dataesr/scientific-paragraphs-categorization huggingface dataset:
They range from market opportunities to legislative aspects, hazards and fate of products, to possible pathways of emission of nanomaterials and mitigation strategies to limit emissions. In parallel, issues relating to the stakeholders involved, their issues, responsibilities, obligations and mutual relations as well as issues relating to societal consequences cover the technological development part. All these issues are very broad, and some will only concern certain categories of actors. L. G. Soeteman-Hernandez et al. applied this SbD concept with PhD students by taking case studies of nanomaterials from current research and published a first database addressing nanosafety aspects for nanomaterials as an example to raise awareness of the importance of risk assessment in the early stages of research and to implement RATA to academic laboratories  [27].
In the future, these databases should be used to make decisions throughout the design stages of a nanotechnology-based product in a circular economy approach. They will make it possible to find the best compromise between the lowest inherent toxicity and the highest functionality, to define ways of applying the material and using the product to limit their emission or, in the least favorable cases, to establish minimizing measures to prevent undesirable effects right from the product conception. In the present study, we investigate emerging multifunctional nanoparticles of pristine gadolinium oxysulfide Gd2O2S and Ce-doped (Gd,Ce)2O2S still far from the market, in the spirit of RATA as part of an ethical and civic approach. Gadolinium oxysulfide (Gd2O2S) nanoparticles are promising candidates for biotechnologies as they have been used as MRI contrast agent or X-ray absorbing materials.
Light Absorption and Optical Bandgap Absorption spectra of the powders of the nanoplatelets were recorded using UV-visible diffuse reflectance spectroscopy (Figure  2 ). The Kubelka-Munk function of reflectance F(R) was calculated from obtained apparent absorbance. This function is directly proportional to the extinction coefficient of the material, thus to its absorption properties. The data were fitted assuming a direct bandgap, as discussed in a previous study  [35]. The Ce/Gd substitution strongly affects the absorption of the nanomaterials, in accordance with their color. In fact, spectrum of the synthesized Gd2O2S nanoparticles indicates only weak UV absorption with a threshold around 270 nm. The bandgap of the material was previously estimated at ca. 4. 7 eV using the Tauc plot suggesting that Gd2O2S is closer to an insulating material than a semiconductor.
The gel was run at a constant voltage of 60 V for 1 h then at 120 V for 1. 5 h. The proteins were transferred to a nitrocellulose membrane by electroblotting at 20 V, 4 °C for 18 h. Blocking of unreacted sites of the membrane was performed by incubation with a solution of milk 5% for 1 h. The membrane was incubated with primary antibody (HO-1 or β-actin) at 4 °C for 16 h then with secondary antibody coupled with peroxidase ECL or alkaline phosphatase at room temperature for 2 h. It was finally reacted in the dark with peroxidase substrate (ECL, Bio-Rad) or alkaline phosphatase (Bio-Rad) for revelation of HO-1 or β-actin, respectively. Detection of luminescence signals and acquisition of images of the membrane were performed on a G-box (Syngene, UK). Quantification of luminescence intensity was carried out with ImageJ software.
The silicon nitride (Si3N4) membrane windows were mounted onto a pre-cooled copper sample holder immersed in liquid nitrogen and were rapidly inserted into the vacuum chamber. µXRF maps were acquired with 0. 5 µm 2 steps and an integration time of 100-150 ms. Analysis of the XRF images and spectra was carried out using the multiplatform program PyMCA  [46]. Elemental mass fractions were calculated from fundamental parameters with the PyMca software package, applying pixel-by-pixel spectral deconvolution to hyperspectral maps normalized by the incoming current. The detector response was calibrated using reference sample on Si3N4 membrane RF8-200-52454-17 purchased from AXO DRESDEN GmbH (Dresden, Germany). To calculate weight fractions, the thickness of the cells and the density of the medium were estimated at 15 µm and 1 g/mL, respectively.
Acknowledgments:  Sorbonne Université ,  CNRS  and  Collège de France  are acknowledged. The Program " Interface pour le Vivant " at  Sorbonne Université  is acknowledged for the funding of the PhD of  A. -M. N. Audrey Ridoux  is acknowledged for the help with in vitro experiments. Tamar Saison  is acknowledged for the preparation of BiVO4 nanoparticles. We acknowledge  ESRF  for allocation of beamtime on ID21 under project number  75737  and we acknowledge  Hiram Castillo  and  Murielle Salomé  for technical support on the beamline.
