More Like this

1. High-Temperature Superconductivity in Hydrides: Experimental Evidence and Details

   https://doi.org/10.1007/s10948-022-06148-1  

   Eremets, M. I. ; Minkov, V. S. ; Drozdov, A. P. ; Kong, P. P. ; Ksenofontov, V. ; Shylin, S. I. ; Bud’ko, S. L. ; Prozorov, R. ; Balakirev, F. F. ; Sun, Dan ; et al ( March 2022 , Journal of Superconductivity and Novel Magnetism)

   Since the discovery of superconductivity at ~ 200 K in H₃S [1], similar or higher transition temperatures,T_cs, have been reported for various hydrogen-rich compounds under ultra-high pressures [2]. Superconductivity was experimentally proved by different methods, including electrical resistance, magnetic susceptibility, optical infrared, and nuclear resonant scattering measurements. The crystal structures of superconducting phases were determined by X-ray diffraction. Numerous electrical transport measurements demonstrate the typical behavior of a conventional phonon-mediated superconductor: zero resistance belowT_c, shift ofT_cto lower temperatures under external magnetic fields, and pronounced isotope effect. Remarkably, the results are in good agreement with the theoretical predictions, which describe superconductivity in hydrides within the framework of the conventional BCS theory. However, despite this acknowledgement, experimental evidences for the superconducting state in these compounds have recently been treated with criticism [3–7], which apparently stems from misunderstanding and misinterpretation of complicated experiments performed under very high pressures. Here, we describe in greater detail the experiments revealing high-temperature superconductivity in hydrides under high pressures. We show that the arguments against superconductivity [3–7] can be either refuted or explained. The experiments on the high-temperature superconductivity in hydrides clearly contradict the theory of hole superconductivity [8] and eliminate it [3].

    

   more » « less
2. In Situ Characterization of Phase-Change Materials

   Tripathi, s. ; Janish, M. ; Noor, N. ; Jungjohann, K. ; Pete, D. ; Kotula, P. ; Silva, H. ; Carter, C. ( November 2018 , Materials Research Society symposia proceedings)

   To understand the mechanism underlying the fast, reversible, phase transformation, information about the atomic structure and defects structures in phase change materials class is key. PCMs are investigated for many applications. These devices are chalcogenide based and use self heating to quickly switch between amorphous and crystalline phases, generating orders of magnitude differences in the electrical resistivity. The main challenges with PCMs have been the large power required to heat above crystallization or melting (for melt-quench amorphization) temperatures and limited reliability due to factors such as resistance drifts of the metastable phases, void formation and elemental segregation upon cycling. Characterization of devices and their unique switching behavior result in distinct material properties affected by the atomic arrangement in the respective phase. TEM is used to study the atomic structure of the metastable crystalline phase. The aim is to correlate the microstructure with results from electrical characterization, building on R vs T measurements on various thicknesses GST thin films. To monitor phase changes in real-time as a function of temperature, thin films are deposited directly onto Protochips carriers. The Protochips heating holders provides controlled temperature changes while imaging in the TEM. These studies can provide insights into how changes occur in the various phase transformations even though the rate of temperature change is much slower than the PCM device operation. Other critical processes such as void formation, grain evolution and the cause of resistance drift can thereby be related to changes in structure and chemistry. Materials characterization is performed using Tecnai F30 and Titan ETEM microscopes, operating at 300kV. Both the microscopes can accept the same Protochips heating holders. The K2 direct electron detector camera equipped with the ETEM allows high-speed video recording (1600 f/s) of structural changes occurring in these materials upon heating and cooling. In this presentation, we will describe the effect of heating thin films of different thickness and composition, the changes in crystallinity and grain size, and how these changes correlate with changes in the electrical properties of the films. We will emphasize that it is always important to use low-dose and/or beam blanking techniques to distinguish changes induced by the beam from those due to the heating or introduction of an electric current. 

   more » « less
3. Self-reported and biometrically measured hot flashes in relation to ambient temperature and humidity.

   Sievert, Lynnette L ; Shreyer, Sofiya S ; Brown, Daniel E. ( December 2022 , Menopause)

