Zero or not? Causes and consequences of zero-flow stream gage readings
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Four grades of nuclear graphite with various microstructures were subjected to accelerated oxidation tests in helium with traces of moisture and hydrogen in order to evaluate the effects of chronic oxidation on graphite components in high temperature gas cooled reactors. Kinetic analysis showed that the Langmuir-Hinshelwood (LH) model cannot consistently reproduce all results. In particular, at high temperatures and water partial pressures oxidation was always faster than the LH model predicts, with stronger deviations for superfine grain graphite than for medium grain grades. It was also found empirically that the apparent reaction order for water has a sigmoid-type variation with temperature which follows the integral Boltzmann distribution function. This suggests that the apparent activation with temperature of graphite reactive sites that causes deviations from the LH model is rooted in specific structural and electronic properties of surface sites on graphite. A semi-global kinetic model was proposed, whereby the classical LH model was modified with a temperature-dependent reaction order for water. The new Boltzmann-enhanced model (BLH) was shown to consistently predict experimental oxidation rates over large ranges of temperature (800-1100 oC) and partial pressures of water (3-1200 Pa) and hydrogen (0-300 Pa), not only for the four grades of graphite but also for the historic grade H-451. The BLH model offers as more reliable input for modeling the chemical environment effects during the life-time operation of new grades of graphite in advanced nuclear reactors operating at high and very high temperatures.
The development of molecular targeted drugs with radiation and chemotherapy is critically important for improving the outcomes of patients with hard-to-treat, potentially curable cancers. However, too many preclinical studies have not translated into successful radiation oncology trials. Major contributing factors to this insufficiency include poor reproducibility of preclinical data, inadequate preclinical modeling of intertumoral genomic heterogeneity that influences treatment sensitivity in the clinic, and a reliance on tumor growth delay instead of local control (TCD50) endpoints. There exists an urgent need to overcome these barriers to facilitate successful clinical translation of targeted radiosensitizers. To this end, we have used 3-dimensional (3D) cell culture assays to better model tumor behavior in vivo. Examples of successful prediction of in vivo effects with these 3D assays include radiosensitization of head and neck cancers by inhibiting epidermal growth factor receptor or focal adhesion kinase signaling, and radioresistance associated with oncogenic mutation of KRAS. To address the issue of tumor heterogeneity, we leveraged institutional resources that allow high-throughput 3D screening of radiation combinations with small-molecule inhibitors across genomically characterized cell lines from lung, head and neck, and pancreatic cancers. This high-throughput screen is expected to uncover genomic biomarkers that will inform the successful clinical translation of targeted agents from the National Cancer Institute Cancer Therapy Evaluation Program portfolio and other sources. Screening “hits” need to be subjected to refinement studies that include clonogenic assays, addition of disease-specific chemotherapeutics, target/biomarker validation, and integration of patient-derived tumor models. The chemoradiosensitizing activities of the most promising drugs should be confirmed in TCD50 assays in xenograft models with or without relevant biomarker and using clinically relevant radiation fractionation. We predict that appropriately validated and biomarker-directed targeted therapies will have a higher likelihood than past efforts of being successfully incorporated into the standard management of hard-to-treat tumors.
Highlights: • Construct self-supported porous Ni/CNTs composite arrays. • Porous Ni/CNTs arrays show superior capacitive performance. • Ni/CNTs arrays structure boosts fast electron/ion transfer. - Abstract: Rational synthesis of advanced carbon materials is of great importance to the development of supercapacitors. In this work, we propose a facile combined electrodeposition-chemical vapor deposition for tailored construction of carbon nanotubes (CNTs) arrays on highly porous Ni matrix forming integrated 3D Ni/CNTs electrode. Interconnected CNTs with diameters of 20–30 nm are strongly anchored on the cross-linked porous Ni branches consisting of nanoparticles of 500–600 nm. Capacitive performance of the obtained porous Ni/CNTs electrode is thoroughly characterized by electrochemical means including charge/discharge tests and cyclic voltammetry. The 3D Ni/CNTs electrode shows good high-rate performance with a capacitance of 186 F g{sup −1} at 2.5 A g{sup −1}, and excellent cycling stability with a capacitance retention of 99% after 10,000 cycles. The integrated conductive network composed of 3D Ni/CNTs offers large active area, good electronic conductivity and fast transportation paths of ions/electrons, leading to superior capacitive performance.
