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We hereby propose and theoretically investigate a new scheme for simultaneous generation and manipulation of terahertz (THz) waves through difference frequency generation facilitated by a metasurface-assisted nonlinear leaky waveguide antenna. The proposed structure integrates a nonlinear optical waveguide, composed of multiple Al_xGa_1−xAs layers, with a THz leaky waveguide, wherein a bianisotropic metasurface realizes the radiating aperture. By explicitly utilizing the electric, magnetic, and magnetoelectric coupling responses of the metasurface, we demonstrate that the generated THz wave can be induced as a tightly confined, phase-matched guided mode for efficient generation of the THz wave. Additionally, this approach allows the THz wave to be transformed into a directive beam, radiating at a user-defined leakage rate and direction. Our numerical analyses suggest that THz beams ranging from 2.85 THz to 3.05 THz can be steered from 4^∘to 40^∘, utilizing the inherent beam-steering capabilities of the leaky-waveguide antenna. Within this THz frequency spectrum, the phase matching condition is achieved by adjusting the optical wavelengths between 1.6μmand 1.52μm. In particular, the nonlinear conversion efficiency is 2.9 × 10⁻⁵[1/W] at 3 THz.

 

Abstract Recent experiments continue to find evidence for a liquid-liquid phase transition (LLPT) in supercooled water, which would unify our understanding of the anomalous properties of liquid water and amorphous ice. These experiments are challenging because the proposed LLPT occurs under extreme metastable conditions where the liquid freezes to a crystal on a very short time scale. Here, we analyze models for the LLPT to show that coexistence of distinct high-density and low-density liquid phases may be observed by subjecting low-density amorphous (LDA) ice to ultrafast heating. We then describe experiments in which we heat LDA ice to near the predicted critical point of the LLPT by an ultrafast infrared laser pulse, following which we measure the structure factor using femtosecond x-ray laser pulses. Consistent with our predictions, we observe a LLPT occurring on a time scale < 100 ns and widely separated from ice formation, which begins at times >1 μs. 

Motivated by regulating/eliminating the population of herbivorous pests, we investigate a discrete-time plant–herbivore model with two different constant control strategies (removal versus reduction), and formulate the corresponding optimal control problems when its dynamics exhibits varied types of bi-stability and fluctuating environments. We provide basic analysis and identify the critical factors to characterize the optimal controls and the corresponding plant–herbivore dynamics such as the control upper bound (the effectiveness level of the implementation of control measures) and the initial conditions of the plant and herbivore. Our results show that optimal control could be easier when the model has simple dynamics such as stable equilibrium dynamics under constant environment or the model exhibits chaotic dynamics under fluctuating environments. Due to bistability, initial conditions are important for optimal controls. Regardless of with or without fluctuating environments, initial conditions taken from the near the boundary makes optimal control easier. In general, the pest is hard to be eliminated when the control upper bound is not large enough. However, as the control upper bound is increased or the initial conditions are chosen from near the boundary of the basin of attractions, the pest can be manageable regardless of the fluctuating environments.

 

Abstract The use of silicon nanoparticles for lithium-ion batteries requires a precise control over both their average size and their size distribution. Particles larger than the generally accepted critical size of 150 nm fail during lithiation because of excessive swelling, while very small particles (<10 nm) inevitably lead to a poor first cycle coulombic efficiency because of their excessive specific surface area. Both mechanisms induce irreversible capacity losses and are detrimental to the anode functionality. In this manuscript we describe a novel approach for enhanced growth of nanoparticles to ∼20 nm using low-temperature flow-through plasma reactors via pulsing. Pulsing of the RF power leads to a significant increase in the average particle size, all while maintaining the particles well below the critical size for stable operation in a lithium-ion battery anode. A zero-dimensional aerosol plasma model is developed to provide insights into the dynamics of particle agglomeration and growth in the pulsed plasma reactor. The accelerated growth correlates with the shape of the particle size distribution in the afterglow, which is in turn controlled by parameters such as metastable density, gas and electron temperature. The accelerated agglomeration in each afterglow phase is followed by rapid sintering of the agglomerates into single-crystal particles in the following plasma-on phase. This study highlights the potential of non-thermal plasma reactors for the synthesis of functional nanomaterials, while also underscoring the need for better characterization of their fundamental parameters in transient regimes. 

Flow recession analysis, relating dischargeQand its time rate of change −dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the recession plot, the plot of −dQ/dtversusQ, typically form a wide point cloud due to noise and hysteresis in the storage‐discharge relationship, and it is still unclear what information we can extract from the plot and how to understand the information. There seem to be two contrasting approaches to interpret the plot. One emphasizes the importance of the ensemble characteristics of many recessions (i.e., the lower envelope or a measure of central tendency), and the other highlights the importance of the event scale analysis and questions the meaning of the ensemble characteristics. We examine if those approaches can be reconciled. We utilize a machine learning tool to capture the point cloud using the past trajectory of daily discharge. Our model results for a catchment show that most of the data points can be captured using 5 days of past discharge. We show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage‐discharge relationship can be estimated based on the attractor.

