dataesr/scientific-paragraphs-categorization huggingface dataset:
Manipulating the PME To investigate the influence of the sample surfaces on the PME, several experiments were performed in Detroit: (i) the surfaces of the Nb disks were abraded (thereby the sample thickness was reduced by about 10%), and (ii) the sample edges were treated using sandpaper. Sanding off the edges of the sample did not lead to a change in the PME characteristics, so these data are not shown here. In contrast, abrading the sample surface (sample Nb2) was found to have a significant effect on the PME of the Nb disks as illustrated in Figure  14a-d. DC magnetic fields of 2 mT (a,b), 20 mT (c), and 60 T (d) were applied. The as-prepared sample (red datapoints, sample Nb1) exhibits all the features of the PME as discussed before, whereas the abraded sample does not show any signature of the PME at any applied field.
The positive peak in χ was termed diffraction paramagnetic effect (DPE), and enabled the determination of T surface c as well as the onset of diamagnetic response, T irr (see also Figure  23 below ). Measurements of the AC susceptibility were performed in Nancy using the ACoption of the MPMS3 SQUID system [145] to obtain further information on the details of the superconducting transition of the Nb disks from Detroit (see Figure  17a-d ). These experiments were carried out after ensuring the PME effect in the Nb disks by DC SQUID measurements. Figure  17a    In Figure  17c , the frequency dependence of χ at a DC field of 1 mT and an AC amplitude of 1 mT is shown. All data were recorded during cooling and warming. There are only small differences seen at the high-T side of the loss peak, and the peak height increases upon an increase in the frequency.
However, at 1 Hz there is a strong difference between cooling and warming modes. On cooling, the same path as for the higher frequencies is followed (T peak ∼9. 05 K), but on warming, the peak is shifted to about 9. 17 K and a characteristic shoulder at the low-T side appears. We must note here that the measurement during cooling was carried out with a sweep rate of 72 mK/min, whereas the warming was measured with a rate of 30 mK/min, so this may have an influence on this observation. On the other hand, this was done in all the measurements and the entire measurement run was repeated three times, but only the data recorded with an AC amplitude of 1 mT and at a frequency of 1 Hz showed this behavior. The higher position of the loss peak recorded during warming falls directly in the region between the two characteristic temperatures T p and T 1 , so this loss peak may have directly to do with the formation of a giant vortex state.
We also must note here that there is also a hysteresis in the chi-data (not shown here). This requires further investigation. Figure  17d  gives the frequency dependence of the AC loss peak taken at a DC field of 5 mT and an AC amplitude of 0. 1 mT. The data taken at 1000 Hz obviously suffer from a skin effect at the metallic surface. No trace from PME (positive moment) is visible in all the curves. Thus, we may summarize these AC experiments by saying that traces of PME in Nb disks can only be observed at low frequencies (1 Hz) and small applied DC fields (1 mT) and AC amplitudes (1 mT). These observations demonstrate the presence of PME signatures also in the AC susceptibility measurements, and the signatures are present in both the real and imaginary part of the susceptibility.
To investigate the paramagnetic Meissner effect, implantation energies in the 3-6 keV range were used to determine the B loc (z) profile in the Au layer. The overall recorded T c of the trilayer film is 8. 52 K. Figure  21a -d present the results from such an experiment performed at 5 K (a,b) and at 3 K (c,d). The data recorded at 10 K (i. e. , above T c ) are shown by • and the magenta line corresponds well to the applied field of 101 G. The measurement at 3 K shows the most significant PME response within the Au layer. A conventional Meissner effect (i. e. , B loc (z) < B loc (z)(10 K)) is measured in Nb up to the interface with Ho, where the contribution of spin-singlet Cooper pairs to the screening supercurrent J x (z) is larger than that due to the long-ranged spin-triplet pairs. The PME is obtained in the Au layer (i.
Very recently, van Weerdenburg  [175]  showed that in ultrathin Al films (8. 5 ML film) a paramagnetic contribution from odd s-wave superconductivity gives rise to an enhanced magnetic penetration depth and an enhanced vortex size due to the Meservey-Tedrow-Fulde (MTF) effect [176]. The vortex size was measured in this paper via dI/dU-maps in STM/STS, and no magnetization data were reported. However, this is the first experimental proof that odd s-wave superconductivity may also play a role for pure, superconducting materials when reaching the atomic limits. It is obvious that odd s-wave superconductivity does not provide an explanation for the PME in the bulk or mesoscopic s-wave superconductors, but provides an unique way to design superconductor/ferromagnet structures for superconducting spintronics [165], which may have important consequences for such applications.
Conclusions and Outlook To conclude, we have presented a review on the experimental data of PME observed on various metallic samples using either magnetometry, AC susceptibility, or various magnetic imaging techniques. Several different materials were found in the literature to exhibit the PME, and several different shapes of samples were involved; single crystals, polycrystalline bulks, nanowires and nanowire arrays, thin films, mesoscopic samples patterned from thin films, multilayers, and bulk wires (in the case of MgB 2 ) were studied. Among them, the bulk Nb disks, the Al and Nb mesoscopic films, and the ZrB 12 crystals were analyzed in detail to refine the flux compression and giant vortex state models. Thus, there are now well-defined theoretical explanations for most features of PME.
Acknowledgments: We would like to thank  A. Silhanek  ( U Liège, Belgium ),  R. Prozorov  ( Ames Lab. , USA ),  L. E. Wenger  ( †), and  M. Murakami  ( ISTEC/SRL, Div. 3/Shibaura Institute of Technology, Tokyo, Japan ) for valuable contributions and/or discussions on the PME in Nb and other metallic superconductors.
Along with the molecular building block (MBB) and supermolecular building block (SBB) approaches  5  giving access to topologies never observed before, researchers successfully targeted the assembly of MOFs with well-known underlying topologies, zeolitic nets  6, 7. These zeolite-like MOFs (ZMOFs) have proven their potential for Article https://doi. org/10. 1038/s44160-023-00401-8 (Supplementary Figs. 123  and 124 ), a survey of the zeolitic nets in the IZA  19  , RCSR  18  and Deem  20  databases allowed us to identify, among more than 300,000 topologies, a curated set of nets combining the following (Supplementary Table  20 ): (1) Eclipsed tetrahedra exclusively (2) No more than three vertices (distinct nodes) (3) Reasonably high TOT angles As the only edge-transitive net in the list, the semi-regular sodalite topology (SOD, sod) appears as the most promising candidate compared with AST, RHO, LTA, NPT, SAS or RWY.
Vertex dependency equations To find the suitable cSDA linkers fitting square or hexagonal windows of the targeted ZMOFs, the analysis of general ST structure and resulting myriad applications, for example in biochemistry  8, 9  , catalysis  10, 11  , gas storage  12  and separation of hydrocarbons  [13] [14] [15]  among other gas mixtures of interest  16. While it is recognized that highly connected nets are ideal targets for the successful practice of reticular chemistry 3 , such observation consequently highlights remaining design challenges for nets with low connectivity. Indeed, the Reticular Chemistry Structure Resource (RCSR) database  17, 18  contains 666 entries for assembling 4-connected (4-c) nodes together 3 , more than 250 of which being zeolitic nets  19  or nets based on the assembly of tetrahedral nodes (diamond dia, quartz qtz and so on); moreover, researchers have enumerated over 2.
1 ) inspired by architecture techniques, combining the SBB approach with the use of various centring structure-directing agents (cSDAs). For the construction of arches, bridges, oculus windows and so on, architects have developed elaborated systems to overcome the laws of gravity, using wooden formworks (also named centring) that would both help support and shape the edifice. This approach permits to alter and control the TOT angle between adjacent STs and consequently deviate from the mtn topology of MIL-100 type of MOFs  29, 32, 35  , a rarely observed feature  [36] [37] [38]. Analysis of the targeted sodalite topology, a semi-regular net with transitivity (1121), recognized as the ideal target blueprint through a systematic analysis of the zeolitic nets contained in the RCSR (>3,000 nets)  17, 18  , International Zeolite Association (IZA) (>250 nets)  19  and Hypothetical Zeolites Structures  20, 21  databases, allowed us to identify two types of cSDAs, namely expanding-cSDA (Fig.
Topology databases survey While mtn is recognized to be the default topology for assembling eclipsed tetrahedra/STs with a TOT angle close to 180° (ref. 31)  Article https://doi. org/10. 1038/s44160-023-00401-8 sodalite windows was carried out. The arm length of tightening-and expanding-cSDAs (L T and L E , respectively) can be expressed as a function of the tritopic carboxylate ligand (L A ) forming the STs, by applying trigonometry rules (Fig. 3a )  41. Accordingly, the following relations are obtained (Supplementary Fig. 9 ): L T = √1. 5L A -0. 9Å L E = √3L A +0. 3Å Suchwise, simple geometrical relations allow to express a rational dependence between the size of any equilateral tricarboxylate linker and corresponding N-ligands to fit either in square or hexagonal windows to promote the sod topology versus mtn in the formation of ST-based ZMOFs (Fig.
