dataesr/scientific-paragraphs-categorization huggingface dataset:
(b) The alternative is to use a 1 × N switch (TDM arrangement), to single out each individual channel  [17, 22] ; in this arrangement, the OBR loses a large portion of real-time sensing, due to the significant increase of measurement time (N-fold increase, with ideal switches and optimal software), well over 1 s. (c) The SLMux setup  [18, 19] , presented in this work; the switch is substituted by a splitter, hence the detection is simultaneous and real-time (0. 3 s). A network of single-mode fiber delayers and high-scattering fiber multiplex from a single fiber to the N-size sensing network, each constituted by a distributed sensor.
The fiber is displaced in a 2D/3D configuration, as in  [15] ; this is impractical for several medical devices, due to limited spacing, tight bending and excessive mechanical torsions of the fiber. (b) The alternative is to use a 1 × N switch (TDM arrangement), to single out each individual channel  [17, 22] ; in this arrangement, the OBR loses a large portion of real-time sensing, due to the significant increase of measurement time (N-fold increase, with ideal switches and optimal software), well over 1 s. (c) The SLMux setup  [18, 19] , presented in this work; the switch is substituted by a splitter, hence the detection is simultaneous and real-time (0. 3 s). A network of single-mode fiber delayers and high-scattering fiber multiplex from a single fiber to the N-size sensing network, each constituted by a distributed sensor.
2 The preform type characterizes the label used by INPHYNI Institute to characterize the fibers. Multiple fibers have been drawn, according to the methods described in  [18] [19] [20] [21] 25] , and the scattering parameters of seven fibers drawn with these methods, and reported on the OBR instrument, are reported in Table  2. Data report multiple fibers, drawn in different preforms that account for different location and distribution of elongated nanoparticles in the core. Table  2  shows the parameters of all fibers used in the analysis. 1 Fibers are listed in chronological order of testing. 2 The preform type characterizes the label used by INPHYNI Institute to characterize the fibers. Multiple fibers have been drawn, according to the methods described in  [18] [19] [20] [21] 25] , and the scattering parameters of seven fibers drawn with these methods, and reported on the OBR instrument, are reported in Table  2.
Data report multiple fibers, drawn in different preforms that account for different location and distribution of elongated nanoparticles in the core. The scattering gain recorded with the M01 preform, which appears as the most interesting method for SLMux, has a range of 5. 7 dB (37. 2-42. 9 dB), while the two-way loss achieves a minimum of 22. 1 dB/m and a maximum of 30. 8 dB/m. This fiber has been used by Beisenova et al. and Molardi et al. [18, 19, 30] , and the data are in agreement with Table  2 , with some fluctuations also due to the random nature of nanoparticle distribution. Conversely, fibers drawn from the G22 fiber, achieve a higher gain (47. 5-49. 3 dB), but losses fall with a 10× higher rate, up to almost 300 dB/m  [30]. In the middle between the two preforms, the R04 fiber pattern achieves a gain of 40.
Funding: The research was funded by Nazarbayev University, under grants FOSTHER (code: 090118FD5314), EPICGuide (code: 240919FD3908), and SMARTER (code: 091019CRP2117). This work was partly supported by ANR Projects Nice-DREAM (ANR-14-CE07-0016-03) and NanoSlim (ANR-17-CE08-0002). This research is partially developed by Fundação para a Ciência e a Tecnologia (FCT) through the program within the scope of the project i3N, UIDB/50025/2020 & UIDP/50025/2020, financed by national funds through the FCT/MEC, the European Regional Development Fund under the PT2020 Partnership Agreement, and by national funds (OE), through FCT I. P. , in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19.
1989) , were of 0. 53 nm 3 for IBP, 2. 02 nm 3 for IL1 and 2. 27 nm 3 for IL2. N + CH 3 C H 3 CH 3 C H 3 Br - N + CH 3 C H 3 CH 3 O O H Br - OH CH 3 C H 3 CH 3 O The octanol/water partition coefficient values of IBP, IL1 and IL2 calculated by using Chemsketch software and model of ref. (Viswanadhan et al. 1989 ) are 3. 72, -1. 84 and -2. 7, respectively. Thus, IL1 and IL2 are hydrophilic whereas IBP is hydrophobic. Owing to the presence of carboxylic group on IL2, it is more hydrophilic than IL1.
