<p>Aluminum is present throughout the environment and in many industrial processes and consumer goods. While very useful in everyday lives, it has no inherent biological functions in humans. High quantities in the human body can be toxic, resulting a range of skeletal, neurological, and hematopoietic effects. A system has been developed to analyze aluminum using the neutron activation analysis (NAA) technique in vivo. NAA was performed with a transportable neutron generator as a neutron source and a high purity germanium (HPGe) detector for spectroscopy. The neutron generator and HPGe detector were completely modelled in MCNP6. Measurements were carried out to evaluate the accuracy of the MCNP6 simulations and to determine the detection capabilities of the system for aluminum. Simulations were also conducted to determine the acceptability of radiation dose to subjects undergoing analysis. The detection limit for the system was evaluated using skeletal bone as a long-term aluminum biomarker. The detection limit was determined to be 3.41 x 10¹ μg of Al per g of dry bone for an irradiation time of six minutes. This detection level is below a point at which physiological effects have been observed in humans. A lower detection level was demonstrated to be possible with a longer irradiation time. The radiation absorbed dose was determined to be 7.30 mGy for an irradiation of six minutes. The system can therefore be utilized as a potential screening and monitoring tool for high skeletal burdens of aluminum that may lead to physiological effects.</p> <p>The simulation and calculation techniques developed herein were applied to a set of human subject data that were acquired for a purpose other than evaluating aluminum. The human subject data included both bone Al from NAA and fingernail Al from mass spectrometry measurements. No significant aluminum signals were observed when assessing the in vivo NAA spectra data. Through simulation and calculation, it was demonstrated that the NAA experimental parameters resulted in an elevated detection limit for aluminum that is above Al skeletal loads observed in healthy individuals. The elevated detection limit prevented the in vivo detection of aluminum in a healthy population, thus confirming the NAA results. </p>

<div>We propose an order parameter theory of the quantum Hall nematic in high fractional Landau levels in terms of an Ising description. This new model solves a couple of extant problems in the literature: (1) The low-temperature behavior of the measured resistivity anisotropy is captured better by our model than previous theoretical treatments based on the electron nematic having XY symmetry. (2) Our model allows for the development of true long-range order at low temperature, consistent with the observation of anisotropic low-temperature transport.<br></div><div><div> We furthermore propose new experimental tests based on hysteresis that can distinguish whether any two-dimensional electron nematic is in the XY universality class (as previously proposed in high fractional Landau levels), or in the Ising universality class (as we propose). Given the growing interest in electron nematics in many materials, we expect our proposed test of universality class to be of broad interest.</div><div> </div><div> Whereas the XY model in two dimensions does not have a long-range ordered phase, the addition of uniaxial random field disorder induces a long-range ordered phase in which the spontaneous magnetization points perpendicular to the random field direction, via an order-by-disorder transition. We have shown that this spontaneous magnetization is robust against a rotating driving field, up to a critical driving field amplitude. Thus we have found evidence for a new non-equilibrium phase transition that was unknown before in this model. Moreover, we have discovered an incredible anomaly at this nonequilibrium phase transition: the critical region is accompanied by a cascade of period multiplication events. This physics is reminiscent of the period bifurcation cascade signaling the transition to chaos in nonlinear systems, and of the approach to the irreversibility transition in models of yield in amorphous solids~\cite{reichhardt-dahmen,leishangthem_yielding_2017}. This period multiplication cascade is surprising to be present in a statistical mechanics model, and suggests that the non-equilibrium transition as a function of driving field amplitude is part of a larger class of transitions in dynamical systems.</div><div> Moreover, we show that this multi-period behavior represents a new emergent classical discrete time-crystal, since the new period is robust against changes to initial conditions and low-temperature fluctuations over hundreds of driving period cycles.</div><div><br></div><div> We expect this work to be of broad interest, further encouraging cross-fertilization between the rapidly growing field of time-crystals with the well-established fields of nonequilibrium phase transitions and dynamical systems.</div><div> </div><div> Geometrical configurations gave us a better understanding of the multi-period behavior of the limit-cycles.</div><div> Moreover, surface probes are continually evolving and generating vast amounts of spatially resolved data of quantum materials, which reveal a lot of detail about the microscopic and macroscopic properties of the system. <br></div><div> Materials undergoing a transition between two distinct states, phase separate.</div><div> These phase-separated regions form intricate patterns on the observable surface, which can encode model-specific information, including interaction, dimensionality, and disorder. </div><div> While there are rigorous methods for understanding these patterns, they turn out to be time-consuming as well as requiring expertise. </div><div> We show that a well-tuned machine learning framework can decipher this information with minimal effort from the user.</div><div> We expect this to be widely used by the scientific community to fast-track comprehension of the underlying physics in these materials.</div><div><br></div></div>

