dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurement results for pure R1243zf Thermal conductivity measurements of pure R1243zf were carried out in the temperature range from (314. 2 to 405. 6) K and pressures from (0. 9 to 6. 1) MPa in the homogeneous liquid and vapour phases. The experimental data (T, p, λ) of pure R1243zf are listed in Table  4. Overall, a total of 35 values of thermal conductivity were acquired in the range from (0. 020 to 0. 068) W⋅m -1 ⋅K -1 at densities from (32 to 964) kg⋅m -3. The pressure-temperature phase diagram of pure R1243zf showing the measurement conditions, the measured thermal conductivities, λ exp , as a function of density, and the relative deviations of λ exp from values calculated using the ECS model  (Chichester and Huber, 2008)  in REFPROP 10 (  Lemmon et al. , 2018)  are illustrated in Fig.
Conclusions In this paper, we report experimental thermal conductivity data for pure R1243zf and two binary systems, R32+R1234yf and R32+R1243zf with molar fractions 0. 25, 0. 50 and 0. 75 of R32. The thermal conductivity in the homogenous vapour and liquid phases was measured using the transient hot wire technique in the temperature range from (264. 1 to 405. 6) K with pressures ranging between (0. 9 and 6. 1) MPa. The relative combined expanded uncertainty (k = 2) in the experimental thermal conductivity was approximately 2. 0 %. The relative deviations of the measured thermal conductivities from the default extended corresponding states (ECS) model, which is implemented in the REFPROP 10, were between (-13 and +10) % in the vapour phase and between (-14 and +1) % in the liquid phase. For R32+R1234yf mixtures, using the improved interaction parameters suggested by Arami-Niya et al.
(  2020 ) and  Kim et al. (2020)  for the Helmholtz and ECS models implemented in REFPROP 10 reduced the RMS difference between measured and predicted liquid phase thermal conductivities from 7. 9 % to 3. 9 %. Additionally, the parameters of the residual entropy scaling model incorporating cubic-plus-association equation of state (RES-CPA model) for the thermal conductivity of pure R1243zf and binary R32+R1243zf system were determined. The measured pure and mixture data can then be readily mapped onto a univariate function of the residual entropy through the RES approach. The RES-CPA model predicts the experimental mixture thermal conductivities generally within 10 %, which is similar to the ECS model but is achieved with no binary-specific   (Chichester and Huber, 2008) , as function of densities.
In order to allow higher EGR rates, a choke valve was also added in the intake air-path, just upstream the EGR junction. A diagram of the engine layout and flow connections is attached in Figure  1. The engine was fully instrumented and installed in a dynamic test bench, which was controlled with the AVL PUMA software. It allows regulating the engine speed and torque through the AVL AFA 200/4-8EU dynamometric brake. The main cycle-averaged pressure and temperature values, air and fuel mass flows, turbocharger speed and exhaust emissions were also recorded by means of the AVL PUMA software. The location of the cycle-averaged pressure (Kistler 4260A piezoresistive type transmitters) and temperature (3mm K-type thermocouples) sensors can be found in Figure  1. The turbocharger speed and the air and fuel mass flows were acquired with the systems MICRO-EPSILON DZ140, AVL FLOWSONIX and AVL 733S, respectively.
Modeling tools The model used in this study is built on a 1D model of an analogous GTDI engine, also with VVT and VNT technologies, but without an EGR system. This base model was developed by means of the market-leading software GT-Power and calibrated according to the procedure described by Serrano et al  [19]. In summary, this procedure is mostly characterized by isolating the different engine parts, decoupling the turbocharger, and using proportional-integral (PI) controls to achieve fitting parameters. It also includes the determination of empirical correlations to estimate heat losses and pressure drops in engine systems, and the training of an artificial neural network (ANN) to predict the Wiebe parameters and so to reproduce the combustion process  [19]. After that, in order to complete the model calibration, the cycle-averaged variables (the most critical values of pressure and temperature and the air mass flow) and crank-angle resolved pressure signals (in the cylinders and both manifolds) are checked to validate the ANN and correlations performance.
