Antiferromagnetic spin chain behavior and a transition to 3D magnetic order in Cu(D,L-alanine)2: Roles of H-bonds

We study the spin chain behavior, a transition to 3D magnetic order and the magnitudes of the exchange interactions for the metal-amino acid complex Cu(D,L-alanine)2.H2O, a model compound to investigate exchange couplings supported by chemical paths characteristic of biomolecules. Thermal and magnetic data were obtained as a function of temperature (T) and magnetic field (B0). The magnetic contribution to the specific heat, measured between 0.48 and 30 K, displays above 1.8 K a 1D spin-chain behavior that can be fitted with an intrachain antiferromagnetic (AFM) exchange coupling constant 2J0 = (-2.12 $\pm$ 0.08) cm$^{-1}$, between neighbor coppers at 4.49 {\AA} along chains connected by non-covalent and H-bonds. We also observe a narrow specific heat peak at 0.89 K indicating a phase transition to a 3D magnetically ordered phase. Magnetization curves at fixed T = 2, 4 and 7 K with B0 between 0 and 9 T, and at T between 2 and 300 K with several fixed values of B0 were globally fitted by an intrachain AFM exchange coupling constant 2J0 = (-2.27 $\pm$ 0.02) cm$^{-1}$ and g = 2.091 $\pm$ 0.005. Interchain interactions J1 between coppers in neighbor chains connected through long chemical paths with total length of 9.51 {\AA} are estimated within the range 0.1 < |2J1| < 0.4 cm$^{-1}$, covering the predictions of various approximations. We analyze the magnitudes of 2J0 and 2J1 in terms of the structure of the corresponding chemical paths. The main contribution in supporting the intrachain interaction is assigned to H-bonds while the interchain interactions are supported by paths containing H-bonds and carboxylate bridges, with the role of the H-bonds being predominant. We compare the obtained intrachain coupling with studies of compounds showing similar behavior and discuss the validity of the approximations allowing to calculate the interchain interactions.Comment: 10 pages, 4 figure

Interaction-induced charge and spin pumping through a quantum dot at finite bias

We investigate charge and spin transport through an adiabatically driven, strongly interacting quantum dot weakly coupled to two metallic contacts with finite bias voltage. Within a kinetic equation approach, we identify coefficients of response to the time-dependent external driving and relate these to the concepts of charge and spin emissivities previously discussed within the time-dependent scattering matrix approach. Expressed in terms of auxiliary vector fields, the response coefficients allow for a straightforward analysis of recently predicted interaction-induced pumping under periodic modulation of the gate and bias voltage [Phys. Rev. Lett. 104, 226803 (2010)]. We perform a detailed study of this effect and the related adiabatic Coulomb blockade spectroscopy, and, in particular, extend it to spin pumping. Analytic formulas for the pumped charge and spin in the regimes of small and large driving amplitude are provided for arbitrary bias. In the absence of a magnetic field, we obtain a striking, simple relation between the pumped charge at zero bias and at bias equal to the Coulomb charging energy. At finite magnetic field, there is a possibility to have interaction-induced pure spin pumping at this finite bias value, and generally, additional features appear in the pumped charge. For large-amplitude adiabatic driving, the magnitude of both the pumped charge and spin at the various resonances saturate at values which are independent of the specific shape of the pumping cycle. Each of these values provide an independent, quantitative measurement of the junction asymmetry.Comment: 17 pages, 8 figure

Laser-induced effects on the electronic features of graphene nanoribbons

We study the interplay between lateral confinement and photon-induced processes on the electronic properties of illuminated graphene nanoribbons. We find that by tuning the device setup (edges geometries, ribbon width and polarization direction), a laser with frequency {\Omega} may either not affect the electronic structure, or induce bandgaps or depletions at \hbar {\Omega}/2, and/or at other energies not commensurate with half the photon energy. Similar features are also observed in the dc conductance, suggesting the use of the polarization direction to switch on and off the graphene device. Our results could guide the design of novel types of optoelectronic nano-devices.Comment: 4 pages, 3 figure

