCL) were assessed in conjunction with the treatments to exclude their influence on cell viability. The results highlighted that cell viability for E-LCL was 99% (data not presented), and all the reported results exclusively represent the influence of the treatments.
Thereby, a secondary risk analysis using the FMEA was performed at first to reduce the number of factors that need to be optimized. The factors that were formerly identified as critical for the SIM-DOX-LCL CQAs, namely the concentration of PL, the concentrations of DOX and SIM and the pH of the AS solution  [14] , were re-evaluated considering the severity of their produced effects (S) on the CQAs of the SIM-DOX-LCL formulation, the occurrence (O) and how difficult it was to detect the source of the problem (D). For each CMA and CPP, a RPN was calculated as follows: RPN = S × O × D (2) The factors that counted the highest RPN were included in a DoE conceived in Modde 13 software (Sartorius Stedim Data Analytics AB, Umea, Sweden). The CQAs that constituted the matrix of dependent variables were the entrapped drug concentration, EE%, liposomal size, PdI and zeta potential.
Statistical Analysis The statistical analysis of the DoE was performed using Modde 13 software (Sartorius Stedim Data Analytics AB, Umea, Sweden), while the data that resulted from the antiproliferative studies was analyzed using SigmaPlot 11 software (Inpixon GmbH, Dusseldorf, Germany). The ANOVA test was used to evaluate the differences between the investigated factors and the obtained results at 95% confidence level. The release profile of SIM and DOX were fitted through multiple kinetic models using DDSolver add-ins software.
Cryo-EM The whole sample preparation and data acquisition were done at CEITEC MU. The 3. 5 μl of SDBC sample (5 mg/ml) was applied to freshly glow-discharged transmission electron microscopy grids (Protochips, Cu, 300 mesh, R1. 2/1. 3) and vitrified into liquid ethane using ThermoScientific Vitrobot Mark IV (4 C, 100% rel. humidity, 30 s waiting time, 3 s blotting time). The grids were subsequently mounted into the Autogrid cartridges and loaded to a FEI Talos Arctica (Ther-moScientific) transmission electron microscope for screening prior to data acquisition. The SDBC data were collected using a FEI Titan Krios operated at 300 kV using the SerialEM software (https://bio3d. colorado. edu/SerialEM/; 28). The microscope was aligned for fringe-free imaging. The data were collected on the K2 direct electron detection camera (Gatan) positioned behind the Gatan Imaging Filter at the calibrated pixel size of 0.
818 Å/px (nominal magnification of 1,650,00×). The energy selecting slit was set to 10 eV. The data from a 5. 0 s exposure were saved into 40 frames containing overall dose of 55 e/Å 2. The dataset comprised 9594 movies in total. The image defocus was set to to vary between -0. 5 μm and -1 μm.
Data analysis The movies were first corrected for the drift during data acquisition using program MotionCor2 (29) and the contrast transfer function (CTF) parameters were estimated using GCTF  (30). The images with astigmatism larger than 2500 Å and estimated resolution based on the contrast transfer function fit analysis worse than 4. 0 Å were excluded from subsequent data analysis. A set of 20 micrographs was randomly selected from dataset for manual particle picking using e2boxer. py program  (31). The manually picked particles were used for generation of the model for automated particle picking using program CrYOLO  (32). The overall number of 670,560 particles were extracted from the dose-weighted micrographs using Relion3. 1  (33)  and imported in CryoSPARC (https://cryosparc. com/; 34) for the reference-free 2D classification.
The damaged particles were further removed using the ab initio reconstruction task in CryoSPARC. Finally, 252,122 particles were selected for the model refinement using the C3 symmetry, which was followed by defocus refinement and another round of 3D refinement, which resulted in 2. 54 Å cryo-EM map (according to the FSC(0. 143) criteria). Finally, another round of the 3D refinement was performed without imposing the symmetry, which provided 2. 88 Å cryo-EM map. A de novo model was built using Coot software (https://www2. mrc-lmb. cam. ac. uk/personal/pemsley/coot/; 35), and the resulting model was refined with the Phenix software (http:// www. phenix-online. org/; 36). Metal assignment was performed using the metal-binding site validation server (CheckMyMetal)  (20). Model visualization and fittings were done using the Chimera software (https://www.
