dataesr/scientific-paragraphs-categorization huggingface dataset:
Chemists aim to meet modern sustainability, health, and safety requirements by replacing conventional solvents with deep eutectic solvents (DESs). Through large melting point depressions, DESs may incorporate renewable solids in task-specific liquids. Yet, DES design is complicated by complex molecular interactions and a lack of comprehensive property databases. Even measuring pure component melting properties can be challenging, due to decomposition before melting. Here we overcame the decomposition of the quintessential DES constituent, choline chloride (ChCl). We measured its enthalpy of fusion (13. 8 ± 3. 0 kJ ⋅ mol) and melting point (687 ± 9 K) by fast scanning calorimetry combined with micro-XRD and high-speed optical microscopy. Our thermodynamically coherent fusion properties identify ChCl as an ionic plastic crystal and demonstrate negative deviations from ideal mixing for ChCl-contradicting previous assumptions.
16 ). The calorimeter-on-a-chip design of FDSC  14, 15  grants access to heating rates up to 50 000 K ⋅ s -1 , because the thermal lag of the furnace and microscopic samples (1 ng to 100 ng) is substantially reduced as compared to regular DSC. Since ChCl is strongly hygroscopic, we adapted the experimental setup such that all samples were prepared and measured under dry argon or nitrogen atmosphere. A full description of the experimental setups and (tedious) sample preparation for ChCl is given in the Supplementary Methods. With FDSC, we experimentally assessed the phase transitions of ChCl at extreme heating rates. The underlying structural processes of these transitions cannot be identified based on enthalpic data alone, so direct access to structural information is needed. We therefore combined fast scanning calorimetry (FSC) with synchrotron micro-XRD, which allowed us to follow the evolution of the crystal structure before and after liquefaction of ChCl.
15 K at 1000 K ⋅ s -1 (about 10 K below T trs ), kept isothermally for 60 s, and cooled to 303. 15 K at 1000 K ⋅ s -1. The heat flow signals were recorded using the Star-e software package, they were then integrated and visualised using Python 3. Δ trs H was calculated from the integral of the solid-solid peak, which was numerically integrated using a sigmoidal baseline that scales with the peak integral. The start and end of the peak were determined by the point at which a rolling linear fit from the inflection point towards the baseline merges with the baseline. Δ fus H was determined in two ways: (i) fitting all data to a melting + decomposition model, (ii) fitting a single melting peak to the data until the inflection point after the first major peak (Fig. 3 , Supplementary Methods: Fast differential scanning calorimetry).
The sample temperature thus drops rapidly to ambient temperature prior to the fast heating run. A trigger scheme was implemented to ensure synchronisation of the micro-XRD data and the FSC data. The FSC triggered the activation of the fast X-ray shutter as well as the data collection of the Eiger 4M detector. As a result, the sample would only be exposed to X-rays during the actual temperature programme, reducing extensive exposure of the sample prior to and after FSC data collection. A single temperature programme was applied for each run: (1) an isotherm of 25 ms to stabilise at 383 K before heating at controlled rates, (2) a heating ramp to the maximum temperature (~730 K) at 100, 1000, 2000, or 5000 K ⋅ s -1 , (3) an isotherm of 10 ms, and (4) cooling as fast as possible (uncontrolled) to prevent decomposition.
XRD patterns were recorded simultaneously every 2 ms. For some samples, a picture was taken before and after heating to evaluate sample morphology changes owing to the temperature programme and/or the X-ray beam exposure. The used diffraction geometry, including the modulus of the scattering vector q, was calibrated using alpha-alumina. The collected two-dimensional diffraction patterns were resampled and reduced to one-dimensional scattering curves using the pyFAI package (Supplementary Methods: Fast scanning calorimetry + μ-XRD). The FSC was also combined with in-situ high-speed optical microscopy imaging. To this end, an Olympus BX50 optical microscope was equipped with a Phantom v7. 3 fast CCD-camera from Vision Research US with a maximum frame rate of 15 kHz. The measurements were done using bright field transmission and reflection light illumination taking advantage of the good transparency of the SiN x membranes.