This paper studies a power transmission system with both conventional generators (CGs) and distributed energy assets (DEAs) providing frequency control. We consider an operating condition with demand aggregating two dynamic components: one that switches between different values on a finite set, and one that varies smoothly over time. Such dynamic operating conditions may result from protection scheme activations, external cyber-attacks, or due to the integration of dynamic loads, such as data centers. Mathematically, the dynamics of the resulting system are captured by a system that switches between a finite number of vector fields - or modes -, with each mode having a distinct equilibrium point induced by the demand aggregation. To analyze the stability properties of the resulting switching system, we leverage tools from hybrid dynamic inclusions and the concept of ..omega.. -limit sets from sets. Specifically, we characterize a compact set that is semi-globally practically asymptotically stable under the assumption that the switching frequency and load variation rate are sufficiently slow. For arbitrarily fast variations of the load, we use a level-set argument with multiple Lyapunov functions to establish input-to-state stability of a larger set and with respect to the rate of change of the loads. The theoretical results are illustrated via numerical simulations on the IEEE 39-bus test system.
Lignin decomposition is critically linked to terrestrial carbon (C) cycle due to the enormous C mass of lignin and its importance in controlling overall rates of litter decomposition. Interactions between lignin and iron (Fe) minerals have been increasingly recognized as key mediators of lignin decomposition in experimental studies. However, we still lack a quantitative understanding of how Fe minerals interact with microbes to control lignin decomposition. Here, we leveraged experimental results from an incubation of Fe-rich soil, in which lignin decomposition rates were measured at aerobic conditions after four levels of pre-treated O2 availability, to examine microbe-Fe (MiFe) interactions in lignin decomposition with a MiFe model. We quantified how Fe redox cycling interacted with microbial activities to control lignin decomposition via data-model integration. Our results showed that the MiFe model with time-dependent growth and mortality functions better represented CO2 release from lignin decomposition (R2 ranging from 0.96 to 0.97) than models assuming either first-order or Michaelis-Menten kinetics. Reduction of Fe(III) to Fe(II) after pre-treatments with lower O2 availability stimu-lated the Fenton reaction to break down macro-molecular lignin into small molecules available to microbes. The small molecules of lignin and necromass bounded with oxidized Fe and were protected from decomposition. After 1-year incubation, the model implied that most of C stabilized with Fe minerals was derived from small molecular lignin C. Our quantitative analysis of microbe-Fe interactions sheds new light on lignin decomposition and preservation and helps improve model prediction of soil C persistence under global change.
Cathodic corrosion of copper (Cu) has posed a significant challenge for over a century, impeding various technological progresses such as electrochemical conversion of CO 2 (eCO 2 RR) into fuels and other value-added carbon products. Here, in this study, employing a combined Density Functional Theory (DFT) and kinetic Monte Carlo (kMC) simulation approach, we delve into the atomistic level mechanism driving this phenomenon in Cu. Our hypothesis posits the pivotal role of alkaline hydrogen evolution reaction (HER) in facilitating cathodic corrosion in Cu. We rigorously develop a pH-dependent hydroxide (OH) adsorption mechanism and calculate the equilibrium OH coverage (𝜃 OH ) at varying pH levels, the thermodynamic stability of subsurface oxygen (O sub ), as well as the Cu-vacancy mediated diffusion of subsurface oxygens (O sub ). Through comprehensive analysis, we establish correlation among various microenvironments, including oxygen diffusion in subsurface layers, pH-dependent OH adsorption, and Cu dissolution into the electrolyte as (Cu-OH) complexes. Furthermore, our investigation explores the correlation between surface coordination environment of active sites and cathodic corrosion of Cu. Finally, by integrating DFT-derived thermodynamic data into a kMC model, we successfully predict the formation of experimentally observed corrosion pits on Cu-surfaces. This combined approach not only advances our fundamental understanding of Cu cathodic corrosion but also offers insights crucial for developing effective corrosion mitigation strategies.