org/10. 1038/s44160-023-00401-8 material (Fig. 5b )  23, 26, 43. Experimental total pore volumes of In-sod-ZMOFs materials are 2. 89, 2. 26 and 2. 07 cm 3 g -1 for In-sod-ZMOF-320, In-sod-ZMOF-307 and In-sod-ZMOF-327, respectively, and predictably decreasing with inclusion of additional organic moieties. The corresponding series of BTTC-based frameworks Co-sod-ZMOF-210, Co-sod-ZMOF-204 and Co-sod-ZMOF-214 has close total pore volume values giving 1. 06, 1. 27 and 1. 18 cm 3 g -1 , respectively. The pore size distribution (PSD) for In-sod-ZMOF-3TE and Co-sod-ZMOF-2TE series (Supplementary Figs. 110-118) was assessed using the Ar adsorption data (Supplementary Figs. 98, 101-104 and 106) and revealed two types of pore for In-sod-ZMOF-3T7 and Co-sod-ZMOF-204 series with mesopore sizes of 43 Å and 33 Å, respectively, which are in good qualitative agreement with the above derived values of 42 Å and 33 Å from the corresponding calculated structure.
Co-sod-ZMOF-205 has a lower expected total pore volume than the isoreticular In-sod-ZMOF due to its anionic framework, which requires the presence of counterions inside the cavities, and ligand arms pointing inside the cavities (Supplementary Figs. 99  and 100 ). The total pore volume of Co-sod-ZMOF-205 is 0. 65 cm 3 g -1. In-sod-ZMOF-205 demonstrates high Ar uptake with total pore volume of 0. 94 cm 3 g -1 (calculated 1. 09 cm 3 g -1 ). The PSD for In-sod-ZMOF-205 was assessed using the Ar adsorption data (Supplementary Fig. 100 ) and revealed two types of pore, with mesopore size of 33 Å, in perfect qualitative agreement with the above-derived values from the corresponding calculated structure. Fe-sod-ZMOF-102 has a total pore volume of 0. 80 cm 3 g -1 (Supplementary Fig. 96 ). This experimental value is close to the calculated one (0.
Acknowledgements Research reported in this publication was supported by the  King Abdullah University of Science and Technology (KAUST). S. R. G. B. was supported with Grant  POSTDOC_21_00069  funded by  Consejería de Transformación Económica, Industria, Conocimiento y Universidades  and  Agencia Andaluza del Conocimiento, Junta de Andalucía. We are thankful to  C3UPO  for the HPC facilities provided.
Data availability The X-ray crystallographic coordinates for structures reported in this study have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition numbers CCDC 2261737 (In-sod-ZMOF-102), 2261738 (In-sod-ZMOF-103), 2261739 (Co-sod-ZMOF-210), 2261740 (Co-sod-ZMOF-204), 2261741 (Fe-sod-ZMOF-204), 2261742 (Co-sod-ZMOF-205), 2261743 (Co-sod-ZMOF-214), 2261744 (In-sod-ZMOF-320), 2261745 (Co-sod-ZMOF-307), 2261746 (In-sod-ZMOF-307), 2261747 (Ni-sod-ZMOF-307), 2261748 (In-sod-ZMOF-327) and 2261749 (In-sod-ZMOF-427). These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/ data_request/cif. Article https://doi. org/10. 1038/s44160-023-00401-8.
Acknowledgements: PD holds a visiting professor association with the  Department of Mathematics, Imperial College London, UK. SM's research was supported by the  Austrian Science Fund (FWF)  through the project  F65  and by the  Vienna Science and Technology Fund (WWTF)  with a  Vienna Research Groups for Young Investigators , grant  VRG17-014. AF acknowledges support from the  Project EFI   ANR-17-CE40-0030  of the  French National Research Agency. AF thanks the hospitality of the  Laboratoire de Mathématiques et Applications (LMA, CNRS)  in the  Université de Poitiers , where part of this research was conducted.
We collected the data using an interview guide based on these themes. In the present report, we focus on the parts of the interviews related to the sustainable cuisine theme. The opening question on this theme was "If I say 'sustainable cuisine' ('cuisine durable' in French), what words and adjectives come to mind? What images, what sensations does it evoke for you?". The follow-up question asked them to link the ideas or concepts mentioned with sustainable cuisine: "How do you relate 'this' to sustainable cuisine?" The interviews were audio-recorded, pseudonymized and fully transcribed. A thematic analysis was performed on the corpus of our subsample of 15 chefs, based on their response to our question on sustainable cuisine. This thematic analysis follows a deductive approach by being guided by the analytical interest of a specific research question.