Thermogravimetric analysis Thermogravimetric analysis (TGA) measurements were carried out on a Setaram Labsys Evo TGA under a nitrogen flow of 30 mL Á min À1. About 10 mg was weighed into a 100 μL aluminium oxide crucible under argon atmosphere. When placing the crucible in the instrument, a short exposure to the moist atmosphere could not be avoided. The sample was therefore heated from 303 K to 393 K at 10 K Á min À1 and kept isothermally for 30 min to dry the sample. Then the sample was heated from 393 K to 473 K at 10 K Á min À1 and further from 473 K to 573 K at 0:1 K Á min À1. Finally, the crucible was cleaned by heating from 573 K to 1773 K at 30 K Á min À1. (Programme Investissements d'Avenir ANR-16-IDEX-0005). We acknowledge the European Synchrotron Radiation Facility (ESRF) for provision of synchrotron radiation facilities and we thank Michael Sztucki for assistance and support in using beamline ID13.
Acknowledgements The authors thank  Dimitri A. Ivanov  from  Institut de Sciences des Matériaux de Mulhouse  for providing the high-speed camera for the insitu fast optical microscopy measurements. We thank  Ahmad Alhadid  for the scientific discussions on plastic crystals and ChCl-rich liquidus temperatures. We thank  Nicolas Scaglione  for helping with the thermogravimetric analysis and  Inês Vaz  for attentively evaluating the manuscript. We acknowledge  IDEX-LYON  for financial support Article https://doi. org/10. 1038/s41467-023-42267-6 Nature  Communications | (2023) 14: 6684.
Data availability All FSC + micro-XRD data recorded at the European Synchrotron Radiation Facility is publicly available as of 2025 at https://doi. org/10. 15151/ESRF-ES-708312842. All numerical data obtained from the fast differential scanning calorimetry are listed in Supplementary Tables  1  and 2 , and corresponding thermograms are shown in Supplementary Figs. 19 20 21.
Indeed, when the physical properties of the agitated medium, the diameter of the vessel and the desired mixing time are known, it is very easy to compute the dimensionless number 2 (using the left-hand side of Eq. (  4 )). Hence, the numerical values of 1 can be read off at the intersection of the 2 value with the curve appearing on the worksheet. The power consumption, P, can be then deduced by knowing 1 values. Then the following information can be brought out: -the stirrer type, tank and baffling conditions which have to be used; can be read off from the abscissa. Then, the diameter of the stirrer can be determined from data on the stirrer geometry in the sketch. -finally, the numerical value of Re can be read off its proper scale at the same intersection. This, in turn, it makes possible to determine the stirrer's rotational speed, which should be selected to reach such optimum mixing efficiency.
1 Å -1 < Q < 3. 2 Å -1. The sample was loaded into a disposable quartz capillary with a nominal thickness of 1. 0 mm and sealed with hot glue. Measurements were conducted at an ambient temperature (ca. 20 °C), and standard background subtraction and data normalization were applied. Molecular dynamics simulations were performed using the GROMACS 5. 1. 1 package. 47, 48 Concerning bonded and nonbonded parameters for the two components, LevAc (see Scheme 1), was described using an all-atom potential force field. 49, 50 ] [53] [54]  Simulations were performed using a cubic box (box size ∼57 Å) containing 30 DiMeβ-CD molecules and 810 LevAc molecules and (1:27 molar ratio) (hereinafter indicated as the DiMeβ-CD-LevAc system); periodic boundary conditions were applied. Initial configurations were created by Packmol software.
After that, 2nd PCR was carried out using 2 µL of 1st PCR purified product and NexteraXT Indexed Primer to construct the final library. The 2nd PCR cycle conditions were analogous to those of the 1st PCR cycle, except that 10 cycles were performed instead of 25. The purification of 2nd PCR product was conducted with AMPure beads, and the quantification was carried out using qPCR following the protocol guide (KAPA Library Quantification kits for Illumina platforms). TapeStation D1000 ScreenTape (Agilent Technologies, USA) was used to qualify the 2nd PCR product. Sequencing of pairedend (2 × 300 bp) was conducted by Macrogen using the MiSeq™ platform (Illumina, San Diego, CA, USA). QIIME (version 1. 9. 0) was employed to analyze the sequencing data  [30]. BLASTn (version 2. 4. 0) was used to align the sequence of each operational taxonomic unit (OTU) sequence against the NCBI 16S rRNA database reference sequences to obtain the taxonomic affiliation of each OTU.
Funding: This work was supported by the  C1 Gas Refinery Program  from the  Ministry of Science and ICT (MSIT)  through the  National Research Foundation (NRF) of Korea  ( NRF-2015M3D3A1A01064926 ) and the  NRF  grant funded by the  MSIT  ( NRF-2019K1A3A1A21031925 ).
