dataesr/scientific-paragraphs-categorization huggingface dataset:
In detail, there are enrichments in H 2 (majorly for NH 3 -poor ices) and CHN as well as depletions in H 2 O and CO 2 for component 1 (relative to component 0). Furthermore, the transition from the H 2 O-rich cluster toward the NH 3 -rich cluster in component 1 is governed by CHN transformations instead of H 2 O, which indicates a more prominent role of nitrogen chemistry for component 1 molecules, compared to those of component 0. Subnetworks and PubChem Database Unlock Structural Information. Figure  3  shows detailed information about molecular structures as inferred via two approaches: (i) splitting the minimal set of transformations and (ii) comparison to the PubChem database. 45 We want to remind here that structural information has been unlocked from mass spectrometric MS 1 data, without fragmentation information (no MS n data required) that at has not been reported to date (to the best of our knowledge).
Subnetworks were generated by splitting the minimal set of transformations. Each element from {H 2 , O, CO, NH 3 } ("minimal set" of transformations) got iteratively removed to unveil detailed information about molecular subsets (Figure  3A ). The resulting subnetworks consisted of both many small components and moderate-sized ones (Supporting Figure  7 ). This implies that removing each of the four transformations has a significant effect on the stability of the network (many small components). However, some local stable molecular groups remain connected (moderate-sized ones). In a next step, the key components have been identified based on (i) size of components and (ii) maximal variation of map The comparison with the PubChem database  45  has revealed further insights into molecular structures and compound classes.
We have applied the same workflow for network generation and analysis for the PubChem data as done for those of the ice (transformation set of {H 2 , O, CO, NH 3 }"minimal set", extraction of two largest graph components, Figure  3B ). We have randomly sampled two PubChem subsets with 10,000 compounds each and compared their two major graph components with those of the ice molecular network, using element maps and transformation frequencies (Supporting Figure  9 ). Qualitatively, element maps and transformation frequencies match between the ice and PubChem data, but H maps showed significant variation for the PubChem subset 10,001-20,000. Thus, we continued with the detailed analysis using the PubChem subset 1-10,000 only (details about the PubChem data are given in the Supporting Information).
g. , C or N counts, inferred from splitting the "minimal set" of transformation) and were assigned with compound class likelihoods (inferred from PubChem database). We further show that a gradient in N-vs C-backbone molecules in each component highlights the competitive two different types of chemistry, namely, the carbon-and nitrogen-driven ones (in relation to N/C ratios). Four subsets were identified:  (based on ternary diagrams of atomic-ratios),  27  the here presented network analysis sets emphasis species' interactions, and both confirm but also refine current knowledge. The previously reported two distinct molecular groups were validated, but the network analysis significantly refined their characterizations because of enhanced resolution of the molecular space (multidimensional representation) that enabled the spatial differentiation between NH 3 -medium and NH 3 -poor/NH 3 -rich ices (H 2 O:CH 3 OH:NH 3 = 3:1:1 from H 2 O:CH 3 OH:NH 3 = 3:1:0.
Conclusions. In a proof-of-concept study, we have analyzed the network of complex astrochemical data (from high-resolution Orbitrap MS 1 analysis of H 2 O:CH 3 OH:NH 3 interstellar ice analogs). The molecular network is clustered according to the initial NH 3 content and unlocked HCN, NH 3 , and H 2 O as spatially resolved key transformations. In comparison with the PubChem database, four subsets were structurally annotated. This method was validated with previous results by Fresneau et al. 26  and Gautier et al. 27  Current knowledge was confirmed but also refined (e. g. , characterization of two distinct molecular groups). The implications of the presented network analysis are not specific to astrochemistry but manifold for complex chemical systems, including (i) the characterization of nondetected species in any sample, (ii) the comprehensive elucidation of chemical reactivity (e.
Furthermore, the identified transformation patterns might be generalized that would allow the characterization of nondetected species as well. This has strong implications for astrochemistry to probe the chemistries of large complex organic molecules that would otherwise be hidden to observations. ■ DATA SET AND METHODS Data Set of Interstellar Ice Analogs. This data set represents 15 different H 2 O:CH 3 OH:NH 3 ice samples (3:1:0. 2, blue, 3 replicates; 3:1:1, green, 3 replicates; 3:1:5, red, 2 replicates; 10:1:1, gray, replicates/over-irradiation, orange, 3 replicates). Ice deposition/irradiation time was 48 h except for the "3:1:1 16 h" ice with a deposition/irradiation time of 16 h. All details about the experiments and their high resolution Orbitrap mass spectrometric analysis have been described by Fresneau et al.