During the experiments the spectra were recorded in the spectral range 4000-700 cm -1 every 5 min by integrating 120 interferograms. The used spectral resolution was 1 cm -1. The products were identified and quantified by FTIR spectroscopy, where recorded spectra were compared with references available in IR databases at the Wuppertal laboratory. The quantification of propanal was completed using tabulated IR absorption cross-sections determined in QUAREC. Glyoxal was quantified using absorption crosssections determined by Volkamer et al. [18]. T2P, propanal and glyoxal concentrations were retrieved using the following IR absorption bands centered at 1150 and 2726 cm -1 , 850 and 2992 cm -1 , and 2835 cm -1 , respectively, by iterative subtraction of calibrated reference spectra. A few gas samples taken on Carbotrap 202 cartridges were analyzed by GC-MS/FID at the IMT laboratory after thermal desorption  [19].
Characterization Characterization is mainly performed on the exposed face of the disc referenced "P5" for LG-Al and of the disc referenced "P6" for HG-Al, especially for SEM and TEM analysis. However, in order to acquire representative data for the mass and density of VACNTs, all the collected substrates (ten) placed in the isothermal area and covered with VACNTs on both faces are weighted. This allows us to calculate the carbon conversion yields on the overall substrate faces, which is representative of the CVD growth configuration. The carpet thickness was measured using a Scanning Electron Microscope (SEM Carl Zeiss Ultra 55, Zeiss France, Rueil-Malmaison, France) through the analysis of the carpet cross-section on the exposed face of the Al disc referenced P5 (LG-Al) and P6 (HG-Al). Thickness measurement was performed on five different locations of the cross section: one at the extremities, two at the center and two in-between.
The geometry optimization of compound 1o was performed using the B3LYP hybrid functional with the D3BJ model to describe weak interactions, and the 6-31G(d,p) basis set, using the Gaussian 16 software (Figure  2 ). 36 The anti-parallel conformation of 1o was found to be the most stable, confirming a majority of anti-parallel conformations in solution. In this conformation, the two pyrenes are facing each-other, at a distance of 3. 5 Å, evidencing their possible interaction in the ground state and their capacity to form an excimer in the excited state. Scan calculations performed at the B3LYPGD3BJ / 6-31G level of theory allowed us to estimate the rotational energy barrier around the thiophene -perfluorocyclopentene bridge. It was estimated at 75 kJ. mol -  for the C10-C3' bond and 161 kJ. mol -1 for the C6-C3 bond (Figure  2 ).
ACKNOWLEDGMENT This work was supported by financial supports from the  CNRS  and the  University of Rennes 1. The PhD grant of the first author has been provided by the  University of Rennes 1. This work was granted access to the computing resources of CINES (Montpellier, allocations  2020-AP010811752 ,  2021-A0100805032 ). Part of this work has been performed using the  PRISM core facility  ( Biogenouest©, UMS Biosit, Université de Rennes 1-Campus de Villejean-35043 RENNES Cedex, FRANCE.
Note that since the pressure was controlled by a pressure controller, the gas flow rate in the drying experiment was not controlled quantitatively. The influence of the drying rate on the morphology of gelled films will be the scope of a future study. The drying time t = 0 min was taken at the time at which the gas outlet was opened. To prevent the film from being replenished by the liquid in the microfluidic channels upon drying (via osmotic effects), the liquid inlet was disconnected from the chip as soon as the gas inlet was opened. Film Thickness via Colorimetric Mapping: To be able to estimate the thickness of the thin films, a colorimetric map was generated using a MatLab code based on Fresnel optics: the spectrum of reflected light intensity can be derived from Fresnel equations for reflection and transmission.
Acknowledgements The authors acknowledge funding from an  ERC   Consolidator Grant  (agreement 819511 -METAFOAM), from the  region Grand Est (Young Investigator Grant ), and from the  Interdisciplinary Institute "Hierarchical Functional Materials" supported by IdEx Unistra  ( ANR-1″0-IDEX-0002 ) and  SFRI  ( STRAT ' US  project,  ANR-20-SFRI-0012 ) under the framework of the  French Investments for the Future Program.