Apparatus Fluorescence spectra were obtained at room temperature (298 K) on a Shimadzu RF-6000 spectrofluorimeter equipped with a pulsed xenon lamp and interfaced to a microcomputer, processed by a software LabSolutions CS, Shimadzu (Shimadzu Co. ). Slit widths for both excitation and emission monochromators were set at 5 nm, and all measurements were performed in standard Hellma 1-cm pathlength quartz fluorescence cells. Absorption spectral measurements were also realized at room temperature with a Shimadzu UV1800 absorption spectrophotometer. An Osram 200W HBO highpressure mercury lamp with an Oriel Model 8500 power supply was used for photolysis measurements. During photolysis, the quartz fluorescence cell was placed on an optical bench 30 cm from the mercury lamp.
The total GC analysis time was 432 s. The scheme of the setup is provided in Figure  S6. A fixed catalyst amount of 50 mg was used during the switching feed experiments. In a typical experiment, pelletized (250-500 μm) catalyst was pretreated at 550 °C in a flow of artificial air for 1 h and then cooled to 350 °C in He flow. Then, the first switch to methanol was performed, and the online analysis by GC and MS was started. After, the feed was switched from methanol-containing He flow to dry He flow every 1098 s (±10 s) and samples of the reactor effluent were injected into the GC every 432 s (±1 s). As a result of the asynchronous switching and GC injections, an array of about 100 chromatograms, corresponding to different points along the 36. 6 min MeOH in He/dry He cycle, was obtained. Data processing involved assigning each chromatogram to a specific time after the switch was performed, which is further referred to as "time on switch".
In contrast, intense signals of aromatic eluents are observed for HZSM-5 and Na/ZSM-5. By inspecting the individual products, more propylene (as a product of olefin cycle) and much less toluene (as a product of aromatic cycle) were observed for Ca/ZSM-5 catalyst after methanol was switched off (Figure  S11 ). The total yield of eluents after the switch to dry He was the highest for Ca/ZSM-5 catalyst followed by Na/ZSM-5 (Figure  5b ). We should note that our GC does not allow analyzing hydrocarbons heavier than C 9 , such as tetramethylbenzenes and naphthalenes. Therefore, we cannot estimate their contribution in these transient experiments. Overall, the step-response data demonstrate that (i) Ca/ZSM-5 retains a much larger amount of water and a significantly larger amount of hydrocarbons than HZSM-5 and Na/ZSM-5 during the MTH reaction; (ii) the hydrocarbon pool intermediates inside the pores of Ca/ZSM-5 that can leave the pores are mostly aliphatic in nature; and (iii) HZSM-5 and Na/ZSM-5 contain more aromatic hydrocarbon pool species.
83 Nevertheless, the spectra in the 1350-1500 cm -1 region are similar for HZSM-5 and Na/ZSM-5 samples and very different from that of Ca/ZSM-5 (Figure  9g, h, i ). The main inference from these data is the much stronger interaction of Ca/ZSM-5 with methanol and water and suppressed formation of aromatics over this sample. In addition to methanol switches, we studied the interaction of water with catalysts by IR spectroscopy upon exposure of catalysts to water vapor in a temperature-programmed regime. Ca/ZSM-5 retained a larger amount of water in a broad temperature range (Figures  11  and S22 ), indicating a much stronger interaction with water under reaction conditions. We also used IR spectroscopy of adsorbed pyridine  104  on dry and wet samples (Figures  S23-S24 ), where Ca/ZSM-5 catalyst showed a weaker intensity of pyridine bands upon adsorption of pyridine on wet samples.
Operando XRD. 6] [107]  With the aid of operando synchrotron-based XRD experiments, we followed the structural changes of catalyst during the MTH reaction over HZSM-5, Ca/ZSM-5, and Na/ZSM-5 catalysts and explored correlations with the amount of retained adsorbates. For operando XRD measurements, a home-built Clausentype flow cell  27  placed on a movable sample stage was used. First, we acquired diffractograms of working catalysts at 5 different positions along the bed (Figures  12a  and S25 ). 9] [110]  Figure  12  shows the evolution of the unit cell volume together with MS data of the reaction cell effluent. The MS signals related to hydrocarbons (m/z = 27, 56, 92, and 106) point to the buildup of the hydrocarbon pool and the formation of common MTH products. The XRD results are generally in line with the TGA-MS results.
