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The transport of charge carriers throughout an active conjugated polymer (CP) host, characterized by a heterogeneous morphology of locally varying degrees of order and disorder, profoundly influences the performance of CP-based electronic devices.
A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.
This work presents a study of plasma transport at low aspect ratio on the National Spherical Torus Experiment tokamak, where the turbulent and neoclassical energy fluxes calculated by the quasilinear Trapped Gyro Landau Fluid (TGLF) model and the multi species drift-kinetic Neoclassical solver (NEO) are validated against experimental data. The turbulent energy transport of two plasma discharges, one in the L-mode confinement regime and another in the H-mode regime, is dominated by electrostatic drift-wave instabilities, while the ion heat transport has a significant neoclassical contribution. The data analysis workflow is described in detail to understand how the variations of mapping and fitting of experimental data affect the power balance solution and subsequent flux-matching plasma profile predictions with the TGYRO solver. On average, the predicted plasma profiles are consistent with experimental data. However, the solutions are sensitive to various input parameters, including boundary conditions, and the electron-ion coupling. Linear gyrokinetic stability analysis demonstrates close agreement of the real frequencies of unstable modes between TGLF and CGYRO gyrokinetic simulations, but higher growth rates are predicted by TGLF, especially for the H-mode case. Estimates of the low-k modes' contributions to the total flux are consistent with linear stability analysis and the E × B suppression of turbulence in TGLF simulations with the SAT1 saturation model, while the SAT2 saturation model over-predicts the low-k modes' contribution in the H-mode case. Moreover, the results with SAT1 model are consistent with power balance analysis, which indicates only neoclassical ion energy fluxes inside ρ < 0.4 in the L-mode case and $\rho \unicode{x2A7D} 0.7$ in the H-mode case. The presence of multi-scale turbulence and ion-scale driven zonal flow mixing effects are also observed in TGLF scans of the electron turbulent heat flux over a range of temperature gradients and the electron-ion temperature ratio, which could explain the strong model sensitivity to variations of input parameters.
In plants, nitrogen and carbon metabolism are tightly interconnected, and nitrogen availability often negatively correlates with phenylpropanoids that are associated with xylem formation and stress responses. A nitrate transporter 1/peptide transporter (NRT1/PTR) family (NPF) gene (PtNPF6.1), which is expressed in the vasculature, was previously found to have a genetic association with the variation in syringyl lignin content in poplar trees (Populus trichocarpa). PtNPF6.1 belongs to an evolutionarily distinct NPF superfamily with limited taxonomic distribution. RNAi-mediated suppression of PtNPF6.1 led to increases in total foliar nitrogen and amino acids related to nitrogen transport and storage in source leaves. There was also a concomitant decrease in soluble phenolics, including attenuated stress-induced production of anthocyanins and condensed tannins. The proportions of syringyl and p-hydroxyphenyl units in lignin were slightly but significantly decreased in down-regulated lines grown under high nitrogen conditions, while there was an increase in the level of ester-linked p-hydroxybenzoate groups. Together, these results suggest that PtNPF6.1 is involved in maintaining internal nitrogen homeostasis in trees, indirectly impacting the production of nitrogen-free phenolics including lignin and soluble secondary metabolites.
Agave species are widely cultivated crassulacean acid metabolism (CAM) plants for alcoholic beverages, food and fiber production. Among these, the Agave hybrid H11648 ((A. amaniensis × A. angustifolia) × A. amaniensis) is the main cultivar for sisal fiber in the tropical areas of Brazil, China, and African countries. The plants of Agave hybrid H11648 have a long life cycle and large leaves, which require a huge amount of nitrogen nutrient. However, the molecular basis of nitrogen transport and allocation has not been well understood in agave. In this study, we identified 19 NITRATE TRANSPORTER 1/PEPTIDE TRANSPORTER FAMILY(NPF) genes (called AhNPFs) with full-length coding sequences in Agave hybrid H11648. Our analysis of gene expression in various types of tissues revealed the tissue-specific expression pattern of AhNPFs. We further examined their expression patterns at different leaf developmental stages, under abiotic/biotic stresses and nutrient deficiency. The results reveal several candidate regulators in the agave NPF family, including AhNPF4.3/5.2/7.1. We first characterized the NPF genes in agave based on published leaf transcriptome datasets and emphasized their potential functions. The study will benefit future studies related to nitrogen nutrient in agave.