Themes are identified at a semantic level  (Braun and Clarke, 2006). We used MAXQDA (2022) for data analysis. We draw on  Braun and Clarke's (2006)  and  Nowell et al. (2017)  work to clarify how the thematic analysis was conducted. They divide the process of thematic analysis into six phases, each of which contains points requiring vigilance to conduct rigorous and reliable research (Table  1 ). During the first phase of the thematic analysis, familiarization with the data, the interviews were transcribed manually and in full and the initial reflective thoughts were kept. The part of the corpus corresponding to "sustainable cuisine" was color coded on MAXQDA to facilitate the work. The second phase consisted in the generation of the initial codes. The coding framework was based on the structured interview guide as well as on a first global reading of the corpus.
The first keywords were identified. The lexical field of meat was identified in the corpus by automatic searches on MAXQDA for the word "meat" or types of meat such as "beef" or "lamb", and completed by a manual search. The third phase consisted of researching the themes. A second reading of the corpus resulted in a list of 9 themes to be identified more precisely in the whole corpus. The fourth phase was the examination of the themes. The framework was adjusted and validated by the members of the research team. Themes and sub-themes were implemented in MAXQDA. The fifth phase was based on naming and defining the themes. To facilitate the organization of the coding, each code specific to this analysis was named with its own prefix ("sustainability_") and a specific color. A "memo" was added to each code directly on MAXQDA to define the code.
Atmospheric reanalyses are valuable datasets for driving ocean-sea ice general circulation models and for proposing multidecadal reconstructions of the ocean-sea ice system in polar regions. However, these reanalyses exhibit biases in these regions. It was previously found that the representation of Arctic and Antarctic sea ice in models participating in the Ocean Model Intercomparison Project Phase 2 (OMIP2, using the updated Japanese 55-year atmospheric reanalysis, JRA55-do) was significantly more realistic than in OMIP1 (forced by the atmospheric state from the Coordinated Ocean-ice Reference Experiments version 2, CORE-II). To understand why, we study the sea ice concentration budget and its relations to surface heat and momentum fluxes as well as the connections between the simulated ice drift and the ice concentration, the ice thickness and the wind stress in a subset of three models (CMCC-CM2-SR5, MRI-ESM2-0 and NorESM2-LM).
g. ,  Hunke and Holland, 2007; Lindsay et al. , 2014; Sterlin et al. , 2021) , the Arctic and Antarctic sea ice concentration in the marginal ice zones  (Chaudhuri et al. , 2016)  and the Antarctic sea ice extent, motion and thickness  (Barthélemy et al. , 2018). Wu et al. (2020)  also showed the positive impacts of highfrequency (hourly to daily) atmospheric fluctuations on the Antarctic sea ice simulation, which implies that driving an ocean-sea ice model with a reanalysis that is developed at enhanced temporal and spatial resolution can help capture the small-scale atmospheric processes and eventually improve the representation of sea ice. The CMIP6 Ocean Model Intercomparison Project (OMIP,  Griffies et al. , 2016)  provides global ocean-sea ice model simulations in two streams of model experiments: OMIP1, forced by the Coordinated Ocean-ice Reference Experi-ments, version 2 interannual dataset (CORE-II;  Large and Yeager, 2009) , and OMIP2, forced by the updated Japanese 55-year atmospheric reanalysis (JRA55-do;  Tsujino et al.
, 2018). The design of the CMIP6 OMIP simulations has been coordinated by the World Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on Ocean Model Development Panel (OMDP), and ongoing research collaboration is done through the OMDP to further develop OMIP2  (Griffies et al. , 2016). The same configuration is used under two different atmospheric forcing datasets as mentioned in  Tsujino et al. (2020). The JRA55-do atmospheric forcing is relatively new with major improvements, e. g. , increased temporal frequency (3 h) and horizontal resolution (0. 5625 • ), compared to CORE-II forcing (6 h and 1. 875 • ). The Arctic and Antarctic sea ice concentration and drift simulations in CMIP6 OMIP2 models forced by JRA55-do are improved compared to those in OMIP1 models forced by CORE-II  (Tsujino et al.
7  (Kimura, 2004; Holland and Kwok, 2012). This paper complements a companion publication  (Lin et al. , 2021)  that documents a new Sea Ice Evaluation Tool (SITool v1. 0) and applies this tool to assess the sea ice simulations in CMIP6 OMIP models. In that study, the improved Arctic and Antarctic ice concentration and drift simulations in CMIP6 OMIP2 compared to OMIP1 were highlighted from performance metrics and diagnostic spatial maps. In the present study, the thermodynamic and dynamic processes that contribute to the improved ice concentration simulation in OMIP2 compared to OMIP1 are assessed. The related surface sensible and latent heat fluxes, net shortwave and longwave radiation fluxes and surface wind stress on sea ice are investigated to trace the origin of simulated sea ice differences back to the forcing datasets.