■ ACKNOWLEDGMENTS This work was supported by  The Netherlands Center for Multiscale Catalytic Energy Conversion (MCEC) , an  NWO Gravitation programme  funded by the  Ministry of Education, Culture and Science of the government of The Netherlands. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 801359. We acknowledge synchrotron SOLEIL for provision of beamtime to record the PEPICO data under proposal no. 20200257, and we thank  J. F. Gil  for his technical help around the SAPHIRS chamber and  L. Nahon  for his constant support of this project, and the  SOLEIL  staff for smoothly running the facility. We acknowledge the  European Synchrotron Radiation Facility (ESRF)  for provision of synchrotron radiation facilities: to record the XRD data under proposal no.
Acknowledgements We gratefully acknowledge the  ESRF and Swiss-Norwegian beamlines (SNBL)  for access to facilities, as well as the time invested in the experiments. We are also very thankful for the technical support supplied by  Joel Jenni  and  Fredy Mettler  of  ETH. We further acknowledge  Technical University Munich (TUM) , especially  Professor Andreas Jentys , as well as  ETH Zürich , for enabling JW to undertake his master thesis at  ETH Zürich. We also thank  Dr. Zoran Jovanovic  and  Jonas Meidinger  (MSc), for insightful discussions and the reading of this manuscript. Open Access funding provided by  Eidgenössische Technische Hochschule Zürich.
I i is not a sca- lar quantity but a function of the position in the x-y plane of the image so that I i (x, y) is the pixel intensity collected at positions x and y from laser excitation at wavelength i. A dataset was assembled to calibrate pH predictions. Samples contained only smart soils kept at 11 different pH levels achieved using MES acid monohydrate and Na buffers (1. 06126 and 1. 06197, Merck, UK) at respective pH of 4. 97, 5. 20, 5. 37, 5. 58, 5. 78, 6. 03, 6. 18, 6. 38, 6. 63, 6. 80 and 6. 97. Image data were collected from 3 experimental runs. Four Images were collected for each pH and for each of the four wavelengths (i. e. 44 images for each location measured). Points of interests were automatically extracted from images obtained at 488 nm excitation and 530 nm emission (Fig. S1 ).
These points were then used to collect a dataset describing the relation between pixel intensities at all wavelengths and pH values. Regressions were established between pH and image intensity ratios pH = f I i I j , i ≠ j (Fig. S2 ). To further improve predictions, neural network models of the type pH = f I i , x, y , were developed using a Multi-layer Perceptron regressor with rectified linear unit function and trained using a Limited-memory BFGS optimiser. The final neural network model contained 3 layers of 12 neurons each (Fig. S3  and S4 ).
Computation and statistical analysis Computations were performed using the Scikitlearn library  (Pedregosa et al. 2011). Datasets of pixel intensity at known pH was compiled with macros and scripts developed in ImageJ. Analysis of Variance and Tuckey's Post Hoc tests were performed in the R software package with a statistical significance level 0. 05. Analysis of pH variations surrounding plant roots was done using the nlme library  (Pinheiro et al. 2013).
4B  & C ). Results showed the machine learning approach improved predictions significantly in a test dataset (Fig. 4C , t = 79. 9, p < 0. 001), with 95% of the predictions falling within -0. 4 and 0. 3 units of the true pH value, almost doubling the precision of the ratiometric fluorescence predictions. Ratiometric fluorescence predictions also introduced bias leading to underestimation of large pH values (supplementary Fig. S2 ). Up to 80% of variation in pH was correctly predicted using neural networks with only 46% of the variance explained by the ratiometric fluorescence approach.
Smart soil particles help understand the formation of soil chemical heterogeneity Using the fabricated smart soil particles, pH dynamics in the rhizosphere of plants were observed up to one week after germination. The first indication of root driven pH change was visible after 4 days (data not shown). Following growth in the smart soils, we could observe changes in chemical properties by the naked eye (Fig. 5A ) and subsequently analyse how these changes relate to soil and root biological activity. Maps of entire seedlings and surrounding smart soils were assembled by tessellating images to produce volume data corresponding to up to 3 cm 3 of substrate at 10 μm resolution with each time point ). B The stability of coatings was studied using FTIR. FEP FTIR spectrum (top red) was compared to those of the coatings immediately, after 1 week, 2 weeks, one month and two months (from green top to bottom black respectively).