Output signals were filtered by a low-pass Bessel filter at 10 kHz and saved at a sampling frequency of 50 kHz using an Axon Digidata 1440A digitizer (Molecular Devices, LLC). Data acquisition and analyses were performed using, a homemade LabVIEW (National Instruments), Clampfit 10. 0. 3 (Molecular Devices, LLC), and Origin Lab 2018 (Northampton).
Acknowledgments-We acknowledge the  Cryo-electron microscopy and tomography core facility CEITEC MU of CIISB (Brno, Czech Republic ),  Instruct-CZ Centre  supported by  MEYS CR  ( LM2018127 ) and iNEXT-Discovery, project number  871037 , funded by the  Horizon 2020 program  of the  European Commission. We are grateful to  Dr J. Nová cek  for the support at the cryo-EM core facility.
Data availability The SDBC maps, the coordinates files, FSC data, and other relevant information about data acquisition and processing have been deposited in the Electron Microscopy Data Bank: for the SDBC with C3 symmetry, 2. 54 Å resolution, EMD-14715 and PDB ID-7ZGY; for the SDBC with C1 symmetry, 2. 88 Å resolution, EMD-14714 and PDB ID-7ZGX. All data will be made publicly available upon acceptance of the manuscript. Supporting information-This article contains supporting information.
[6] In the present article, the authors intend to embrace all these existing results concerning VO 2 technologies in order to build a prospective life cycle assessment (LCA) of the environmental impacts of VO 2 -glazed smart windows from its production to its end-of-life. Therefore, this article addresses the sustainability aspects of the use of VO 2based technologies. [20] CA is a standardized method to assess the environmental impact of all kinds of products and services. [21] It is widely applied in the construction sector, from the material to the building level,  [22]  most often for well-known industrialized systems, such as concrete, glass, steel, etc. The use of prospective LCA to study technologies in early stage development is challenging. LCA studies have been initially performed in well-known industrial systems because the required data for the LCA analysis are more exact at that scale than at an early stage development system.
[23, 24] The laboratory or pilot plant procedures are less representative of the future production because some steps are done by hand and there are higher losses during the process. Moreover, a process efficiency is usually lower at laboratory scale. Hetherington et al. [25]  performed an analysis of the main concerns related to an early research LCA. They identify challenges such as the issues related to the scaling-up of the production, the lack of raw materials data or use phase information, and the probably unknown future applications. However, they provide guidance to overcome those challenges. As also stated by Cucurachi et al. ,  [24]  it is necessary to perform LCA studies in the early stage developments of a technology in order to introduce the necessary changes. Otherwise, if we perform the LCA study when the product is already commercialized, the changes to improve the environmental sustainability will involve high costs.
Previous studies have also reported methodologies to shift a laboratory process to an industrial scale approach so that the production data are closer to the final product. [26] Thus, taking into account the uncertainties, useful perspectives can be drawn to design a product with low environmental impacts from its foundations. Therefore, the aim of this study is to conduct a LCA of the VO 2 -based smart windows to analyze the environmental impacts during its whole life cycle for the intended application. The objective is to address the sustainability aspects of the technology taking into account all its life cycle, and to identify the features that require a special attention to avoid high environmental impacts in the final product. The analysis was first done taking into account only the production phase of the window (cradle-to-gate) and then, the complete LCA, from the production phase to the product end-of-life, was evaluated (cradle-to-grave).