26  and Gautier et al. 27  Data will be made available on request. Method: Molecular Network Generation and Analysis. The algorithmic workflow of the method mol2net 24 is organized as follows (Figure  5 ): (1) input, (2) identify transformations, and (3) network analysis (using NetworkX). (1) Input: Theoretical masses of prior assigned 3270 unique molecular formulas were used for network generation, as performed via Python. 50 (2) Identify transformations: Theoretical masses (nodes in the network) were connected by edges (transformations) if their mass differences match with the theoretical mass differences of the transformations (e. g. , for the "minimal set" of transformations, used as basis in this study): {ΔH 2 , ΔO, ΔCO, ΔNH 3 }, with ΔH 2 = 2. 01565 amu, ΔO = 15. 994915 amu, ΔCO = 27.
(3) Network analysis (using NetworkX): Molecular network analysis was performed via Python,  50  and specifically, NetworkX 51 was used for graph computations a n d n e t w o r k l a y o u t s ( g r a p h v i z _ l a y o u t , "nx. nx_pydot. graphviz_layout(G)"). The following parameters were majorly addressed: filtered graph components, shape of degree distributions, network statistics, key transformation patterns, and plotted network. All computations were performed on a stand-alone computer. The Python code of mol2net is available on Zenodo and GitHub. 24 ubChem Database. The PubChem database was downloaded via the Python package pubchempy (https:// pubchempy. readthedocs. io/en/latest/). We have filtered the downloaded data for the same chemical elements (CHNO) and mass range (199 and 365 amu) as present in the ice data set.
Introduction Since their seminal report  [1]  in the 1960s and their first structural characterization by Albertsson  [2]  in the early 1970s, lanthanide tris-dipicolinate complexes (dipicolinate = DPA = 2,6-pyridine dicarboxylate) became ubiquitous in the field of f-elements coordination chemistry. These trisanionic complexes, of general formula [Ln(DPA) 3 ] 3-present a threefold D 3 symmetry where the lanthanide ion is coordinated in an almost perfect trigonal tricapped geometry. 3a, 6] The nature of the counterion generally controls the crystal packing and ensures the structural diversity of the global architecture. More than 380 structures have been deposited in the Cambridge Structural database (November 2021 version). These complexes are mostly soluble in water, and the tris-terdentate character of the dipicolinate chelate induces a propeller-like molecular structure with a helical axis of chirality resulting in the existence of two enantiomers  and  in rapid interconversion in solution at room temperature.
Instrumentation Characterizations and NMR experiments were carried out on a NMR Bruker Avance III 300 spectrometer at 298 K, using perdeuterated solvents. 1 H DOSY NMR spectra were analyzed with MestReNova software. MALDI-TOF-MS spectra were recorded on a MALDI-TOF Bruker Biflex III instrument using a positive-ion mode. ESI-FTICR mass spectra at very high resolution were performed in positive detection mode on a 7T Solarix 2xR (Bruker Daltonics, Marne la Vallée, France).
The total amount of polymer-bound T was quantified by deconvoluting the spectrum of the sample, rather than individual fractions. DFT calculations. All computations were performed with the Gaussian 09. E software package at the (u)MPW1K/6-31+G(d,p) level. In addition we calculated six key activation enthaplies with 5 other functionals (Supplementary Table  3 ) to assess how sensitive the results were to the model chemistry. To find the minimum number of segments (chains of two or more repeat units terminated by CH 3 groups, Supplementary Fig. 18 ) that quantitatively capture the kinetics and stoichiometry of mechanochemical fragmentation of a chain of styrene/butadiene copolymer of an arbitrary length and stretched to an arbitrary force at an arbitrary loading rate (because these parameters in sheared samples are unknown), we divided all backbone bonds of the styrene/butadiene copolymer into those formed during polymerisation (the "between" bonds) and those carried over from each monomer (the "within" bonds).