In the realm of enzyme engineering, the dual enhancement of thermostability and activity remains a challenge. Herein, we employed a computer-aided approach integrating folding free energy calculations and evolutionary analysis to engineer Paecilomyces thermophila xylanase into a hyperthermophilic enzyme for application in the paper and pulp industry. Through the computational rational design, XynM9 with superior thermostability and enhanced activity was designed. Its optimal reaction temperature increases by 10 °C to 85 °C, its T m increases by 10 °C to 93 °C, and its half-life increases 11-fold to 5.8 h. Additionally, its catalytic efficiency improves by 57% to 3926 s –1 mM –1 . Molecular dynamics simulations revealed that XynM9 is stabilized by more hydrogen bonds and salt bridges than wild-type xylanase. The mutant’s narrower catalytic cleft enhances the substrate-binding affinity, thus improving the catalytic efficiency. In harsh conditions at 80 °C and pH 10, using XynM9 significantly reduced both hemicellulose and lignin, which makes it a good candidate for use in the paper and pulp process. Finally, our study presents an accurate and efficient strategy for the dual enhancement of enzyme properties, guiding further improvement of computational tools for protein stabilization.
Significantly reducing platinum group metal (PGM) loading while improving catalytic performance and durability is critical to accelerating proton-exchange membrane fuel cells (PEMFCs) for transportation. In this study, we report an effective strategy to boost PGM catalysts through integrating PGM-free atomically-dispersed single metal active sites in the carbon support toward the cathode oxygen reduction reaction (ORR). We achieved uniform and fine Pt nanoparticle (NP) (~2 nm) dispersion on an already highly ORR-active FeN 4 site-rich carbon (FeN 4 –C). Furthermore, we developed an effective approach to preparing a well-dispersed and highly ordered L1 2 Pt 3 Co intermetallic nanoparticle catalyst on the FeN 4 –C support. DFT calculations predicted a synergistic interaction between Pt clusters and surrounding FeN 4 sites through weakening O 2 adsorption by 0.15 eV on Pt sites and reducing activation energy to break O–O bonds, thereby enhancing the intrinsic activity of Pt. Experimentally, we verified the synergistic effect between Pt or Pt 3 Co NPs and FeN 4 sites, leading to significantly enhanced ORR activity and stability. Especially in a membrane electrode assembly (MEA) with a low cathode Pt loading (0.1 mg Pt cm –2 ), the Pt/FeN 4 –C catalyst achieved a mass activity of 0.451 A mg Pt –1 and retained 80% of the initial values after 30 000 voltage cycles (0.60 to 0.95 V), exceeding DOE 2020 targets. Furthermore, the Pt 3 Co/FeN 4 catalyst achieved significantly enhanced performance and durability concerning initial mass activity (0.72 A mg Pt –1 ), power density (824 mW cm –2 at 0.67 V), and stability (23 mV loss at 1.0 A cm –2 ). The approach to exploring the synergy between PGM and PGM-free Fe–N–C catalysts provides a new direction to design advanced catalysts for hydrogen fuel cells and various electrocatalysis processes.