LCA Methodology The goal of the LCA in the present study is to assess the environmental benefits of using a VO 2 -based TC window as compared to a standard (STD) window. For this purpose, a comparative analysis was done between a STD window and a VO 2 -coated TC window. The VO 2 production is currently at laboratory stage development and thus, a scale-up approach to industrial production is also proposed to perform the prospective LCA. A cradle-to-grave LCA has been undertaken in this study using the OpenLCA 1. 10. 3 software with the ecoinvent 3. 6 and 3. 8 cutoff databases. The use scenario is based on an office room comprising a double-pane window (STD) and being renovated to improve the energy efficiency. The life cycle impact assessment (LCIA) was done through the analysis of the nonrenewable and renewable resources and climate change (global warming potential for 100 years, GWP 100a).
The transport was also doubled to take into account the transport required for the film replacement after 10 years. Regarding the installation, it was assumed that the VO 2 film was electrostatically adhered to the window. Therefore, no inputs were associated to this phase. In the use phase, the cooling, lighting and heating demands inputs were associated also to low voltage electricity and natural gas, respectively. The energy values were obtained from Aburas et al. work. [11] Moreover, the VO 2 film was assumed to be treated as inert waste at its end-of-life using the dataset from the ecoinvent database. Transports related to the inert waste treatment are included in the mentioned dataset. The VO 2 film LCI described in Table  3  consists of a 100 μm PET film. Commercialized pigmented polymer films for window application are usually made of a core pigmented PET layer coated with two protective layers on each side.
Therefore, three PET layers form the VO 2 film and the VO 2 particles are mixed as a pigment in the PET middle layer. However, for the LCA calculation, a 100 μm-thick PET film containing VO 2 particles with a filling factor f ¼ 2. 5 Â 10 À3 was considered, corresponding to a VO 2 mass thickness of 250 nm. The raw materials for the film production are VO 2 and PET. As the unit process for the VO 2 powder production is not available in the ecoinvent database, a custom-made unit process was created rather than using an average dataset because VO 2 is a key material of the present study. The PET production and film manufacturing were approximated by the unit process of packaging film production available in the ecoinvent database. The custom-made unit process done for the VO 2 powder production is shown in Table  4.
This LCI corresponds to Barcelona city because the laboratory production was only analyzed in that city. The place where the manufacture takes place is assumed to be at the same industry where the dispersion is fabricated. The VO 2 powder is still not commercialized at industrial scale and thus, a hydrothermal synthesis process was assumed following a procedure reported in the literature. [10] In order to fit with the smart window application, W-doped VO 2 is considered rather than VO 2 to lower the transition temperature because a transition temperature around 25 C was found to be the most efficient for building energy efficiency. [12, 30] The process consists in a hydrothermal synthesis using vanadium pentoxide (V   Europe without Switzerland. precursors. [10] The first two precursors are not available in the ecoinvent database either.
Therefore, the V 2 O 5 unit process was obtained from the literature  [31]  and the one for the H 2 C 2 O 4 was approximated by the citric acid dataset, as it has been previously done in other works. [32, 33] The V 2 O 5 LCI is related to a manufacturing process taking place at Evraz Highveld Mine (South Africa) and thus, transport from the mine to Barcelona was estimated. Regarding the other precursors transport processes, they were included in their corresponding datasets (Table  4 ). To start the synthesis, the precursors are added to a H 2 C 2 O 4 water-based dispersion of (75 mL) and they are mixed by magnetic stirring during 30 min. Then, the dispersion is placed within a 100 mL hydrothermal reactor vessel and the synthesis process is performed at 280 C for 72 h. The product of the synthesis is a W-doped VO 2 dispersion.
The article states that the H 2 C 2 O 4 was first mixed in water and then the other reactants were added and mixed for 30 min. Thus, it has been assigned a 30 min mixing for each stirring step. The water washings were considered to be done by filtration with a pump using 75 mL water per washing. Regarding to the electricity estimations, the following equipment were used: a magnetic stirrer allowing a 5 L maximum water volume (2. 6 W), a hydrothermal reactor of 100 mL in capacity (350 W), a pump of 65 W for the water washings, a centrifuge Europe without Switzerland. with a speed allowance of 6000 rpm (100 W), and a 25 L vacuum oven (1300 W). The equipment's materials and production were considered outside the LCI boundaries. Moreover, the washing water was considered as a waste product applying the "market for wastewater, average" treatment from the ecoinvent database.