Acknowledgements The work was supported by  Manufacture Française des Pneumatiques MICHELIN. We thank  Waters Corporation  for the gift of Acquity UPLC system and the technical help in converting it to SEC. We thank  Melanie Ray  ( Michelin ) for synthesis of the polymer,  Jericho Timbol  ( University of Liverpool ) for optimizing analytical method for quantitation of carbonyl and hydroxy groups in remodeled material and  Robert T O'Neill  ( University of Liverpool ) for useful comments on the paper draft.
Catalytic Activity Test The catalytic activity of the Ni/kaolin catalyst was tested in the catalytic oxidation of malachite green dye using H 2 O 2 (30%) as an oxidant reagent at the flow rate. All the samples were prepared and distributed in 250 mL glass breakers, into which 200 mL of the solution of MG with C = 20 mg/L and 0. 2 g of the catalyst for different amounts (2 wt. %, 5 wt. %, and 7 wt. %) were added. The reaction was kept going for 3 h with constant stirring at 320 rpm. Samples were collected every 15 min and analyzed by UV-Vis spectroscopy. The percentage removal of dye was calculated using the following equation: Dye removal (%)= C 0 -C t C 0 × 100 (1) where C 0 and C t are the concentrations of dye in solution (mg/L) at reaction times 0 and t, respectively. The experimental data are fit using a pseudo-first-order kinetics model defined by the Matthews-Weber equation, where k (min -1 ) is the reaction rate constant.
Reversibility of the dehydration process After the ex situ thermal treatment, Pb-STI crystals were exposed to high humidity conditions (relative humidity > 90%) in order to allow reabsorption of H 2 O. IR absorption curves of the crystals obtained after 15 days equilibration time are shown in Fig. 4c , Fig. S1c. The absorption on the left shoulder of the broad peak between 2700 and 3700 cm -1 increased and so did the absorption of the H 2 O bend and stretch combination peak, illustrated by purple-coloured arrows in Fig. 4c. This indicates that the structure of Pb-STI is able to reabsorb H 2 O and that the dehydration process is reversible. The reversibility of the dehydration process was also investigated by SC-XRD. After an equilibration time of 15 days under high humidity conditions (relative humidity > 90%), new data were collected at RT on a previously dehydrated crystal.
Before each data collection, the crystal was equilibrated for at least 30 minutes. The same experimental set up was used for the investigation of the thermal stability of the other cationic forms of stellerite (i. e. Ca-, Na-, Ag-, and Cd-STI)  [9, 10, 11]  and is regarded as dry experimental conditions. The data were integrated and corrected for absorption using the software package Apex3. v. 2018. 7-2 [29]. Structure solution (ShelxS  [30] ) for the data set at 50, 75 and 100°C pointed to the monoclinic space group C2/m, transformed to the non-standard setting A2/m for better comparison with literature data. From 125°C to 150°C, the structures were solved and re ned (ShelxL  [31] ) in the Amma space group, and from 200 to 450°C in Fmmm. The reversibility of the dehydration process (rehydration) was also investigated by SC-XRD.
Another Pb-STI crystal was selected, because the one used during the rst set of high-temperature measurements was lost after the experiment. After a stepwise thermal treatment up to 200 °C for approximately 6h, the crystal was exposed to high humidity conditions (relative humidity > 90%) for 15 days. Subsequently, diffraction data were collected at RT with data collection strategy similar to that used for the rst crystal. Structural data of Pb-STI obtained at different temperatures have been deposited in form of Crystallographic Information File (Cif). All structural drawings were produced with the software VESTA  [32].
Molecular Dynamics Simulations Ab initio simulations were performed with the CP2K package  [33]. The interatomic forces were obtained based on the Density Functional Theory (DFT)  [34]. The electron exchange and correlation energies were approximated with the Perdew-Burke-Ernzerhof (PBE) functional  [35]. Dispersion correction was applied using the semi-empirical pair potential type DFT-D3  [36]. The Gaussian and plane waves basis sets were used to represent electron wave function and the electron density, respectively  [37]. Valence electron wave functions were described by the Gaussian short range Double-Zeta Valence Polarized basis set (DZVP-MOLOPT)  [38]. Core electrons were represented by dual space norm conserving pseudopotentials  [39]. MD simulations were performed in an isothermal-isobaric (NPT) ensemble with a exible cell.