   Self-reported and biometrically measured hot flashes in relation to ambient temperature and humidity Lynnette L. Sievert, PhD1, Sofiya Shreyer, MA,1 Daniel E. Brown, PhD2 1Anthropology, UMass Amherst, MA; 2Anthropology, University of Hawaii at Hilo, HI Objective: Warm ambient temperatures provoke hot flashes in the laboratory, but outside the laboratory the temperature to hot flash relationship is less consistent. A study in Bangladesh and London found that temperature and humidity at 12:00 and 18:00 were not associated with self-reported or biometrically measured hot flashes. However, in Spain and three South American countries, higher temperatures and humidities were associated with more frequent and problematic hot flashes. The study reported here differs from previous work in that we asked women to carry an ambulatory temperature and humidity monitor while wearing an ambulatory hot flash monitor. The purpose of this study was to examine the relationship between concurrent temperature, humidity, and hot flash experience. We hypothesized more frequent hot flashes with higher ambient temperatures. Design: Women aged 45 to 55 years were drawn from western Massachusetts for an ongoing cross-sectional study (n=195) from October through April (2019-2023). Exclusion criteria included use of medications that dampen hot flashes. Hot flashes were queried with a semi-structured questionnaire: During the past two weeks, have you been bothered by hot flashes (not at all, a little, somewhat, a lot)? Currently, how often do they occur (from less than 1/month to 5+ times/day, scored 0-8)? Hot flashes were also assessed by sternal skin conductance using a Biolog ambulatory hot flash monitor (3991x/1-SCL, UFI, Morro Bay, CA). Subjective hot flashes during the 24-hour study period were recorded with buttons on the hot flash monitor. Ambient temperature and humidity were continuously recorded with the GSP-6 Temperature and Humidity Data Logger Recorder (Elitech Technology, San Jose, CA). Menopausal status was categorized as pre-, peri- (change in cycle length >6 days) and post- (absence of menses for 12 months). Univariate relationships between temperature (maximum, minimum, mean), humidity (maximum, minimum, mean), and hot flashes (yes/no) were examined by t-tests. Temperature, humidity, and hot flash bothersomeness were examined by ANOVA. Pearson correlations were used to evaluate temperature, humidity, and hot flash frequencies (from the questionnaire and Biolog monitor). Logistic regression was also applied to examine temperature and humidity measures in relation to hot flashes while adjusting for menopausal status. Results: Mean ambient temperatures ranged from 16.3 to 30.1oC (mean 24.5oC, s.d. 2.8); mean humidities ranged from 18.9 to 68.6% (mean 40.8%, s.d. 9.2). Minimum temperature was positively associated with minimum (r=0.508, p<0.001) and mean (r=0.316, p<0.001) measures of humidity. Hot flash bothersomeness was described as not at all (31%), a little (23%), somewhat (23%), and a lot (24%). In univariate analyses, maximum, minimum, and mean temperatures and humidity levels were not associated with hot flashes (yes/no) or with the bothersomeness of hot flashes. Temperature measures were not correlated with current frequency of hot flashes or with the frequency of objective or subjective hot flashes during the study period. However, the current frequency of hot flashes was negatively correlated with minimum (r=-0.205, p<0.01) and mean (r=-0.196, p<0.01) levels of humidity, so that as humidity levels increased, the likelihood of hot flashes decreased. Although participants were keen to wear the monitor for 24-hours, the Biolog monitor quit during 38% of the studies. In the majority of cases, participants restarted the monitor. Compared to monitors that continued to function, monitors that quit were more likely to be worn when minimum temperatures were lower (mean 6.8oC vs. 8.9 oC, p=0.03), minimum humidity levels were lower (mean 17.2% vs. 22.6%, p<0.001), and mean humidity levels were lower (mean 37.2% vs. 43.0%, p<0.001). Conclusion: The hypothesized positive relationship between temperature and hot flashes was not supported. Instead, as humidity levels increased, the likelihood of hot flashes decreased. This preliminary study will be followed by syncing of the temperature, humidity, and hot flash data in order to study how changes in temperature and/or humidity may provoke hot flashes. Funding: NSF Grant #BCS-1848330 

   more » « less
4. Work function and temperature dependence of electron tunneling through an N-type perylene diimide molecular junction with isocyanide surface linkers

   https://doi.org/10.1039/C7NR06461F  

   Smith, Christopher E. ; Xie, Zuoti ; Bâldea, Ioan ; Frisbie, C. Daniel ( January 2018 , Nanoscale)

   Conducting probe atomic force microscopy (CP-AFM) was employed to examine electron tunneling in self-assembled monolayer (SAM) junctions. A 2.3 nm long perylene tetracarboxylic acid diimide (PDI) acceptor molecule equipped with isocyanide linker groups was synthesized, adsorbed onto Ag, Au and Pt substrates, and the current–voltage ( I – V ) properties were measured by CP-AFM. The dependence of the low-bias resistance ( R ) on contact work function indicates that transport is LUMO-assisted (‘n-type behavior’). A single-level tunneling model combined with transition voltage spectroscopy (TVS) was employed to analyze the experimental I – V curves and to extract the effective LUMO position ε l = E LUMO − E F and the effective electronic coupling ( Γ ) between the PDI redox core and the contacts. This analysis revealed a strong Fermi level ( E F ) pinning effect in all the junctions, likely due to interface dipoles that significantly increased with increasing contact work function, as revealed by scanning Kelvin probe microscopy (SKPM). Furthermore, the temperature ( T ) dependence of R was found to be substantial. For Pt/Pt junctions, R varied more than two orders of magnitude in the range 248 K < T < 338 K. Importantly, the R ( T ) data are consistent with a single step electron tunneling mechanism and allow independent determination of ε l , giving values compatible with estimates of ε l based on analysis of the full I – V data. Theoretical analysis revealed a general criterion to unambiguously rule out a two-step transport mechanism: namely, if measured resistance data exhibit a pronounced Arrhenius-type temperature dependence, a two-step electron transfer scenario should be excluded in cases where the activation energy depends on contact metallurgy. Overall, our results indicate (1) the generality of the Fermi level pinning phenomenon in molecular junctions, (2) the utility of employing the single level tunneling model for determining essential electronic structure parameters ( ε l and Γ ), and (3) the importance of changing the nature of the contacts to verify transport mechanisms. 

   more » « less
5. Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

   https://doi.org/10.5061/dryad.8sf7m0cpx  

   Hussain, Mir Zaman ; Hamilton, Stephen ; Robertson, G. Philip ; Basso, Bruno ( January 2021 , Dryad)

   Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 

   more » « less