Simulation Studies on External Electric Field-Dependent Anisotropy of Two-Dimensional Fullerene Nanomaterials qTP C ₆₀ and qHP C ₆₀: Implications for Photoelectric Nanodevices

The method of dispersion correction as an add-on to standard Kohn-Sham density functional theory (DFT-D) has been refined regarding higher accuracy, broader range of applicability, and less empiricism. The main new ingredients are atom-pairwise specific dispersion coefficients and cutoff radii that are both computed from first principles. The coefficients for new eighth-order dispersion terms are computed using established recursion relations. System (geometry) dependent information is used for the first time in a DFT-D type approach by employing the new concept of fractional coordination numbers (CN). They are used to interpolate between dispersion coefficients of atoms in different chemical environments. The method only requires adjustment of two global parameters for each density functional, is asymptotically exact for a gas of weakly interacting neutral atoms, and easily allows the computation of atomic forces. Three-body nonadditivity terms are considered. The method has been assessed on standard benchmark sets for inter- and intramolecular noncovalent interactions with a particular emphasis on a consistent description of light and heavy element systems. The mean absolute deviations for the S22 benchmark set of noncovalent interactions for 11 standard density functionals decrease by 15%-40% compared to the previous (already accurate) DFT-D version. Spectacular improvements are found for a tripeptide-folding model and all tested metallic systems. The rectification of the long-range behavior and the use of more accurate C(6) coefficients also lead to a much better description of large (infinite) systems as shown for graphene sheets and the adsorption of benzene on an Ag(111) surface. For graphene it is found that the inclusion of three-body terms substantially (by about 10%) weakens the interlayer binding. We propose the revised DFT-D method as a general tool for the computation of the dispersion energy in molecules and solids of any kind with DFT and related (low-cost) electronic structure methods for large systems.

In the soil N cycle, the rate-limiting step of microbial decomposition of organic matter is the depolymerization of proteins to oligopeptides and amino acids by extracellular proteases, rather than the subsequent mineralization of amino acids to ammonium1 2 3. The products of the depolymerization process can be directly and rapidly utilized by microbes as both energy and nutrient sources4 5. However, most studies on soil N cycling have focused on N mineralization, rather than on the partitioning of organic N between incorporation into microbial biomass and release as ammonium. A thorough understanding of microbial nitrogen use will thus strongly improve our knowledge about how heterotrophic microbes control soil inorganic N availability, and thereby regulate ecosystem functions, such as plant productivity. Microbial nitrogen use efficiency (NUE) reflects the partitioning of organic N taken up (U N), between N incorporation into microbial biomass (growth; G N) and N recycling to the environment as inorganic N (mainly as ammonium; N mineralization, M N; see also Supplementary Fig. 1; Supplementary Note 1 for integration of NUE in the microbial N balance), which can be formulated as: High NUE expresses efficient N sequestration of the N taken up into microbial biomass and the concomitant release of only a small fraction of that N back to the environment as inorganic N (that is, low N mineralization). In contrast, low NUE indicates that less N is converted to biomass, whereas a relatively large fraction of the organic N taken up is released as ammonium (that is, high N mineralization). While microbial NUE has not been studied so far, microbial carbon use efficiency (CUE; that is, the efficiency by which organisms convert organic C into biomass C) has been the focus of many studies in soil biogeochemistry, and has been established as a main factor determining microbial growth, nutrient immobilization and ultimately soil C sequestration6 7. Microbial C metabolism is a highly regulated interplay between anabolic (providing the biochemical basis for growth) and catabolic processes (for example, respiration)8. The metabolic control of microbial anabolism and catabolism therefore effectively drives changes in microbial CUE. Empirical as well as modelling studies demonstrated stoichiometric and environmental controls on microbial CUE7 9 10 11 12, and illustrated its importance for a range of different biogeochemical processes in marine and terrestrial ecosystems6 11 13. Although NUE has been taken into account in some theoretical and conceptual models of organic matter decomposition14 15, its plasticity and regulation have not yet been empirically tested, despite the critical importance of N as a limiting nutrient for terrestrial primary productivity16. However, similar to microbial C metabolism, N metabolism