Vibrational Density of States (VDOS) calculations Vibrational frequencies of hydrogen (H) atom in different structural environment were derived from MD simulations. The analysis of the 15 ps long equilibrated trajectory of the RT Pb-STI  [19]  was performed with the program TRAVIS  [41]. The total and partial VDOS was calculated for: (i) all H atoms, (ii) H atoms belonging to H 2 O (H (water) ) and H atoms belonging to OH -groups (H (hydroxyl) ) and (iii) H atoms belonging to H 2 O molecules that are not bonded to Pb (distance Pb-O > 3 Å) (H (water) 0), H atoms belonging to H 2 O molecules that are bonded to Pb (distance Pb-O < 3 Å) (H (water) 1), H atoms belonging to OH -groups that are bonded to 1 Pb (H (hydroxyl) 1) and H atoms belonging to OH -groups that are bonded to 2 Pb (H (hydroxyl) 2).
Acknowledgments Jorg Hermann is acknowledged for his assistance during the IR measurements. We are thankful to  Thomas Armbruster  for fruitful comments and discussion. We acknowledge access to the  Swiss National Supercomputing Center (CSCS)  and  UBELIX HPC cluster at the University of Bern. GC acknowledges the  Swiss National Science Foundation (SNSF)  for the grant nr. PZ00P2  173997.
Acknowledgements We thank the  Deutsche Forschungsgemeinschaft  for financial support. We acknowledge  Dr. G. Fink  for assisting with NMR experiments, and  Prof. Dr. U. Englert  and  Dr. T. P. Spaniol  for advice regarding crystal structure refinement. L. M. is a senior member of the  Institut Universitaire de France. The  Alexander von Humboldt Foundation  is acknowledged for financial support as well as CalMip for a generous grant of computing time. Open Access funding enabled and organized by Projekt DEAL.
Funding: The authors acknowledge startup funding from the  Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology , the  International Quantum Academy , the funding from  European Union  under the  Horizon 2020 research and innovation programs FET-Open  grant  EXTREME-IR 964735 , the  French National Research Agency  ( ANR-18-CE24-0013-02-"TERASEL" ),  Australian Research Council Discovery Project  ( DP160103910  and  DP200101948 ),  National Science Foundation -"Room temperature high-power terahertz semiconductor laser with high-quality beam shape and stable spectral emission" Grant #  2149908. X. Q. acknowledges support under the  Advance Queensland Industry Research Fellowships program.
And this was only achieved following many iterations of structure-based design, synthesis and assays of analogues  [78, 81, 82]. Also of concern, a subsequent virtual hit expansion/ligandbased screening starting with the isoquinoline-bearing inhibitors such as 6 and using the Ukrainian REAL database which contains 1. 37 billion of compounds, actually failed to go beyond this chemical motif  [83]. In any case, what would have been the results of all these computer-based endeavors had the hits 4 and 5, which led to compounds 6 and 7, not been reported  [79, 80]  in 2013? In fact, it is a well-designed and quite "blind" screening of 293. 000 compounds from the NIH molecular libraries  [79, 80] , luckily containing such fruitful hits, which saved the day almost a decade later! For another very recent illustration of the risks of only relying on virtual docking for drug discovery, a reported attempt to design peptidic but non-covalent inhibitors of SARS-CoV-2 main protease is also worth consulting  [84].
Acknowledgements This text owes some of its existence to many debates and suggestions on drug discovery with colleagues at the  Institut Pasteur and elsewhere , including journal editors as well as anonymous referees. Their comments, suggestions and critics were certainly instrumental in honing and amending some part of it. Fund-wise, aside from my salary which comes from the  CNRS  and a ground support which came from the  Institut Pasteur , it would be very relevant to mention a Medicen-supported initiative which was established and set in motion by the keen and pressing impulsion of President Jacques Chirac. In 2005, this initiative provided us with a 5 years-long grant to design, prepare and evaluate, on many biological assays, series of new chemical entities (Chemical Library Project; grant of the  Région Ile de France n° I   06-222/R and I 09-1739/R ).
SBET represents the total surface area of the hierarchical MFI. Mesopore diameters were calculated following the Broekhoff and De Boer (BdB) desorption method  [51]  at the inflection point of the desorption step, as it was demonstrated to be one of the most appropriate methods for the analysis of mesoporous materials such as MCM-41  [52]. The results obtained using the BdB method are in quite good agreement with the results obtained using the NLDFT method  [43, 53]. The cumulative pore volumes, pore size distribution, and cumulative surface areas were calculated by the NLDFT method by analyzing the adsorption branch of the isotherms included in the Micromeritics software ("Microactive") and choosing the parameters corresponding to the cylindrical pores for micro-and mesopores and oxide surfaces at 87 K for Ar and 77 K for N2.