Experimental validation After the parametric studies, modeling results were compared with experimental data from a DOE carried out in the engine test bench to minimize fuel consumption. The DOE procedure was composed of two stages in which more than 300 experiments were completed. In a first step, 100 tests were performed for each operating point by varying four parameters: IVO angle, EVC angle, EGR rate and spark timing. If needed, the VNT vanes were also actuated to reach the desired load. In a final step, the best combinations of IVO, EVC and EGR were again tested, but now the spark timing was adjusted in real time to set the CA50 inside the optimum range  [23]  for each test. Moreover, it must be stated that the experiments with an IMEP coefficient of variation (COV) higher than 3% were not accepted for the optimization of engine fuel efficiency.
min -1. SDSC heating scans were also performed from -30 to 190 • C using an average heating rate of 2 • C. min -1 and a stabilization step duration of 60 s, i. e. a temperature increment of 1 • C after each stabilization step. The data were normalized by the baseline obtained from an experiment achieved with empty pans following the same thermal cycle. The heat flow data derived from the iso-scan SDSC heating program were used to obtain two curves, i. e. the apparent thermodynamic heat capacity (Cp, ATD) signal (reversible) and the IsoK baseline heat capacity (Cp, IsoK) signal (related to kinetic and non-reversible events). Analyses were done in duplicate.
Tensile properties ISO½ samples were cut from the films obtained by thermocompression and stabilized during 5 days at 25 • C and 50 %RH prior to testing. Tensile properties were measured on stabilized samples using a tensile tester (Zwick BZ2. 5/TN1S, Metz, France) with a cross-head speed of 1 mm. min -1. Young's modulus, strain and stress at break as well as energy at break were calculated from strain-stress curves with the Matlab program. Ten replicates were characterized for each formulation.
7 ). Each peak observed was assigned to a specific monomer unit of the copolymer structure (HBi or HVi, i being the carbon number, are the different monomer units present in the copolymer structure, as illustrated in the top right corner of Fig. 7 ). For the PHBV18% raw powder sample, intermediate peaks were observed (X, Y and Z peaks in Fig. 7 ) between the specific peaks attributed to HB and HV units. Indeed, if no thermal degradation has occurred, PHB-rich and PHV-rich domains are formed with no macrophase separation and their dimensions is related to the length of the Fig. 6. WAXS diffractograms obtained after melting at various Tph and crystallization at 21 • C during 60 min. PHBV18% (A) and PHBV3% (B). Raw data available at https://doi. org/10. 15454/MNEKPC. different units. In that case, these specific signals are observed and constitute markers of the interactions between PHB and PHV units  [7].
[61] Within the two series of isostructural complexes, we focussed on the comparison of the Sr II and Sm II compounds; the theoretical structural results are in very good agreement with the experimental values. The basis sets were of def2-SV(P) quality for all atoms as given in the program package TURBOMOLE. [62] For iodine, samarium and strontium effective core potentials (ecp) of 46, 52 and 28 core electrons were used. The calculated molecules are of symmetry D 4 ([MI 2 (IMe) 4 ] with M = Sm, 4, and the hypothetical complex with M=Sr, 7') and C 2 ([MI 2 (IMe) 2 THF 2 ] with M = Sr, 7, and the hypothetical complex with M=Sm, 4'), respectively. A good overview on the bonding situation is obtained from an energetic comparison of the compounds. The formation of the respective tetra-NHC adduct from the bis-NHC adduct according to Equation (1) indicates a slightly stronger bond of the IMe ligand compared to THF.
Acknowledgements This work is supported by the  German Federal Ministry of Education and Research (BMBF)  under Contracts  02NUK059F. The authors acknowledge computational support by the state of  Baden-Württemberg  through bwHPC and the  Deutsche Forschungsgemeinschaft (DFG)  through grant No  INST 40/467-1 FUGG. T. S. thanks the  Alexander von Humboldt Foundation  for a postdoctoral fellowship. Open access funding enabled and organized by Projekt DEAL.