The office room location has also been analyzed considering the corresponding transports and providers in the LCA for each site. The LCA was performed also in Cairo and London using the use phase data from Aburas et al. work. [11] The energy demand results (Figure  4 ) show that the higher demands were associated to Cairo, followed by Barcelona and London. Regarding the energy savings, the higher savings were observed when using the TC window in Barcelona city. These outcomes are similar to those obtained from the energy savings study by Aburas et al. [11]  because of the high electricity demands of the use phase.
On the contrary, STM method is not convenient for quick screening as it requires longer experimental time, more material, and solubility curve determination work prior to screening. Nonetheless, it allows to acquire a large amount of accurate solubility data (see the Supporting Information) that can be collected in databases for future use. This is particularly relevant for pharmaceutical industry as the same pool of coformers are regularly used. Furthermore, when detecting a cocrystal with STM, experimental parameters to grow single crystals are also measured as stability domains of cocrystals are identified. The same compositions can therefore be used for slow cooling crystallization or temperature cycling growth experiments. These data can also be used later, for instance, to design a cocrystal production process.
CONCLUSIONS Three common and accessible methods with different principles in their crystallization mechanism were investigated in a vast screening for cocrystals of PZQ. The methods were applied to PZQ with 30 coformers, which were identified based on a link prediction algorithm 67 using data mining of the Cambridge Structural Database (CSD). A total of 17 cocrystals were identified, with 14 showing stability, and 12 new structures were resolved and reported. 67 The large amount of data obtained in the screening helped to compare the efficiency of the cocrystal screening methods. LAG highlighted the best results, with the largest screenable coformer fraction (90%) and the highest number of cocrystals found that are suspected to be stable (13), even though amorphous phases are obtained in a few cases.
1 ). Theoretically, the artificial route has a higher carbon yield than that for the native biosynthetic route from their respective terpenoid precursors (65% vs 46. 7%, Fig. 1 ). Serendipitously, a bifunctional methyltransferase and cyclase (TleD or pMT1 in this study) was recently discovered from Streptomyces  12  and structurally elucidated (PDB id: 5GM1 (substratefree), 5GM2 (substrate-bound))  13. TleD methylates the terpene moiety of teleocidin A1 and cyclizes it to form a 6-member ring. Along with TleD, we also tested another three methyltransferases with known structures (Supplementary Table  1 ): cyclopropane mycolic acid synthase 1 from Mycobacterium tuberculosis (Mt. CMAS)  14  , geranyl diphosphate 2-C-methyltransferase from Streptomyces lasalocidi (Sl. GdpMT)  15  , and a methyltransferase-like protein from Saccharopolyspora spinosa (Ss.
SpnF)  16. By incubating cell extracts overexpressing the four respective methyltransferases with psi-ionone, only reaction with pMT1 gave rise to detectable levels of trans-α-irones (84%) and cis-α-irones (16%) (Fig. 2a  and b ). To identify a potentially more active enzyme, the pMT1 sequence was mined using the Basic Local Alignment Search Tool (BLAST) against the non-redundant protein sequence database  17. Two sequences were The chemical route uses acid to catalyse cyclization of methyl-3-psi-ionone, which is not a known natural product. Chemical synthesis produces a racemic mixture containing (1 R, 5 R)-trans-α-irone (1R5R), (1 S, 5 S)-trans-α-irone (1 S5S), (1S, 5 R)-cisα-irone (1S5R), (1 R, 5 S)-cis-α-irone (1R5S), (5 R)-β-irone and (5 S)-β-irone. The native biosynthesis route was proposed based on a radiolabelling study  9  , and a bifunctional methyltransferase and cyclase (bMTC) was hypothesized to convert iridal directly to cycloiridal.