162 nm 2. The micropore and mesopore volumes were calculated from the pore size distributions (PSD) of the activated carbons determined by using NLDFT (non local density functional theory) models applied on both the adsorption isotherms of N2 at 77 K and CO2 at 273 K. For that, infinite slit pores were assumed for CO2 adsorption (pore diameters from 0. 35 to 0. 8 nm), while finite slit pores having a diameter-to-width aspect ratio of 12 were used for N2 adsorption simulations (pore diameters from ~ 0. 45 to 25 nm)  (Jagiello and Olivier 2009). N2 adsorption data at P/P0< 0. 01 were obtained using incremental fixed doses of 10 cm 3 g -1 (STP), setting the equilibration interval at 300 s. CO2 adsorption data were obtained at P/P0 ranging from 4×10 -4 and 3. 5 ×10 -2 , using 45 s equilibration interval  (Jagiello and Thommes 2004).
5) has been also an exothermic phenomenon (Fig. 4c ) as the IBP uptake has decreased from ~ 2. 30 mmol. g -1 at 286K to ~ 2. 02 mmol. g -1 at 313K. Exothermal phenomena were also evidenced for the adsorption of IBP from a methanol/water (10%v/90%v) solution on the same activated carbon cloth (T0)  (Guedidi et al. 2014)  as well as on a microporous-mesoporous granular activated carbon  (Guedidi et al. 2013 ). The exothermic nature of the adsorption phenomenon remains consistent with the solute distribution between the aqueous phase and the activated carbon as the ILs and IBP solubilities increase together with the temperature. All the adsorption isotherms were tentatively reproduced with the Langmuir  (Zhu et al. 2009 ), Freundlich, Langmuir-Freundlich  (Yao 2000)  and Redlich-Peterson (Kumar and Porkodi 2007) models, using an iterative procedure based on a non-linear least-squares algorithm (with MATLAB® software).
I. Introduction COLUMBUS is a collection of programs for high-level ab initio molecular electronic structure calculations. The programs are designed primarily for extended multi-reference (MR) calculations on electronic ground and excited states of atoms and molecules. Since its early versions, 1, 2 the COLUMBUS program system  3, 4  was always at the forefront of the development of proper methodology to solve chemically challenging problems, relying, of course, on the actual state-of-the-art theoretical knowledge and computer architecture. The primary focus in the '80s were small molecules and mostly properties related to electronic energy. Besides the MR methodology based on the standard non-relativistic Hamiltonian, the treatment of relativistic effects in the form of spin-orbit (SO) configuration interaction (CI) was a unique feature.
0] [11] [12]  Today allows applications in many fields of Chemistry, Materials Science and Biochemistry. COLUMBUS is dedicated to variational calculations based on multireference configuration interaction with single and double excitations (MR-CISD)  13  and related methods, of which the MR averaged quadratic coupled-cluster approach (MR-AQCC)  14, 15  is probably the most popular one because it includes size extensivity corrections. The program can also perform calculations on excited states in the form of a linear-response theory (LRT). 16 A formulation optimizing the total energy (TE) in place of the correlation energy (TE-AQCC) is available as well. 17 In COLUMBUS, the expansion of the wavefunction is performed in an uncontracted (uc)   form in which no internal contraction (ic) of the reference wavefunction is used.
The examples mentioned, and many others, demand MR methods because of the intrinsically complicated electronic structures involved in the problems. This is the point where COLUMBUS excels. The uncontracted nature of the CI expansion provides the required high flexibility and allows precise benchmark calculations. As mentioned above, the simplicity of the variational calculations of this formulation is also the basis for the availability of analytic MR energy gradients and nonadiabatic couplings. These are features, which stand out, and are not shared by many other MR program packages. Thus, because of the availability of analytic energy gradients at MRCI level, consistent geometry optimization at the same high-level method can be performed as the final energy calculation. This situation must be contrasted to the case where, because of the lack of analytic energy gradients for the high-level method, geometry optimizations need to be performed at a lower level.