Models, observational references and atmospheric reanalysis data Five CMIP6 OMIP models have been forced by both CORE-II (OMIP1) and JRA55-do (OMIP2) reanalysis data so far, and they are marked as < model name + /C and /J >, such as CMCC-CM2-SR5/C and CMCC-CM2-SR5/J, respectively. Details of the CMIP6 OMIP models can be found in Sect. 2. 2 of our previous paper  (Lin et al. , 2021). The sea ice components of five CMIP6 OMIP models are given in Table  1. Three of the five models (CMCC-CM2-SR5, MRI-ESM2-0 and NorESM2-LM) provide the tendencies of sea ice con-centration attributed to dynamic vs. thermodynamic processes and surface wind stress on sea ice, while two of them (CMCC-CM2-SR5 and NorESM2-LM) provide surface heat fluxes (sensible and latent heat fluxes and downward/upward shortwave and longwave radiation fluxes).
The first observed ice drift product is processed by the NSIDC and enhanced by the Integrated Climate Data Center (ICDC-NSIDCv4. 1, Tschudi et al. , 2019) , and the second ice drift data (KIMURA) are processed by  Kimura et al. (2013). More information on the observational references can be found in Sect. 2. 2 of  Lin et al. (2021). The evaluation period is chosen according to available historical model outputs and observations and is consistent with the analysis in  Lin et al. (2021). The ice concentrations, concentration tendencies and their relations to surface heat fluxes and wind stress are evaluated from 1980 to 2007, while the ice drift and its links to the ice concentration, ice thickness and wind stress are assessed from 2003 to 2007. The monthly mean surface air temperature, specific humidity, downward shortwave and longwave radiation fluxes and wind speed in the CORE-II and JRA55-do reanalysis datasets from 1980 to 2007 are used to evaluate the differences between two forcing datasets.
org/10. 5194/tc-17-1935-2023 The Cryosphere, 17, 1935-1965, 2023   and 8e ). Compared to the OMIP1 simulation, the Arctic interior icemotion MKE from January to December is not much improved in CMCC-CM2-SR5/J, MRI-ESM2-0/J, EC-Earth3/J and MIROC6/J (Fig. A8a to d ). The positive September ice-motion MKE bias in NorESM2-LM/C is larger than in other OMIP1 models, and this positive bias is reduced in NorESM2-LM/J but not in other models. In the Arctic exterior region, the ice-motion MKE in the five OMIP1 simulations (Figs. 7a, A8a to d, dashed orange) is much larger than those in KIMURA (dashed blue) and ICDC-NSIDCv4. 1 data (dashed purple), and the positive biases in OMIP1 simulations are largely reduced in OMIP2 simulations (dashed green) from November to April. The decreased Arctic ice-motion MKE in OMIP2 simulations in the exterior region from November to April is mainly induced by the decreased surface wind stress (Figs.
Compared to the OMIP1 simulation, the Antarctic interior ice-motion MKE from January to December is not much improved in CMCC-CM2-SR5/J, MRI-ESM2-0/J, EC-Earth3/J and MIROC6/J (Fig. A9a to d ). In the Antarctic exterior region, the ice-motion MKE in NorESM2-LM/C (Fig. 8a , dashed orange) is larger than those in KIMURA (dashed and ICDC-NSIDCv4. 1 data (dashed purple), and this positive bias is reduced in NorESM2-LM/J from July to September (dashed green, smaller than -1 × 10 -3 m 2 s -2 as a baseline). The icemotion MKE positive bias is reduced from July to October in CMCC-CM2-SR5/J, MRI-ESM2-0/J, EC-Earth3 and MIROC6 (Fig. A9a.
Ice drift direction and its connections to wind stress We  ulated ice drift direction (independently of the improvements in sea ice drift magnitude noted in the previous section). To that end, the vector correlations between simulated and observed ice drift fields (KIMURA and ICDC-NSIDCv4. 1 data) are diagnosed, as done in  Lin et al. (2021). In general, the vector correlation coefficients between the modeled ice drift and observational data during 2003-2007 are larger in NorESM2-LM/J than those in NorESM2-LM/C in the Arctic (Fig. 9d  and e ) and Antarctic (Fig. 9j  and k ). The links with the surface wind stress are assessed. The vector correlation coefficients between modeled ice drift and surface wind stress are much larger in NorESM2-LM/J than those in NorESM2-LM/C in the Beaufort Gyre area (Fig.