Developing efficient energy storage systems that capture and convert CO 2 is critical for mitigating carbon emissions. Here, we report a Na–CO 2 battery with ruthenium dioxide (RuO 2 ) cathode catalysts and propane-1,3-diamine (PDA) as an electrolyte additive to enhance CO 2 capture and conversion efficiency. The integration of CO 2 adsorption and electrochemical reduction facilitates activation of the inert CO 2 molecule and circumvents gas–solid–liquid ternary-phase reactions at the interface. We employed density functional theory (DFT) calculations to systematically unravel the reaction mechanisms and energetics governing CO 2 reduction, both with and without PDA. Our results reveal an energetically favorable pathway toward the formation of Na 2 CO 3 and C as final discharge products, rather than sodium oxalate (Na 2 C 2 O 4 ). The CO 2 –amine adduct facilitates charge transfer from PDA to CO 2 , which results in activation of CO 2 . The kinetics of CO 2 conversion and regeneration of PDA were found to be significantly enhanced on the RuO 2 surface compared to the bulk electrolyte. More importantly, pre-activation of CO 2 via the amine–CO 2 adduct lowers the total overpotential to 2.44 V, compared to 3.13 V without PDA. This study provides fundamental insights into CO 2 electroreduction in Na–CO 2 batteries and underscores the promise of electrolyte engineering for sustainable CO 2 utilization and high-performance energy storage.
We present an application of the Portable Diagnostic Package (PDP) on the Wisconsin High-temperature-superconducting Axisymmetric Mirror (WHAM), which integrates an optical emission spectroscopy (OES) system and an active Thomson scattering (TS) system. The OES system facilitates a comprehensive impurity line survey and enables flow measurements through the Doppler effect observed on impurity lines. Plasma rotation profiles were successfully derived from doubly charged carbon lines. The TS system enabled the first measurements of the electron temperature in commissioning plasmas on WHAM. Notably, the PDP was installed, commissioned, and used to obtain OES and TS data within ∼6 months from project start, enabled by its designed portability and standardized interfaces. These results demonstrate the PDP’s potential to accelerate diagnostic readiness and advance experimental plasma studies.
Strategies to mitigate carbon dioxide emissions through forestry activities have been proposed, but ecosystem process-based integration of climate change, enhanced CO 2 , disturbance from fire, and management actions at regional scales are extremely limited. In this work, we examine the relative merits of afforestation, reforestation, management changes, and harvest residue bioenergy use in the Pacific Northwest. This region represents some of the highest carbon density forests in the world, which can store carbon in trees for 800 y or more. Oregon’s net ecosystem carbon balance (NECB) was equivalent to 72% of total emissions in 2011–2015. By 2100, simulations show increased net carbon uptake with little change in wildfires. Reforestation, afforestation, lengthened harvest cycles on private lands, and restricting harvest on public lands increase NECB 56% by 2100, with the latter two actions contributing the most. Resultant cobenefits included water availability and biodiversity, primarily from increased forest area, age, and species diversity. Converting 127,000 ha of irrigated grass crops to native forests could decrease irrigation demand by 233 billion m 3 ∙y –1 . Utilizing harvest residues for bioenergy production instead of leaving them in forests to decompose increased emissions in the short-term (50 y), reducing mitigation effectiveness. Increasing forest carbon on public lands reduced emissions compared with storage in wood products because the residence time is more than twice that of wood products. Thus, temperate forests with high carbon densities and lower vulnerability to mortality have substantial potential for reducing forest sector emissions. Our analysis framework provides a template for assessments in other temperate regions.
Abstract NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.
ABSTRACT Our ability to describe the highly diverse pool of low abundance populations present in natural microbial communities is increasing at an unprecedented pace. Yet we currently lack an integrative view of the key taxa, functions and metabolic activity which make-up this communal pool, usually referred to as the ‘rare biosphere’, across the domains of life. In this context, this review examines the microbial rare biosphere in its broader sense, providing an historical perspective on representative studies which enabled to bridge the concept from macroecology to microbial ecology. It then addresses our current knowledge of the prokaryotic rare biosphere, and covers emerging insights into the ecology, taxonomy and evolution of low abundance microeukaryotic, viral and host-associated communities. We also review recent methodological advances and provide a synthetic overview on how the rare biosphere fits into different conceptual models used to explain microbial community assembly mechanisms, composition and function.