Nitrous oxide (N 2 O), ammonia (NH 3 ), and methane (CH 4 ) emissions from the manure management chain of livestock production systems are important contributors to greenhouse gases (GHGs) and NH 3 emitted by human activities. Several studies have evaluated manure-related emissions and associated key variables at regional, national, or continental scales. However, there have been few studies focusing on the drivers of these emissions using a global dataset. An international project was created (DATAMAN) to develop a global database on GHG and NH 3 emissions from the manure management chain (housing, storage, and field) to identify key.
variables influencing emissions and ultimately to refine emission factors (EFs) for future national GHG inventories and NH 3 emission reporting. This paper describes the "field" database that focuses on N 2 O and NH 3 EFs from land-applied manure and excreta deposited by grazing livestock. We collated relevant information (EFs, manure characteristics, soil properties, and climatic conditions) from published peerreviewed research, conference papers, and existing databases. The database, containing 5,632 observations compiled from 184 studies, was relatively evenly split between N 2 O and NH 3 (56 and 44% of the EF values, respectively). The N 2 O data were derived from studies conducted in 21 countries on five continents, with New Zealand, the United Kingdom, Kenya, and Brazil representing 86% of the data.
In livestock systems, NH 3 emissions are largely derived from manure storage and handling  (Webb et al. , 2005). Therefore, the manure management chain is an important source of direct and indirect GHG emissions as a consequence of its substantial concen-tration of N, carbon (C), and water  (Chadwick et al. , 2011). Although N leaching is also an indirect source of N 2 O (IPCC, 2006), we have not included this loss pathway within the existing database due to limited resources. In the future, we hope to be able to expand the database to include this source. Several studies have focused on understanding the variables influencing N 2 O and NH 3 emissions from manure management and identifying possible mitigation strategies  (Broucek, 2018; Hafner et al. , 2018; Montes et al. , 2013; Sajeev, P.
Over the last decade, a large body of data has been generated that quantifies emissions arising from each step of the manure management chain. However, there has been no consolidation of these data into one single database. Recently, the DATAMAN project was created to build a publicly available global database of CH 4 , N 2 O, and NH 3 emissions (plus relevant activity and ancillary data) relating to livestock housing, storage, and field application of manure (including excreta deposited during grazing) (GRA, 2020a). The overall aim of the DATAMAN project is to provide researchers and policy makers alike with the most up-to-date knowledge on methods for managing GHG and NH 3 emissions from manure. The DATAMAN database, disaggregated into (a) housing, (b) storage, and (c) field-based emissions, provides an opportunity to identify possible variables influencing gaseous emissions from the manure management system.
Because fluxes from grassland and cropland soils are relatively small compared with N 2 O emissions (IPCC, 2006), these emissions were not included in the field database. The objective of the current study was to describe the field-based component of the DATAMAN database (http://www. dataman. co. nz), which focuses on N 2 O and NH 3 EFs for land-applied manure and excreta collated from a wide range of countries. 2019) , where all relevant N 2 O and NH 3 studies on excreta and manure (27 studies in total) were included in DATAMAN.
Process of data collection Figure  1  presents a summary of the process for collating data. All studies were checked for their suitability for inclusion in the database. The selection criteria for N 2 O studies were: (a) the studies were field-based (i. e. , excluded laboratory, greenhouse, and modeling studies), (b) the measurements were made using static or dynamic chamber-based methods, (c) the total study duration was 14 d or more, (d) cumulative N 2 O emissions or EF values were reported, (e) the trials included a nonamended control, (f) the trials continued until the N 2 O emissions and soil mineral N concentration from the N treatment had returned to background levels (i. e. , there was no significant difference between the N treatment and the nonamended control), (g) the N treatments were limited to one N source to avoid additive or multiplicative effects (e.
g. , manure + fertilizer treatments were excluded), and (h) the activity data on the N load were available or could be calculated from information provided. These criteria ensured that data were representative of emissions from manure and excreta under field conditions. We acknowledge the criteria for duration of experiments is smaller than that used for recent meta-analyses of N 2 O field experiments (30 d or more)  (IPCC, 2019; López-Aizpún et al. , 2020) ; the influence of experiment duration on N 2 O EF will be analyzed in a subsequent study. Inclusion of relatively short-term experiments broadens the potential use of the database beyond revised EFs and identifying key drivers. For example, a future application of the database may include identifying proxies of EF values based on short-term experiments.