Polarization-transfer efficiencies (e) can become too low when dipolar couplings are averaged by motion or when INEPT transfers are inefficient due to fast T2' relaxation times, which lead to loss of signal. d) Summary of different polarization-transfer schemes available in solid-state NMR and their best efficiencies for different motional regimes. In contrast to heteronuclear CP and INEPT transfers, spin diffusion (SD)/NOE transfer is homonuclear and mediated by the network of strong 1 H-1 H dipolar couplings. e) Representation of the hepatitis B virus capsid 3D structure (PDB: 1qgt  [36]  ), with a zoom on the dimeric subunit structure, and its domain organization with the amino-acid sequence of the CTD in red. The X-ray structure was determined on the isolated NTD. The stars show positions of the arginine residues in the NTD.
Acknowledgement This research was supported by an  ERC   Advanced Grant  (B. H. M. , grant number  741863 , Faster), by the  Swiss National Science Foundation  (B. H. M. , grant number  200020_159707  and  200020_188711 ), the  French ANRS  ( ECTZ71388  and  ECTZ100488 ) and the  CNRS  ( CNRS-Momentum 2018 ).
Introduction Crime scene investigation often involves the assessment and analysis of paint traces (e. g. , graffiti), which are generally carried out by means of spectroscopic and/or microscopic techniques  [1, 2]. Among these, Fourier Transform Infrared (FTIR) spectroscopy has proven to be one of the most reliable and performant approaches, especially for paint discrimination and characterisation  [3] [4] [5] [6]. Paints, in fact, can be regarded as mixtures of two primary constituents: i) an organic polymeric resin enabling their binding to the target surface and ii) organic/inorganic pigments dispersed in this polymeric resin (for instance, titanium dioxide, talc, kaolin or calcium carbonate), regularly at sufficient concentration levels to allow FTIR determination. In such scenarios, two of the basic tasks forensic agents and examiners usually need to address are i) the identification of the chemical composition of the unknown samples collected on-field through the interpretation of the spectral profiles registered and ii) the comparison between these spectroscopic profiles and those of databased specimens for similarity recognition.
On the other hand, database searching/matching is ordinarily performed either manually through pairwise comparisons of spectra or by commercial software suites (like KnowItAll Spectroscopy Edition -John Wiley & Sons, Inc. , Hoboken, United States of America -and OMNIC Spectra Software -Thermo Fisher Scientific, Inc. , Waltham, United States of America) that rely on proximity measures directly estimated from the spectroscopic data at hand. Although both these strategies constitute the state of the art in paint forensics, they suffer from severe drawbacks which hamper their utilisation particularly when large amounts of spectral profiles are to be handled and processed. Indeed, being mostly based on visual/manual procedures, such methodologies are extremely time consuming and prone to errors principally induced by the mood/fatigue of the operators and/or by the complexity and the pronounced entanglement of the instrumental response recorded  [9].
Furthermore, even when computer algorithms are employed for this purpose, it is well-known that chance matches are frequent and the risk of false positives/negatives remains high, mainly when sample differentiation strictly depends on minor chemical components  [10]. For overcoming these limitations while guaranteeing a rapid, automatic and robust characterisation of paint samples, multivariate (chemometric) methods can be alternatively applied. Recent studies have already demonstrated that they can represent feasible solutions for the discrimination of synthetic resins  [11]  and proteinaceous binders  [12]  contained in varnishes for art purposes, architectural finishes (household paints  [13] ), automotive clear coats  [14] , and spray paint specimens of distinct colours  [9]  as well as for developing spectral pre-filters to facilitate library or database comparisons  [15].
Materials and methods The proposed workflow for paint identification and matching encompasses two sequential steps, whose detailed description will be given in the following subsections: 1. The SIMCA-based classification of paint specimens according to their FTIR spectral signature; 2. The recognition of the databased samples most spectroscopically similar to such specimens. Notice that this assessment is conducted through the estimation of a pairwise Mahalanobis distance metric representative of how resemblant the individual spectral response of these latter is to the spectral profiles of paints with the same chemical composition.