C The particles were tested for imaging using light sheet scattering (blue) and fluorescence (green) signals (scale bar 1 mm). D Water contact angle measurement of FEP (hydrophobic, > 90, left), and that of the functional polymer (hydrophilic, < 90, right). E Water retention of the terpolymers containing PEGMA (red x) increased the water retention of FEP soil particles (cyan +) with comparison to virgin FEP particles (blue Δ). van Genuchten curves (dashed lines) for gravel  (Tokunaga et al. 2002) , sand, sandy loam and loam soils  (Šimůnek et al. 2006)   Vol:. (  1234567890 ) producing a dataset of approximately 4 Gb. pH predictions obtained from the data acquired 7 days after inoculation (Fig. 5B-D ) revealed how soil chemical heterogeneity may form as a result of root exudation, and how this process is influenced by the generation of microsites and the type of FEP particles.
Acknowledgements This work was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 647857-SENSOILS ). We also acknowledge the funding from the  Spanish Ministry of Science and Innovation (MICINN)  under de project  MICROCROWD  ( PID2020-112950RR-I00 ). Researchers at the  James Hutton Institute  also receive financial support from the  Rural & Environment Science & Analytical Services Division of the Scottish Government. The authors thank Akzo and  Tosoh Finechemicals Corp. for supplying with tert-butyl peroxypivalate and 2-trifluoromethacrylic acid, respectively. BA also thanks the  French Fluorine Network (GIS).
Acknowledgements O. W. acknowledges funding from the  EPSRC  and  SFI Centre for Doctoral Training in Advanced Characterisation of Materials Grant Ref :  EP/S023259/1. M. S. and I. E. L. S. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  (grant agreement no. 866402 ). A. B. acknowledges funding from the  Carlsberg Foundation  (grant number  CF21-0144 ). R. T. acknowledges funding from the  Royal Academy of   Engineering Chair in Emerging Technologies Fellowship. R. J. acknowledges funding from the  Faraday Institution Degradation project  ( EP/ S003053/1 , grant  FIRG024 ). The authors acknowledge the assistance of  James Murawski ,  Daisy Thornton , and  Rose Oates  for helpful discussions on platinum poisoning and the RHE, lithium overpotentials and pH variation in electrochemical CO 2 reduction.
ACKNOWLEDGMENTS We thank the  TEM platform (M. Schmutz)  from  ICS  for technical support. Support from  ANRT  through  Cifre PhD  grant  2018/0723  is acknowledged (MB). Céline Kiefer  and  Guillaume Rogez  ( IPCMS, Strasbourg ) are acknowledged for giving access to the  FTIR spectroscopy laboratory. Mélanie Legros  is acknowledged for performing the GPC analysis of the PLLA materials. We thank also  Catherine Saettel  for the DSC measurements. We want also to thank  Bernard Lotz  for a critical reading of the manuscript and fruitful discussions on PLA polymorphism.
Catalyst characterization The Ru loading was determined by inductively coupled plasma optical emission spectrometry (ICP-OES), using a mixture of HNO 3 and HCl and microwaves activation to dissolve the sample. The analysis was performed in an Agilent 5110S instrument. The crystallographic structure of the samples were analyzed before and after isotopic exchange by PXRD. The data were collected on a D8 ADVANCE Bruker AXS diffractometer in Bragg-Brentano setup equipped with a copper anode source (λ = 1. 5418 Å), and a linear detector LYNXEYE XE-T. The measurements were performed at room temperature over an angular range: 8 • < 2θ < 80 • with a step of 0. 02 • and an acquisition time of 1 s per step. PXRD patterns were analyzed using the Fullprof Suite program and the unit cell parameters were determined through full-pattern matching  [19].
Transmission Electron Microscopy (TEM) was performed in a JEOL 2100 UHR apparatus, equipped with a LaB 6 filament and a Gatan Ultra scan camera. The equipment uses the range of 0-20 keV of X-rays from the sample to electron impact. EDX spectroscopy was carried out with a Hypernine (Premium) detector (active area: 30 mm 2 ) using the software SM-JED 2300 T for data acquisition and treatment. The thermal stability of hydrides contained in the interstitial sites of Ru/LaScSi and Ru/CeTiGe intermetallic phases were evaluated by temperature-programmed desorption (TPD) under 30 mL min -1 of Ar flow from RT to 1000 • C (heating ramp rate of 5 • C min -1 ). The measurements were performed on a Micromeritics Autochem 2920 apparatus equipped with a thermal conductivity detector (TCD) to monitor the H 2 release resulting from the hydride desorption.