Using corrections of PSS absorption and scattering (for details see the caption of Figure  S2 , Supporting Information), the contribution of DNA absorbance as A = 0. 1351 could be estimated. Using Beer Lambert's law and the cuvette path length of 1 cm, this value corresponded to a DNA Calculation of the Spectral Overlap Integral: The spectral overlap integral (J) in the 500-600 nm wavelength range between the green UCNP emission and Cy3 dye absorption was calculated by using the following equation • • • A D 4 J F d ε λ λ λ λ ( ) ( ) = ∑ (7) where ε A (λ) is the wavelength-dependent molar extinction coefficient of the Cy3-DNA acceptor (with ε(552 nm) = 150 000 cm -1 m -1 ), F D is the area-normalized (to unity) emission spectrum of the donor, and λ is the wavelength. Fitting of UCL Kinetics: The rise (τ R ) and decay times (τ D ) were determined by fitting the data to Equation (  8 ) • 1 • 0 0 0 y y A e e t t t t R D = + -         τ τ - - - - (8) Ionic Strength: The ionic strengths (I) of the different buffers were calculated as follows 1 2 2 I cz i i ( ) = ∑ (9) where c i and z i are the concentration and the charge of each ion present in the buffer, respectively.
UC Luminescence Measurements: A 96-well microplate reader (Edinburgh Instruments) equipped with a 2 W 980 nm laser (CNI lasers) was used for UCL measurements. A 900 nm dichroic mirror (Edmund Optics Inc) was used to focus the laser beam into the samples. UCL decays were collected using PMTs with the appropriate bandpass filters (Semrock): 542/20 nm for the green UCNP emission, 660/13 nm for the red emission, and 585/11-25 nm for dye emission. The pulsing parameters of the 980 nm excitation were controlled with a PM-1 laser modulation box (Edinburgh Instruments). Steady-state PL spectra were obtained using a fiber-coupled CCD-USB-spectrometer and SpectraSuite software (Ocean Optics). Fluorescence Spectrum of Cy3: The Cy3 PL spectrum was collected with a Xenius fluorometer (SAFAS). For that, 100 µL of the dye-labeled DNA solution (1 × 10 -6 m, HEPES buffer) were placed in a black 96-well microplate.
The measurement was carried out with a bandwidth of 2 nm, a photomultiplier voltage of 1000 V, and an integration time of 0. 1 s. Infrared Spectra Measurements (FTIR): FTIR spectra were performed using a FTIR Thermo Nicolet 6700 spectrophotometer at room temperature. TEM images were obtained using an FEI TECNAI 12 BIOTWIN microscope operating at 80 kV equipped with a digital camera (ES500W GATAN). The samples were prepared by depositing 10 µL of a 0. 1 mg mL -1 solution of the UCNPs on a Formvar/carbon film supported on a 300mesh copper grid and left to dry at room temperature. The sizes of UC@ PSS and UC@PSS@DNA were determined manually by using Image J software and averaging the obtained size of 500 particles from different representative TEM images. DLS and ζ-Potential: UCNPs dispersions in aqueous solution (0.
001 mg mL -1 ) were prepared by sonication for 30 min and then were placed in disposable folded capillary cells. The measurements were carried out in a Zetasizer Nano ZS instrument (Malvern Panalytical) at 25 °C in triplicates. Zetasizer software was used to analyze the data. UV-Vis Characterization: A PerkinElmer Lambda 35 dualbeam spectrophotometer was used to perform all the absorbance measurements. Briefly, 100 µL of each sample were placed in a quartz cuvette (1 cm path length, 150 × 150 µm 2 sample window). The spectra were acquired with a bandwidth of 1 nm. The concentration of the UCNPs dispersions was 1 mg mL -1. miRNA Assays: All FRET assays were measured in black 96-well microtiter plates with a working volume of 150 µL. All samples were prepared in hybridization buffer (20 × 10 -3 m TrisCl + 500 × 10 -3 m NaCl + 0.
Acknowledgements This work was supported by the  European Union 's  Horizon 2020 research and innovation programme  ( Marie Skłodowska-Curie Individual Fellowship   794410 ),  Universitat de València  ( Subprograma Atracció de Talent Contractes Postdoctorals ),  COST Action   CM1403  (The  European Upconversion Network) , the  Brain Pool program  funded by the  Ministry of Science and ICT  through the  National Research Foundation of Korea  (No. 2021H1D3A2A0204958912 ), the  French National research agency  ( LaPin  project  ANR-20-CE09-0021-02 ),  Seoul National University, Université Paris-Saclay ,  Université de Rouen Normandie ,  Normandie Université ,  INSA Rouen ,  CNRS ,  European Regional Development Fund, Labex SynOrg  (No. ANR-11-LABX-0029 ), Carnot Institute I2C, XL-Chem graduate school (No.