is central to the physiological functioning of microbes and therefore has been shown to be strongly controlled8 17. The balance between anabolic processes (such as protein biosynthesis and growth) and catabolic processes (that eventually lead to the exudation of catabolic products in excess of microbial N demand) in microbes is highly regulated; we can thus predict microbial NUE to be flexible and regulated, similar to CUE. Moreover, NUE is expected to follow its own controls, although controls of microbial CUE and NUE may overlap due to cross-talk between microbial C and N metabolism8 18, and given that elements other than C or N (for example, P) can become limiting for microbial growth, in which case both CUE and NUE must be regulated independently to adjust biomass to resource stoichiometry. Although microbial CUE and NUE are expected to be flexible and may have divergent controls, the mass balance equation predicts that microbial CUE, NUE and biomass C:N (B C:N) are interrelated and dependent on resource C:N (R C:N), and therefore in strictly homeostatic microbes these parameters are not fully independent. Ecological stoichiometry uses elemental ratios and the concept of stoichiometric invariance (homeostasis) to predict nutrient retention and biomass production, from subcellular to ecosystem scales14. The theory of ecological stoichiometry suggests that at low substrate C:N ratios (N-sufficient conditions), strictly homeostatic organisms have low NUE but high CUE14. In contrast, at high substrate C:N ratios (N-deficiency) they are expected to lower their CUE while increasing their NUE. A key approach for understanding nutrient limitation in decomposers is therefore the threshold elemental ratio (TER) that expresses the elemental ratio at which metabolic control of an ecological system switches from C limitation to nutrient (N or P) limitation19 20 21. TERC:N denotes the threshold of the resource C:N below which N will be in excess in relation to the organism’s N requirements, and consumers therefore become C or energy limited, and above which N will limit organismic growth. By integrating the regulation of NUE and CUE by resource C:N into the TER concept, we expect microbial NUE to decrease below the TERC:N when N is in excess, and C is the limiting element (Fig. 1). In contrast, above this threshold microbial communities are expected to be N limited, while C is in excess, and NUE should consequently reach a maximum accompanied by down-regulation of CUE. Non-homeostatic decomposers, in turn, may alter their cellular composition to limit the stoichiometric imbalance to their resource14. However, elemental imbalances are likely to emerge, even when microbial biomass C:N varies, because the variability in their cellular composition is limited by physiological bounds, and adjustments in CUE and NUE are therefore expected to occur. Towards a more mechanistic understanding of the soil N cycle, we here explore the ability of terrestrial decomposer communities to adjust their N metabolism (NUE) to N availability. We analyse microbial NUE by measuring gross fluxes of organic N uptake and ammonium excretion (N mineralization) in soils and decomposing plant litter varying in their C:N ratios. By applying these simplifying assumptions of ecological stoichiometry14, we are able to capture the metabolic flexibility of microbial communities across different substrate types and qualities. Our results show that microbial NUE varies considerably among microbial communities in different substrate types, and that the C:N imbalance between resource and microbial biomass is also compensated by adjustments in microbial NUE and not solely by flexibility in microbial CUE and biomass C:N. Results The magnitude of microbial NUE Microbial NUE in different substrate types (that is, decomposing plant litter, mineral and organic soil horizons) varied between 0.15 and 1 (Fig. 2a). Microbial NUE in soil exhibited greater variation (coefficient of variance, CV=30.7% and 17.8% for mineral and organic soil horizon, respectively) than in plant litter (CV=8.9%). Microbial NUE was lower in mineral soil horizons (0.70±0.21 s.d.; n=36) compared with organic soil horizons (0.83±0.15 s.d.; n=19) and plant litter (0.89±0.08 s.d.; n=38), although this difference was significant only between the mineral soil horizon and plant litter (Kruskal–Wallis test followed by Dunn’s test, H(2)=18.2, P 0.05; organic versus mineral, Q=2.02, P>0.05). To meet their N demand, microbial communities also take up inorganic N that becomes available through N mineralization and nitrification. In our study, ammonium concentrations were on average 14.1 (±7.1 s.d.) in plant litter, 18.9 (±39.8 s.d.) in organic and 5.3 μg N g−1 dry weight (±6.7 s.d.) in mineral soil horizons. Average nitrate concentrations ranged between 4.1 (±2.1 s.d.) in plant litter, 1.4 (±3.0 s.d.) in organic and 1.0 μg N g−1 dry weight (±0.7 s.d.) in mineral soil horizons. For plant litter samples, we also measured gross nitrification and nitrate consumption rates in addition to gross ammonification and ammonium consumption rates in order to include inorganic N assimilation in microbial NUE (NUE+inorg). When accounting for inorganic N assimilation, we found an average NUE+inorg of 0.81 (±0.09 s.d.). Microbial NUE and NUE+inorg were strongly correlated with a slope close to 1 (NUE +inorg=1.012 × NUE−0.089; R 2=0.801; P 8, which points at the pivotal role of free amino acids as the major form of N to meet the microbial N demand39. Therefore, we measured gross rates of amino-acid consumption to determine U N. M N was determined by the gross N mineralization rate (that is, gross production of NH4 +). The term U N−M N represents the fraction of organic N incorporated into microbial biomass. Gross amino-acid consumption rates were determined using the isotope pool dilution technique according to ref. 39. Litter and soil samples were slightly differently treated owing to difference in physical properties and in total free amino-acid pool size. We added 4–40 μg (litter) or 1.25–5 μg (soil) of uniformly-15N-labelled amino-acid mix (98 atom% 15N, 20 amino acids) to 2 g (litter) or 1–4 g (soil) fresh material in triplicates (litter) or duplicates (soil). The samples were incubated at 15 °C (litter, temperate grassland soil and boreal forest soil) or 7 °C (tundra soil) and the assays were terminated after 2, 10 and 20 min (litter) or 10 and 30 min (soil) by adding 14 ml (litter) or 19.5 ml (soil) 10 mM CaSO4 containing formaldehyde (end concentration 3.7%), which effectively inhibited protease activity without lysing microbial cells. Concentrations and 15N:14N ratios of individual amino acids were determined by compound-specific isotope analysis via Gas chromatography–mass spectrometry. To quantify gross N mineralization and nitrification, quadruplicate samples of moist soil (2–4 g) were treated by addition of 2.5 ml 0.1 mM NH4Cl or KNO3 (10 atom% 15N) and litter samples (1.5 g) by addition of 0.5–1 ml 0.125 mM NH4Cl or KNO3 (10 atom% 15N), respectively. Assays were terminated after 4 and 24 h by extraction with 13 ml or 12.5 ml 2 M KCl for soil and litter samples, respectively. The samples were shaken (soil samples, 30 min; litter samples, 60 min) and filtered through ash-free cellulose filter paper. To stabilize the soil extracts, 20 μl of 5 mM phenylmercuric acetate were added and samples were frozen until further processing. Ammonium and nitrate were isolated by microdiffusion with sequential addition of MgO and Devarda’s alloy46. Acid traps were dried and analysed for N content and atom% 15N by a continuous-flow IRMS consisting of an elemental analyzer (EA 1110, CE Instruments) coupled via a ConFlo III interface (Finnigan MAT, Thermo Fisher) to the IRMS (DeltaPLUS, Finnigan MAT, Thermo Fisher). Data and statistical analyses In order to analyse the effect of substrate type (decomposing plant litter, mineral and organic soil) on microbial NUE, we used the Kruskal–Wallis test (assumptions for parametric procedure were not met) followed by a non-parametric multiple comparison test (Dunn’s test), which compares the difference in the sum of ranks between the groups with the expected average difference. Across all substrate types, the relationship between NUE and resource C:N was first analysed using CART analysis, which is inherently non-parametric. In other words, no assumptions are made regarding the underlying distribution of values of the predictor variables. Optimal tree size was determined using the 1-SE rule47. Following the CART analysis, we used piece-wise linear regression analysis to more thoroughly investigate the relationship that emerged in the CART analysis. In piece-wise regression models that are effective in modelling abrupt thresholds, two or more lines are joined at unknown point(s), called ‘break point(s)’, representing threshold(s)48. Assumptions for the piece-wise regression model (normal distribution and homoscedasticity of residuals) were met. We used a saturating nonlinear model to describe the relationship between NUE and C:N imbalance. For the litter decomposition experiment, simple linear regression analyses were used to relate NUE to resource C:N. The Kruskal–Wallis test, Dunn’s test and piece-wise regression analysis were performed in Sigmaplot 11.0 (Systat Software Inc., San Jose, CA, USA; www.sigmaplot.com), linear and nonlinear regression analyses in Statgraphics 5.0 (Statistical Graphics Inc., Rockville, MD, USA; www.statgraphics.com). CART analysis was performed with the rpart library in R (version 2.12.1)49 50. Author contributions W.W., A.R. and S.Z.-B. designed research. M.M., I.H., J.S., B.W., M.W., L.F., A.K., M.T. and K.M.K. performed the experiments. M.M., F.H. and W.W. analysed the data. M.M., W.W. and A.R. wrote the manuscript. Additional information How to cite this article: Mooshammer, M. et al. Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling. Nat. Commun. 5:3694 doi: 10.1038/ncomms4694 (2014). Supplementary Material Supplementary Information Supplementary Figures 1-2, Supplementary Note 1 and Supplementary References