 

Process‐based modeling of soil water movement with the Richards equation requires the description of soil hydraulic material properties, which are highly uncertain and heterogeneous at all scales. This limits the applicability of the Richards equation at larger scales beyond the patch scale. The experimental capabilities of the three hillslopes of the Landscape Evolution Observatory (LEO) at Biosphere 2 provide a unique opportunity to observe the heterogeneity of hydraulic material properties at the hillslope scale. We performed a gravity flow experiment where through constant irrigation the water content increases until the hydraulic conductivity matches the irrigation flux above. The dense water content sensor network at LEO then allows mapping of the heterogeneity of hydraulic conductivity at a meter scale resolution. The experiment revealed spatial structures within the hillslopes, mainly a vertical trend with the lowest hydraulic conductivity close to the surface. However, the variation between neighboring sensors is high, showing that the heterogeneity cannot be fully resolved even at LEO. By representing the heterogeneity in models through Miller scaling we showed the impact on hillslope discharge. For the hillslope with the smallest heterogeneity, representing the dominant structures was sufficient. However, for the two hillslopes with the larger overall heterogeneity, adding further details of the local heterogeneity did impact the discharge further. This highlights the limitations of the Richards equation, which requires the heterogeneous field of material properties, at the hillslope scale and shows the relevance to improving our understanding of effective parameters to be able to apply the process‐based model to larger scales.

 

Spatially integrated transport models have been applied widely to model hydrologic transport. However, we lack simple and process‐based theoretical tools to predict the transport closures—transit time distributions (TTDs) and StorAge Selection (SAS) functions. This limits our ability to infer characteristics of hydrologic systems from tracer observations and to make first‐order estimates of SAS functions in catchments where no tracer data is available. Here we present a theoretical framework linking TTDs and SAS functions to hydraulic groundwater theory at the hillslope scale. For hillslopes where the saturated hydraulic conductivity declines exponentially with depth, analytical solutions for the closures are derived that can be used as hypotheses to test against data. In the simplest form, the hillslope SAS function resembles a uniform or exponential distribution (corresponding to flow pathways in the saturated zone) offset from zero by the storage in the unsaturated zone that does not contribute to discharge. The framework is validated against nine idealized virtual hillslopes constructed using a 2‐D Richards equation‐based model, and against data from tracer experiments in two artificial hillslopes. Modeled internal age, life expectancy, and transit time structures reproduce theoretical predictions. The experimental data also support the theory, though further work is needed to account for the effects of time‐variability. The shape and tailing of TTDs and their power spectra are discussed. The theoretical framework yields several dimensionless numbers that can be used to classify hillslope scale flow and transport dynamics and suggests distinct water age structures for high or low Hillslope number.

 

Understanding transit times (TT) and residence times (RT) distributions of water in catchments has recently received a great deal of attention in hydrologic research since it can inform about important processes relevant to the quality of water delivered by streams and landscape resilience to anthropogenic inputs. The theory of transit time distributions (TTD) is a practical framework for understanding TT of water in natural landscapes but, due to its lumped nature, it can only hint at the possible internal processes taking place in the subsurface. While allowing for the direct observation of water movement, Electrical Resistivity Imaging (ERI) can be leveraged to better understand the internal variability of water ages within the subsurface, thus enabling the investigation of the physical processes controlling the time‐variability of TTD. In this study, we estimated time‐variable TTD of a bench‐scale bare‐soil sloping soil lysimeter through the StorAge Selection (SAS) framework, a traditional lumped‐systems method, based on sampling of output tracer concentrations, as well as through an ERI SAS one, based on spatially distributed images of water ages. We compared the ERI‐based SAS results with the output‐based estimates to discuss the viability of ERI at laboratory experiments for understanding TTD. The ERI‐derived images of the internal evolution of water ages were able to elucidate the internal mechanisms driving the time‐variability of ages of water being discharged by the system, which was characterized by a delayed discharge of younger water starting at the highest storage level and continuing throughout the water table recession.

 

Spatially integrated water transport dynamics at the hillslope scale have rarely been observed directly, and underlying physical mechanisms of those dynamics are poorly understood. We present time‐variable transit time distributions and StorAge Selection (SAS) functions for a 28 days tracer experiment conducted at the Landscape Evolution Observatory, Biosphere 2, the University of Arizona, AZ, USA. The observed form of the SAS functions is concave, meaning that older water in the hillslope was preferentially discharged than younger water. The concavity is, in part, explained by the relative importance of advective and diffusive water dynamics and by the geomorphologic structure of the hillslopes. A simple numerical examination illustrates that, for straight plan‐shaped hillslopes, the saturated zone SAS function is concave when the hillslope Péclet (Pe) number is large (and thus when the advective water dynamics are more pronounced). We also investigated the effect of hillslope planform geometry on the saturated zone SAS function using a model and found that the more convergent the plan shape is, the more concave the SAS function is. A numerical examination indicates that the unsaturated zone SAS function is concave for straight and convergent hillslopes when the soil thickness is uniform. The concavity of those subcomponent SAS functions signifies that the hillslope scale SAS function is concave for straight or convergent plan shape hillslopes when the hillslope Pe number is high.