Photocatalytic Activity Evaluation The materials were further tested as photocatalysts for the degradation of MO. Before starting the catalytic process, the mixture containing 90 mg•L -1 MO and 20 mg of the material was stirred in dark conditions for 1. 5 h, as indicated by the adsorption experiments. The shaded area in Figure  6a  corresponds to the adsorption process. After adsorption/desorption equilibrium was achieved, the sample was irradiated with UV light. The kinetics of MO photocatalytic degradation was monitored for 2 h. The data are presented in Figure  6a. No obvious degrease in MO concentration was detected by the Pal, pure UiO-66, and PU composite. Clay minerals do not possess any photocatalytic activity. On the contrary, UiO-66 is photoactive  [37, 53]. However, on the basis of the U and PU light absorption properties (Figure  4 ), light irradiation at wavelengths lower than 310 nm was required for their activation.
In order to further evaluate the photocatalytic efficiency of the prepared materials, the photocatalytic degradation rates were calculated. The data were well fitted with a fistorder kinetic model. Rate constants (k) were estimated by fitting the experimental data with the following equation: ln C 0 C =kt, (4) where k is the apparent first-order rate constant (min -1 ), t is the irradiation time (min), C0 is the initial concentration (i. e. , after the adsorption/desorption process reached equilibrium), and C is the concentration of MO at reaction time t. The data are presented in Figure  6b , and the photocatalytic degradation rates (k) are summarized in Table  1. The PUT composite presented higher degradation rates than the pristine TiO2. This has previously been seen in UiO-66/TiO2  [24, 60]  composites, as well as in clay/TiO2 or TiO2-based composites containing other natural porous minerals  [37] [38] [39] 61].
The improved photoactivity in TiO2 composites has been linked with synergistic effects related to the increase in charge availability  [11, 25, 45] , as well as the deposition of In order to further evaluate the photocatalytic efficiency of the prepared materials, the photocatalytic degradation rates were calculated. The data were well fitted with a fist-order kinetic model. Rate constants (k) were estimated by fitting the experimental data with the following equation: ln C 0 C = kt, (4) where k is the apparent first-order rate constant (min -1 ), t is the irradiation time (min), C 0 is the initial concentration (i. e. , after the adsorption/desorption process reached equilibrium), and C is the concentration of MO at reaction time t. The data are presented in Figure  6b , and the photocatalytic degradation rates (k) are summarized in Table  1.
The large size of MO molecular structure and the fast adsorption kinetics suggest that MO was most likely adsorbed onto the external surface of UiO-66 where TiO 2 particles were present. This potentially increased the contact between TiO 2 and MO, improving photoactivity. To further verify the photocatalytic activity of the composites, in situ spin-trap electron paramagnetic resonance (EPR) spectroscopy was performed using DMPO to study the reactive oxygen species formed under irradiation. Figure  7  presents the EPR spectrum obtained at room temperature after 15 min irradiation of a PUT aqueous suspension under light irradiation conditions identical to the photocatalytic experiments. An EPR signal with a 1:2:2:1 intensity ratio corresponding to the DMPO-OH • spin adduct was observed. These data clearly indicate that the PUT composite was able to produce OH • under irradiation.
Acknowledgements The authors from France acknowledge the  ANR  Project  MeaCoPA  ( ANR-17-CE29-0003 ) and the  European Community Seventh Program  (FP7/2007-2013) for funding the research under Grant Agreement 608490 (Project  M4CO2 ). S. W. acknowledges support from the  National Natural Science Foundation of China  ( 22071234 ) and the  Fundamental Research Funds for the Central Universities  ( WK2480000007 ). T. P. V. ,  C. Simms  and H. G. L. T. thank  Research Foundation Flanders (FWO)  for funding ( 68090/11C9320N ). T. P. V. thanks the  European Commission (Horizon 2020 ,  FoodEnTwin  project, GA No. 810752 ) for funding. The computational work was granted access to the HPC resources of CINES under the allocation A0100907613 made by  GENCI.
Discussion We illustrate advancements in HRMAS NMR and genome-scale metabolic modeling to track single carbon and nitrogen flow through complex anaerobe metabolism. The sealed rotor chamber used in HRMAS NMR maintains an anaerobic environment, ensuring maintenance of reducing conditions, mass conservation, and biological containment. The non-destructive nature of HRMAS NMR spectroscopy enables high-resolution analyses of living cells in small reaction volumes, on the order of tens of microliters, whereas alternatives using GC, MS, or regular solution NMR often require orders of magnitude more volume and cellular mass to support longitudinal sampling and analyses. The acquisition of HRMAS NMR datasets is also far more rapid and does not require further extraction or preparation beyond loading of living cells in the rotor.
Longitudinal tracking of fermentable 13 C-labeled substrates supports high-resolution monitoring of individual pathways within complex nutrient conditions. The cellular scale of genome-scale metabolic models provides means to incorporate multiple NMR datasets tracking fermentable substrates into a unified dFBA solution with inference of flux through pathways not directly captured by NMR analyses, and of cellular or experimental objectives that may include ATP generation, biomass, or production of industrially important metabolites. 13 C-NMR analyses of 13 C-and  15  N-labeled substrates quantified  15  N flow to 13 C backbones, confirming integration points identified by dFBA. We apply this framework to identify electron-and nitrogen-cycling systems driving ATP production, including promising gene-level targets for downstream therapeutic interventions against C.
In total, these approaches represent a versatile toolbox that overcomes a critical obstacle to the application of 13 C-NMR datasets to flux constraints for FBA simulations. Our use of  15  N-13 C J-coupling to amplify less NMR-sensitive  15  N nuclei with more sensitive 13 C-NMR presents a conceptual foundation for the simultaneous tracking of atomic species to determine their biological and chemical importance. This concept may be extended to covalent bonding with other NMR-active nuclei such as 1 H or 17 O. We leveraged this technique to track two distinct spin-active nuclei in an NMR time series, study interactions between metabolic pathways involving multiple substrates, and experimentally confirm integration points predicted by dFBA. NMR time series and genome-scale metabolic analyses identified a unique strategy Stickland fermenters employ to integrate Stickland metabolism with high-flux glycolytic metabolism, generating alanine to support cellular growth, energy generation, and more energyefficient nitrogen handling  13, 22.
MRS spectra were processed using the TopSpin 3. 6. 2 (Bruker BioSpin Corporation, Billerica, MA, USA), as well as with NUTS (Acorn NMR Inc. , Livermore, CA, USA). All free induction decay files will be made available on the Metabolomics Workbench (https://www. metabolomicsworkbench. org/). Identification of metabolite productions with 2D NMR 13 C labeled metabolites produced from [U-13 C]glucose, L-[U-13 C]proline, and L-[U-13 C]leucine in live cells were identified through 2D NMR (Extended Data Fig. 2 ) according to reported 13 C and 1 H chemical shift values availed from Human Metabolome Database (HMDB, https://hmdb. ca) and Biological Magnetic Resonance Bank (BMRB, https://bmrb. io). 1 H and 13 C spectra analyses Individual free induction decay (fid) files were processed using NMRPipe 44 , nmrglue  45  , and in-house python scripts.
Fourier transformed spectra were normalized by the noise root-meansquare error of the sparse 130-160 ppm region (Supplementary Figs. 1, 2 , and 3). The normalized spectrum stack was rendered as a surface plot in MATLAB R2019b (MathWorks, Natick, MA, USA) with face lightness mapped to the log base-2 of signal. Peaks with height ≥6 times the noise root-mean-square error (130-160 ppm) and separated from other peaks by 0. 08 ppm were classified as detectable signals and assigned to compounds using the following algorithm: (1) Subpeaks within 0. 3 ppm of each other were clustered. (2) Reference shifts for expected compounds within 0. 45 ppm of the cluster were associated with the cluster. (  3 ) All subpeaks from a cluster that associated with a single reference peak were assigned to the compound producing that reference peak.
Surface regions within 0. 5 ppm of each reference peak were colorized. Assigned peaks ≤100 ppm were curve-fit using a Voigt (Gaussian-Lorentzian convolution) lineshape and integrated. Logistic curve fits of metabolite integral vs. time were calculated in python using a least-squares regression (SciPy 46 1. 6. 2) according to equation (  1 ) (Supplementary Table  2 ). The custom python and MATLAB scripts are available via GitHub (https://github. com/Massachusetts-Host-Microbiome-Center/nmr-cdiff).
Metabolic modeling A previously published genome-scale metabolic model of C. difficile strain 630, icdf834 30 , was modified using the COBRApy toolbox  47  and custom python scripts. Exchange reaction bounds were set to 3% of the millimolar concentration of media components used experimentally (Supplementary Table  1 ). The following changes were made based on experimental data and to support biologically relevant processes energetically and thermodynamically. The updated model, icdf843, is included within the GitHub repository. (1) An exchange reaction was added for iron(II), transport and secretion reactions were added for propionate, phenylacetate, indole-3-acetate, butyrate, and n-butanol, and proton-motive force dependent transport reactions were added for acetate, L-alanine, L-leucine, L-proline, L-isoleucine, isovalerate, isobutyrate, 2-methylbutyrate, isocaproate, and 5-aminovalerate (not listed in Supplementary Table  10 ).