A pore-resolved modeling framework is developed to quantify how pore-scale transport affects the intrinsic microkinetics of CO 2 -to-CO on Au. A DFT-informed microkinetic model is coupled self-consistently to a Generalized-Modified Poisson–Nernst–Planck (GMPNP) transport description in a single, electrolyte-filled cylindrical pore, allowing local concentrations and potential to feed back into site-specific reaction rates. FIB-SEM is used to determine pore sizes within realistic electrode materials. Across pore diameters, d p = 10–6000 nm, the surface-averaged CO 2 reduction rate is systematically reduced relative to the ideal microkinetic baseline where mass transport is not accounted for; the effectiveness factor 𝜂 𝑠,CO 2 , which quantifies this ratio, decreases rapidly at more negative potentials and is about 1% near −1.0 V vs SHE due to reactant depletion. Spatial maps reveal pore-bulk alkalization that emerges at higher cathodic bias, with a small, near-wall pH dip due to electrostatic repulsion of hydroxide at the cathode interface. For a fixed aspect ratio L p /d p , narrower pores exhibit larger 𝜂 𝑠,CO 2 by shortening diffusion paths, whereas variations in the aspect ratio L p /d p play a secondary role. A dimensionless analysis (surface/bulk Damköhler numbers) delineates operating regimes. In conclusion, this work offers a concept for incorporating microkinetic models into homogenized porous-electrode models through effectiveness factors and pore-size distribution.
With suitable water dissociation (WD) catalysts, bipolar membranes (BPMs) can efficiently dissociate water into H + and OH – at the junction between anion- and cation-exchange layers (AEL and CEL, respectively). First, however, water must be transported through the AEL or CEL and thus against the outward flow of hydrated H + and OH – . This is a challenge intrinsic to the BPM architecture and limits operation to current densities typically less than ~0.5 A·cm –2 . Here we explore how water transport affects durability and performance in reference alkaline and acidic membrane electrolyzers, and we use the insight gained to design BPMs with improved water transport. We demonstrate a thin-CEL BPM (2-μm Nafion CEL|~200 nm TiO 2 |~200 nm NiO + ionomer|50 μm Sustainion AEL) which maintains a pH difference of ~14 units between the anode and cathode for current densities of up to 3.4 A·cm –2 with a total water electrolysis voltage of ~4 V and an estimated WD overpotential of ~1.5 V. Finally, such high-current-density operation is crucial for key emerging BPM applications, including in water and carbon-dioxide electrolyzers and in (regenerative) fuel cells.
We investigate how mineral dissolution reshapes flow pathways and solute transport in three‐dimensional discrete fracture networks using a computationally efficient graph‐based reactive transport model. The DFNs are inspired by field‐site observations of fractured carbonate and represent realistic connectivity and structural heterogeneity. Flow is simulated with the Reynolds equation, and dissolution follows first‐order kinetics with diffusive limitations captured through an effective mass‐transfer coefficient. By systematically varying two key dimensionless parameters, the effective Damköhler number (Da), governing reaction versus advection rates, and a transport parameter (Da), analogous to the Thiele modulus, distinct flow channelization regimes emerge: mildly channelized at low G, highly channelized at intermediate Da, and extreme wormhole formation at high Da and low G. Eulerian and Lagrangian analyses, including breakthrough curves, particle tortuosity, dispersivity, and flow channeling indicators quantitatively characterize the progression of dissolution‐driven network restructuring. Across all regimes, initial fracture heterogeneity persists. The results underscore how the interplay between this initial structure, advection, reaction, and diffusion critically shapes subsurface flow pathways, with implications for applications ranging from groundwater remediation to enhanced geothermal systems.