Recent experimental studies indicate that polymeric structures with a well-adjusted balance of amphiphilic parts may translocate through self-assembled phospholipid bilayers and enhance the passive trans-membrane transport of smaller molecules. Using a coarse grained lattice Monte Carlo model with explicit solvent we investigate self-assembled lipid bilayers interacting with a linear polymer chain under variation of the hydrophobicity of the chain. Here, we focus on the relationship between the chain's hydrophobicity and its translocation behavior through the membrane as well as induced membrane perturbations. We show, that there is an adsorption transition of the polymer at the bilayer interface, where effectively the solvent phase and the tail phase of the bilayer are equally repulsive for the polymer. Close to this adsorption threshold of the polymer both the translocation probability of the polymer as well as the permeability of the membrane with respect to solvent are enhanced significantly. The frequency of polymer translocation events can be understood quantitatively assuming a simple diffusion along a one-dimensional free energy profile, which is controlled by the effective lipophilicity of the chain and the tail-packing in the bilayer's core. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

<p>Cereal rye (<i>Secale cereale</i>, L., CR) is the most commonly utilized cover crop species within the United States. Yet, the total land area planted to CR on an annual basis remains relatively low despite its numerous proven environmental benefits. The relatively low rates of CR adoption could be due to a dearth of knowledge surrounding certain agronomic and economic components of CR adoption. Currently, there exists knowledge gaps within the scientific literature regarding CR performance, N cycling, and associated economic risk. <a>Thus, to address the above-mentioned knowledge gaps, three individual studies were developed to: i) investigate the fate of scavenged CR nitrogen (N) amongst soil N pools, ii) assess the suitability of visible-spectrum vegetation indices (VIs) to predict CR biomass and nutrient accumulation (BiNA), and iii) characterize the economic risk of CR adoption at a regional scale over time.</a></p> <p>In the first study, ¹⁵N, a stable isotope of N, was used in an aerobic incubation to track the fate of CR root and shoot N among the soil microbial biomass, inorganic, and organic N pools, as well as explore CR N bioavailability over a simulated corn growing season. In this study, the C:N ratio of the shoot residues was 16:1 and the roots was 31:1 and differences in residue quality affected the dynamics of CR N release from each residue type. On average, 14% of whole plant CR N was recovered in the soil inorganic N pool at the final sample date. Correspondingly, at the final sampling date 53%, 33%, and less than 1% of whole plant CR N was recovered as soil organic N, undecomposed residue, and as microbial biomass N, respectively. Most CR N remained unavailable to plants during the first cash crop growing season subsequent to termination. This knowledge could support the advancement of N fertilizer management strategies for cropping systems containing cereal rye.</p> <p>In the second study, a commercially available unmanned aerial vehicle (UAV) outfitted with a standard RGB sensor was used to collect aerial imagery of growing CR from which visible-spectrum VIs were computed. Computed VIs were then coupled with weather and geographic data using linear multiple regression to produce prediction models for CR biomass, carbon (C), N, phosphorus (P), potassium (K), and sulfur (S). Five visible-spectrum VIs (Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Green (ExG)) were evaluated and the results determined that MGRVI was the best predictor for CR biomass, C, K, and S and that RGBVI was the best predictor for CR N and P. Furthermore, the final prediction models for the VIs selected as the best predictors developed in this study performed satisfactorily in the prediction of CR biomass, C, N, P, K, and S producing adjusted R² values of 0.79, 0.79, 0.75, 0.81, 0.81, and 0.78, respectively. The results of this study have the potential to aid producers in making informed decisions regarding CR and fertility management. </p> <p>In the final study, agronomic data for corn and soybean cropping systems with and without CR was collected from six states (Illinois, Indiana, Iowa, Minnesota, Missouri, and Wisconsin) and used within a Monte-Carlo stochastic simulation to characterize the economic risk of adopting CR at a regional scale over time. The results of this study indicate that average net returns to CR are always negative regardless of CR tenure primarily due to added costs and increased variability in cash crop grain yields associated with CR adoption. Further, the results demonstrate that the additional risk assumed by adopting CR is not adequately compensated for with current CR adoption incentive programs and that the risk premium necessary can be 1.7 to 15 times greater than existing incentive payments. Knowledge gained from this study could be used to reimagine current incentive programs to further promote adoption of CR.</p>