COLUMBUS has served that purpose for almost 40 years, and it will continue to do so. Beyond this benchmark role, the available procedures in COLUMBUS are so efficient that reliable production work can be done on many interesting problems, at a precision level that is hardly achievable with other approaches. The second focus of this paper is on delivering a showcase of many examples of applications using COLUMBUS for electronic structure (Section II) and nonadiabatic dynamics problems (Section III). These examples, spanning fields of Materials Science, Biological Sciences, Atmospheric Chemistry, and Heavy Metal Chemistry, should provide a practical guideline for applying the methods available in COLUMBUS. COLUMBUS is freely available from the website https://www. univie. ac. at/columbus/. It includes executables for a simple compilation-free installation of the serial code along with the source code for the compilation of the parallel section of the CI calculation.
E. Parallel calculations on graphene nanoflakes The calculations described in Section II. D were performed using the parallel MRCI implementation in COLUMBUS,  12, 80  which is based on the Global Array toolkit  9, 10  for managing the parallel environment and one-sided data access. These calculations were performed on an HPE MC990X symmetric multiprocessing (SMP) system with 2 TB RAM und 8x Intel E7-8867 v4 CPUs, each of which has 18 cores. In the following, we will use the example of the triplet computation for the system shown in Fig. 4b , C84H26, to discuss the performance characteristics of the parallel calculations. The computation included 1. 3 billion configurations, i. e. , the MRCI problem corresponds to determining eigenvalues of a matrix of dimension 1. 3 billion x 1. 3 billion.
These quantities are calculated on-the-fly at the nuclear position defined by classical trajectories. Thus, at every time step of integration of the Newton equations, the dynamics program automatically invokes COLUMBUS to calculate the required electronic quantities. After COLUMBUS execution, the dynamics program automatically extracts the electronic information and uses that data to continue the propagation. Alternatively, the wavefunctions produced by COLUMBUS can be used to compute time-dependent wavefunction overlaps,  149, 150  which are used either in Hammes-Schiffer-Tully time-derivative couplings  151  or in local diabatization procedures. 152 n the calculation of spectra based on nuclear ensembles, the procedure is similar. Having a collection of nuclear geometries, for instance, generated from a Wigner distribution  153  for the quantum harmonic oscillator or even extracted from a previously run dynamics, the third-party program invokes COLUMBUS to calculate transition energies and transition dipole moments for each of these geometries.
Acknowledgements R. S. and S. R. B. were supported by the  US Department of Energy (DOE), Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division ,  Gas Phase Chemical Physics program  through  Argonne National Laboratory  under Contract  DE-AC02-06CH11357. E. A. C. is grateful for support from the  U. S. Department of Energy, Office of Science, Offices of Basic Energy Sciences and Advanced Scientific Computing Research ,  Scientific Discovery through Advanced Computing via  Award No. DE-AC02-05CH11231. S. M. was funded by the  Department of Energy , Awards No. DEFG02-08ER15983. L. B. was funded by the  High-Energy Laser Joint Technology Office, Albuquerque, NM. This work was supported by the  US Department of Energy  ( DE-SC0015997 ) to D. R. Y. C.
After three cycles, sulfur particles were almost absent from the biomass sample. By using the before mentioned protocol, no sulfur was observed. Furthermore, we also would like to state that proteins attached to the sulfur are also considered biomass, even though they are extracellular. Moreover, data from our experiments on biomass concentration are compatible with results from our colleagues  (Rink et al. , 2020; Roman et al. , 2016b). Sulfate and thiosulfate were measured by ion chromatography (Compact IC 761, Metrohm Nederland, Barendrecht, the Netherlands) with an anion column (Metrohm Metrosep A Supp 5, 150/ 4. 0 mm, Metrohm Nederland, Barendrecht, the Netherlands) equipped with a pre-column (Metrohm Metrosep A Supp 4/5 Guard, Metrohm Nederland, Barendrecht, the Netherlands) to eliminates any particles.
16S amplicon sequencing and qPCR 16S rRNA gene amplicon sequence libraries were sequenced on an Illumina MiSeq using the V3 chemistry to generate 300 bp paired-end reads with 515f (5′-GTGCCAGCMGCCGCGGTAA-3′)  (Caporaso et al. , 2012)  and 926r (5′-CCGYCAATTYMTTTRAGTTT-3′)  (Quince et al. , 2011)  primer-set at MrDNA Molecular Research LP, Shallowater, TX, USA  (Chiodini et al. , 2015). Sample identifier barcode sequences were extracted from forward and reverse reads in QIIME  (Caporaso et al. , 2011). We then used the bioinformatics toolkit implemented in QIIME2  (version 2018. 2 and 2018. 11 ) to perform quality control and filtering of sequence data  (Bolyen et al. , 2019). Briefly, samples were first demultiplexed, and primer sequences were then trimmed from sequence reads using cutadapt  (Martin, 2011).
, 2011)  trained on full 16S rRNA sequences from the SILVA database version 132  (Quast et al. , 2013). The pre-trained classifier is publicly available from qii-me2. org/2018. 11/data-resources. Data analysis was performed using phyloseq version 1. 22. 3 (Mcmurdie and Holmes, 2013)  in R statistical software version 3. 5. 0 (R Core Team, 2018). QIIME and R scripts are available as supplementary information. The EMBL-EBI accession number for presented 16S rRNA sequencing set is PRJEB31230. For absolute quantification of three species of interest, qPCR was used with designed species-specific primers. Detailed method description can be found in  Kiragosyan et al. (2019b).
2 %) in the alkaline biotrickling filter that was used to simultaneously treat DMDS and H 2 S (Arellano-  García et al. , 2018). Other studies did not observe proliferation or a high abundance of either Thioalkalivibrio sulfidiphilus nor presence of Thb. halophilus and Alk. ehrlichii  (Chen et al. , 2016; Tu et al. , 2019). To confirm the capacity of the pure culture of Thb. halophilus for sulfide oxidation in the presence of DMDS, we performed a respiration test at different DMDS concentrations. The obtained data were used to calculate an IC 50 value, indicating the DMDS concentration at which 50 % inhibition occurs at a given sulfide concentration  (Roman et al. , 2016c). Results showed that Thb. halophilus maximum sulfide oxidation rate was reached at 0. 12 mM sulfide, and the IC 50 value was reached at 2.
Acknowledgments This work has been performed within the cooperation framework of  Wetsus, European Centre of Excellence for Sustainable Water Technology  (wetsus. nl) and  Wageningen University and Research  (wur. nl). Wetsus is co-funded by the  Netherlands' Ministry of Economic Affairs and Ministry of Infrastructure and Environment , the  European Union's Regional Development Fund , the  Province of Fryslan  and the  Northern Netherlands Provinces. Wetsus is also a coordinator of the  WaterSEED  project that received funding from  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie  grant agreement No. 665874. This research was cofinanced by Paqell B. V. We want to thank all full-scale facilities for kind supply of the inoculum for this study. We would like to acknowledge  Pieter van Veelen  for the help with 16S amplicon sequence processing.
The point charges used for the framework atoms and extraframework cations are collected in Table  S1. Note that we used different point charges for oxygen atoms bridging one Si and one Al atom (O Al ), and oxygen atoms bridging two Si atoms (O Si ). 18 However, the set of point charges are the same for all the zeolites independently of the number Si/Al ratio (Table  S1 ). The pure silica structures for the screening are taken from the International Zeolite Association (IZA) database. 31 The structure of LTA5A were reported by J. J. Pluth and J. V. Smith. 32 The structure of NaY and NaX were created as follows, starting with the pure silica structure. 1) Using random substitution, silicon atoms were replaced by aluminium atoms obeying Löwenstein's rule. 4] [35] [36] [37]  Schematic representation of FAUtype, and LTA-type zeolites and the description of the cations sites can be found in Figure  S1  of the ESI.
Propane and propylene adsorption in pure silica zeolites To identify the optimal topology for propane/propylene separation, we used the models and force field from the literature  8, 21  to calculate the adsorption isotherms of propane and propylene in most zeolites of the IZA database in the pure silica form (more than 200 topologies). Adsorption selectivity was used as a measure of the separation factor of the mixture. Adsorption selectivity is defined as  = (    ) ⁄ (    ) ⁄ ⁄ were x i is the molar fraction in the adsorbed phase for the component i and y i is the molar fraction in the bulk phase. Figure  1  shows the adsorption selectivity as a function of the pore volume at 100 kPa and 298 K, for the studied zeolites. For clarity purposes, we have classified the zeolite structures in those exhibiting preferential adsorption for propane (Figure  1a ) or for propylene (Figure  1b ).
The privilege of this class of organic coatings lies in the ability to be multidisciplinary coatings whose properties can be tailored by manipulating their size, porosity, concentration, and curing circumstances  [60] [61] [62] [63]. The anti-corrosion, flame-retardant, mechanical, and dielectric properties of epoxy/MOFs coatings have been discussed in this regard. [39] , (b) The number of different kinds of fabricated MOF structures (Source: The Cambridge Structural Database (CSD))  [64]  revealing that more than 70,000 different structures of MOFs have been introduced prior to 2016. The exponential trend in the number   [64]  revealing that more than 70,000 different structures of MOFs have been introduced prior to 2016. The exponential trend in the number of publications released in the last decade extracted from the Scopus database (based on the keywords of "metal-organic framework" and "MOF") can be observed.
1 wt. %. Such a significant rise in the cure enthalpy was rated as good and excellent according to the Cure Index  [71, 72] , due to the large pore size of the MOF, having two internal cages with diameters of 29 Å and 34 Å which allow epoxy pre-polymer diffusion inside the 3D structure of the MOF, therefore accelerating the cross-linking by the aid of the Lewis acid sites of Cr that catalyzed epoxy ring opening (Figure  4 ). Similar behavior was observed for epoxy systems containing halloysite of publications released in the last decade extracted from the Scopus database (based on the keywords of "metal-organic framework" and "MOF") can be observed.
Characterization of films 1. 5. 1 Wide angle X-ray diffraction (WAXD) The crystal structures and crystallinity (χc) were evaluated using an X-Ray diffractometer D5000 from Bruker AXS. The radiation was λCo = 1. 789 Å powered by a current of 40 mA and a voltage of 40 kV. The data were acquired on a scanning range 2θ = 5°-55° with a scanning speed of 0. 004° s -1. Intensity correction, crystallinity determination and peak separation were performed using PeakFit ® software.
Mechanical properties The tensile tests of polyester films were performed at room temperature (23 °C) using a ZwickRoell Z010 testing machine (TestXpert Ⅱ-V3. 5 software). The load cell was 500 N, the crosshead speed was 10 mm⋅min -1 , and the distance between the grips was 40 mm. The dumbbell shaped specimens were 1BB type according to the ISO 527-2:2014-04 standard. The tensile modulus (E) was determined in the linear part of the stress-strain curve. More than 10 tests were performed on each sample and the obtained values were averaged.
Water vapor sorption Water vapor sorption measurements were conducted with the automatic gravimetric dynamic vapour sorption DVS1 Advantage (Surface Measurement Systems Ltd) as previously described  [48]. Different water vapor activity steps (aw) ranging from 0 to 0. 95 were applied at 25 °C on the film (m ≅ 15 mg). At each step, the mass gain was measured as a function of time until the equilibrium was reached. The mass gain at equilibrium Meq was calculated for each aw according to Eq. 2, which allowed the plotting of water vapor sorption isotherms. M eq = m -m d m d ×100 (Eq. 2) with meq the film mass at equilibrium. The accuracy on the values was lower than 4%. Kinetic data were extracted from the normalized mass gain Mt/Meq vs reduced time (t 1/2 /L), where Mt is the mass gain at time t (Eq.
Thermal degradation The thermal degradation of polyesters was investigated by thermal gravimetric analysis. The degradation curves were presented in Figure  3  and detailed data were recorded in Table  S1. All polyesters showed a thermal stability up to 320 °C with a rapid decomposition around 400-450 °C. The degradation seemed to begin at higher temperatures when the length of the aliphatic diacid chain increased, with 5% weight loss temperature (T5%) values T5%(ISC4) < T5%(ISC6) < T5%(ISC10) and T5%(IMC4) < T5%(IMC6) < T5%(IMC10), which was probably due to the increased molar masses. Even if IMC10 showed the highest thermal stability, we cannot generally conclude that IM polyesters were more stable than IS homologues. It means that the diacid chain length has more influence on the thermal degradation than stereoisomerism.
As predicted by Chemdraw ® Software, IMC10 has more regular bond connections in a short range that can crystallize, but the highly curved chains make it hard to crystallize by chain-chain packing. On the contrary, the exo-links in ISC10 induces a more oriented chain which can readily crystallize and give a more dense microstructure  [73, 74] : the more the exo-exo links are repeated in the chain the easier the chains crystallize by chain-chain packing. This difference in chain orientation explained why ISC10 showed better barrier properties. Six aliphatic bio-based polyesters including isosorbide and isomannide were successfully synthesized by bulk polycondensation. The chain length of the diacid units influenced the degradation and glass transition temperature as well as the crystallization.