The knowledge of minority and majority charge carrier properties enables controlling the performance of solar cells, transistors, detectors, sensors, and LEDs. Here, we developed the constant light induced magneto transport method which resolves electron and hole mobility, lifetime, diffusion coefficient and length, and quasi-Fermi level splitting. We demonstrate the implication of the constant light induced magneto transport for silicon and metal halide perovskite films. We resolve the transport properties of electrons and holes predicting the material’s effectiveness for solar cell application without making the full device. The accessibility of fourteen material parameters paves the way for in-depth exploration of causal mechanisms limiting the efficiency and functionality of material structures. To demonstrate broad applicability, we further characterized twelve materials with drift mobilities spanning from 10 –3 to 10 3 cm 2 V –1 s –1 and lifetimes varying between 10 –9 and 10 –3 seconds. The universality of our method its potential to advance optoelectronic devices in various technological fields.
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Here, this work demonstrates a first-principles-based approach to obtaining finite temperature thermal and electronic transport properties which can be employed to model and understand mesoscale structural evolution during electronic, magnetic, and structural phase transitions. A computationally tractable model was introduced to estimate electron relaxation time and its temperature dependence. The model is applied to Ca 3 Ru 2 O 7 with a focus on understanding its electrical resistivity across the electronic phase transition at 48 K. A quasiharmonic phonon approach to the lattice vibrations was employed to account for thermal expansion while the Boltzmann transport theory including spin-orbit coupling was used to calculate the electron-transport properties, including the temperature dependence of electrical conductivity.
Here, an analytical model for resistance to oxygen transport in air electrodes containing carbon black supports with high surface area was developed by combining a Thiele modulus—effectiveness factor approach at the agglomerate scale (~150 nm) with nanoscale diffusional resistance in carbon micropores/pits (~5 nm). This paper extends an earlier model for transport resistance to platinum nanoparticles on low surface area carbon. Differences in transport resistances between catalyst layers with high and low surface area carbon blacks predicted by the model with reasonable geometric dimensions and physical properties are consistent with experimental observations.
This document presents a summary and framework of available transport data and other information directly relevant to the development of the groundwater transport model for Central and Western Pahute Mesa (PM) corrective action units (CAU). This model is referred to as the “PM CAU transport model” in this document. Central and Western PM are two areas of the Nevada National Security Site (NNSS) that historically were used for underground nuclear testing. These nuclear tests resulted in groundwater contamination in the vicinity of the underground test areas. As a result, the U.S. Department of Energy (DOE), Environmental Management (EM) Nevada Program is currently conducting a corrective action investigation (CAI) of the PM underground test areas. This work is a part of the Underground Test Area (UGTA) Activity in accordance with the Federal Facility Agreement and Consent Order (FFACO) (1996, as amended).
The main objectives of the composite analysis (CA) compliance case vadose zone (VZ) facet are to simulate transport of radionuclide releases from the surface to the water table, and to provide radionuclide transfer rates to groundwater for inclusion into the Plateau-to-River (P2R) saturated zone (SZ) model (CP-57037, Model Package Report: Plateau to River Groundwater Model, Version 8.3). The parallel exascale version of the Subsurface Transport Over Multiple Phases (eSTOMP) simulator is used to simulate flow and transport for the CA VZ models. The CA compliance case approach to address the scale and distribution of contaminant sources in the VZ facet is to subdivide the Hanford Site Central Plateau into 26 models that contain contaminant sources and liquid discharges likely to commingle during migration through the VZ to the water table.
Motivation: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As `omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results: We propose a workflow (`Omics to Reactive Transport – ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Live, interactive models, which incorporate the results from this narrative into a PFLOTRAN simulation, are available (without login) at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/.
Machine learning (ML) offers a promising avenue for international freight transportation management (IFTM) given its capability to harness the power of data that have become increasingly available to freight transportation researchers and practitioners. This paper conducts a comprehensive investigation of the state-of-the-art in developing ML models for applications to different aspects of IFTM. We start by giving an overview of various fundamental ML methods. Then, how different ML methods have been employed, adapted, and applied to a multitude of subject areas in IFTM are discussed, including demand forecast, operation and asset maintenance, and vehicle trajectory and on-time performance prediction. The potential data sources that may be used to develop ML models are further examined. Subsequently, a synthesis of the exiting work is performed to identify the specific topics addressed in the existing research, ML methods used, the trends of research, and opportunities for further explorations. Four directions for future research are proposed in the end.