Non-perturbative laser effects on the electrical properties of graphene nanoribbons

The use of Floquet theory combined with a realistic description of the electronic structure of illuminated graphene and graphene nanoribbons is developed to assess the emergence of non-adiabatic and non-perturbative effects on the electronic properties. Here, we introduce an efficient computational scheme and illustrate its use by applying it to graphene nanoribbons in the presence of both linear and circular polarization. The interplay between confinement due to the finite sample size and laser-induced transitions is shown to lead to sharp features on the average conductance and density of states. Particular emphasis is given to the emergence of the bulk limit response.Comment: 14 pages, 8 figures, to appear in J. Phys.: Condens. Matter, special issue on "Ultrafast and nonlinear optics in carbon nanomaterials

Role of coherence in quantum-dot-based nanomachines within the Coulomb blockade regime

During the last decades, quantum dots within the Coulomb blockade regime of transport have been proposed as essential building blocks for a wide variety of nanomachines. This includes thermoelectric devices, quantum shuttles, quantum pumps, and even quantum motors. However, in this regime, the role of quantum mechanics is commonly limited to provide energy quantization while the working principle of the devices is ultimately the same as their classic counterparts. Here, we study quantum-dot-based nanomachines in the Coulomb blockade regime, but in a configuration that resembles the quantum mechanics' paradigmatic experiment: the double-slit. We show that the coherent superposition of states appearing in this configuration can be used as the basis for different forms of "true" quantum machines. We analyze the efficiency of these machines against different non-equilibrium sources (bias voltage, temperature gradient, and external driving) and the factors that limit it, including decoherence and the role of the different orders appearing in the adiabatic expansion of the charge/heat currents.Comment: 16 pages, 7 figure

Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry

OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc). METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers. RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group. CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies

A Comparison of indexes to estimate corn S uptake and S mineralization in the field

The development of simple predictors of sulfur (S) mineralization and its correlation with field-derived data may help improving corn S availability diagnosis. The objectives of this study were (1) to compare methods to estimate soil S mineralization, (2) to develop a model to predict soil S mineralization from S mineralization indexes and edaphic variables, and (3) to predict fieldgrown corn S uptake (Suptake) and apparent S mineralization (Smin-app) from different S mineralization indexes and edaphicclimatic variables.We evaluated 26 experimental sites where we measured edaphic variables as soil organic C (SOC), organic C in the particulate fraction (C-PF), S mineralization potential (Smin-10wk), S mineralized during a short-term (7 days) aerobic incubation + initial inorganic S (Smin-7d+ Sinorg), and N mineralized during a short-term (7 days) anaerobic incubation (Nan). Additionally, 18 field experiments were carried out to quantify Suptake and Smin-app. TheC-PF, Smin-7d+ Sinorg, Nan, and SOC were variables significantly correlated with Smin-10wk (r = 0.89, 0.89, 0.88, and 0.85, respectively). We developed a simple model to predict Smin-10wk from selected edaphic variables (Smin-10wk= 0.038*Nan + 0.106*SOC + 0.74; Ra 2 = 0.87). The Smin-10wk, C-PF, and Smin-7d+ Sinorg showed a liner-plateau association with Suptake (R2 = 0.73, 0.53, and 0.48, respectively). We modified the method to estimate Smin-app to account for S losses (Smin-app (modified)) and developed a model to predict Smin-app (modified) from CPF (Smin-app (modified)= 4.65*C-PF + 9.86; R2 = 0.62) or Smin-10wk (Smin-app (modified)= 3.0*Smin-10wk+ 7.4; R2 = 0.54). Our results demonstrate that S mineralization indexes can be used to predict corn S availability under field conditions.EEA BalcarceFil: Carciocchi, Walter Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Wyngaard, Nicolás. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Divito, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Cabrera, Miguel L. University of Georgia. Crop and Soil Sciences Department; Estados UnidosFil: Reussi Calvo, Nahuel Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Echeverria, Hernan Eduardo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentin

Attainable yield and soil texture as drivers of maize response to nitrogen: a synthesis analysis for Argentina

The most widely used approach for prescribing fertilizer nitrogen (N) recommendations in maize (Zea Mays L.) in Argentina is based on the relationship between grain yield and the available N (kg N ha−1), calculated as the sum of pre-plant soil NO3--N at 0−60 cm depth (PPNT) plus fertilizer N (Nf). However, combining covariates related to crop N demand and soil N supply at a large national scale remains unexplored for this model. The aim of this work was to identify yield response patterns associated to yield environment (crop N demand driver) and soil texture (soil N supply driver). A database of 788 experiments (1980−2016) was gathered and analyzed combining quadratic-plateau regression models with bootstrapping to address expected values and variability on response parameters and derived quantities. The database was divided into three groups according to soil texture (fine, medium and coarse) and five groups based on the empirical distribution of maximum observed yields (from Very-Low = 13.1 Mg ha−1) resulting in fifteen groups. The best model included both, attainable yield environment and soil texture. The yield environment mainly modified the agronomic optimum available N (AONav), with an expected increase rate of ca. 21.4 kg N Mg attainable yield−1, regardless of the soil texture. In Very-Low yield environments, AONav was characterized by a high level of uncertainty, related to a poor fit of the N response model. To a lesser extent, soil texture modified the response curvature but not the AONav, mainly by modifying the response rate to N (Fine > Medium > Coarse), and the N use efficiencies. Considering hypothetical PPNT levels from 40 to 120 kg N ha−1, the expected agronomic efficiency (AENf) at the AONav varied from 7 to 31, and 9–29 kg yield response kg fertilizer N (Nf)−1, for Low and Very-High yield environments, respectively. Similarly, the expected partial factor productivity (PFPNf) at the AONav ranged from 62 to 158, and 55–99 kg yield kg Nf−1, for the same yield environments. These results highlight the importance of combining attainable yield environment and soil texture metadata for refining N fertilizer recommendations. Acknowledging the still low N fertilizer use in Argentina, space exists to safely increasing N fertilizer rates, steering the historical soil N mining profile to a more sustainable agro-environmental scenario in the Pampas.Fil: Correndo, Adrián A.. Kansas State University; Estados UnidosFil: Gutiérrez Boem, Flavio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: García, Fernando O.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Alvarez, Carolina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Álvarez, Cristian. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Angeli, Ariel. I+D CREA; ArgentinaFil: Barbieri, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Barraco, Mirian Raquel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Berardo, Angel. Laboratorio de Suelo S.a.; ArgentinaFil: Boxler, Miguel. Private Consultant; ArgentinaFil: Calviño, Pablo Antonio. Private Consultant; ArgentinaFil: Capurro, Julia E.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Carta, Héctor. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Diaz Zorita, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Pampa. Facultad de Agronomía; ArgentinaFil: Díaz Valdéz, Santiago. Bayer Crop Science; ArgentinaFil: Echeverría, Hernán E.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Espósito, Gabriel Pablo. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Ferrari, Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ferraris, Gustavo Nestor. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Gambaudo, Sebastian Pedro. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentina. Private Consultant; ArgentinaFil: Gudelj, Vicente. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ioele, Juan P.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Melchiori, Ricardo J. M.. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Molino, Josefina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Orcellet, Juan Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Pagani, Agustin. Clarion Inc.; ArgentinaFil: Pautasso, Juan Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Laboratorio de Suelo S.a.; ArgentinaFil: Redel, Matías. Private Consultant; ArgentinaFil: Rillo, Sergio. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Rimski-korsakov, Helena. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Sainz Rozas, Hernan Rene. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Saks, Matías. Bunge Argentina S.A; ArgentinaFil: Tellería, María Guadalupe. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Ventimiglia, Luis. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Zorzín, Jose L.. Private Consultant; ArgentinaFil: Zubillaga de Sanahuja, María de Las Mercedes. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Salvagiotti, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentin