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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Genomic insights into redox-driven microbial processes for carbon decomposition in thawing Arctic soils and permafrost

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. This exposes large carbon stocks to microbial decomposition, possibly worsening climate change by releasing more greenhouse gases. Understanding how microbes break down soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes. Here, we studied the microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following the thawing of permafrost, there was a notable shift in microbial community structure, with fermentative Firmicutes and Bacteroidota taking over from Actinobacteria and Proteobacteria over the 60-day incubation period. The increase in iron and sulfate-reducing microbes had a significant role in limiting methane production from thawed permafrost, underscoring the competition within microbial communities. We explored the growth strategies of microbial communities and found that slow growth was the major strategy in both the active layer and permafrost. Our findings challenge the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, they indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors, and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Success Path Method: Introduction to the Success Path Method Software Tool©

As part of its commitment to advancing safety and reliability assessment methodologies, Argonne National Laboratory pioneered the use of an evaluation method called the Success Path Method (SPM) to improve risk management for offshore oil and gas operations. The development of the SPM at Argonne has been driven by the need to improve existing risk assessment methodologies by focusing on the steps necessary for success rather than failure modes alone. This is particularly important for industrial environments like offshore facilities that perform multiple functions under a continuously evolving set of operational conditions – such as water depth and temperature, currents, and weather conditions. In these dynamic environments, the traditional Probabilistic Risk Assessment (PRA) approach is far too complex as it focuses on what can go wrong – which comprises an infinite failure space that must be fully explored and understood. By shifting the focus to a finite space of success paths, the SPM enables operators and decision makers to prioritize a manageable number of steps that must go right to ensure success. Building on its five decades of experience in safety assessments for the nuclear industry, Argonne made major adaptations to existing risk assessment methods utilizing features similar to fault trees that are traditionally used in PRA to map all pathways in which the system can malfunction. In contrast, SPM identifies the components and processes that must function correctly to achieve specific outcomes – such as preventing the uncontrolled release of hydrocarbons during drilling operations. The SPM framework integrates equipment, procedures, software, processes, and human actions to ensure that physical barriers meet critical safety functions in dynamic operational conditions. This approach helps identify failure modes and improve operational risk management by narrowing the focus to key success elements, which in turn reduces uncertainty and helps users understand, manage, and respond to failures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
DOE OSTI2022

Exchange Coupling Determines Metal-Dependent Efficiency for Iron- and Cobalt-Catalyzed Photochemical CO 2 Reduction

Catalysts promoting multielectron charge delocalization offer selectivity for the CO 2 reduction reaction (CO 2 RR) over the competing hydrogen evolution reaction. Here, we show metal–ligand exchange coupling as an example of charge delocalization that can determine the efficiency for photocatalytic CO 2 RR. A comparative evaluation of iron and cobalt complexes supported by the redox-active ligand tpyPY2Me establishes that the two-electron reduction of [Co(tpyPY2Me)] 2+ ([Co] 2+ ) occurs at potentials 770 mV more negative than the [Fe(tpyPY2Me)] 2+ ([Fe] 2+ ) analogue by maximizing the exchange coupling in the latter compound. The positive shift in the reduction potential promoted by metal–ligand exchange coupling drives [Fe] 2+ to be among the most active and selective molecular catalysts for photochemical CO 2 RR reported to date, maintaining up to 99% CO product selectivity with total turnover numbers (TONs) and initial turnover frequencies exceeding 30,000 and 900 min –1 , respectively. In contrast, [Co] 2+ shows much lower CO 2 RR activity, reaching only ca. 600 TON at 83% CO product selectivity under similar conditions accompanied by rapid catalyst decomposition. Further, the spin density plots of the two-electron reduced [Co] 0 complex implicate a paramagnetic open-shell doublet ground state compared to the diamagnetic open-shell singlet ground state of reduced [Fe] 0 , rationalizing the observed negative shift in two-electron reduction potentials from the [M] 2+ species and lowered CO 2 RR efficiency for the cobalt complex relative to its iron congener. This work emphasizes the contributions of multielectron metal–ligand exchange coupling in promoting effective CO 2 RR and provides a starting point for the broader incorporation of this strategy in catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Corrosion of Zinc Cold Spray Coatings in a Wet Sweet and Sour Gas Environment

Internal corrosion is a problem for steel pipelines transporting natural gas or CO 2 containing water and partial pressures of H 2 S higher than 0.3 kPa (0.05 psi). This work aims to mitigate internal corrosion in steel pipelines transporting natural gas containing H 2 S using cold spray coatings. Two types of the cold spray binary metallic coatings (zinc chromium [ZnCr]. zinc niobium [ZnNb]) were studied using electrochemical techniques: potentiodynamic polarization, linear polarization resistance, and electrochemical impedance spectroscopy. The corrosion resistance of cold spray coatings (ZnCr, ZnNb) was evaluated in an environment containing 4 bar CO 2 pressure, simulating the partial pressures found in gas transmission lines over a solution of 3.5 wt% NaCl heated to 40°C. A concentration of 0.003 M Na 2 S 2 O 3 ·5H 2 O, corresponding to H 2 S partial pressures around 0.079 bar (1.146 psi), was used to simulate sour conditions. Postcorrosion surface characterization was performed using a scanning electron microscope (SEM) equipped with an energy-dispersive x-ray spectroscope (EDS) and x-ray diffraction analysis. The data showed that the presence of 0.003 M Na 2 S 2 O 3 ·5H 2 O shifted the corrosion potential to more anodic values and decreased the corrosion current density. Both coatings showed similar behavior after 1 h of exposure in the CO 2 /H 2 S environment, indicating that similar electrochemical reactions were occurring on ZnNb and ZnCr. SEM images and EDS surface analyses for specimens showed a significant change in the surface chemical composition of carbon steel coated with ZnNb and ZnCr after 24 h of immersion. In the presence of thiosulfate (under sour conditions), the formation of corrosion product layers (ZnCO 3 and ZnS) on top of ZnNb and ZnCr coatings increased their corrosion resistance, which helped to reduce their corrosion by a factor of 2. Under a sweet environment, the corrosion rates for steel coated with cold spray coatings after 14 d of exposure are lower than that for galvanized steel by a factor of 5 due to the ZnCO 3 layer formed on top of the coatings. The ZnCO 3 layer formed on the steel surface acts as a physical barrier against corrosion by blocking the diffusion of corrosive species to the surface. No localized attack was observed. ZnCr Cold spray coating with defect showed promising corrosion protection against CO 2 corrosion (sweet corrosion) after 14 d of exposure to a CO 2 environment. Here, the scratch on the coating simulated damage created in service, and it was deep enough to expose the substrate material (steel). The formation of zinc oxide (ZnO) and zinc carbonate (ZnCO 3 ) on the scratch confirmed the cathodic protection of the steel by ZnCr and ZnNb coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Occupant-Centric Demand Response for Thermostatically-Controlled Home Loads

Efficiently managing energy usage to balance supply and demand on the electric grid is crucial, especially with the widespread deployment of distributed variable renewable electricity generation. This paper introduces two duty-cycle control methods for heating systems, adjusting thermostat setpoints to limit and shift electricity demand. The control approaches employ innovative techniques, such as adaptive duty cycling, to prioritize household thermal comfort while reducing peak demand. These control methods can respond to signals from the electric grid, including demand targets and time-of-use tariffs, and were tested physically on an electric furnace and heat pump in a test home during winter conditions in 2021 and 2022. The results are given as average demand reductions and energy use impacts with respect to the average indoor-outdoor temperature difference during the control period. For heat pumps, demand limiting control reduced power by 18.5% and 23.3% for indoor-outdoor temperature differences of 30°F and 40°F. Preheating-based demand shifting achieved reductions of 34.8% and 33.2% for the same temperature differences. Electric furnace tests showed demand reductions of 33.8% and 25.3% for demand limiting, and 56.1% and 45.7% for preheating-based demand shifting. These findings highlight the potential for innovative control methods to enhance grid efficiency and reduce energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Hydrogen Adsorption, Reactivity, and Catalysis on Colloidal Iron Carbide Nanoparticles

Supported iron carbide particles have long served as catalysts for CO hydrogenation (the Fischer Tropsch synthesis, FTS) and continue to be attractive. Despite this, little is known about their chemistry. Reported here is a colloidal Fe x C nanoparticle (NP) model system that allows direct observation of surface hydrogen and CO, as well as quantification of the surface H. Dodecylamine-capped Fe x C NPs (DDA-Fe x C NPs) were synthesized through solution-phase carburization of Fe NPs and form stable colloids in low-polarity organic solvents. Treatment of these colloids with H 2 or D 2 produced highly hydrogenated materials, and FTIR spectra of DDA-Fe x C-D n showed that most of the D binds to carbides, with at least four distinct v(C-D) modes. The surface C-H(D) bonds were all reactive, transferring hydrogen to alkenes and other reagents in solution. Titration and ICP measurements showed a 0.17:1 ratio of added H:total Fe, or roughly. 40 H per 1.8 nm DDA-Fe x C NP. Conversely, CO was preferentially bound to surface Fe sites, with FTIR spectra showing a single broad v(CO) that shifted with CO coverage or co-adsorption of H 2 . The DDA-Fe x C NPs were active catalysts for both olefin hydrogenation and the FTS, under mild conditions and without catalyst pre-treatment. The CO hydrogenation reactions yielded a broad distribution of long-chain linear paraffins and olefins. Though quantitative comparisons with typical FTS results are not possible because of our use of sealed batch reactors and other factors, the observations of high catalytic reactivity demonstrate the relevance of this model system to iron-carbide catalysis. DFT calculations on model slab surfaces with varying iron carbide stoichiometries revealed that the thermodynamically preferred surface adsorption sites are C for H ads and Fe for CO ads . A variety of binding sites and binding energies were found for each adsorbate. We are unaware of previous studies indicating that a diverse array of C-H bonds is the primary source of reactive H on iron carbides. Experimentally, the diversity of *C-H sites was evident in reactions with H-atom donors and abstractors of different strengths, from both the reaction stoichiometries and IR spectra. The different surface–H binding energies correlate with the v(C-D) stretching frequencies. Furthermore, these insights into complex iron carbide surfaces and catalysis could assist catalyst design, and they showcase the importance of stoichiometric studies of reaction intermediates.

08 HYDROGEN↗
DOE OSTI2026

Correlating binding energies of adsorbed CO and H on model surfaces with CO/H 2 selectivity from co-electrolysis of CO 2 and H 2 O over copper–palladium bimetallic catalysts

Binding energies of adsorbed CO and H are key descriptors governing the activity and selectivity of the co-electrolysis of CO 2 and H 2 O to produce syngas with desired CO/H 2 ratios. Palladium hydride (PdH), which forms in situ at negative overpotentials, has been identified as the active Pd phase for CO 2 reduction to syngas. Herein, binding energies of CO and H are determined using temperature programmed desorption (TPD) of CO and H 2 from Pd(111), PdH/Pd(111), and Cu/PdH/Pd(111) under ultra-high vacuum (UHV) conditions. TPD results reveal that desorption of H 2 from subsurface PdH occurs at 460 K, while desorption from surface PdH is more facile at 320 K. CO desorption temperatures shift 20 K lower on PdH/Pd(111) compared to on Pd(111). The presence of 0.7 ML Cu further increases the desorption temperature of H 2 by 30 K while simultaneously reducing CO desorption temperatures by 70 K. Density functional theory (DFT) calculations show that CO adsorption onto Pd sites is hindered on the 0.7 ML Cu/PdH/Pd(111) surface while the kinetic barrier for H 2 desorption is increased. The trends in the binding energies of CO and H on model surfaces are consistent with electrochemical measurements of CuPd powder catalysts in a membrane electrode assembly (MEA), where H 2 evolution is reduced while CO production is enhanced compared to unmodified Pd catalysts. Overall, the results from model surface studies (TPD and DFT) provide a prediction and explanation for the activity and CO/H 2 ratios observed in electrochemical experiments. This study also demonstrates that CuPd is a promising catalyst with reduced Pd-loading to produce CO-rich syngas.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray phase contrast and absorption imaging for the quantification of transient cavitation in high-speed nozzle flows

In this work, high-flux synchrotron radiation has been employed in a time-resolved manner to characterise the distinct topology features and dynamics of different cavitation regimes arising in a throttle orifice with an abrupt flow-entry contraction. Radiographs obtained though both X-ray phase- contrast and absorption imaging have been captured at 67,890 frames per second. The flow lied in the turbulent regime (Re=35,500), while moderate (CN=2.0) to well-established (CN=6.0) cavitation conditions were examined encompassing the cloud and vortical cavitation regimes with pertinent transient features, such as cloud-cavity shedding. X-ray Phase-Contrast Imaging (XPCI), exploiting the shift in the X-ray wave phase during interactions with matter, offers sharp-refractive index gradients in the interface region. Hence, it is suitable for capturing fine morphological fluctuations of transient cavitation structures. Nevertheless, the technique cannot provide information on the quantity of vapour within the orifice. Such data have been obtained utilising absorption imaging, where beam attenuation is not associated with scattering and refraction events, and hence can be explicitly correlated with the projected vapour thickness in line-of-sight measurements. A combination of the two methods is proposed, as it has been found is capable of quantifying the vapour content arising in the complex nozzle flow, while also faithfully illustrating the dynamics of the highly-transient cavitation features.

42 ENGINEERING↗
DOE OSTI2025

Role of perturbed parallel magnetic field effects in predicting turbulent transport in NSTX

This study presents analysis of gyrokinetic simulations on the National Spherical Torus Experiment (NSTX) to investigate the effects of electromagnetic fields on plasma turbulence and transport. The simulations, performed with varying levels of fidelity using the gyrokinetic CGYRO code, include electrostatic (ES), single-field electromagnetic (EM1), and two-field electromagnetic (EM2) models. A detailed comparison across the simulation database reveals that electromagnetic effects increase both predicted growth rates and quasilinear fluxes, with EM2 simulations producing stronger turbulence than ES and EM1 cases. Quasilinear modeling using QLGYRO demonstrates that while the perturbed parallel magnetic field (δB ∥ ) does not drastically affect the total flux at experimental gradients, it leads to a shift in the dominant instability, altering mode structures from microtearing to kinetic ballooning modes (KBMs). The proximity of the plasma profiles to the KBM threshold is explored, with the experimental conditions being near the onset of KBM-driven transport. The KBM, with its large growth rates, is identified as a potential driver of electron temperature flattening, as it can rapidly transport heat across flux surfaces. Performing stability analysis shows core-localized unstable a low- mode that could contribute to the flattening at the early times of the discharge. TGYRO predictive modeling, incorporating both TGLF and QLGYRO, indicates that the inclusion of δB ∥ significantly improves the accuracy of temperature profile predictions in NSTX high-beta plasmas, although challenges remain in modeling the sharp flux discontinuities caused by KBM-driven instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Safe Reinforcement Learning for Emergency Load Shedding of Power Systems

The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid’s security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening in the grid. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)- based control techniques, RL control usually cannot guarantee the safety of the power systems. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulation on the IEEE 39-bus testcase is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

reinforcement learning, emergency control, power g↗
DOE OSTI2024

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

Abstract Historical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Opportunistic Short‐Term Water Uptake Dynamics by Subalpine Trees Observed via In Situ Water Isotope Measurements

Abstract Variations in tree water sources are important to understand in semi‐arid ecosystems because climatic shifts towards lower snowpack and increased drought affect water availability in subalpine forests of the western US. Here, we use daily in situ measurements of stable isotopes ( 2 H & 18 O) in soil and tree stem water, soil matric potential and sap flow to study tree water uptake dynamics. We instrumented three soil profiles down to 90 cm, as well as three aspen and engelmann spruce trees near Gothic, Colorado, in the East River watershed. We observed the fate of natural isotopic variations in rainfall, soil, and plants from June to October 2022, and in August 2023 we conducted a 2 H labeled irrigation experiment. Our observations showed that all studied aspen trees compensated for water scarcity in the shallow soil by shifting the dominant water source at 60(±20) cm to ⅔ of uptake from 90 cm within a few days of a dry period. Both species relied on snowmelt stored in the subsoil to sustain transpiration. Intense rainfall caused the plant water uptake to shift partially to top soil layers within 2 days. Spruce transpiration was lower and relied more on snowmelt, because rainfall infiltration was low in the spruce stand due to high canopy interception. Our findings highlight the important role of snowmelt stored in the deep soil layers for subalpine forest drought response and the dominant fate of monsoonal rainfall to become transpiration rather than recharging groundwater and streams in the Upper Colorado River. Plain Language Summary There is a need to understand how trees in mountainous regions respond to dry conditions that lead to water scarcity, because climate projections suggest that such conditions will become more frequent in the future. Here we present a novel data set of measurements of daily stable isotopes of water across soil profiles and in tree stems of aspen and spruce. Our data show that when the upper soil dried out, aspen trees shifted to using water from deeper layers (beneath 60 cm) to keep transpiring. For spruce trees the uptake pattern is less clear, but both types of trees mainly used snowmelt stored in the deeper soil layers to survive the dry summer. After heavy rain, aspen and spruce trees switched to using water from the top 20 cm of soil. However, for spruce, only some rain reached the soil because the dense tree canopy intercepted it, so spruce trees stayed more dependent on snowmelt and used less water overall. This study shows how important deep snowmelt water is for helping forests survive dry periods and suggests that most summer rain is quickly used by trees rather than replenishing streams and groundwater in the headwaters of the Colorado River. Key Points Tree water resources changed within a few days from snow dominated to higher share of rainfall as soils wetted up after a dry period Compensatory plant water uptake by aspen from the deep layer (90 cm), while uptake from soil depths that became drier (60 cm) declined Strong differences between water sources and availability beneath aspen and spruce, respectively

Sprenger, Matthias↗
DOE OSTI2024

Molecular shifts in dissolved organic matter along a burn severity continuum for common land cover types in the Pacific Northwest, USA

Increasing wildfire severity is of growing concern in the western United States, with consequences for the production, composition, and mobilization of dissolved organic matter (DOM) from terrestrial to aquatic systems. Our current understanding of wildfire impacted DOM (often termed pyrogenic DOM) composition is largely built from temperature-based studies that can be difficult to extrapolate to field conditions, which are often defined by ‘burn severity’, or the post-wildfire impact observed at a site. Thus, burn severity can encapsulate a broader range of fire and environmental conditions not exclusive to temperature. Biogeochemical studies that describe DOM along burn severity continuums remain limited but are needed to better link DOM composition with field conditions post-fire. Here, in this study, we addressed this need with an experimental open air burn simulation that generated chars from vegetation representative of major land cover types in the western United States. The chars were leached to simulate DOM mobilization potential. The DOM composition was characterized by ultra-high resolution mass spectrometry (HR-MS) and UV/VIS absorbance and fluorescence. Our results indicated that the shifts of DOM production and composition along a burn-severity gradient depends on the land cover type that was burned, with the degree of change dependent on the composition of the starting parent vegetation material. Fluorescence signatures indicated a strong convergence across land cover types to more aromatic DOM with increasing severity, while HR-MS indicated an increase in the production of aromatic nitrogen containing DOM with increasing severity. Results from this study enhance our ability to describe DOM composition in a framework that can be more directly related with field and remote-sensing based metrics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Theoretical Analysis of Resonant Tunneling Enhanced Field Emission

In this paper, we develop an exact analytical quantum theory for field emission from surfaces with a nearby quantum well, by solving the one-dimensional time-independent Schrödinger equation. The quantum well, which may be introduced by ions, atoms, nanoparticles, etc., is simplified as a square potential well with depth H, width d, and distance to the surface L. The theory is used to analyze the effects of the quantum well (d, H, and L), the cathode properties (work function W and Fermi energy E F ), and dc field F. It is found that the quantum well can lead to resonant tunneling enhanced field emission up to several orders of magnitude larger than that from bare cathode surfaces. In the meantime, the electron-emission-energy spectrum is significantly narrowed. The strong enhancement region is bounded by the conditions eFL + H ≥ W + C and eFL ≤ W, with e being the elementary charge (positive) and C a constant dependent on dc field F. It is also found that the linear shift of resonance peaks in the electron-emission-energy spectrum with dc field F follows ε p =ε p⁢0 –e⁢FL, with ε p⁢0 being approximately the eigenenergies for electrons confined in a square potential well without a dc field. Finally, the theory provides insights for the design of high-efficiency field emitters, which can produce a high current and highly collimated electron beams.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Quantum fate of the Choptuik naked singularity

Classical critical collapse provides a dynamical route from smooth initial data to a naked singularity, representing a sharper violation of predictability than ordinary black hole singularities. We argue that this distinction is erased by quantum backreaction. Building on the semiclassical interior analysis, where quantum self-energy of the collapsing matter generates a universal growing mode and a finite mass gap, we study the exterior naked singularity region that determines global visibility in the Einstein-scalar system. We analyze controlled exterior models in both $2+1$ and $3+1$ dimensions. In the former, smooth matching and physical boundary conditions analytically select a vacuum polarization state, whose backreaction cloaks the classically naked region by a quantum trapped branch. In the latter, numerical horizon tracing shows that near a quantum-shifted threshold the exterior develops finite-mass marginally trapped surfaces rather than a zero-mass naked endpoint. These results suggest a global quantum picture in which the Choptuik naked singularity shares the fate of an ordinary black hole singularity: quantum effects push the putative Cauchy horizon behind a quantum-generated horizon, thereby reducing the loss of predictability to the standard black hole evaporation problem.

FOS: Physical sciences↗
DOE OSTI2022

Thermodynamic Model and Initial Experimental Investigation of Air Dehumidification through Electrically Charged Vapor Capturing Electrostatic Droplets

The removal of water vapor from the air to reduce relative humidity is a well known indoor environmental comfort requirement. Common dehumidification approaches require a substantial amount of energy and usually involve the cooling of atmospheric humid air below its dew point or the use of absorbent/adsorbent materials to extract water vapor out of the air. More recently, researchers investigated the effect of electrostatic forces for enhancing water vapor condensation with the goal to reduce the energy consumption associated with dehumidification. However, the studies are limited, and there is a lack of correlations that can predict the dehumidification rate. In this thesis, a broad theoretical investigation to study the electrostatically enhanced condensation processes was carried out. These processes consist of the use of highly charged particles, preferably highly charged water droplets to attract polar water vapor molecules to their surfaces and promote condensation, a phenomenon known as dielectrophoresis. The electrical charge promotes the reduction of the vapor pressure on the droplets' surface with respect to the saturated pressure predicted by the Kelvin equation on curved surfaces and, consequently, the equilibrium between evaporation and condensation is shifted towards condensation. This investigation resulted in the development of a thermodynamics model, which was able to predict an effective size range of the charged droplets for optimal dehumidification, under ideal conditions. The range resulted in about 2 to 4 m in diameter, while the electrical charge was kept to the maximum limit predicted by the Rayleigh model. A sensitivity analysis on the variation of the size and the charge of the charged droplets was also performed. In terms of dehumidification rates, when six electrospray heads, i.e. the maximum number of heads tested in the experimental campaign, were considered, the model predicted very limited rates, way lower than the target of this research, i.e. a dehumidification rate in terms of relative humidity of 5 % with an air flow rate of 5 cfm. While ways to increase this rate existed, their implementation proved difficult for this initial investigation. According to the requirements predicted by the model, the use of electrosprays appeared the most suitable solution for the production of small but highly charged droplets. The electrospray features and operational modes were studied in detail and an electrospray assembly was designed to be tested in this initial experimental investigation. For this initial investigation, an experimental system was designed and built to validate the thermodynamics model and with the goal to achieve a dehumidification rate of 5 % in terms of relative humidity with an air flow rate of 5 cfm. The experimental system consisted of a wind tunnel test apparatus, dew point sensors, thermocouples for temperature measurements, pressure sensors and other devices. The experimental system was equipped with a computerized data acquisition and storage system. The system was suitable to evaluate the air water content differential before and after the test section where the electrospray heads were installed. The air water content differential was evaluated in terms of the ∆ω/ω_1 ratio, to eliminate the dependence on the dry bulb temperature along the test apparatus. This work presented initial experimental data for electrostatically enhanced dehumidification processes. Working parameters, such as air and water flow rates, high voltage potentials and polarity, deionized water types with different electrical conductivity, were varied to find the best combination for an improved dehumidification. With the set up used and the conditions considered, it was never possible to achieve a 5 % dehumidification rate with 5 cfm. In general, the dehumidification rate was always limited and lower than 1 % for all the air flow rates considered and mainly within the uncertainty of the dew point sensors. These results confirmed the already limited predictions of the thermodynamics model, for ideal conditions and the interesting and promising results obtained in some experimental investigations available in the open literature were not achievable in this thesis. The more probable reasons were the higher air flow rates considered and the complete absence of the use of cooling power, which was mainly implemented to facilitate the vapor condensation.

Morcelli, Stefano↗
DOE OSTI2021

Pigment modulation in response to irradiance intensity in the fast-growing alga Picochlorum celeri

Picochlorum celeri has among the fastest photoautotrophic growth rates (~2 h doubling time in optimal conditions) reported to date for a marine alga. This study comprehensively analyzes the levels of Picochlorum celeri photosynthetic pigments, which can reach up to ~13% of the total particulate organic carbon (POC) under light-limiting conditions. The main Picochlorum celeri pigments identified include: chlorophyll a, chlorophyll b, lutein, β-carotene, canthaxanthin, violaxanthin, neoxanthin, zeaxanthin and antheraxanthin. The ketocarotenoid canthaxanthin can accumulate up to 120 mg L -1 in Picochlorum celeri liquid culture; ~12% of it was found in an extracellular polysaccharide matrix. Using a solar-simulating automated photobioreactor, we monitor the photoacclimation of cultures maintained in unshaded conditions (<0.5 μg mL -1 of total chlorophyll) through transitions from high irradiance (1000 μmoles photosynthetically active radiation (PAR) m -2 s -1 ) to low irradiance (60 μmoles PAR m -2 s -1 ), and conversely from low to high irradiance. Canthaxanthin and zeaxanthin accumulation are among the most rapid modulation responses when cultures are shifted from low to high irradiance. The violaxanthin, antherazanthin, and zeaxanthin (VAZ) pool is ~3-fold higher in high-light cultures, suggesting that the VAZ cycle combined with a dramatic reduction in chlorophyll levels are among the major mechanisms used in Picochlorum celeri to efficiently acclimate to high-irradiance levels. Responsive pigment modulation was also observed in denser cultures (~0.65 g L –1 ) grown under a diel cycle (pond-mimicking conditions). Furthermore, this research provides unique insights into the dynamics of pigment modulation and photoacclimation in response to changing irradiance in a biotechnologically promising alga and will inform future pigment engineering strategies to further improve light capture and biomass accumulation.

59 BASIC BIOLOGICAL SCIENCES↗