ABSTRACT We present a method to identify type-1 active galactic nuclei (AGNs) in the central 3 arcsec integrated spectra of galaxies in the MaNGA DR15 sample. It is based on flux ratios estimates in spectral bands flanking the expected h α broad component h αBC. The high signal-to-noise ratio obtained (mean S/N = 84) permits the identification of h αBC without prior subtraction of the host galaxy (HG) stellar component. A final sample of 47 type-1 AGN is reported out of 4700 galaxies at z < 0.15. The results were compared with those from other methods based on the SDSS DR7 and MaNGA data. Detection of type-1 AGN in those works compared to our method goes from 26 per cent to 81 per cent. Spectral indexes were used to classify the type-1 AGN spectra according to different levels of AGN-HG contribution, finding 9 AGN-dominated, 14 intermediate, and 24 HG-dominated objects. Complementary data in NIR-MIR allowed us to identify type I AGN-dominated objects as blue and HG-dominated as red in the WISE colours. From NVSS and FIRST radio continuum data, we identify 5 HERGs (high-excitation radio galaxies) and 4 LERGs (low-excitation radio galaxies), three showing evidence of radio-jets in the FIRST maps. Additional X-ray data from ROSAT allowed us to build [O iii] and h αBC versus X-ray, NIR-MIR, and radio continuum diagrams, showing that L(h αBC) and L([O iii]) provide good correlations. The range in h αBC luminosity is wide 38 < logL(h αBC) < 44, with log FWHM(h αBC) ∼ 3–4, covering a range of Eddington ratios of −5.15 < log Lbol/LEdd < 0.70. Finally, we also identify and report ten possible changing-look AGN candidates.
Abstract Recent breakthroughs in structural biology have provided valuable new insights into enzymes involved in plant cell wall metabolism. More specifically, the molecular mechanism of synthesis of (1,3;1,4)-β-glucans, which are widespread in cell walls of commercially important cereals and grasses, has been the topic of debate and intense research activity for decades. However, an inability to purify these integral membrane enzymes or apply transgenic approaches without interpretative problems associated with pleiotropic effects has presented barriers to attempts to define their synthetic mechanisms. Following the demonstration that some members of the CslF sub-family of GT2 family enzymes mediate (1,3;1,4)-β-glucan synthesis, the expression of the corresponding genes in a heterologous system that is free of background complications has now been achieved. Biochemical analyses of the (1,3;1,4)-β-glucan synthesized in vitro, combined with 3-dimensional (3D) cryogenic-electron microscopy and AlphaFold protein structure predictions, have demonstrated how a single CslF6 enzyme, without exogenous primers, can incorporate both (1,3)- and (1,4)-β-linkages into the nascent polysaccharide chain. Similarly, 3D structures of xyloglucan endo-transglycosylases and (1,3;1,4)-β-glucan endo- and exohydrolases have allowed the mechanisms of (1,3;1,4)-β-glucan modification and degradation to be defined. X-ray crystallography and multi-scale modeling of a broad specificity GH3 β-glucan exohydrolase recently revealed a previously unknown and remarkable molecular mechanism with reactant trajectories through which a polysaccharide exohydrolase can act with a processive action pattern. The availability of high-quality protein 3D structural predictions should prove invaluable for defining structures, dynamics, and functions of other enzymes involved in plant cell wall metabolism in the immediate future.
This paper proposes an adaptive DC-bus stabilizer for single-phase grid-connected voltage source converter (VSC) with small-scale renewable energy integration. To enhance converter's power density, the conventional active power decoupling (APD) techniques were adopted to reduce the DC capacitance by compensating the inherent second-order harmonic power ripples in the single-phase VSC. However, it would potentially make the DC voltage vulnerable to transient power ripples, which is a critical issue for the grid-connected renewable energy source (RES) system during grid faults. The proposed DC-bus stabilizer can not only compensate the second-order harmonic power ripples at normal operation, but also enhance the fault ride-through capability of the converter. The circuit topology and its corresponding control strategy are presented, and then simulation results are provided to demonstrate the feasibility and validity of the proposed DC-bus stabilizer under normal operation and grid fault condition.
Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.