Selection criteria for NH 3 emission studies were: (a) the studies were field based; (b) the NH 3 emissions were measured using micrometeorological methods, aspirated F I G U R E 1 Summary of process for collating data of DATAMAN database and the focus of the current paper. AEDA, Agricultural and Environmental Data Archive (AEDA, 2020); ALFAM2, Ammonia Loss from Field-Applied Manure  (Hafner et al. , 2018) ; ELFE, ELevage et Facteurs d'Emission  (Vigan et al. , 2019)  chambers, wind tunnels, or passive sampling techniques; (c) the measurements were conducted for at least 1 d for all manures (except for poultry) and for 7 d or more for poultry manure experiments (due to the slow rate of urea hydrolysis of uric acid present in poultry manure); (d) cumulative NH 3 emissions or EF values were reported; (e) the N treatments were limited to one N source; and (f) activity data on N load were available or could be calculated from information provided.
Description of variables included in the database Field data were collated using a purpose-designed template developed in Microsoft Excel. The field template (accessible at https://www. dataman. co. nz/Home/About) included 96 variables, which were grouped into six categories: "General," "Gas measurement," "Animal," "Manure," "Land," and "Climate. " The "General" category contained 23 variables, including trial description, country, research institute that conducted the study, replicate number, latitude, longitude, database identification, online link to published research paper, reference of research paper, degree of variation in reported EF means, statistical method used for determining EF means, comments, and experiment identification. The "Gas measurement" category contained 13 variables, including gas measured, emission measurement technique, application start date, start and end of gas measurements, number of measurements, number of chambers per plot, chamber area, number of gas samples per chamber, cumulative emissions, and EF.
Climate zone was divided into four categories: temperate wet and dry and tropical wet and dry. The division between wet and dry in the tropics was based on 1,000 mm of precipitation (>1,000 mm equating with wet/moist climate), and the division in the temperate region was based on mean annual precipitation/potential evapotranspiration ratio of 1 (>1 equating with a wet/moist climate) (IPCC, 2019). We used GIS data on the IPCC climate zones to categorize zones for each study. In most cases, this was a simple process (e. g. , country or coordinates obtained from the research publication). However, in several instances where the study location was close to the boundary of two climate zones and coordinates were not available, it was necessary to conduct a visual comparison of the climate zone GIS layer with towns noted in the research publication.
These variables were selected based on the variables included in the ALFAM2 database (building on earlier informal discussions between members of the Global Research Alliance Manure Management Network  [GRA, 2020b; Hafner et al. , 2018]  and expert judgment of DATAMAN researchers). No one study had the full suite of variables available; however, studies were accepted if either cumulative losses and/or emission factors were included. Several studies had limited information relating to soil and manure characteristics and climate. In those cases, the first or corresponding authors of these studies were contacted for additional information. A similar process was carried out when EF values were not reported or could not be calculated from the information supplied in the publication.
Quality control The database was subjected to a quality control check to identify and correct errors during the data entry phase, thereby increasing the accuracy and confidence of data analysis. The quality control process involved two people: one person sourced the data from publications, conference papers, or theses and entered this into the database. Thereafter, a second person independently checked each data entry by comparing the value against the original source. This quality control process was limited to the entry of data sourced from individual publications, conference papers, or theses. The data obtained from existing databases (i. e. , ALFAM2, ELFE, and AEDA) had already undergone a quality control process when they were collated  (Hafner et al. , 2018; Vigan et al. , 2019; R. Thorman, personal communication, 2020).
Summary of database per country, continent, and climate zone Table  1  and Figure  2  present a summary of the database, which is comprised of 5,632 observations that were compiled from 184 studies (existing datasets, theses, conference papers, and peer-reviewed papers) conducted by 91 different institutes. Nitrous oxide emissions represented 56% of the total database, supplying 3,167 observations, and NH 3 represented 44% of the total data. A small number of studies reported both N 2 O and NH 3 emission factors, representing 0. 2% of the total dataset. Nitrous oxide EF data were collated from five continents (Africa, the Americas, Asia, Europe, and Oceania) and 21 countries, with New Zealand, United Kingdom, Kenya, and Brazil representing, respectively, 56, 18, 6, and 6% of the total N 2 O dataset (Table  1 ).
The Americas represented 11% of the dataset (344 observations), of which Brazil represented 51% of the American data, followed by the United States, Canada, Colombia, Argentina, Chile, and Nicaragua. Europe represented 24% of the dataset, where the United Kingdom was the main source of data (73% of the European dataset), followed by Ireland, The Netherlands, Sweden, Germany, Austria, France, and Belgium. Oceania represented 57% of the total N 2 O dataset, with 99% of these data sourced from New Zealand studies and the remaining data sourced from Australian studies. Africa represented 7% of the total dataset, where information was collated from Kenya and Zimbabwe (94 and 6% of data from Africa, respectively). Asia represented 1% of the total dataset, with data collated from China and Japan.
Ammonia EF data were collated from 17 countries spread across three continents, where the United Kingdom contributed most of the NH 3 dataset (54%) and Denmark, Canada, and The Netherlands combined supplied 25% of the dataset (Table  1 ). Europe represented 88% of the observations, with the United Kingdom and Denmark supplying 63% of European data. America represented 12% of the NH 3 dataset, with Canada and Brazil representing 91% of the American data. Oceania and Asia represented <1% of the dataset. We were unable to source NH 3 EF data from African countries. Results indicate that Oceania, Asia, and Africa have generally focused on evaluating N 2 O emissions from manure and direct deposition of urine and dung, whereas Europe and America have conducted studies on both N 2 O and NH 3 emissions from livestock manure and excreta.
Our data suggest a large imbalance in the dataset for both N 2 O and NH 3 emissions. Although the current dominance of data from wet temperate climates is partly influenced by the countries participating in the DATAMAN project, a large effort was made to identify and include studies from tropical (dry and wet) and temperate dry climates. Given the importance of tropical regions such as South, East, and Southeast Asia; Latin America; and the Caribbean as hotspots of N 2 O and NH 3 emissions from livestock systems  (Uwizeye et al. , 2020) , we encourage researchers to conduct and publish more field studies from these climates to improve our understanding of emissions from land application of livestock manure and excreta. This knowledge will help to further improve default EFs according to climate zone and to increase our understanding of drivers influencing emissions in these regions.
Type of animal and manure The numbers of N 2 O and NH 3 EF observations are summarized based on animal and manure types in Tables  2  and 3. Dairy cattle represented 64% of the N 2 O dataset, followed by beef cattle (18%) and sheep (13%). Other animal categories represented <7% of the N 2 O data. Regarding manure type, urine (real and synthetic) was the most common N source (51% of the dataset);the next most common N sources were dung (26%) and slurry (12%). The remaining 11% was represented by dirty water, solid manure, farmyard manure, broiler litter, layer manure, and composted manure. Given that urine represents 29% of total N 2 O emissions from livestock production  (Gerber et al. , 2013) , it was not surprising to see a large proportion of studies focused on this N source. For NH 3 , cattle, swine, and dairy cattle were the most important animal categories, roughly equally contributing to a total of 96% of the dataset.
As noted earlier, "cattle" represents the studies where we were unable to disaggregate into dairy or beef cattle. Slurry was the main manure type, representing 92% of the NH 3 dataset. The large number of studies evaluating NH 3 emissions from cattle and pig slurry is likely to be associated with the significant NH 3 emissions from those sources  (Aita et al. , 2019; Amon, Kryvoruchko, Amon, & Zechmeister-Boltenstern, 2006; Bourdin, Sakrabani, Kibblewhite, & Lanigan, 2014; Webb, Pain, Bittman, & Morgan, 2010)  and the extensive use of slurry as an N source for grassland  (Salazar, Martínez-Lagos, Alfaro, & Misselbrook, 2014)  and crop production  (Velthof & Mosquera, 2011).
Manure treatment The majority of NH 3 and N 2 O studies (82%) have determined emissions from untreated manures (Tables  2  and 3 ). The effectiveness of nitrification inhibitors as a mitigation treatment was examined in 14% of N 2 O studies, whereas other manure treatments were either not or very occasionally included (1% of data). For NH 3 , a small number of studies focused on storage-based solid separation (4%) or digestion (4%); fieldbased inclusion of nitrification inhibitors (3%) were also assessed. It was found that only 9. 4% of total data (5. 1 and 4. 3% for the N 2 O and NH 3 datasets, respectively) included information on the duration of manure storage, which ranged between 1 and 7 mo.